California Infill Estimation Methodology Project Phase II

Transcription

California Infill Estimation Methodology Project
Phase II
Final Report
June 1, 2007
Prepared for
City of Los Angeles Department of City Planning
California Department of Transportation
William Fulton
Greg Goodfellow
Ryan Aubry
Darren Greve
Aaron Engstrom
Table of Contents
I. EXECUTIVE SUMMARY.......................................................................................................................... I
1. INTRODUCTION .................................................................................................................................... 2
2. HISTORY, GOALS AND DISSEMINATION ......................................................................................... 3
2.1 DEVELOPMENTAL HISTORY .................................................................................................................... 3
2.2 DESCRIPTION OF THE TOOL ................................................................................................................... 4
2.3 GOALS OF PHASE II ................................................................................................................................ 4
2.4 DISSEMINATION STRATEGY .................................................................................................................... 6
3. ENVIRONMENTAL JUSTICE METHODOLOGY ................................................................................ 8
3.1 INTRODUCTION: DEFINITIONS AND DIFFICULTY .................................................................................... 8
3.2 PROMOTING EQUITABLE INFILL: A FIVE-STEP METHODOLOGY............................................................... 9
3.3 SUMMARY AND CONSTRAINTS ............................................................................................................. 18
4. ECONOMIC METHODOLOGY............................................................................................................ 20
4.1 METHODOLOGICAL BASICS.................................................................................................................. 20
4.2 STUDY AREA OPPORTUNITY ANALYSES ................................................................................................ 22
4.2.1 Prototype Selection and Market Analysis of Study Areas....................................................................... 22
4.3 KEY FINDINGS ...................................................................................................................................... 31
4.4 POLICY RECOMMENDATIONS ............................................................................................................... 32
4.4.1 Increase Densities and Height Limits on Land in C1 and CM Zones .................................................... 32
4.4.2 Increase Densities in R2 and RD2 Zones............................................................................................ 33
4.4.3 Increase Height Limits in C1.5, C2, C4, and CR Zoning .................................................................... 33
4.4.4 Limit commercial space requirements for mixed-use projects................................................................... 33
4.4.5 Reduce Minimum Parking Standards in Commercial Zones.................................................................. 34
5. INTEGRATIVE PILOT PROGRAM DEVELOPMENT: THE INFILL SCENARIO MAPPING
SYSTEM ....................................................................................................................................................... 35
5.1 FUNCTIONALITY................................................................................................................................... 35
5.2 TRAINING AND TESTING ...................................................................................................................... 41
5.3 CONSTRAINTS AND IMPROVEMENTS ..................................................................................................... 41
6. PARTICIPATORY NEIGHBORHOOD WORKSHOP ....................................................................... 43
6.1 NEIGHBORHOOD SELECTION AND WORKSHOP FORMAT ...................................................................... 43
6.2 WILMINGTON WORKSHOPS ................................................................................................................. 46
6.2.1 Wilmington Workshop One.............................................................................................................. 46
6.2.2 Wilmington Workshop Two.............................................................................................................. 48
6.3 PACOIMA WORKSHOPS ........................................................................................................................ 50
6.3.1 Pacoima Workshop One................................................................................................................... 50
6.3.2 Pacoima Workshop Two................................................................................................................... 53
6.4 SILVER LAKE WORKSHOPS ................................................................................................................... 55
6.4.1 Silver Lake Workshop One ............................................................................................................... 55
6.4.2 Silver Lake Workshop 2 ................................................................................................................... 56
6.5 NEIGHBORHOOD WORKSHOPS: CONCLUSIONS AND APPLICATIONS .................................................... 58
6.5.1 Community Knowledge as an Infill “Screen”....................................................................................... 58
6.5.2 A Tool for Community Planning ....................................................................................................... 59
6.6 NEIGHBORHOOD WORKSHOPS SUMMARY ........................................................................................... 60
7. CONCLUSIONS AND NEXT STEPS.................................................................................................... 61
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APPENDIX A: DETAILED DESCRIPTION OF INFILL ESTIMATION TOOL ................................... 63
APPENDIX B: DETAIL, SAMPLE ECONOMIC FEASIBILITY PRO FORMA..................................... 74
APPENDIX C: MANUAL FOR THE INFILL SCENARIO MAPPING SYSTEM ................................... 87
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I. Executive Summary
This report provides the results of Phase II of the California Infill Estimation Project. The
project is two-phase effort to design a methodology for estimating infill potential using the
California Infill Estimation Tool (Tool), a GIS-based application developed as part of the
project.
With the “stand alone,” technical success of the Tool proven in Phase I, the goal of Phase
II is to demonstrate that the utility and accessibility of the Tool can be expanded into a
methodology addressing all elements of well-managed infill practice. In order to accomplish
this, we applied the Tool to a series of selected Los Angeles study areas, fully integrating it
into the analytical, practical and political processes that are vital to infill decision making.
In Phase I, we demonstrated that the Tool allows local jurisdictions and developers to
locate infill-suitable properties, and then test the likely success of different infill strategies
in those locations. In Phase II, we have demonstrated that the Tool can deepen relevant
analyses, promote and organize community input and integrate smoothly into existing,
municipal GIS resources. These outcomes stem from the project’s focus on the following
four goals of methodological expansion:
•
Environmental Justice Analysis
Any method of estimating infill potential must account for the varying potential impact of
infill development on surrounding populations. We developed a methodology, based on
the introduction of socioeconomic indicators and community input to the Tool’s mapping
functions, for identifying the “overlap” of infill ripe parcels and populations that may be
sensitive to adverse impacts of development, such as displacement. The result is a unique
analytical assessment of environmental justice.
•
Economic Analysis
We designed a methodology for integrating relevant economic factors into the Tool’s
geographical analysis, in order to better estimate the geographical distribution of infill
development. Developed using the current conditions of our Los Angeles study areas, the
methodology successfully combines the Tool’s ability to screen for infill opportunities with
pro-forma analyses of development prototypes and local market conditions.
•
Technical Integration and Increased Accessibility
In an effort to establish the Tool’s accessibility and compatibility, we demonstrated that the
Tool can be incorporated into the web-based GIS applications made available by many
public agencies. Through engagement with City of Los Angeles planning staff, we outlined
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a pilot program for integrating the capabilities of the tool directly into the City Los
Angeles, Department of City Planning's Zone Information and Map Access System (ZIMAS).
•
Community Workshops
We designed a series of sequential neighborhood workshops with residents of the study
areas we researched. By involving neighborhood residents in the infill analysis process and
inputting their feedback into the Tool’s mapping functions, we concluded that the input of
neighborhood groups can act as a final, context-sensitive infill “screen” during the parcel
analysis process, and that the Tool has an emerging value in the community planning
process.
Finally, throughout the various stages of this project, we “marketed” the history and
potential of the infill estimation methodology to the multiple local agencies, developers
and community groups with which we worked. Using presentations, tutorials and internet
links, we educated an expanding pool of potential users in an effort that will continue
beyond the publication of this report.
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1. Introduction
In Phase I of the “California Infill Estimation Methodology Project,” we reported on the
development and feasibility of the California Infill Estimation Tool. The study verified that
the tool—a unique hybrid of “off-the-shelf” Geographic Information System (GIS) and
database software—can be used to identify and quantify parcels with a high potential for
infill development. It was demonstrated that with the tool, local jurisdictions, developers
and community groups could use assessor and zoning data to locate infill-suitable
properties, and then test the likely success of different infill strategies in those locations.
The urgency for well-planned infill development has only increased throughout California.
Infill has varying impacts on varying neighborhoods, and infill projects affect different
socioeconomic groups in different ways. This has intensified the demand for an infill
analysis tool that is context-sensitive, flexible and user-friendly.
In this second phase of the project, we report on our efforts to address a series of upgrades
to the California Infill Estimation Tool. Most were identified in the previous study, and
each contributes to a final product that will allow communities of all types to better
manage infill growth on their own. We have undertaken a series of technical
improvements, methodological refinements, outreach efforts and participatory workshops.
As will become evident, we have demonstrated that our tool can be used by multiple
groups to promote both economically feasible and environmentally just infill.
Initially, this methodology for estimating infill capacity was a response to a regional
housing crunch. We believe the following project signals the tool’s eventual role in
mitigating a statewide crisis.
2. History, Goals and Dissemination
The California Infill Estimation Tool is the result of a collaborative effort between the City
of Los Angeles, the County of Los Angeles and the Environment Now Foundation. In
2002, the three entities committed to solving a conundrum faced by local jurisdictions
across the region: That of fulfilling—in the face of a severe shortage of vacant suburban
land—the Housing Element requirements set by the State Department of Housing and
Community Development. The team was awarded a $300,000 Environmental Justice grant
from the State Department of Transportation (Caltrans) to prepare a study that would
“examine various methodologies to identify infill sites and to develop an accurate, widely
applicable methodology.”
The grant partners hired Solimar Research Group (Solimar) to provide organizational,
outreach, and technical assistance. A Task Force of volunteer state, county and local
officials, as well as developers, architects and housing/transportation professionals, was
brought on board to consult and review.
The result of this collaboration was the California Infill Estimation Tool. This unique
application of commonly-used software programs was designed to provide local
jurisdictions, developers and community groups with a flexible method of identifying
parcels ripe for infill development, and of testing the likely success of different infill
strategies in a given area.
2.1 Developmental History
This infill estimation methodology is the result of a decade-long effort, in the State of
California, to accommodate growth via the development of vacant or underutilized land in
existing communities. In recent years, infill development has proven a viable alternative to
“suburban sprawl,” or the expansion of urbanized land on the metropolitan fringe.
Exponential increases in land prices, intensifying traffic congestion and the environmental
consequences of low-density, auto-oriented development have all contributed to the vitality
of infill practices.
With growth forecasters predicting that California’s demand for new homes will exceed 4
million units over the next 20 years, municipalities across the state are considering policies
to facilitate infill development. These include increasing allowable densities and directing
public subsidies to infill. As these policies solidify, communities and developers must seek
new ways to identify promising infill sites.
The California Infill Estimation Tool facilitates this identification process. It permits users
to draw upon a wide variety of data, including both parcel- and district-level statistics, and
to conduct analyses at multiple geographical scales, from the level of the single parcel to
that of the county. The Tool is not “fixed;” it evolves in response to changing
circumstances and the availability of new datasets. It is a flexible method of identifying
locations for housing, parks, commercial and mixed use projects. Additionally, it can be
used to assess public policy by testing the likely success of different infill strategies.
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2.2 Description of the Tool
The California Infill Estimation Tool uses of “off-the-shelf” GIS and database software
(ArcGIS and Microsoft Excel) to combine a series of data that is already used by local
governments and available to constituents.
Central to this data are the three basic parcel-level datasets typically available at the city
and/or county level. Vector data contains the information required to draw parcel shapes in
GIS software. Parcel attribute data, available from the county assessor, includes the basic
attributes of each property. Finally, zoning or land-use designation data provides information
about the zoning district each parcel is located in.
The Tool is flexible enough to accept a large variety of additional parcel-level data, from
government ownership data to transportation nodes and routes. As will become evident in
Section 3 of this report, the analytical power of the Tool is increased further with block or
district-level data such as census and jobs data, special areas data (redevelopment areas, state
enterprise zones) and mortgage lending information. Finally, a less explored but high
potential application of the tool is its ability to analyze market data, such as data on
increased development activity resulting from infill-oriented subsidy policies.
There are two main analytical features of the Tool:
1) Geographical Screening: This refers to the ability of the Tool to filter infill
opportunities at the parcel level in order to identify those opportunities
geographically, and then quantify them in terms of parcels, acreage, and (in the case
of infill housing) potential units. This feature combines the power of statistical
databases with the power of mapping and aerial photography.
2) Infill Strategy Evaluation: This feature allows users to test the likely effectiveness of
various infill strategies based on different assumptions and scenarios. The feature
estimates the effectiveness of a given infill strategy by quantifying two factors: the
increase in allowable density that would result from the strategy and the expected
increase in the amount of infill development over a specified period of time that
would result from employing the strategy.
For a complete technical description of the California Infill Estimation Tool, please refer
to Appendix A.
In the first phase of this project, we demonstrated the feasibility of the California Infill
Estimation Tool as a stand-alone, analytical implement. Relying only on the above datasets
and the above processes of screening and evaluation, it was proven to be a valuable
mechanism for government agencies, developers and community groups.
2.3 Goals of Phase II
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In this second phase of the project, we have improved upon the “stand-alone” success of
the Tool. This has involved developing a series of infill estimation methodologies that
expand the Tool’s analytical range and increase its accessibility. In addition, we have
developed methods to integrate the Tool into existing, municipal GIS resources. These
improvements can be summed up in the following five project goals:
1) Project Dissemination: An important element of this study has been informing and
educating a range of potential users about infill estimation using the Tool.
Throughout the various stages of this project, we “marketed” the history and
potential of the infill estimation methodology to the multiple local agencies,
developers and community groups with which we worked. Using presentations,
tutorials and internet links, we sought to continually educate this expanding pool
of potential users.
2) Design of Environmental Justice Methodology: A major effort of this project was
expanding the analytic capacity of the Tool to include identifying those population
sectors likely to be adversely affected by a particular infill strategy. To achieve this,
we researched the social and legal history of the adverse impact of development,
integrated current local reality via community outreach, and then designed a
methodology for analyzing the potential disproportionate impacts of infill
development. This environmental justice methodology involves “layering” block
and district-level socioeconomic data onto parcel attribute data in our GIS-based
infill analysis.
3) Design of Economic Methodology: We have also expanded the analytic capacity of the
Tool to include using economic analysis to better estimate the geographical
distribution of infill development. We developed a methodology for this approach
in which the results of the Tool’s geographic screening feature are used to identify
context-appropriate infill project prototypes, the financial feasibility of which are
then analyzed based on current market conditions and standard industry returns.
4) Creation of Integrative Technical Pilot Program: With this study, we have demonstrated
that the Tool can be incorporated into the web-based GIS applications already
made available by many public agencies. Through engagement with City of Los
Angeles planning staff, we outlined a pilot program for integrating the capabilities
of the tool directly into the City Los Angeles, Department of City Planning's Zone
Information and Map Access System (ZIMAS).
5) Coordination of Neighborhood Workshops: Finally, we have demonstrated the value of
the Tool to the community planning process, and have demonstrated that the Tool
is a valuable mechanism for organizing and quantifying community input toward
environmentally just infill. These conclusions came from a series of sequential
neighborhood workshops that we designed, in which the concerns of
neighborhood residents were inputted into the infill analysis process and used to
refine an actual infill screen of their community.
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2.4 Dissemination Strategy
Interest in the California Infill Estimation Tool has been strong since its inception and
throughout its entire development. During the previous phase of this study, it became clear
that the continuing development effort should include a strategy for building that interest
and educating relevant groups about the potential of the Tool. Therefore, integral to each
element of the present study was our effort to ensure that all potential users were made
aware of the Tool’s history and applications. These targeted groups included community
groups, developers, local and state agencies and educators in the fields of planning and
public policy.
At the beginning of this project, we finalized a comprehensive introduction to tool,
including a history and tutorial on the process of geographic screening for infill potential.
This PowerPoint presentation was titled “Infill Opportunity Scenario Analysis”. This
introduction includes various GIS maps and a step-by-step presentation of the processes of
Geographic Screening and Infill Strategy Evaluation. Accessible via links on both the
Solimar Research Group and California Planning & Development Report web sites, this
tutorial has been the foundation of our educational strategy.
The outreach and participatory nature of much of this project has allowed us to inform a
wide swath of potential users:
•
Community Groups: We have worked closely with the Los Angeles Department of
Neighborhood Empowerment (DONE) and it’s Congress of Neighborhood
Councils in order to organize our neighborhood workshop series. Through DONE,
we have distributed information about our tool and the infill estimation
methodology to approximately 100 Neighborhood Councils covering the entire Los
Angeles Region. During our workshops, members of independent community
groups such as Pacoima Beautiful and the Wilmington Citizens Committee were
provided with our introductory PowerPoint presentation.
•
Developers: In completing the pro forma and market analyses central to our
economic methodology, we communicated with both affordable and market-rate
housing developers in Los Angeles. All were interested in the direction of our
research, and each was provided with information about the tool and notified of
web-based information and links to the tool.
•
Local Agencies: We developed “points of contact” with both the City of Los
Angeles and the Los Angeles Community Redevelopment Agency. In concert with
these individuals, and as a result of the outreach efforts integral to this project, we
presented material directly to 17 local agencies in the Los Angeles region. This does
not include those agencies directed to the tool via the internet.
Most recently, we are planning a comprehensive presentation of both phases of the
California Infill Estimation Methodology Project to the State of California, Housing and
Community Development department. With completion of this phase, Cathy Creswell,
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Deputy Director of the Housing Policy Development Division has asked to distribute the
reports to her office, for review and presentation planning.
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3. Environmental Justice Methodology
The Geographic Screening Feature of the California Infill Estimation Tool permits users to
identify parcels that are likely to be “in-play” as the result of an infill policy or strategy. In
Phase I, we realized that this capacity to estimate the “location of impact” may provide for
an important analytical expansion: With the likely locations of infill development
identified, the socioeconomic and demographic character of those locations could also be
assessed. As a result, the Tool could be integrated into a methodology for predicting the
adverse environmental affects of urban redevelopment on low-income, disadvantaged or
similarly sensitive populations.
In the following section, we outline our development of a method to maximize the
promotion of environmental justice in the urban infill process. Although the method is
based on inputting socioeconomic datasets into the parcel analysis process, it was refined
following a series of community mapping efforts, as well as a group of neighborhood
participatory workshops that we organized in three Los Angeles communities.
As questions of inequitable impact become increasingly integral to the politics and practice
of urban infill, we believe that this methodology could prove influential to successful infill
in California.
3.1 Introduction: Definitions and Difficulty
First and foremost, we sought to understand past and current perspectives on
environmental justice in the State. According to the California Department of
Transportation’s Standard Environmental Reference (SER), environmental justice refers to “the
fair treatment and meaningful involvement of all people regardless of race, color, national
origin or income, from the early stages of transportation planning and investment decision
making through construction, operations and maintenance.” Although the SER refers
specifically to transportation projects, promoting the equitable distribution of
environmental impact is critical to all project types. This reality assumed a legal standing in
California in 1999, when legislation was passed mandating that the California
Environmental Protection Agency administer programs in a way that “ensures fair
treatment of people of all races, cultures, and income levels, including minority
populations and low-income populations.” (Public Resources Code [PRC] section
71110(a)).
Regardless of the increasing recent legal weight of the issue, defining what exactly
constitutes those sensitive groups—those “environmental justice populations”—has proven
difficult. There is no quantitative absolute that divides such a population from all others.
Although the term refers to one or more of three socioeconomic indicators—race, class and
income—the limits of the term have varied across circumstance and time. Not surprisingly,
there has been little clarification from the either state or federal courts; many have refused
to enforce environmental justice regulation. “In a 2003 lawsuit over the siting of a Seattle
area transit line,” writes Paul Shigley in the California Planning & Development Report (2003),
“the Ninth U.S. Circuit Court of Appeals ruled that a Department of Transportation
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environmental justice regulation (against disparate impacts) cannot be enforced in court
under the Federal Civil Rights Act.”
Developing a methodology for analyzing a subject that defies straightforward definition
forced us to utilize a series of established parameters. Therefore, in integrating
environmental justice analysis into the infill estimation methodology, we have relied on the
guidelines of “minority populations and low-income populations” set by the U.S.
Department of Transportation to assess Environment Justice:
•
Low-Income refers to persons whose median household income is at or below the
Department of Health and Human Services poverty guidelines. A “low income
population” refers to any readily identifiable group of low-income persons who live
in geographic proximity.
•
Minority refers to a person who is Black, Hispanic, Asian American, American
Indian or Alaskan Native. A “minority population” refers to any readily identifiable
group of minority persons who live in geographic proximity, and, if circumstances
warrant, geographically dispersed persons who will be similarly affected by a
proposed program, policy or development.
It is important to stress that these are not definitions of environmental justice populations.
They are definitions of the groups that most often comprise such populations. A minority
or low-income group will not necessarily bear the brunt of a given development, just as a
population that has been disproportionately, adversely impacted will not necessarily be
composed of low-income or minority individuals. Rather, recent history has revealed a
significant imbalance in the environmental impact of development on communities, with
low-income and minority populations very typically disproportionately affected. A key to
our methodology, then, is introducing datasets into the infill estimation process that
highlight the presence of such groups.
3.2 Promoting Equitable Infill: A Five-Step Methodology
The environmental impact of infill development must be assessed on a case-by-case basis.
Unlike the siting of, for example, a hazardous industrial facility, which has an inarguable
potential for negative impact, infill projects can either impact or benefit the environment.
While they have the potential to meet housing demand, revitalize community character
and support low-income groups, they can also result in population displacement and
neighborhood fracture. Our methodology is based on detailed case analysis, free of initial
assumptions.
As explained earlier in this report, property data is central to the functionality of our infill
analysis tool. Such data is required to draw and map parcel shapes, define parcel attributes
and provide information about the zoning of parcels. These and other land use-oriented
datasets allow the tool to estimate where infill development is likely to occur.
Yet this is not the only type of data that can inform the Tool’s analysis. In addition, data
on entire blocks, block groups and districts can be inputted into the tool. Unlike parcel
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data, this information is not limited to property characteristics. It includes the myriad
socioeconomic indicators of the US Census, as well as data on specially-labeled “areas” or
“zones,” such local Redevelopment Areas and State Enterprise Zones. These data, when
inputted into a GIS-based mapping analysis, offer a window into community social
characteristics. They can highlight the presence—both directly and indirectly—of those
historically sensitive populations described above.
Our environmental justice methodology began with experimenting with a large group of
such socioeconomic datasets. The individual steps of the methodology and the processes
leading to their development are outlined below. From the start, it was assumed that this
process would be performed in conjunction with the infill estimation methodology made
possible with our tool.
1. Perform Opportunity Analysis of Study Area
The first step in our environmental justice analysis is to establish the appropriate
study area and assess its infill potential. This is facilitated by the Geographic
Screening feature of the California Infill Estimation Tool. The inherent flexibility
of this feature means that study area limits can be established at multiple
geographic levels, from that of a particular Neighborhood Council boundary to
that of an entire community, or even county.
With the study area established, an infill opportunity analysis is then performed. As
was demonstrated in the previous phase of this study, the result of this analysis is
the selection of parcels containing attributes known to facilitate infill development.
Based on this parcel-level data alone, these are properties that represent significant
opportunities for infill development.
2. Input Selected, Economic Justice-Relevant Datasets
With the study area defined and infill opportunity sites identified, the next step in
this methodology is to input, into the GIS mapping process, those datasets that
offer the clearest picture of potentially sensitive populations. As explained earlier,
our assessment of equitable infill development is founded on a GIS-based analysis
of various block- and district-level socioeconomic data in concert with a parcel
analysis.
We have selected six such datasets that have proven consistently functional in
highlighting potential environmental justice populations. Before continuing to the
next step in the methodology, it is important to explain how we made these data
selections.
The range of available datasets related, at least some degree, to the presence of “lowincome” or “minority” populations is vast. Some, such as Census statistics of the
percent of a given ethnic group by block group, are direct lenses into potentially
sensitive groups. Most others, such as education-level or domestic overcrowding
statistics, are indirect indicators. They are attributes known to exhibit statistically
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significant variation between low-income and high-income groups, or white and
minority groups. Some of these have proven relevant to our environment justice
methodology. Most have not.
In developing this methodology, we selected the following list of datasets as our
original “field” of potential inputs:
Possible Datasets
% White by Blockgroup
% Black by Blockgroup
% Hispanic by Blockgroup
% Asian by Blockgroup
Mean Household Income
Mean Home Value
% without High School
Diploma
% without College Degree
% Unemployed
% White Collar Occupations
% Blue Collar Occupations
% Owner Occupied
% Renter Occupied
Population Density
Structure Year Built
Parcel Investment Index
Land Use
Data Source
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Assessor, County of Los Angeles
Assessor, County of Los Angeles
City of Los Angeles
As we developed this methodology, it was clear that we needed to refine this list to
include only those that consistently match “on the ground” reality. In order to
determine this, we undertook a two-part process of mapping a variety of Los
Angeles communities.
First, we created a series of Environmental Justice maps of three communities with
significant minority and/or low-income populations: San Pedro, Sylmar and
Westlake. In this mapping series, we sought to gauge the relevancy of various
datasets to “real-world” conditions, and thus assess their utility in an environmental
justice methodology. In our testing of various datasets, we mapped “direct” inputs
such as ‘Majority Race by Blockgroup,’ ‘Percent Hispanic by Blockgroup’ and
‘Percent of Median Household Income’ (see figure 3.2.1). We also tested those less
direct indicators of sensitive populations such as ‘Average Number of Autos per
Adult’ and ‘Percent of Workforce that Commutes via Public Transit’ (see figure
3.2.2).
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Figure 3.2.1 Environmental Justice map of San Pedro: Percent Hispanic by Blockgroup (U.S Census)
12
Figure 3.2.2 Environmental Justice map of Westlake: Percent of workforce that commutes via public
transit (U.S Census)
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Although these maps allowed us to compare data-based conditions to those ‘on the
ground,’ the effort did not allow us to select, with certainty, those datasets to be
included in our methodology. It was clear that the only true test of the relevancy of
socioeconomic data would be a mapping effort in which our GIS maps could be
scrutinized by actual members of the communities mapped. Only such a process
would allow us to select those datasets that most consistently “match” reality.
Therefore, in our second mapping effort to select environmental justice datatsets,
we again created environmental justice maps of three very different Los Angeles
communities, each of varying socioeconomic composition and varying potential for
infill development. In this series, we compared our results to insight into current
conditions that we received from active members of the communities themselves.
We also subjected these communities to the Geographical Screening process,
allowing us to compare infill opportunity sites, socioeconomic conditions and local
knowledge. This was all accomplished via a series of neighborhood participatory
workshops with Neighborhood Council members in the communities of
Wilmington, Pacoima and Silver Lake (see Section 6 for full reporting on these
workshops).
The final result of this process was the selection of three datasets that, when
mapped, most consistently reveal “grounded” patterns of low income and minority
populations. Still, we stress again that while these can reveal the location of such
groups, they do not necessarily reveal the location of an environmental justice
population. Rather, they have proven to be extremely helpful in completing a predevelopment Environmental Justice analysis that will promote critical thinking
about the impact of local infill practices. For that reason we have included them as
the data foundation of our methodology. These three datasets are:
•
•
•
Percent race by blockgroup
Percent of median household income
Overcrowding: Percent of units with more than one person per room
In addition to these three, we have found that the following three datasets, while
less direct indicators, also consistently match the “real-world” location of potentially
sensitive populations:
•
•
•
Average number of automobiles per adult
Percent of workforce that commutes via public transit
Percent of housing units owner v renter occupied
Again, Step 2 of the methodology is to map these datasets in the same study area
for which an infill opportunity analysis was performed in Step 1.
3.
Combine Selected Datasets into Single Environmental Justice Sensitivity Map
Although mapping race, income and overcrowding will highlight different potential
environmental justice groups, a thorough equitability analysis will not weight one
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type of potentially sensitive population over any other. For this reason, Step 3 in
our methodology is to combine the three indicators into a single ranking of
individual blockgroups by overall environmental justice sensitivity.
In order to facilitate this ranking, we established quantifiable definitions of
blockgroup “sensitivity” based on the statistical strength of race, income and
overcrowding indicators. The definitions of blockgroup “sensitivity” for each
dataset are as follows:
•
Race: Blockgroup contains a population composed of over 50% minority
race(s)
•
Income: Median income of blockgroup is below 50% national median
income
•
Overcrowding: Blockgroup is more than 75% overcrowded
Next, a general sensitivity ranking, ranging from 0 (Not sensitive) to 3 (Very
Sensitive) is applied to each blockgroup. This ranking is based on the presence of
the above statistical attributes:
0 (No Sensitive EJ Population): Blockgroup contains none of the above
statistical attributes
1 (Less Sensitive EJ Population): Blockgroup contains 1 of the 3 statistical
attributes
2 (Moderately Sensitive EJ Population): Blockgroup contains 2 of the 3
statistical attributes
3 (Very Sensitive EJ Population): Blockgroup contains 3 of the 3 statistical
attributes
Finally, these rankings are mapped in GIS to provide a visual analysis of economic
justice populations in the study area. As revealed in Figure 3.2.3, this spatial
analysis is a valuable window into outlying areas, clusters and even swaths of
populations whose presence must be considered in the pre-development politics of
potential infill practices.
15
Figure 3.2.3 General sensitivity analysis maps; Silver Lake, Pacoima and Wilmington communities
16
4.
Compare Socioeconomic Maps to Geographic Screen Results
With an infill opportunity analysis performed and the above environmental justice
datasets mapped, the next step in our methodology is to compare the results of the
former with the community maps of the latter. It is this step that may reveal parcels
or parcels clusters that, although ripe for infill, are located within areas of lowincome or minority populations, or areas that show signs of economic imbalance
such as severe overcrowding. This comparison offers the first indications of
potential adverse impact of infill development.
However, we found in the development of this methodology that this purely GISbased analysis, due to data imperfections and the inherent dynamism of urban
conditions, must be “ground-truthed” against actual conditions. We found that
while not an absolutely reliable foundation for environmental justice analysis, this
comparative mapping provides a critical platform on which to collect contemporary
input from community members themselves. Step 4 of our methodology is to
integrate the feedback of the local population for a truly context-sensitive and
contemporary set of maps.
5.
Integrate Community Feedback
It wasn’t until completing our neighborhood workshop series in Wilmington,
Pacoima and Silver Lake that we realized the value of local input to an
environmental justice methodology. From awareness of current development
schemes, involvement in local development conflicts and sensitivity to local social
priorities, we collected feedback from citizens that acted as a final “editing pen” to
our maps. We now believe Public involvement is crucial and must be included in
the analysis of socially equitable infill.
Just as important, we have seen that the integration of public participation in this
process is vital step toward procedural equity in the infill practice. As has been
demonstrated throughout both phases of this study, infill development is
inherently complex due to its definitive placement in dynamic urban settings.
Hearings, meetings or charets that are both open to, and easily accessible by, all
involved parties, add transparency to the infill process. In addition, our experiences
with Los Angeles community councils revealed that the very nature of an equity
analysis, often steeped in ethnic difference, demands sensitivity to language
barriers. In order that all groups may truly respond and contribute, presentations
and other materials should be circulated in the language(s) of dominant minority
group(s) (in our case, Spanish).
Again, the procedural intricacies of our participatory meetings, as well the details of
community feedback and resulting maps, are recounted fully in Section 6 of this
report.
7. Revise Mapping and Analyze Potential “Low Impact” and “High Impact” Parcels
17
The last step in our methodology is to revise the infill opportunity/environmental
justice dataset map, such that it fully represents community input. This involves a
process of either removing or adding areas of either infill potential, and/or
environmental sensitivity. We found that certain parcels deemed “ripe” for infill by
parcel data alone may be deselected, while certain blocks, blockgroups or areas that,
according to the six datasets alone, are in reality less sensitive that the data would
suggest. Others areas of the study area deemed “insensitive” by data may be selected
as potential “high impact” sites, vulnerable to infill projects.
The final result of this process is a context-sensitive, comparative tool for assessing
the relationship of community socioeconomics and infill potential. It is a product
that accounts for three elementary “forces” that have, and will continue, to shape
infill practice: parcel/land use, community characteristics and citizen response.
Once again, see Section 6 for the full results of this process.
3.3 Summary and Constraints
As has been stressed throughout this five-step methodology, to identify and define a
“sensitive” population is an extremely sensitive undertaking in and of itself. The fact that
there is no definition of an environmental justice population means that there are simply no
data available to identify, for certain, those populations. Neither is there a method of truly
verifying their presence. This is certainly the overriding constraint of our methodology.
Even using a combination of relevant data with modern mapping techniques and the
equitable input of all stakeholders, they process is far from perfect: While such quantifiable
data is potentially dated, incomplete and incorrect, individual input is always subject to
bias, false information and emotion.
In assessing the relationship of infill development and surrounding populations, there is
also an inherent difficulty in determining “cause and effect:” Does a development with
known adverse effects result in a surrounding environmental justice population, or do
populations of known sensitivity transform any development into an “impactful” project?
While history has shown that just as most hazardous development is sited in industrial
areas of low-income populations, land value surrounding such development is reduced,
thus attracting populations of potential sensitivity. In developing our methodology, we
experimented with mapping established sites of adverse impact, such as known
Environmental Protection Agency (EPA) Hazards, as a means of identifying environmental
justice populations. Although the areas surrounding such sites often coincided with the
areas deemed “sensitive” by data and local input, we decided not to include any hazardous
site analysis in our methodology, because of the “cause and effect” conundrum. For that
reason, the methodology may have lost certain analytical capacity.
Finally, the influence of market forces is not integrated into our analysis. These are
becoming increasingly critical to environmental justice assessment because of the
prevalence of mixed-use development. Mixed-use projects—fast becoming the defining face
of urban infill development—may result in significant adverse impact in the form of
residential displacement. These projects are plagued with a notorious feasibility gap under
18
current market conditions. This is due to high construction and land costs, as compared to
low selling prices and lease rates, in inner urban settings. As a result, developers and
jurisdictions, seeking to avoid eminent domain and ease the parcel assemblage process, are
prone to displace rental residents, who are much less “connected” to property than
homeowners. In addition, the very attempts to mitigate the impact of mixed-use projects,
such as low-income housing requirements and the preservation of manufacturing/
industrial jobs so prevalent in infill-ripe areas, only exacerbate this feasibility problem
further.
As mixed-use development gains increasing favor and becomes an increasingly common in
the urban center, a social equity analysis that accounts specifically for the difficulties of
mixed-use will be valuable.
19
4. Economic Methodology
In addition to predicting community impact, a critical component of GIS-based infill
estimation is an economic analysis that addresses the nexus between the location of infill
opportunities and the financial feasibility of such opportunities. For that reason, we have
strived to develop an economic methodology that does more than identify the quantity of
infill that might result from the use of a particular policy. In addition, that methodology
should identify the factors most likely to motivate a developer to pursue infill in specific
locations. It is those factors can be used to identify, with increasing certainty, those parcels
most likely to be targeted for development.
Any method of estimating the absorption rate of infill—or the quantity of such
development that would result from a given strategy—must account for the impact of three
factors:
1) Natural market activity
2) Additional development activity likely to result from implementation of an infill
strategy
3) Additional development activity likely to result from the use of public subsidies
In this section, we outline a methodology to estimate the impact of these three factors on a
given area and then incorporate the results into a GIS-based spatial analysis.
4.1 Methodological Basics
The potential for infill development in the urban core is vast. Driving this theoretical
capacity for infill are the existing, often aging commercial corridors that are so common to
the urbanized neighborhoods of built-out metropolitan regions. As will become evident in
the following economic analyses of study areas of such character, these commercial strips,
developed as retail/commercial resources for surrounding neighborhoods, generally
contain little to no housing themselves. Not surprisingly, approximately one-half of all
current housing starts in City of Los Angeles are in the form of high-density units located
on such commercial strips. Were these strips removed from the infill equation, the
resulting potential for new, mixed-use development would decrease drastically, effectively
reducing the potential for urban infill in general.
Regardless of this proven land-use “resource,” infill development is notoriously plagued by
feasibility challenges. Like all development, infill is feasible only when projects are able to
attract financiers. Investors and lending agencies will only provide funding if a developer
can show at least an industry-expected return on borrowed capital.
It is in this context that we performed economic feasibility analyses of infill development in
three Los Angeles study areas: The geographically and demographically varied communities
of Pacoima, Wilmington, and Silver Lake. As stated in the previous section, we also
organized our pilot neighborhood workshop series (Section 6) in these three communities.
20
We have outlined and adhered to a three-step analytical method:
1) Infill Opportunities Analysis. This involves quantifying the underutilized infill potential of
a study area. Underutilized parcels “ripe” for infill development are identified with the
Geographic Screening function of the infill estimation tool (see Appendix A). This
function integrates common planning datasets to identify parcels with common infill
characteristics. Commercially zoned areas are screened for parcels on which no more than
50% of the possible density has been constructed, parcels with an area of more than
20,000 square feet and parcels with a structure-to-property value ratio of less than two.
Residentially zoned areas are screened for parcels on which 3-10 more units can be added
under current zoning and parcels with no more than 1 existing unit.
2) Pro-forma Analyses. We performed a series of economic pro-forma analyses to test the
financial feasibility of infill project prototypes most appropriate to each community’s infillripe land. The infill prototype selected for each community is based on the typical parcel
sizes available for infill development (as identified in Step 1), feedback from neighborhood
workshops and developer insight into building in specific zoning classes.
Our pro-formas were developed following extensive conversations with Los Angeles
development professionals and analysis of data from the Urban Land Institute. They allow
us to model developer costs, revenues, and expected returns in the local real estate market.
In its most basic form, an infill development pro-forma is similar to any equation for real
estate return:
Project Revenue
(# units x selling price + lease rate x leasable space)
- Project Costs (i.e. site development, parking, construction, and financing costs)
- Land Costs
(i.e. land current selling price)
= Project Profit
(industry standard: at least 15% of total costs)
If revenue does not cover costs and provide the industry-expected 15% profit, then the
project is considered financially unfeasible: A cash shortfall or “gap” prevents profitability.
For a detailed sample economic feasibility pro forma see Appendix B.
The last element of our pro forma analyses is a comparative assessment of “baseline” and
“optimizing” conditions. We compare the feasibility gap under “baseline,” or existing,
zoning constraints to the feasibility gap calculated under “optimizing” conditions—those
designed specifically to minimize infeasibility. These are the ideal conditions that attract
developers to pursue infill development.
3) Policy Translation. The third and final step in the process is to translate the results of the
economic analysis – that is, any disparity between the “baseline” and “optimizing”
conditions - into tangible policy options or policy adjustments. The final goal is the
21
establishment of local policy that promotes those “optimizing conditions” and therefore
encourages infill development in the area under analysis.
4.2 Study Area Opportunity Analyses
All residential and commercial zones in the three study areas were analyzed for infill
opportunities. In total, the three communities contain an estimated “opportunity” capacity
of 26,330 units on 1830 parcels. Compared to the 1,542 existing units, the combined
build-out level of the three study areas is only 6%. Wilmington has the most infill
potential: It has the capacity for 10,764 units under existing zoning conditions.
The greatest potential for residential development in commercial zones is also in
Wilmington, where 90 parcels zoned C2 contain an estimated potential for 7809 units.
Silver Lake has the most potential for development in residential zones, with R3 and RD2
zones that contain 612 parcels with an estimated infill potential of 3,374 units. In
Pacoima, where 78% of residential parcels are zoned single family, the potential for infill is
markedly decreased. In addition, the majority of C2 commercial parcels in Pacoima are too
small for the large, mixed-use infill projects that maximize economies of scale and reduce
project costs.
4.2.1 Prototype Selection and Market Analysis of Study Areas
For each study area, a single “best-fit” prototype was selected based on geographic screening
results. The results were presented to members of each community during the
neighborhood workshops, and parcel opportunities were further refined with the input of
knowledgeable citizens. These workshops were instrumental in choosing appropriate
project prototypes, with participants contributing valuable insight to the infill analysis (see
section 6 for workshop details). The resulting prototype for each community, and the zone
class for which it is modeled, are as follows:
Wilmington: Small mixed-use prototype 2A in C2 zone.
Pacoima: Townhome prototype 1 in RD2 zone.
Silver Lake: Townhome prototype 1 in C2 zone.
The results of our pro-forma analyses are reported below. “Baseline” and “optimizing”
conditions are modeled for those projects deemed unfeasible. We introduce the
determinants of infill feasibility with the inclusion of general market trends pertaining to
the entire study area and surrounding zip codes. We then “tailor” these general findings to
each of the three study areas.
Uncertainties in market conditions prevent the accurate prediction of selling prices, lease
rates and absorption rates of future projects. To buffer this uncertainty, we completed
small market surveys of the three study areas; the results are displayed in table 4.2.1. Cells
highlighted in blue represent assumptions inputted into the pro formas. An inevitably
22
uncertain market ensures that reported “gap” values will vary by 5% to 10% of total project
costs. As such, all feasibility gaps reported should be considered estimates.
Pacoima (Townhomes in RD2 )
Avg
freq. SD
Land (sale/sf)
$ 61.49
13 $ 40.04
Res Land (sale/sf)
$ 52.01
8 $ 39.12
Condos/Townhomes (sale/sf) $ 328.15
14 $ 146.37
Commercial (lease/sf/month)
$ 2.34
10 $ 0.66
Cap Rates (retail/office)
6.50% 13
1.61%
Silverlake (Townhomes in C2)
Avg
freq. SD
Land (sale/sf)
$ 108.15
7 $ 38.20
14 $ 107.59
$ 2.13
29 $ 0.71
6.50% 20
2.10%
Wilmington (Small Mixed-Use in C2)
Avg
freq. SD
Land (sale/sf)
$ 60.05
8 $ 25.05
Commercial Land (sale/sf)
$ 62.86
10 $ 23.14
14 $ 93.78
$ 1.77
9 $ 0.51
5.75% 10
0.95%
Pro-Forma Input
$
60
$
50
$
325
$
2.35
6.50%
Pro-Forma Input
$
110
$
390
$
2.25
6.50%
Pro-Forma Input
$
60
$
65
$
300
$
1.75
5.75%
Table 4.2.1: Study Area Market Survey Results
Silver Lake: Townhome Prototype 1, C2 Zone
Of the three study areas, Silver Lake has the highest residential and commercial density as
well as the smallest average parcel size. Centrally located and with few vacant parcels for
sale, the real estate market in Silver Lake is strong. Our market survey revealed that more
than 50% of “land for-sale” is existing apartments, retail, and mixed-use structures.
This for-sale land—characterized largely by older, less utilized structures—contrasts Silver
Lake’s many newly developed parcels, each with condos or townhomes. The community
has extremely high condo/townhome prices fueling a market-driven redevelopment trend.
Developers are maximizing density by demolishing outdated residential and commercial
structures and replacing them with dense, executive style residential units.
Prototype 1(figure 4.2.2) models a townhome project on a hypothetical, 1,500 square foot
C2 parcel. While a typical townhome prototype contains 8 units on medium density
residential, C2 zoning allows for 5 extra units—bringing the allowed total to 13. This
increased density, coupled with high per-unit selling costs, results in project profitability
(see figure 4.2.3). In fact, the analysis shows a negative feasibility gap of $350,000 in excess
capital. This finding is supported by our market analysis, which reveals that many new,
executive-style townhomes are being sold.
23
Prototype #1
Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7
50’
“Townhome Project”
Project”
(Baseline)
driveway
Project Characteristics:
Unit 8 Unit 9 Unit 10
Unit 11
Unit 12
Unit 13
50’
Residential Townhomes: 13 units
Individual Lot Size = .34 ac (15,000 sf)
Zoning:
C2
Allowed Density:
108.9 units/ac
Commercial:
0%
150’
35’
Street level
garage
garage
Unit #1
#2
garage
garage
#3
Figure 4.2.2 “Baseline” townhome prototype, Silver Lake
Townhouse Prototype 1
Land Use Mix Scenario #1(Baseline)
Feasibility Gap
$2,250,000
$2,000,000
$1,750,000
Land Value ($)
$1,500,000
$1,250,000
$1,000,000
$750,000
$500,000
$250,000
$$(250,000)
current land
selling price
residual land
value
feasibility gap
$(500,000)
Figure 4.2.3 “Baseline” pro-forma results, townhome prototype 1, Silver Lake
24
#4
Wilmington: Small Mixed-Use Prototype 2A, C2 Zone
Wilmington has a relatively weak real estate market characterized by expensive land, low
commercial lease rates and low condo prices. Not surprisingly, prospects for successful
infill are minimal. This is unfortunate, as our infill estimation revealed that Wilmington
has the most infill opportunity for residential units of the three study areas. Its commercial
corridors alone hold exceptional opportunity for an estimated 9,041 units.
Geographic Screen results show that 86% percent of the estimated unit potential lies in C2
zones, with an average parcel size of 41,310 square feet. On paper this group of parcels
would be best suited for prototype 3A—a large mixed use project. However, during our
community workshops, Wilmington citizens professed a strong distaste for bulky, mixeduse projects in its downtown core. In addition, our environmental justice methodology
(Section 3) identified a significant, low-income Hispanic population susceptible to
displacement by large, market-rate mixed-use projects.
A more acceptable infill solution for Wilmington is the small mixed use prototype—
prototype 2 (figure 4.2.4). This 26-unit structure with 3,000 square feet of ground-floor
retail and underground parking was modeled in Wilmington’s underutilized C2 zones.
Unfortunately, Wilmington’s difficult market conditions are revealed in the pro-forma
results, which show little chance of such a project being constructed under existing code: A
feasibility gap of $3.1 million exists (figure 4.2.5).
Prototype #2
“Small Mixed-Use Project”
(Baseline)
Mixed-Use Residential: 26 units
Min. Lot Size:
.39 ac (17,000 sf)
Zoning:
C2
Density:
108.9 units/ac
Commercial:
20%
Parking:
‘double podium’
Residential
2 levels of Type V wood construction
over 1 level Type 1 above grade, and 1
level below grade concrete podium
parking
Street level
35’
Residential
Retail
Parking
Parking
Figure 4.2.4 “Baseline” small mixed use prototype, Wilmington
25
Small Mixed-Use Prototype 2A
Feasibility Gap
$1,500,000
$1,000,000
$500,000
Land Value ($)
$$(500,000)
$(1,000,000)
current land
selling price
residual land
value
feasibility gap
$(1,500,000)
$(2,000,000)
$(2,500,000)
$(3,000,000)
$(3,500,000)
$(4,000,000)
Figure 4.2.5 “Baseline” pro-forma results, small mixed use prototype, Wilmington
Typically, the “optimizing” conditions necessary to reduce such a feasibility gap would
include a combined strategy of parking reductions and density increases. This small, mixeduse prototype is typically built on a 0.4-acre or less commercial property in the C1 and CM
zones, each permitting 54 units/acre. However, in the case of Wilmington, we found that
the parcel group with greatest potential is zoned C2, with a maximum allowable density of
108.90 units/acre. One way to maximize this higher allowable density further would be to
relax height requirements, from 35 feet to 45 feet. This would facilitate a third floor of
residential units and increase the number of allowed units by 11, from 27 to 37 (figure
4.2.6).
26
Prototype #2
“Small Mixed-Use Project”
(Optimizing)
Mixed-Use Residential: 37 units
Min. Lot Size:
.39 ac (17,000 sf)
Zoning:
C2
Density:
108.9 units/ac
Commercial:
20%
Parking:
‘double podium’
Residential
2 levels of Type V wood construction
over 1 level Type 1 above grade, and 1
level below grade concrete podium
parking
Street level
35’
Residential
Retail
Parking
Parking
Figure 4.2.6 “Optimized” small, mixed-use prototype, Wilmington
An even more influential element of the “optimizing” of mixed-use feasibility is parking
requirement reduction. The provision of parking can add huge increases to project costs.
Current C2 zoning in Wilmington allows for 2 and 1.5 parking spaces for two- and onebedroom units, respectively. As figure 4.2.7 reveals, these requirements are reduced to 1.25
spaces per unit regardless of the number of bedrooms, and 1 space per 500-square-feet of
retail, the feasibility gap is reduced by 10%. The resulting gap is still far too large to warrant
financing of any kind.
27
Feasibility Gap
Prototype 2A: Small Mixed-Use Project
$0
-$1,000,000
-$2,000,000
-$3,000,000
-$4,000,000
Baseline
Optimizing
Figure 4.2.7 Comparative feasibility; small mixed-use project, Wilmington
Even optimal conditions that include increased density and reduction parking
requirements will not reduce the feasibility gap enough to warrant development. In fact,
for this infill prototype to be feasible in Wilmington, parking requirements would have to
be totally eliminated: Only subsidized neighborhood parking structures could make this
project “pencil.” Given that the assumptions modeled have a 5 – 10% degree of market
uncertainty, the results of this analysis support our market survey findings: Current
constraints prohibit mixed-use infill development in Wilmington and these constraints will
continue without substantial subsidies or severe political action.
Pacoima: Townhome Prototype 1, RD2 Zone
Pacoima has the lowest infill potential of the three study areas. While land costs are nearly
the same as Wilmington, townhome selling prices are 8% higher. Commercial lease rates
are higher than in Wilmington and Silver Lake, yet capitalization rates are also a relatively
high 6.5%. This is indicative of inconsistency in the utilization of leaseable, commercial
space.
As revealed by the Infill Estimation Tool, the C2 zoned commercial corridors of Pacoima
have significant infill opportunity: 5962 high-potential residential units. However, the
surrounding suburban land lacks the density required to build a “critical mass” of
population needed to catalyze the development of high-density, mixed-use projects. In
addition, 78% of residentially zoned Pacoima is zoned single family and is essentially builtout. Still, 10% of residential parcels in Pacoima are zoned RD2, allowing 21.78 units/acre.
The average RD2 parcel size is 15,109 square-feet, an amount of space suitable for 7
townhome units (figure 4.2.8). Moreover, our geographic screening of residential parcels
28
revealed that many of the smaller parcels are adjacent. There are significant opportunities
for parcel assemblage.
Considering the above land-use patterns, the townhome prototype is best-fit to model in
Pacoima. This assumption is supported in the market analysis, which demonstrated that
46% of land for sale in Pacoima is listed as planned or suitable for
condominium/townhome development.
Prototype #1
Unit 1
Unit 2
Unit 3
50’
(Baseline)
driveway
50’
Unit 4
Unit 5
Unit 6
Unit 7
Zoning:
RD2
Allowed Density:
21.78 units/ac
Commercial:
0%
150’
35’
Street level
garage
Unit #1
garage
#2
garage
#3
garage
#4
Figure 4.2.8 “Baseline” townhome prototype, Pacoima
A density increase in the RD2 zones helps to make townhouse development more feasible
– but not completely so. Moving from a “baseline” density of 21.78 units/acre to 29
units/acre under “optimizing” conditions acts to reduce the gap by 64% (figure 4.2.10).
This would permit an increase in project density from 7 units to 9 units on typical RD2
lots (figure 4.2.9).
29
Prototype #1
Project”
Unit 1
Unit 2
Unit 3
Unit 4
Unit 5
50’
(Optimizing)
driveway
Unit 6
Unit 7
Unit 8
Unit 9
50’
Zoning:
RD2
Allowed Density:
21.78 units/ac
Commercial:
0%
150’
35’
Street level
garage
Unit #1
garage
#2
garage
garage
#3
#4
Figure 4.2.9 “Optimizing” townhome prototype, Pacoima
Feasibility Gap
Protoype 1: Townhome Project
$0
-$200,000
-$400,000
-$600,000
-$800,000
-$1,000,000
-$1,200,000
-$1,400,000
Baseline
Optimizing
Figure 4.2.10 Comparative feasibility gap; townhome project, Pacoima
Parking reductions are less of a concern in townhouse development, as townhome units
typically contain a two-car garage. But the allowance of tandem parking would make more
efficient use of space and make greater densities easier to achieve.
30
The results of this analysis indicate townhomes in Pacoima are not feasible: Even with the
inclusion of optimizing conditions into the pro forma analysis, a significant feasibility gap
remains. This conclusion is supported by our market analysis, in which we discovered
multiple instances of for-sale land that has been approved for medium density residential
development, but encountered relatively few condos and townhomes on the market.
4.3 Key Findings
The above analyses revealed “gaps” in the financial feasibility of all selected models except
the townhome prototype in Silver Lake’s C2 zones. The severity of these gaps depends on
the study area and prototype.
One of the main challenges in countering the likely infeasibility of infill projects is
increasing unit count to the point of profitability. A density increase can be a powerful tool
for ensuring feasibility. However, density bonuses only contribute to the financial
feasibility when they generate economies of scale. Minor increases in density – say 10% to
15% - do not generate the unit counts needed to ensure the feasibility of small, mixed-use
projects. Medium-density commercial zones demand an increase in allowable density at
least a 60% to achieve threshold unit counts. This requires the relaxation of municipal
“height limits.” Typically a 4th or 5th floor is required.
As evident in our optimizing pro-formas, reductions in parking requirements can further
catalyze infill development. Typical infill parcels are small; they contain limited space for
parking. This forces developers to provide “below-grade” and “podium” parking at huge
costs: A parking space in each can cost between $17,000 and $35,000.
Not surprisingly, a decrease in parking requirements can significantly increase the financial
feasibility of projects. Reductions to 1.25 parking spaces per unit, regardless of the number
of bedrooms in a unit, in conjunction with 1 space per 500 sf retail, show a sizable positive
impact upon financial feasibility. These trends generally hold true for both for-sale and
rental infill prototypes. Moreover, a reduced parking requirement such as this is politically
tractable – especially when linked with shared parking strategies.
In short, there are three main determinants that create financially feasible infill projects.
These are: (1) the requisite density to achieve “threshold” unit counts to off-set the high
cost of land, (2) a height limit that allows developers to achieve the requisite densities, (3)
parking requirements that reduce the high cost of providing infill parking.
Some additional findings and determinants we found to be true are:
•
Retail space is often considered by developers a “means to an end” in order to get
approval for market rate condo units. Although it offers developers a “less
appealing” source of project revenue than do residential units, it is considered
necessary to get the project approved. Developers often do not even assume retail
31
revenue in their pro-formas. For this reason, the amount of commercial space the
City requires in its mixed-use projects should be carefully considered
•
Rental projects are much more challenging than their for-sale counterparts; they
illustrate the barrier to market-rate rental development in the City. The
combination of high land costs and low rents vastly decrease the chances that rental
projects will “pencil.” Even significant density bonuses and parking reductions are
not enough to produce financially viable rental projects. Until there is a prolonged
upward pressure on rents and a downward pressure on land values, the latter of
which is not conceivable in the short term, new rental projects will only survive
with public subsidies.
4.4 Policy Recommendations
Based on the findings of our economic analyses of the three study areas described above,
we have identified a series of policy ideas, specific to the City of Los Angeles, to “tip” infill
projects into the black. Again, each of these is a result of the various “barriers to feasibility”
that we encountered in our location and economic analyses of infill projects in the above
three Los Angeles communities. These policy recommendations are meant to be illustrative
of the power of the infill analysis tool in identifying policy alternatives in real-life
situations.
4.4.1 Increase Densities and Height Limits on Land in C1 and CM Zones
Our economic analyses of Silver Lake and Pacoima, two contrasting geographical and
market-based communities, revealed the significant role of the townhome project in infill
practice everywhere. As explained, this small, mixed-use prototype is typically built on a 0.4acre or less commercial property in C1 and CM zones, although each was targeted to C2
zones in our community-specific analyses. In order to accommodate townhome
development, it is clear that the City must increase density and height limits in these key
zones.
Currently, C1 and CM zones allow mixed-use and residential projects with a housing
component of 54 units per acre. Considering the value of increased density, the city should
consider increasing allowable densities on C1 and CM land in the station areas from 54 to
85 units per acre (a 60% density increase). This figure represents an economic and land-use
balance of currently permitted density among commercial zones. In order to facilitate this,
the City would need to adjust the language in the “height districts” for these zones to allow
for a 4th floor of residential development. The final result of increase would be 30
allowable units rather than the 20 units that are currently allowed under existing
regulations.
In addition to easing the process and feasibility of development, it is important to note that
this policy—as is the case with all increases in allowable density—will assist cities in reaching
their housing requirement goals. The California Infill Estimation Tool is the result of
32
regional frustration with meeting just such State standards; the implementation of density
increases policy will provide a significant boost in the space that is available for housing
production.
4.4.2 Increase Densities in R2 and RD2 Zones
In the Pacoima, we found that increasing the allowable density of RD2 zoning from 21
units/acre to 29 units/acre resulted in a very significant, 64% decrease in the feasibility gap
of a prototypical townhome project. That this optimizing scenario did not fully close the
financial gap is the result of conditions specific to the Pacoima market and land usage.
Current densities in RD2 are 21 units per acre. This means that in a typical lot assemblage
situation—the combination of two 7,500-square-foot lots—these zones will accommodate
only 7 units. As revealed by our Pacoima baseline pro-forma model, this is not sufficient to
encourage actual development. Increasing these densities by 28% (to 29 units per acre)
would permit up to10 units on this same two-parcel assembly.
4.4.3 Increase Height Limits in C1.5, C2, C4, and CR Zoning
Current allowable density in C1.5, C2, C4, and CR zoning classes is the highest among the
City’s commercial zones: 108 units per acre. This upper limit of commercial density is
generally sufficient to create financial feasibility. However, our optimizing pro-formas
revealed that the “tipping point” of infill project feasibility—the point at which an increased
in units creates an economy of scale—can often only be facilitated by the addition of an
extra floor. It is clear that the City needs to ensure that the height districts regulating these
commercial zones allow a fifth floor of residential development. Only this additional floor
would enable developers to provide the few extra units that are critical to a project’s
bottom line.
4.4.4 Limit commercial space requirements for mixed-use projects
Our economic and infill analysis of Wilmington, which revealed over 9,000 potential
housing units on the community’s commercial strips, exemplifies the role of commercial
corridors in infill strategizing. The mixed-use model of infill development, with residential
units set above retail space, is a cornerstone of urban infill.
The revenue returns of retail space are so much lower than that of residential space that
the latter very often “subsidizes” the former in mixed use projects. Retail requirements can
often jeopardize the financial feasibility of this infill mainstay. Our analyses only solidify
the idea that the City should limit the amount of required commercial space to no more
than 20% of the gross floor area. In addition, while the City currently permits certain
33
retail-free projects in select commercial zones, this flexibility should be expanded to include
all commercial zones.
4.4.5 Reduce Minimum Parking Standards in Commercial Zones
In our economic modeling of townhome projects in Pacoima and Silver Lake, the minimal
unit counts of the projects meant that parking requirements did not play a major role in
optimizing feasibility. However, even in the small mixed-use prototype modeled in
Wilmington, a project of just 37 units (optimal), it became clear that parking requirements
are a major obstacle to project feasibility.
Parking is expensive to provide, uses valuable land area and also challenges the goals of
encouraging transit. While completely eliminating parking is not desirable, reducing
parking requirements to levels that work successfully would be beneficial.
We recommend reducing residential parking standards for all mixed-use buildings to
1.25/per unit, regardless of the number of bedrooms in each unit. The City is currently
looking to implement such a reduction; current policy is 1.5 per 1 bedroom unit and 2 per
2 bedroom unit. Furthermore, we recommend reducing parking standards on infill retail
and office components of mixed use buildings from 4 spaces per 1000 square feet to 2
spaces per 1000 square feet, consistent with standards in other successful mixed-use
districts. In addition the City should encourage tandem parking in the ground floor garage
for townhomes.
These reductions in residential standards are feasible when residents have other
transportation options, including light rail, bus, car sharing, bicycle lanes, and services
within easy walking distance. Greater reductions are also possible if alternative strategies
are created. For example, the City could reduce the parking requirement to one space per
unit and then allow residents to rent or buy parking for a second car somewhere else in the
neighborhood if they need to. Or, rather than easing the parking requirements and
subsidizing a mixed-use project, the city could spend the subsidies on a community parking
structure nearby the project, thus eliminating the need for required parking. This strategy
is especially valuable in reducing the gap in projects with expensive underground parking.
In order to work, however, parking alternatives must be available.
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5. Integrative Pilot Program Development: The Infill Scenario
Mapping System
A major technical goal of this second project phase has been to integrate the GIS-based
infill analysis methodology—proven feasible in the previous study—into a web-based, easilyaccessible platform. IGIS Technologies (IGIST) was contracted to develop this application.
The group was retained due to its involvement in the development of ZIMAS—the City of
Los Angeles Department of Community Planning’s (DCP) existing online parcel query
program. IGIST worked closely with Solimar to integrate the methodology developed in
the previous study into a web-accessible application. Due to the complexity of the system,
the goal for this project was to make the application available on the City’s intranet, and
not to make it available publicly on the internet.
Working closely with DCP staff to ensure a focused, user-friendly design, IGIST was able to
develop and refine a working prototype of the web-based infill screening application. The
Los Angeles DCP has named that application the Infill Scenario Mapping System.
Please refer to Appendix C for IGIST’s complete Manual for the Infill Scenario Mapping
System.
5.1 Functionality
Many of the ideas developed in the methodology of the first infill report were integrated
into the application. Key to the success of the application was ensuring that it enables the
identification of infill parcels based on the selection, or querying, of those characteristics
that define properties with a high potential for infill development. Following is the list of
these attributes that opportunity parcels can be identified by when using this application:
•
Scenario Boundaries. This includes various geographic boundaries such as
Community Plan Areas, Council Districts, and Neighborhood Council Districts.
In addition, the application can establish distance boundaries related to MTA light
rail stations.
•
Zoning Selection. Allows for the breakdown of parcels based on the general zoning
class.
•
Parcel Size. Distinguishes parameters for the size of parcels to be identified.
•
Built:Capacity Ratio. This attribute is determined by dividing total inhabitable units
on a parcel by the total units allowed by the zoning of that parcel. For example, if a
parcel is allowed 10 units by right according to the zoning, but the parcel only has
two units built on the property, the parcel's built capacity ratio is 20 percent (20%).
Parcels can have a built capacity over 100 percent due to variances and other
outstanding situations.
35
•
Remaining Capacity. The number of additional dwelling units that could be built
under the current zoning of a parcel.
•
Investment Index. Ratio of the value of improvements on a property compared to the
value of the land. For example, a parcel whose land is valued at $100,000 has 2
structures with cumulative value of $50,000. The investment index of the property
is 0.5.
•
Year Built. A year range can be set to select parcels that have had structures built on
them within that range.
•
Vacant Parcels. By checking this box, parcels that are identified as vacant are
included in the search.
The four images below represent the three main pages of the Infill Scenario Mapping
System. At these key interfaces, users can select the boundaries of the area in which they
wish to conduct an analysis, and select the attributes (including the parameters of those
attributes) that will inform their parcel identification. The “Home Page” of the application
is represented in Figure 5.1.1, while the scenario selection interfaces, or “search pages,” are
represented in Figures 5.1.2 and 5.1.3. Finally, the completed map of a typical infill
analysis is represented by Figure 5.1.4.
36
Figure 5.1.1 Infill Scenario Mapping System home page
37
Figure 5.1.2 Boundary selection interface
38
Figure 5.1.3 Parcel attribute and parameters selection interface
39
Figure 5.1.4 Results map of typical infill scenario analysis
40
5.2 Training and Testing
On December 6, 2006, at the culmination of application development, a training session
organized by Solimar was held at the DCP offices. Principal participants were Dave Van
Mouwerik from IGIST, Ryan Aubry from Solimar Research Group and City of Los
Angeles, Principal Planner Jane Blumenfeld. During the session, approximately 20 DCP
staff members were educated on, interacted with and commented on the application.
The session revolved around an intensive demonstration of the uses, functionality and
interface of the Infill Scenario Mapping System. Each element of the functionality of the
application was highlighted and demonstrated. The process included running multiple,
various queries suggested by staff planners. These were indicative of the types of analyses
that are vital to day-to-day, department problem solving.
It is important to note that at this point, the application had not been loaded onto the
DCP’s servers due to restricted space and allocation issues. However, planners in the
training session were given the URL of the IGIST webserver, allowing them to use the
application remotely. The functionality of the application was not compromised in any
way.
DCP staff planners were given several weeks to familiarize themselves with and test the
application. Each was asked to report back on their experiences, identify perceived
strengths and weaknesses of the application and suggest possible improvements and/or
additions. Nearly all feedback was positive.
5.3 Constraints and Improvements
The success of our training session verified that the Infill Scenario Mapping System
represents a functional prototype for integrating our infill methodology into larger, more
accessible web-based resources. That the methodology has been made available to a much
larger audience is a significant step forward in the evolution of the tool.
Still, integrating the infill opportunity methodology into a web application was a
challenging task. We discovered that coding the primary functionality of the application in
HTML was extremely time-consuming, so much so that we were prohibited from fully
exploring the potential of application beyond those capabilities identified at the start of the
project. Not surprisingly, many of the planners involved in the training session expressed
an interest in expanding the functionality of the tool, to include a more thorough set of
possible queries.
One common suggestion made by our professional “trainees” was to expand the ability of
the tool to select for parcels based on their current floor-area-ratio (FAR) build-out. As it
stands, the methodology calculates such build-out by comparing the residential unit count
of a parcel to its allowable density.
Another repeated suggestion was to incorporate high-resolution aerial photography into
the mapping interface. At the time the application was being developed, the City did not
41
have a digital aerial photo dataset that could be utilized, but staff was working on
establishing such a resource.
Due to time and budget constraints, these improvements have been relegated to a future
stage of project development.
42
6. Participatory Neighborhood Workshop
We have applied our infill screening tool to a range of built environments in Los Angeles,
from target areas around planned stations of the Exposition Boulevard Light Rail Line to
entire Community Plan Areas. This process has been essential to ensuring the tool’s
adaptability in the urban infill screening process. However, the potential of our tool to
analyze community input and facilitate community planning—a function targeted during its
development—has not, until now, been tested. In this study, as has been demonstrated, our
refined methodologies are shaped by community input.
The assumption that community members will use the tool to manage infill opportunities
in their own neighborhoods is built into our model. The tool’s reliance on public
information ensures that data is accessible by community groups, and it is our belief that
the integration of local knowledge is essential to context sensitive, environmentally-just
redevelopment.
In order to develop a strategy for community-driven infill analysis, we conducted a series of
pilot neighborhood workshops. Neighborhood organizations in Wilmington, Pacoima and
Silver Lake contributed to infill analyses of their own neighborhoods. Each group was
solicited for knowledge of local developmental, geographical, cultural and transit issues,
and that knowledge was inputted into the mapping process. Results were delivered back to
the group for further discussion.
In this section, we first report on the format and results of these interactive neighborhood
workshops. Finally, we share our resulting conclusions about the place of the California
Infill Estimation Tool in a community influenced, infill estimation process.
6.1 Neighborhood Selection and Workshop Format
The three Los Angeles communities selected for this pilot study represent a range of
geographical diversity and a spread of opportunities for infill development.
Nestled in the southwest corner of Los Angeles is the harbor community of Wilmington.
Here, the revitalization of an underutilized waterfront and multiple commercial corridors
are major redevelopment issues. At the other end of the region, in the northeast corner of
the San Fernando Valley, the flat, relatively isolated community of Pacoima struggles with
overcrowding in low income, single family housing and the persistent liability of aging
industry. Finally, growth-resistant Silver Lake, just west of Downtown Los Angeles, creeps
into surrounding hills and contains a mixed landscape of premium residences, parkland
and business districts.
In all three communities, our workshops were attended by members of the respective
Neighborhood Council of the Los Angeles Department of Neighborhood Empowerment
(DONE). In Wilmington, members of the local Citizens Committee also attended. In
Pacoima, community improvement group Pacoima Beautiful hosted the meetings.
43
We adhered to a similar, two-workshop format in all three communities.
Format: Workshop One
Our first workshops contained a three step-process of introduction, demonstration and
feedback. This structure facilitated the community-influenced infill maps that were
delivered back to the groups in Workshop 2.
Step 1: Introduction
At the beginning of each workshop, we introduced the development of our GIS-based infill
screening tool. We stressed the objective nature of our tool, taking time to explain that it
relies wholly on information in the public realm and targets those without specific
development agendas.
We also introduced the various architectural forms that infill housing can assume. We
presented a series of typical infill housing prototypes, from residential townhomes to large
mixed-use projects. We presented images of these prototypes in the context of a typical
streetscape.
Finally, we introduced the process of using appropriate criteria to filter parcels for infill
potential—the Geographic Screening function of our tool. We accomplished this with a series
of sequential maps from previous infill analyses.
Step 2: Demonstration
Next, we sought to demonstrate the vitality of GIS mapping to our infill analysis
methodology, as well as the types of community characteristics that play into an analysis of
infill housing. We presented a series of 10 to 15 maps of the community with which we
were working. All were designed to demonstrate the range of urban characteristics relevant
to redevelopment, as well as the range of datasets available to users of the tool. We used
these maps to facilitate community-oriented discussion on three general topics:
•
Land Use. Representative maps include “General Land Use” and “Redevelopment
Areas”
•
Environmental/Social Justice. Representative maps include “Majority Race by
Blockgroup” and “Median Income by Block Group.”
•
Transit. Representative maps include “LA City Transit Lines” and “Percent who
Commute by Public Transit.”
44
Step 3: Feedback
Step 3 was largely integrated into Step 2, with community members responding to each
map. At this point, we sought to collect relevant, community-focused feedback for use in
preparing a second, more focused infill analysis to be presented in Workshop 2. We
collected feedback concerning:
•
Specific areas of either contested or potential development;
•
Current land use issues, from the scale of the community to that of the individual
parcel;
•
Opinion on, and specific problems with, local public transit;
•
Incorrect data in the maps presented;
•
Local historical perspective;
•
Current social issues.
Format: Workshop Two
In Workshop 2, we sought to demonstrate how the type of neighborhood knowledge
collected in Workshop 1 can be integrated into the process of infill analysis using our
screening tool. This workshop was a two-step process of demonstration and discussion.
Step 1: Demonstration
We presented two series of maps in Workshop 2. The first contained those maps that we
created using the input received in Workshop 1. The second demonstrated the results of
an actual, parcel-level infill analysis of the respective community.
The first series of maps was unique in each community, resulting from Workshop 1
discussion. These maps covered a range of topics, including industrial hazards, historic
overlays, inconsistent uses, public ownership of land, redevelopment areas and State
Enterprise zones.
The second map series displayed the results of a typical infill “screening” the community.
These maps were sequential, revealing the step-by-step process of narrowing a large group of
parcels down to a select group of high-potential properties.
45
We “screened” both commercial and residential parcels using a selection of attribute data.
In our commercial series, these included Parcel Size and Investment Index. For residential
parcels, we used Remaining Capacity and Single Unit parcels to screen properties for
prototypical townhome development potential.
Step 2: Discussion
By the end of Workshop 2, we had introduced arguments for higher density
redevelopment and demonstrated that our tool will allow communities to combine local
knowledge with public parcel data to manage that process. At this point, we elicited general
opinions about the utility of our tool and its perceived place within neighborhood
development, transportation and social contexts.
6.2 Wilmington Workshops
We initiated our first neighborhood workshop with members of the Wilmington
Neighborhood Council. Although this community has distinct character, geography and
history, we encountered themes that would repeat themselves in subsequent workshops.
6.2.1 Wilmington Workshop One
Workshop 1 took place on December 13, 2006. Jane Blumenfeld, head of the City of Los
Angeles, Department of City Planning’s Citywide Planning Division and David Olivo, City
of Los Angeles Planner, attended.
Immediately apparent from reactions to our introduction was that workshop participants—
representative users of our tool—are prone to assumptions about infill “cause-and-effect.”
Participants stated that they do not support new, higher density housing, because, they
believe, it promotes rapid growth and the pricing-out of economically disadvantaged
community members. It was assumed that we had a private development agenda planned
for the immediate area.
Upon clarification of the tool’s publicly-funded development, the use of public
information in the tool, and the importance of infill housing in managing growth,
community reaction shifted. Participants took interest in the simplicity of the parcel
screening process, and were eager to view maps of their own community. This was our first
indication that with our mapping tool, the very roots of community resistance to infill
development—concern for fellow residents, pride in place—could be refocused toward
promoting environmentally just infill strategies.
We covered a range of subjects in our first round of Wilmington maps. Wilmington is a
disadvantaged, largely non-white community with an aging waterfront marred by past
46
industry. It is planning a major waterfront rehabilitation. In our first round of maps, we
focused largely on these issues. Our maps included, but were not limited to:
•
Wilmington- Harbor City: General Land use
•
Wilmington- Harbor City: Neighborhood Businesses
•
Wilmington- Harbor City: State Enterprise Zones
•
Wilmington- Harbor City: Percentage of Workforce that Commutes via Public
Transit
•
Wilmington- Harbor City: Majority Race by Blockgroup
•
Wilmington- Harbor City: Housing, Percent of Units Renter v. Owner Occupied by
Blockgroup
•
Wilmington- Harbor City: EPA Hazards within ½ Mile
It was obvious from the feedback we received that the combination of relevant mapping
and residents’ local knowledge are a potentially powerful combination in community
redevelopment and community planning efforts. Just as our maps covered a range of
topics, we received a range of feedback from the group that would allow us to deliver a
second series of maps targeting housing, transportation and redevelopment concerns.
Topics of discussion in Workshop 1 included:
•
Redeveloping the port brownfield. Participants expressed the community’s desire to
increase/protect Wilmington’s natural marshland and stimulate activity near the
Marina with retail. As a result, they were interested in understanding what public
entities owned what land near the Harbor.
•
Maximizing Redevelopment. One focus of the workshop discussion was the
relationship of infill housing and redevelopment areas. Participants expressed the
desire to compare the location of Wilmington redevelopment areas with related
land use characteristics.
•
Preserving Local History in the Built Environment: Many workshop participants were
interested in locating, protecting and increasing Wilmington’s historic zones, and
working to promote historically-sensitive infill development. They were enthusiastic
about the prospect of mapping historic overlays and building ages.
•
Curbing nonconforming industrial use within residential zones. When confronted with
our land use maps, participants were able to confirm their suspicions of specific
blocks that were zoned residential—yet, according to their observations, were
commonly used for purposes such as storage and disposal of shipping containers.
All participants agreed that beautification of some parts of Wilmington would
depend on curbing such activities.
47
•
Improving local public transit. When reviewing our maps of public transit, the
neighborhood group expressed dissatisfaction with local bus lines. They described
the poorly-planned Wilmington DASH line, and expressed an interest in improving
local transit within the community for Wilmington residents.
In all of these responses, participants stated that clear, visual organization of the right data
into maps could be a powerful tool in promoting and supporting the community agendas
in question, and in making decisions about infill housing. Participants repeatedly stressed
both the need for a Community Plan update in Wilmington and the potential of such
maps in that update process.
6.2.2 Wilmington Workshop Two
On January 10, 2007, we returned to the Neighborhood Council with an infill mapping
series based directly on the community concerns expressed above. These maps were based
on more detailed information; many were scaled to the parcel level. Maps in this series
included:
•
Wilmington-Harbor City: City Transit Lines
•
Wilmington-Harbor City: Parcels—Year-Built
•
Wilmington-Harbor City: Public Owned Land
•
Wilmington-Harbor City: Historic Overlay Zone
•
Wilmington-Harbor City: Parcels—Inconsistent Uses
•
Wilmington-Harbor City: Redevelopment Area—Land use
•
Wilmington-Harbor City: Redevelopment Area—Investment Index
Each of these agenda-targeted maps was immediately used by participants to strengthen,
reassess or even invalidate community development concerns. Our new maps stimulated
reevaluation and enthusiasm for community planning, in that they offered “hard” evidence
of the validity of the ideas and agendas shared in the first meeting. Again, these maps
prompted workshops participants re-state the need for a community plan update. City
planners present at the workshop explained to participants that insufficient staff had
resulted in a backlog of plan updates throughout Los Angeles.
These maps also revealed just how intimate community members are with their
neighborhoods. At multiple times during this meeting, Wilmington residents revealed
48
mistakes in the assessor data, highlighting such inconsistencies as changes in parcel
boundaries and incorrect ownership. For example, upon studying our map of Public
Ownership in Wilmington, one participant pointed to a single waterfront property that
was actually owned by the City of Long Beach. Through her involvement in this waterfront
improvement effort, she explained, she had become very familiar with land-use around the
harbor. We conceded that data from the County Assessor is not always current.
Response to our second round of maps—the series demonstrating the results of a typical,
parcel-by-parcel infill analysis in Wilmington—also demonstrated the potential of our tool
in community planning. Responses continued to validate our notion that neighborhood
knowledge fulfills the role of a final “screen” in a context-sensitive infill analysis.
Upon studying the results of our infill analysis of Wilmington commercial property (see
Figure 6.2.1), one participant drew attention to a group of parcels that, based on its
Investment Index, appeared suitable for infill. He disagreed with this result, explaining that
the underutilized nature of these parcels was actually the result of their being very difficult
to combine and redevelop. This participant had a keen historical perspective, having
observed, he explained, as multiple attempts to combine the parcels had failed over the
years. Clearly, although our infill analysis demonstrated otherwise, these parcels were not
particularly suitable for redevelopment.
Figure 6.2.1 Infill opportunity analysis, Wilmington commercial property
49
Examples of such reactions to individual parcels/properties continued throughout the
Wilmington workshop. It became clear that community members can play an integral role
in the screening process. Using ink at the workshop, we created a map labeled
“Community Input,” whereby we highlighted those parcels on our final geographic screen
deemed “more suitable” or “less suitable” by workshop participants. The result was an infill
analysis, completed in one hour, that accounted for both traditional parcel data and realtime, local familiarity.
Our final map of commercial infill analysis revealed a strip of high-potential parcels leading
down the Avalon Boulevard commercial corridor toward the waterfront. When
participants saw this, they reacted excitedly over the potential of this map to support a
development scheme—waterfront revitalization—that the community had been pushing for.
The enthusiasm of the group—upon realizing that such a scheme was indeed supported by
our infill analysis—again revealed the potential value of the tool to the community planning
process.
We ended the workshop by explaining that we were working to integrate our tool into the
City of Los Angeles’ existing ZIMAS online GIS system. This, we explained, would bring
the mapping and analysis process into real-time, providing immediate access to databases
and results. Participants emphasized that integration into a public sector system would be
of great value, and stated that they would use the system in their own organizing and
analysis.
6.3 Pacoima Workshops
Pacoima is a largely Hispanic community grappling with legacy of industrial land-use
mismanagement. Relatively isolated and with a low-income population, there is a
significant demand for an insufficient transit system. Residents, although driven to
improve their community, strive to maintain the economic and cultural balance integral to
Pacoima’s sense of place.
As was the case in Wilmington, workshop participants in Pacoima demonstrated parcellevel knowledge of current development issues and commitment to certain community
agendas. In Pacoima, we integrated community feedback from workshop 1 directly into the
infill screening analysis presented in workshop 2. Our results contained a contextual
relevancy that highlighted value of community input to achieving environmental justice in
infill planning, as well as the value of our methodology to general community planning.
6.3.1 Pacoima Workshop One
Workshop 1 was held at the headquarters of Pacoima Beautiful on January 31, 2007. Jane
Blumenfeld, head of the LACity DCP’s Citywide Planning Division, Claudia Rodriguez,
City of Los Angeles, Assistant Planner and Dora Huerta, Community Redevelopment
Agency, Assistant Project Manger also attended the workshop.
50
In workshop 1, we used our infill analysis maps from the second Wilmington workshop to
introduce the parcel screening process. Our first round of community-specific maps
addressed the unique transportation, housing and land use mismanagement issues that the
community faces. These maps included, but were not limited to:
•
Arleta-Pacoima: Percentage of Workforce that Commutes via Public Transit
•
Arleta-Pacoima: Los Angeles City Transit Lines
•
Arleta-Pacoima: Majority Race by Blockgroup
•
Arleta-Pacoima: Percentage of Units with More than 1 Person per Room, by Blockgroup
•
Arleta-Pacoima: State Enterprise Zones
•
Arleta-Pacoima: EPA Hazards within ½ Mile
•
Arleta-Pacoima: Housing—Percent of Units Renter v. Owner Occupied, by Blockgroup
As was the case in Wilmington, it was apparent that our community maps were vital to
reducing residents’ confusion about, and negative outlook on, higher density housing. One
participant explained that the visual presentation of current conditions allowed him to
understand infill/redevelopment as a necessary reaction to internal community issues,
rather than an external development scheme.
Workshop 1 feedback revealed that Pacoima residents’ perspective on redevelopment and
infill housing is shaped largely by the following issues:
Problems and Potential of Industrial Lands: Our map of nearby EPA hazards (see Figure 6.3.1)
elicited discussion of specific problem sites as well as long-range considerations of the
populations surrounding such sites. We discussed the problems and potential of notorious
industrial areas, such the heavy industrial zone behind the local airport, as well as the
conditions of individual properties, such as the 25-acre Superfund site on arterial Paxton
Boulevard. Participants made clear that considerations of industrial land and residents
near such land should play in integral part in the placement and strategizing of infill
housing.
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Figure 6.3.1 Map of nearby EPA hazards, Pacoima
52
Coordination of Transit and Housing: Participants repeatedly stressed the vitality of local
public transit to many Pacoima residents. They used our transit map to highlight
overcrowded bus lines and to stress the local value of housing positioned on or near transit
lines.
Building Density v. Human Density: Our maps of persons-per-room in residential units and
renter/owner rates prompted a lengthy discussion of crowding in Pacoima homes.
Participants stressed the common condition of entire families sharing single rooms or
garages, and explained that parking, legal and domestic conflicts are all results of such
crowding. Community members were enthusiastic about the infill strategies to increase the
amount of accessible housing.
Environmentally Just, Culturally Sensitive Housing: In Pacoima, participants stressed that in
addition to economic necessity, the condition of residential crowding is also cultural. It is
tied to the Hispanic tradition of the extended families sharing domestic space. We learned
that although a common desire among many Pacoima residents was a private living space,
the ideal provision of that space is not a traditional high-density housing prototype. Rather,
the group discussed the potential of “cottage style” housing in Pacoima, a multi-unit model
characterized by clusters of small, separate units on a single parcel.
6.3.2 Pacoima Workshop Two
Our second workshop with Pacoima community members was held on February 20, 2007.
In preparing maps for Workshop 2, we were able to integrate community feedback from
Workshop 1 directly into our GIS-based infill analysis. The results confirmed our belief
that communities can input their knowledge into our tool to promote environmental
justice in infill development, and that this community integration will lead to increasingly
focused, context-sensitive results.
In Workshop 1, participants stressed that industrial land, access to transit and culturally
sensitive housing design must figure into any infill housing strategy for Pacoima. Therefore,
in addition to our typical “screening” series of commercial and residential properties, we
analyzed the infill potential of parcels based on the above sets of criteria. Resulting maps
included:
•
Arleta-Pacoima: Industrial Zoning
•
Arleta-Pacoima: Investment Index of Industrial Zoned Parcels
•
Arleta-Pacoima: Assessor Year Built
•
Arleta-Pacoima: Residential Cottage Screen
•
Arleta-Pacoima: Average Number of Autos per Adult, by Blockgroup
53
•
Arleta-Pacoima: Los Angeles City Transit Line Infill Potential Screen
As was the case in the second Wilmington workshop, the parcel-level mapping in these
geographic screens prompted Pacoima participants to contribute their own parcel-level
analyses. Again, this community knowledge added a level of “real-time” relevancy to the
methodology. For example, when viewing our map of the investment index of industrial
parcels, workshop participants “screened” the results beyond simply those that were underinvested. They cited such specifics such as boundary disputes, recent development schemes
and zoning discrepancies, in effect further refining the infill potential.
Likewise, participants responded well to our map of the 451 parcels that, in addition to
being of significant size and less than 50% built out, lie within 500 feet of local transit
lines. Their knowledge of local ridership and of those bus routes deemed “lifelines”
facilitated a valuable refinement of our analysis, resulting in possible locations for infill
housing that maximize local transit and improve transportation capacity.
Most exemplary of the complementary relationship between local knowledge and our infill
analysis methodology was the analysis of “cottage housing” that we presented in Workshop
2. As stated earlier, we did not screen Pacoima’s residential parcels for only remaining
capacity and single unit parcels. Based on the feedback concerning culturally-sensitive infill
housing designs that we received in workshop 1, we also screened parcels for their potential
to host “cottage-style” infill housing (see Figure 6.3.2). This involved an additional screen
of single family parcels greater than 8,000 square feet.
Figure 6.3.2 “Cottage-style” infill opportunity analysis, Pacoima
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Our analysis revealed 2,685 such sizable parcels. In addition, participants highlighted
numerous, small pockets of lots over 9,000 square feet, and some pockets of parcels over
10,000 square feet. All agreed that cottage housing is an infill strategy that fits the unique
parcel and cultural characteristics of Pacoima, with its large residential lots and close-knit
Hispanic population. Upon viewing our map, participants discussed proposing a pilot
“small lot subdivision overlay” to legalize groups of families living in separate structures on
a single family lot. Multiple design options were proposed, most yielding 4 or 5 units with
parking improvements and shared open space.
As a result of these meetings, the idea was officially adopted by City of Los Angeles
Department of Community Planning (DCP), which has initiated an official study of the
community’s cottage housing potential. This is further verification of the potential of this
GIS-based infill analysis to establishing democratic, long-range, community planning goals.
6.4 Silver Lake Workshops
Pacoima and Wilmington are working to change conditions resulting from minimal
external investment. Silver Lake, on the other hand, is resistant to change; it is a
community working to maintain a balance that residents perceives as threatened by
increased investment. In addition, while Wilmington struggles to find support for a
community plan update, Silver Lake’s community plan was recently updated; the
community is resistant to policy change.
We found that the contrasting political context of Silver Lake only highlighted the
adaptability of our infill methodology. By the end of the Silver Lake workshops, we had
incorporated neighborhood concerns into an infill analysis focused on parkland
development strategies.
6.4.1 Silver Lake Workshop One
Workshop 1 was held on March 7, 2007. Jane Blumenfeld, Head of the L.A. City DCP
Citywide Planning Division, attended the session. Unfortunately, the workshop was
constrained by a placement on a packed Neighborhood Council agenda. We were limited
to less than one half-hour for our presentation. In addition to maps introducing the
geographical screening process, we presented maps of the Silver Lake community focusing
on local socioeconomic, transit and housing/development characteristics. These maps
include:
•
Silver Lake-Echo Park: Median Household Income by Blockgroup
•
Silver Lake-Echo Park: General Land Use
•
Silver Lake-Echo Park: Majority Race by Blockgroup
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•
Silver Lake-Echo Park: Percentage of Units with More than 1 person per room, by
Blockgroup
•
Silver Lake-Echo Park: Percentage of Workforce that commutes via public transit
•
Silver Lake-Echo Park: Housing, Percent of Units Renter vs Owner Occupied by Blockgroup
•
Silver Lake-Echo Park: Neighborhood Businesses
Due to time constraints, participants did not respond to each map. Similar to Wilmington
and Pacoima residents, they expressed immediate doubts as to the “community
orientation” of our tool. Some expressed confusion as to who the tool will be available to,
and feared that it could end up “in the wrong hands” of economically-motivated
developers. They worried that infill housing of any kind would exacerbate traffic problems,
and some participants pointed out that they welcome no redevelopment in their
community at all. Participants stressed that with its hilly geography, small lots, tight streets,
single commercial corridor and current high density, infill opportunities in Silver Lake are
of a different sort than those in Pacoima and Wilmington. We agreed, and stressed
throughout our presentation that infill opportunities always vary from community to
community, and that this tool can allow residents to identify and manage customized infill
strategies.
In the short span of workshop 1, we did receive enough community feedback to prepare a
second round of infill analysis maps for Workshop 2. Residents voiced the community’s
wish to increase parkland, while repeating fears that higher housing densities and
associated traffic will jeopardize the character of the hillside community. In response, we
explained that the infill estimation tool can also be used to plan park placement and
strategize project sites, such as maximizing residential/parkland connectivity or avoiding
redevelopment near sensitive historic zones. Members of the Neighborhood Council also
voiced a desire to see the geographic boundaries of the Council integrated into our
mapping of the larger community.
6.4.2 Silver Lake Workshop 2
This meeting was held on March 29, 2007. Unlike the first Workshop, it was a private
event held at the offices of the Neighborhood Council. Time was not limited to an agenda.
Participants stressed early on that they felt our tool had “no particular benefit to this
community.” That sentiment would change.
Although participant input from workshop 1 was limited, we produced a number of
focused maps for workshop 2. Like Pacoima, we found that we were able to integrate
community concerns directly into the infill screening process, thereby facilitating increased
particpant involvement in the process of analysis. In addition to our geographic screens of
commercial and residential parcels, we also created a group of maps focused on the issue of
increased parkland and recreational space. In response to participants, we were able to
locate a GIS shapefile, from the Los Angeles Department of Neighborhood Empowerment,
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of the council boundary. With this data resource, we were able to produce pairs of maps of
each subject, one without the boundary and one depicting the relationship of the
neighborhood council boundary to the community at large. Maps included, but were not
limited to:
•
Silver lake-Echo Park: Government Owned Parcels (with and without Neighborhood Council
boundary)
•
Silver lake-Echo Park: Existing Park Resources (with and without Neighborhood Council
boundary)
•
Silver lake-Echo Park: Vacant Parcels (with and without Neighborhood Council boundary)
These maps, in conjunction with our parcel screening series, prompted detailed responses
from workshop participants. Again, community members took the analytical process to a
level of temporal and context sensitivity that could not be achieved with parcel attribute
data alone. Just as in previous workshops, numerous flaws in the assessor data were
highlighted. For example, multiple individuals contested the “vacant” status of different
parcels, even stating the year in which vacancy status had shifted. They pinpointed small
residential clusters as local and historic points-of-pride; architectural standouts that would
likely resist any change to the built environment. As in other communities, the result was a
final “screening” of potential parcels that would be a vital to any potential locating of infill
projects.
In Silver Lake, the significance of local political knowledge to infill analysis was also
revealed. In this meeting, participants were able to identify residential areas where land use
designation was likely to change. For example, in response to our maps of high-potential
residential parcels, one individual suggested eliminating a particular cluster of properties:
He explained that a City Council initiative to downzone the area was currently underway
and would probably find community support.
Finally, the Silver Lake workshops offered verification that an interactive methodology is
invaluable to developing an infill strategy that is unique to a given community. Here,
geography, demography and urban design restrict traditional infill housing opportunities.
For that reason, our geographic screens of residential and commercial parcels did not result
in enthusiastic discussion of potential community infill agendas, as they did in Pacoima
and Wilmington. Rather, it was our mapping of parks and vacant land that resonated with
Silver Lake residents and initiated discussion of potential community planning. Residents
stressed that the mapping allowed them to visualize the spatial relationship of existing
parks as well as of potential park sites and other land uses.
Specifically, these maps reinvigorated participants’ interest in a longstanding community
initiative to combine two parks in the community. Our map of vacant parcels and
government-owned property within the neighborhood boundary (see figure 6.4.1) allowed
participants to visualize a long-vacant strip of land between to local parks that for years has
resisted development, and for years has been the target of a grassroots campaign to
transform into parkland. Participants explained that our infill mapping allowed them to
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visualize the “bi picture” of surrounding land use and could be vital to following through
with the historic effort.
Figure 6.4.1 Vacant parcels and government-owned property, Silver Lake neighborhood council boundary
6.5 Neighborhood Workshops: Conclusions and Applications
The experience of, and feedback from, these six community workshops have led to us to
two conclusions about the value of the community-managed infill analysis. Each verifies
that the integration of local knowledge into the infill screening process is both possible and
vital.
First, we concluded that neighborhood groups have such familiarity with local land use and
politics that their input can act as a final, context-sensitive infill “screen” during the parcel
analysis process. Second, we concluded that our GIS-based tool has an emerging value in
the community planning process.
6.5.1 Community Knowledge as an Infill “Screen”
Community interaction does more than complement the data-based, infill mapping that
characterizes our tool. Rather, community input can, and should, play a direct role in the
screening process itself. It can act as the “final screen” in the process of locating suitable
parcels. In essence, local input ensures context-specific results.
Just as parcel-level attribute data such as “Parcel Size” or “Investment Index” are inputted
into our tool to screen for infill potential, the information provided by workshop
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participants can do the same. We found this information to be so specific and so current
that in all cases, it acted as the “final word” in our mapping sequences. By providing parcellevel information such as recent or proposed land use changes, incorrect assessor data,
longstanding community opinions, historic facts, developmental histories and surrounding
social patterns, the neighborhood groups added a final level of analysis to our tool’s
screening efforts. Although our typical datasets successfully narrowed the field of potential
parcels in each area, the “dataset” that we have deemed “community knowledge”
continued to refine and screen that field, resulting in a final group of potential parcels that
had passed the scrutiny of intense neighborhood familiarity.
We came to this conclusion during the second round of workshops, when community
members were allowed to critique our customized, parcel-level maps. We saw that the
delivery of this precise data facilitates precise community analysis, and it is this precision
that we are certain has a place in the screening process. In each workshop, the final result
was a group of parcels with a high-potential not only for infill development, but for
community supported and environmentally just infill development.
6.5.2 A Tool for Community Planning
The workshop series also revealed that our tool has an emerging value in the development
of the long-range agendas contained in Community Plans. With the City of Los Angeles in
the midst of a Community Plan overhaul, the communities with which we met were at
different stages in the development of their own Community Plan updates. Still, all three
citied our mapping and screening results as potentially invaluable to the complex decisionmaking process inherent to plan development.
Issues of growth, housing, environmental justice and transportation are inseparable from
any contemporary community development strategy. Due to the nature of infill
development, our tool can analyze, clarify and present just these issues. As will become
clear in upcoming sections of this report, we discovered, in the neighborhood workshops,
that our tool can facilitate the closure of community planning debates by organizing
relevant issues into easily digestible packages of relevant data. During the workshops, our
maps were repeatedly “adopted” by participants to either support or negate a land use plan
or development scheme that either was, or had recently been, a topic of debate. The maps
offered “on the ground” clarification of these larger issues.
At the close of all six workshops, we concluded that our tool is well-suited to highlight
connections between a community’s existing socioeconomics or land use and its
conceptual planning goals.
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6.6 Neighborhood Workshops Summary
These pilot workshops have demonstrated that neighborhood knowledge can be integrated
into the methodology of the California Infill Estimation Tool, and that such integration
increases the level of land use, socioeconomic and political analysis beyond what is possible
with assessor or census data alone. Through this process of integrating intimate
environmental data into infill estimation, the resulting analysis is increasingly
environmentally just. The focused response of workshop participants—introduced to the
tool for the first time—reveals the inherent accessibility and logic of the GIS-based, infill
estimation method.
Just as important, these workshops demonstrated the give-and-take nature of
neighborhood-influenced infill analysis: Just as resident scrutiny of infill prospects can act
as “final screen” in the process of parcel identification, the results of a GIS-based infill
analysis offer clear, visual combinations of “hard” data on which to gauge the viability of
community planning agendas. Through the workshops, have seen the beginnings of a
process in which our tool allows community groups to integrate their own knowledge into
a customized, environmentally-just and context-sensitive infill management process.
Participants of our workshops repeatedly stated that they would utilize this resource were it
integrated into the City of Los Angeles’ ZIMAS system. With this in mind, it is important
to view these two-day workshops as rough, extended versions of a community interaction
process that would take only minutes, and with vastly increased input flexibility, were it
facilitated by a web-based GIS resource. That integration into such a resource is possible
has been demonstrated earlier in this report.
Finally, this methodology—whether facilitated by group workshop or individual computer
interface—is viable regardless of varying infill goals. As demonstrated by our experience in
contrasting communities, the very value of the methodology lies in its capacity to estimate
the potential, in any area, for any type of urban infill. This includes housing, parks,
commercial strips and mixed-use developments.
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7. Conclusions and Next Steps
At the end of the previous phase of this project, it was clear that achieving the full potential
of the California Infill Estimation Tool would demand developing a truly integrated
functionality. The elements of this study—methodological refinement, interface
improvement and community participation—were each constructed to expand the potential
of the tool beyond its “stand alone” capacity. What this study has demonstrated is that at
its highest analytical function, the tool will deliver results to a range of users based on a
comprehensive set of land use, socioeconomic, market and community inputs, all easily
incorporated into a contemporary, GIS-based resource ensuring up-to-date data and a
simple user interface.
The intent of this project was not to bring the tool to this “peak” analytical function.
Rather, we sought to demonstrate that such functionality was attainable by establishing the
necessary methodological and technical strategies. As a result of this study, we are certain
that the California Infill Estimation Tool can be used to inform a holistic infill analyses.
At numerous stages of this project, study results and feedback from various participants
highlighted possible directions for application and refinement of the tool. The following
are those that we feel have the highest potential for improvement:
•
We would like to see the City of Los Angeles—and indeed any jurisdiction with
established Community Plan Areas—use the tool in its community planning efforts
and in the development of individual community plans. During our neighborhood
workshops, it became clear that the tool is a potentially valuable means of
organizing and presenting community-scale planning ideas. The visual advantages
of GIS mapping, combined with the ability to easily input oft-used urban data, has
proven an effective means of sharing complex planning agendas with
nonprofessionals, scenario building, and models of future growth. It is important
to note that this application of the tool would not necessarily focus on infill
development; rather, it would promote “hands-on” participation by all involved,
stimulate discussion about contested ideas and generally increase the learning curve
of active citizens.
•
From a technical standpoint, we are certain that there is a role for digitized
photography in the California Infill Estimation Tool. Recent, very successful
“virtual” internet browsers such as Google Earth have shown that satellite imagery,
maps, and aerial photography are vast improvements to traditional mapping. The
inclusion of digital imagery of the actual built environment into our tool would
vastly improve the analytical power of our maps, transforming the generic “parcel”
to an individual property with its own unique improvements. A focus of our efforts
has been developing a tool that is accessible by the lay user, and as proven by the
popularity of Google Earth, such imagery is both attractive and easy to manipulate.
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•
Not surprisingly, many of the planners involved in our training sessions expressed
an interest in expanding the functionality of the tool to include a more thorough
set of parcel selection queries. Clearly, this is an indication that the Geographic
Screening function of the tool—the root of infill opportunity analysis—will need to
be refined to account for a greater number of parcel attributes. As the tool is used
more frequently, and applied to a wider variety of infill scenarios, it these types of
refinements that will present themselves. Already, it has become clear that the
ability to select for parcels based on their current floor-area-ratio (FAR) build-out
would be a valuable improvement for practicing planners.
Finally, the most obvious “next step” in the development of this tool and infill estimation
methodology is the most straightforward: A cohesive, real-time “dry run” infill analysis is
necessary. The “stand alone” results of the Phase I study and the integrative improvements
of Phase II should be tested in an actual infill development scenario. This would include
all aspects of development, from site selection to opportunity analysis to environmental
justice analysis and the public participation process. It would test the tool’s ability to
facilitate the contribution of all stakeholders, including the developer, local government,
concerned community groups and individual citizens. Only by applying the California
Infill Estimation Tool to an infill effort, from project inception to groundbreaking, will its
utility be fully explored, and thus verified.
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Appendix A: Detailed Description of Infill Estimation Tool
The purpose of the infill estimation tool is to provide users with a flexible method of
identifying parcels that might be ripe for infill development – for housing or other urban
uses such as parks, mixed use, and commerce – and testing the likely success of different
infill strategies.
This tool permits users to draw upon a wide variety of data, including both parcel-level and
district-level data, and conduct analyses at almost any geographical scale, from the parcel
level all the way up to the level of the city or the county. It is not “fixed,” but rather can
evolve in response to changing circumstances and the availability of new datasets. It can be
used to identify individual infill parcels, or it can be used in support of a wide variety of
public policy analyses.
1. Software Requirements
The infill estimation tool is deliberately designed to use “off-the-shelf” software familiar to
most practitioners who work for user agencies and organizations. The only software
required is:
1. A Geographic Information System (ArcGIS was used for this project)
2. Database Software (Microsoft Excel was used for this project)
2. Data Requirements
In addition to using “off-the-shelf” software, the infill estimation tool functions best using a
combination of data that is readily available and commonly used by local governments
throughout California. The power of the tool comes in large part from deftly combining
the data for analytical purposes.
2-1. Basic Parcel-Level Data
The core data required to use the tool are the basic parcel-level datasets typically available at
the city and/or county level. These include:
1. “Vector” data at the parcel level: The data containing information required to draw
the shapes of individual parcels in the GIS. This information is used to create
parcel-level base maps.
2. Parcel attribute data: The data, usually available from the county assessor’s office,
that includes the basic attributes of each parcel, including the Assessor’s Parcel
Number (APN), parcel size, assessed value, current land use as recorded by the
assessor, number of housing units on the parcel, and similar information. This
information is used to attach specific attributes about land, improvements, and
current land use to each parcel.
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3. Zoning or land-use designation data: Data available from the city or county that
provides information about the zoning district that each parcel is located in and the
allowed density or range of densities for each parcel. This data is used to calculate
the maximum allowable buildout under current land-use regulations.
2-2. Additional Parcel-Level Data
In addition to the basic parcel-level data listed above, which is required for the basic
operation of the tool, the tool is flexible enough to accept any other parcel-level data that
might be available and useful in analytical processes. These might include:
•
•
•
•
•
Databases listing parcels designated as “brownfields”.
Government owned parcels.
Infrastructure capacity.
School sites and classroom capacity.
Transportation nodes and routes.
The tool could even accept or be linked to the Multiple Listing Service, so that government
agencies or developers could identify not only which parcels are ripe for infill
development/re-development but also which of those parcels are actually for sale currently.
2-3. Block- or District-Level Data (Infill Study Areas)
The analytical power of the infill estimation tool can be increased by combining parcel-level
data with a wide range of data available at the block or district level. This data does not
always provide information about specific parcels, but information or attributes about the
parcel can be inferred from the larger-scale data. Among the most powerful datasets that
can be loaded into the tool are:
1. Census Data: Census data is available at the Census block level (usually covering
approximately 50 to 100 housing units). The Census provides a wide range of
information about housing stock, demographics, and the socioeconomic
characteristics of households. This information can be very powerful in
environmental justice analysis by suggesting which populations might be heavily
affected by infill development strategies.
2. Jobs Data: As the Census does not cover data collection related to business or jobs,
other data sets must be obtained for this purpose. There are private companies that
create such data sets. One is Dun & Bradstreet. This data can be useful in
estimating jobs – housing balances.
3. Environmental Constraints: A vast array of environmental data is available from
government agencies, especially at the state level. This information is available as
map layers compatible with ArcView. Although it is not always available at finegrained resolution, it can be combined with parcel-level data to determine which
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parcels might be unavailable for infill development because of environmental
constraints.
4. Special Areas: Another valuable district-level dataset that can be added to the infill
estimation tool is a compilation of the special, governmentally designated areas
dealing with revitalization, such as Redevelopment Project Areas, Enterprise Zones,
Community Development Block Grant-eligible areas, and so forth. In testing
strategies under the tool, these designations are especially valuable in determining
which parcels might be eligible for public financial subsidies.
5. Transportation: Various transportation data sets can be utilized identify prime spots
for infill development. These include locations of rapid transit stops as well as
current capacity of transportation ways, as well as transportation analysis zone data
that identifies current vehicle trip generation in a given area. The infill estimation
tool can also be used to feed future trip generation estimates into transportation
modeling software.
6. Infrastructure Capacity & Scheduled Capital Improvements: Individual parcels
require adequate infrastructure to support infill development. Infrastructure
capacity information is often available on the neighborhood or district level, as are
scheduled capital improvements. This data can be used to identify which infill
locations might be preferable from the point of view of public infrastructure
7. Mortgage Lending Information: Under the Community Reinvestment Act and other
applicable laws, a great deal of private lending information is available at the
district level. This data could also be loaded into the tool to identify which infill
sites are receiving – or ought to receive – community-oriented financing.
This data is available at a variety of scales. However, it may be helpful to aggregate the
information to a consistent neighborhood- or district-level area. In the course of this
research project, we approached this issue in the City of Los Angeles by creating “Infill
Study Areas”. ISAs are groupings of Census block groups, that when combined make up
cohesive neighborhoods of like properties, as defined by local planners. (See Appendix C
for a more detailed discussion.) The ISAs are helpful in identifying “neighborhoods” based
on a variety of search criteria. Examples of such criteria would be housing density,
population density, income, home price, infrastructure, and residential-to-commercial land
use ratio.
In some cases where data is missing, expert opinion can be used to fill the gap. In our pilot
project, for example, we asked planners and other experts to assign a numerical ranking to
the water and sewer infrastructure capacity in a given geographical area, such as an Infill
Study Area. This numerical ranking could be aggregated and loaded into the model as an
additional screen or filter if desired.
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2-4. Market Data
In addition to the parcel- and district-level data loaded into the GIS feature, the Infill
Estimation Tool requires the use of local market data that can assist users in estimating the
likely impact of an infill strategy. This component of the tool is not as well developed as
the GIS component (a more complete description of both is included below), and the
possible data sources have not been fully explored.
At a minimum, the tool requires building permit and demolition data over a period of
years for the geographical area under analysis. In addition, the tool works best with datasets
– not necessarily from the geographical area or the jurisdiction in question – that help to
show the effect of different public policies over time.
For example, one of the infill strategies discussed in this pilot project is second-unit
development on single-family lots. Data is beginning to become available from individual
jurisdictions around the state about the increased second-unit activity under the recent
State law AB 1866, which makes it easier for such units to obtain permits. This data could
be used as a starting point for making assumptions about the increase in housing
production that could occur as the result of a more aggressive second-unit strategy.
Similarly, data about increased development activity resulting from subsidies –
redevelopment subsidies, for example – could be used as the basis for creating similar
assumptions.
Again, expert opinion based on local knowledge could be substituted for this data. Local
developers, brokers, and others could be surveyed to gather a consensus on local market
activity and the likely impact of infill strategies. This consensus could be loaded into the
tool rather than actual data. Indeed, in the infill evaluation tool, the user might
deliberately choose a variety of scenarios based on different assumptions and opinions.
3. Main Features of the Tool
The Infill Evaluation Tool has several features that can be manipulated by users in
different ways for multiple purposes. Our project, however, focused on two main features:
1. Geographical Screening: The ability to filter infill opportunities at the parcel level for
the purposes of (a) specifically identifying those opportunities geographically, and
(b) quantifying those infill opportunities in terms of parcels, acreage, and (in the
case of infill housing) potential units.
2. Infill Strategy Evaluation: The ability to (a) identify potential infill strategies; (b) link
those strategies to specific parcels or groups of parcels through the geographical
screening feature; and (c) estimate the potential impact of selected infill strategies
(for example, in the case of infill housing, estimate increased housing production).
3-1. The Geographical Screening Feature
The geographical screening feature combines the power of different databases (parcel-level
and district-level) with the power of mapping and aerial photography to help users visualize
specific infill opportunities. Combined with the statistical information in the underlying
databases, the geographical screening feature can also quantify those opportunities.
The geographical screening feature begins by selecting a particular geographical area for
which data is available and has been loaded into the tool as described in Appendix A.
Once this geographical area is selected, the tool will display the geographical area at any
scale, including a display of parcels, with aerial photography displayed underneath the
parcels. In addition, the tool will calculate and display values of selected parcels which
include:
1. The total number of parcels
2. The total number of acres
3. (In the housing example) the total number of housing units currently existing on
those parcels
4. The total number of housing units permitted under the current zoning or land-use
regulations.
The tool can be modified easily to calculate and display the totals for any other parcelbased attribute contained in the underlying data.
Once the geography has been selected, the user can then screen or filter the parcels
contained in that geography based on any attribute contained in the underlying data. In
our pilot project, we created the following filters:
1. Zoning category
2. Lot size
3. Ratio of existing housing to maximum buildout
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4. Ratio of assessed value of land versus improvements
5. A “wild card” that would permit us to query any other attribute.
We also included a “de-select by hand” screen to permit users to remove individual parcels
that may pass the other screens, but which based on local knowledge, users recognize are
not really available for further development.
It is important to note that in this example we assumed that the maximum capacity for
housing was 100% of allowable zoning. Users could make a different assumption if they
wish – for example, that maximum buildout is only 60% or 70% of theoretical maximum
zoning. This is an assumption that local governments sometimes make in their planning.
Once the data is loaded into the tool and the tool is adapted to activate the filters the user
needs, then the geographical screening process is both simple and powerful. The user
simply selects the filters or screens desired and the results are depicted both visually and
quantitatively.
The example covered in the accompanying tutorial deals with the West Los Angeles
Community Plan Area in the City of Los Angeles. In the example, the user seeks to
identify infill parcels that meet certain criteria for multifamily development. As the tutorial
shows, the tool is able to screen the 14,000 parcels in West Los Angeles based on the
following sequential criteria:
All West Los Angeles Community Plan Area parcels as depicted by Infill Tool
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1.
2.
3.
4.
Parcels zoned for multiple-family residential;
Then parcels of 7,000 square feet or more;
Then parcels which are built to no more than 50% of maximum zoning.
Then parcels where the ratio of improvement:land (assessed value) was 0.9:1 or
less.
5. Finally, hand de-selection of individual parcels that planners identified as unlikely
candidates. In the case of West Los Angeles, we de-selected parcels containing large,
built-out condominium projects in Century City which were already high-density
but theoretically have additional capacity under current zoning
The result of this screening was identification of 177 parcels (approximately 1.2% of the
parcels in West L.A.), concentrated largely in the area around University High School.
These parcels totaled 103 acres of land. At present they have 2,087 housing units on them,
though the theoretical zoning maximum is 9,207.
Remaining parcels after being filtered through screens described on previous page
It is important to note that these results can be visually depicted at any geographical scale.
As the screen images shown in Appendix B show, at the scale of the entire West Los
Angeles Community Plan area, the tool depicts selected parcels in a solid turquoise, so that
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overall geographical patterns become immediately apparent. But the image also permits the
user to “zoom in” to a much finer-grained level of detail. At the zoom level, the selected
parcels are depicted in a turquoise outline on top of the aerial photograph, allowing the
user to visually verify whether the parcel is underutilized. In addition, the user can view
individual parcel attributes simply by clicking on any parcel.
It is also possible to use the geographical screening feature to show the effect of any
additional layers of data the user might load in, such as census data, environmental
constraints, and redevelopment or enterprise zone status. In this way, the user could
further refine the queries to include or exclude parcels that meet these criteria.
The Geographical Screening Feature can be used for a multitude of purposes by a wide
variety of users, including state agency personnel, local government personnel, developers,
and community groups. Once a local government has loaded in the basic data, developers
might use the Geographical Screening Feature to identify specific parcels that meet their
criteria as ripe for infill development. Any user – governmental, private, nonprofit – could
use the Geographical Screening Feature to identify potential sites for any need based on
selected criteria. Urban parks or school planners, for example, could try to identify
potential sites for their purposes. Similarly, planners or developers interested in “use
changes” could also use the Geographical Screening Feature – looking for industrial land
most suited to use for commercial development, for example, or commercial land most
suitable for housing. Local government agencies can use the geographical screening feature
to test the possible effect of different infill development strategies. The process of
evaluating infill strategies will be discussed in the next section.
3-2. The Infill Strategy Evaluation Feature
The Infill Strategy Evaluation Feature allows users to identify infill strategies and then test
their likely effectiveness based on different assumptions and different scenarios. The Infill
Strategy Evaluation Feature uses the Geographical Screening Feature, but incorporates this
feature into the larger task of testing the infill strategies. In addition, the Infill Strategy
Evaluation Features uses a simple Excel file that can be expanded in order to take account
of more variables in the market absorption assumptions.
In simple terms, the Infill Strategy Evaluation Feature seeks to estimate effectiveness by
quantifying the combination of two different factors: the increase in allowable density that
would result from the strategy; and the expected increase in the amount of infill
development over a specified period of time that would result from employing the strategy.
To do so, the Infill Strategy Evaluation Feature involves use of a four-step process that can
be repeated to create an iterative process. The four steps are:
1. Define the Strategy
2. Generate assumptions about increased density, market activity and increased
activity due to the strategy.
3. Conduct the geographical screening and map the results
70
4. Using the statistical results from the geographical screening, estimate the likely
impact of the infill strategy in question.
Step 1: Define the Strategy
The first step is simply for the user to identify one or more potential infill strategies. In the
pilot project we began with 10 different infill housing strategies that have either already
been implemented or are under consideration in the City of Los Angeles. Eventually, we
used the tool to test three different strategies:
1. A density bonus strategy based on the state’s 25% density bonus law.
2. An aggressive second-unit strategy based on the state’s AB 1866 second-unit law.
3. A transit-oriented development strategy that assumed higher densities within
walking distance of transit stops.
Step 2: Assumptions About Density and Absorption
The assumptions about density and absorption are the key assumptions that will drive the
outcome of the strategy evaluation.
The assumption about density is often a simple one. For example, the 25% density bonus
strategy would simply assume a 25% increase in allowable density on eligible parcels. An
aggressive second-unit strategy would simply assume a 100% increase in allowable density
on eligible single-family parcels. In other cases, however, this assumption may need to be
derived from a policy goal. In the case of the transit-oriented development strategy, in the
pilot project we assumed about a 15% density, because in the specific geographical area we
tested, this was the increase required to raise average densities on eligible parcels to 60
units per acre – a figure the research team concluded was the minimum necessary for a
viable strategy.
The assumption about market absorption is more difficult and could involve using several
different types of datasets, expert consensus, or simply good guesses. Essentially, what is
required here is an estimate of how much development activity (infill housing units, in our
pilot examples) will occur over a given period of time once the strategy is in place.
The absorption rate assumption is really made up of three assumptions:
1. An assumption about what the “natural” market absorption would be without the
strategy. This can often be derived from recent building permit and demolition
trends.
2. An assumption about the increase in the absorption rate that might occur once a
strategy is put into place. A potential increase in density, for example, might
stimulate more interest by developers in building on eligible parcels.
3. An assumption about the increase in the absorption rate that might occur as a
result, if a subsidy is also in place. A potential subsidy from the redevelopment
71
agency, for example, might make some projects “pencil” that might not otherwise
do so, even with the increased densities created by the strategy.
In our Excel table, these three factors are folded into one simple annual absorption rate,
which is based on a percentage of the remaining overall capacity permitted under zoning.
These factors could be broken out into separate calculations, and/or additional factors
(such as accounting for demolitions) could also be calculated in order to clarify how they
are accounted for in the absorption rate.
Step 3: Geographical Screening
The third step is simply to conduct the geographical screening in exactly the same manner
as described above. The user can use the screen to identify the universe of eligible parcels
based on the specific parameters envisioned by the strategy – for example, large multifamily parcels for density bonus; ¼ mile walking distance for TOD; single-family units for
second units; and other factors.
Geographical screen for West L.A. 25% density bonus strategy
72
Step 4: Likely Impact
In addition to producing a map of eligible parcels, the Geographical Screening Feature
produces a statistical analysis of the universe of eligible partners, calculating their
combined number, acreage, existing units, and potential units under zoning. These
statistics, along with the assumed density increase and the assumed absorption, then serve
as the inputs into Infill Strategy Estimation Tool.
Sample Calculation for West L.A. 25% bonus density strategy
73
Appendix B: Detail, Sample Economic Feasibility Pro Forma
Wilmington-Harbor City - Small Mixed-Use Prototype
Parcel Size (sf) :
17,000
Zoning:
C2
density (u/ac) :
108.9
allowed # of units :
42
0.39 acres
Land Use Mix #1
% of total area
size (sf)
Open Space*
Building Footprint
Uncovered Surface Parking
10%
90%
0%
Gross Building Composition:
total site building footprint**
(excluding sidewalks and setbacks)
100%
size (sf)
15,300
100%
15,300
65%
20.0%
0.0%
100% residential "for sale" condos
9,945
3,060
14,535
12,240
-
vertical mixed use component
ground floor
parking
retail
office
2nd floor***
3rd floor***
4th floor***
1,700
15,300
-
town house component
0%
(total townhome buildable area is reduced to 70% for driveway and setback requirements)
Residential Composition:
vertical mixed use component****
# of 1 bdr units
unit size (sf)
# 2 bdrm units
unit size (sf)
town house component*****
unit size (sf)
total # units
total residential space (sf)
# units
40%
13
60%
13
700
1,000
1,200
26
26,775
* meets maximum 90% lot coverage requirement in District 3 (public open space)
** area assumption includes driveway space for podium parking and building common areas
*** area assumptions based on above 1st story setback requirements in D3 zone
1st floor - 100% of site buildable area
2nd floor - 90% of site buildable area
3rd & 4th floor - 80% of site buildable area
**** the # of condo units accounts for 20% non-sellable residential common areas
***** the # of townhomes is based on 3-story townhome footprint including 400sf garage
Data Series
chart 2 buillding components
retail
office
residential
chart 3 mix of residential uses
1 bedroom condo
2 bedroom condo
townhomes
chart 4 parking
below grade parking
above grade podium parking
uncovered surface parking
chart 5 Feasibility Gap
current land selling price
residual land value
feasibility gap
-
sf
3,060
26,775
# units
13
13
# spaces
40
27
0
sf
13600
9180
0
$ 1,105,000
$ (2,064,827)
$ (3,169,827)
74
Small Mixed-Use Prototype 2
Land Use Mix Scenario #1
(Baseline)
Open Space*
Building Footprint
Uncovered Surface
Parking
Prototype 2; Scenario #1(Baseline)
Building Composition
residential
90%
office
0%
75
retail
10%
Residential Composition
1 bedroom
condo, 13, 50%
2 bedroom
condo, 13, 50%
Small Mixed-Use Prototype 2A
Feasibility Gap
$1,500,000
$1,000,000
Land Value ($)
$500,000
$$(500,000)
$(1,000,000)
current land
selling price
residual land
value
$(1,500,000)
$(2,000,000)
$(2,500,000)
$(3,000,000)
$(3,500,000)
$(4,000,000)
76
feasibility gap
Parking Composition
below grade
parking, 13600,
60%
above grade
podium parking,
9180, 40%
77
Wilmington-Harbor City - Small MixedUse Prototype
LAND USE MIX #1 (90% building
coverage; 10% open space)
SITE
CHARACTERISTICS
Parcel(s)
Size
Total Open Space (1)
Total Uncovered Surface Parking
(1)
Total Buildable
Footprint (1)
Existing Zoning (2)
zoning density
(units/acre)
height restriciton (ft above grade)
current affordable density bonus
(3)
ac
0.39
17,000
C2
108.9
35
???
DEVELOPMENT
PROGRAM
Residenti
al
Maximum # of residential units w/ existing
zoning
vertical mixed use
component
# market rate units
1 BDR Unit Size (4)
sf
42
size
mix
700
40%
13
2 - 3 BDR Unit Size
(4)
# affordable units
1 BDR
Unit Size
2 BDR Unit Size
townhouse
component
# market rate 2-3 Bdr
units (4)
1,000
60%
13
650
0%
0
1,000
0%
0
1,200
added market rate units w/ aff.
density bonus
added affordable rate units w/
density bonus
Total # of
Units
Total Residential Square
Footage
Infrastructure capacity
requirements (5)
0
0
0
26
26,775
none
Commerc
ial
Retail
Space (1)
3,060
Office
Space (1)
0
Office
Parking
(6)
Residential 1 bdrm
Residential 2 bdrm
20
26
79
Guest
Retail
Regular
Office
Total Required
parking spaces
Total parking square
footage
7
13
0
66
22,440
# spaces
below grade parking
(7)
above grade podium parking (7)
13,600
27
above grade uncovered surface
parking (7)
Vertical mixed use Building
envelope
1st Floor
2nd Floor
3rd Floor
4th Floor
square ft
40
9,180
0
0
height
Office / Commercial Retail / Covered
Parking
Residential / Office
Residentia
l
Residentia
l
10
10
10
0
total
height (8)
35
1.76
FAR
PROJECT
REVENUE
Residenti
al
Total
Market Rate Condo
80
Units
1 BDR market price /
sf (9)
Unit Sale
Price
2 - 3 BDR market
price / sf (9)
Unit Sale
Price
$350
$245,000
$300
$300,000
Commercial Retail
(10)
leasable Retail
square footage
Lease
Rate (10)
Revenue
Periods/year
Gross Annual Income
Less
Vacancy
Less Operating
Expenses
Net Operating Income
Capitalized Value
3,185,000
3,900,000
2,601
$1.75
12
54,621
5%
(2,731)
7%
(3,823)
cap rate
5.75%
48,066
835,939
Office
(12)
Office square footage
Lease
Rate (10)
Revenue
Periods/year
Gross Annual Income
0
$1.75
12
0
81
Less
Vacancy
Less Operating
Expenses
Net Operating Income
Capitalized Value
5%
7%
-
cap rate
5.75%
0
7,920,939
TOTAL PROJECT
REVENUE
PROJECT COSTS
Pre Development
Costs
due
diligence
land carry (% of raw land cost,
see note 13)
land entitlement / legal fees (% raw land, see
note 13)
professional fees (per unit, see
note 14)
RESIDUAL LAND
VALUE (15)
(25,000)
8%
(165,186)
2%
(41,297)
5%
Development Costs
Building
Construction Costs
Demolition Costs (% of land
value)
Site Development
Costs (16)
Residential Construction Costs
Condo Units ($/sf)
82
($121)
(315,829)
2,064,827
10%
(206,483)
$5
(85,000)
$140
(3,748,500)
Townhomes ($/sf)
Retail / Office Construction
Costs $/sf
Parking Construction (see note
17)
Additional Infrastructure enhancement costs
(see note 18)
$145
$110
0
(336,600)
(1,940,000)
0
Indirect
Costs
impact fees (per market rate unit,
see note 19)
building permit (per unit, see note
19)
insurance (% of
revenue)
property tax (% raw land, see
note 20)
$6,928
(180,128)
$5,210
(135,460)
2.4%
(151,598)
1.1%
(68,139)
Developer Fee (21)
3%
(205,557)
Contingency (22)
8%
(505,327)
2.5%
(157,915)
Marketing /
Advertising Costs
(6,203,191)
SUBTOTAL DEVELOPMENT & LAND
COSTS
Financing Costs
Equity (equity interest paid w/ profit
sharing, see note 23)
Permanent Debt (24)
loan horizon (yrs)
loan fees
83
25%
1,756,744
75%
2
2%
5,244,798
(104,896)
average
draw
interest
rate
debt
service
50%
7%
(367,136)
3.0%
Commission &
Closing Costs
(212,550)
(6,887,773)
TOTAL PROJECT
COSTS
1,033,165
Project
Profit
Project Profit less landowner equity
payment (% profit)
Project Profit (% of total costs)
0%
1,033,165
15.0%
SF
RESIDUAL LAND
VALUE
CURRENT LAND SELLING
PRICE (25)
FEASIBILITY GAP
(26)
TOTAL
($121)
$
(2,064,827)
$65
$
1,105,000
$186
$
3,169,827
Lending
Criteria
loan to value ratio
(LTV)
0.66
84
NOTES:
(1) based on Reasonable Assumptions and calculations
in Land Use Mix #1
(2) zoning based on GIS infill screens of
opportunity areas
(3) density bonus (additional market rate units allowed per additional
affordable units built)
(4) based on 4 level building structures; 2,3, and 4th story devoted to residential; includes
25% residential common space
(5) additional infrastructure capacity as determined by
Public Works Division
(6) # parking spaces = 1.5/1bdr, 2/2bdr, guest 1/4unit; 1space/250sf retail & medical office;
1/250 office; each space 340 sf.
(7) 40% and 60% of the parkling spaces devoted to podium and below grade
respectively
(8) each level assumed to be 10' with an
additional 5' roof pitch
(9) Based on local area market survey
research
(10) area standard commercial retail lease rates, vacancy rates and cap rates as
determined in market survey
(11) medical office lease rates, vacancy rates and cap rates as identified in
market survey
(12) area standard office lease rates, vacancy rates and cap rates as
determined in market survey
(13) money paid upfront to hold land through the entitlement process; assumes a 1 year
earnest money contract
(14) Professional Fees include architectual design, engineering &
environmental consultants
(15) Residual land value is the amount of $ the developer is willing to pay for the land after
receiving the project profit
(16) % construction costs for grading, sewer,
water, and roads
(17) Assumes surface parking costs = $3000/stall, podium = $17,000/stall;
underground = $27,000/stall
(18) additional infrastructure capacity enhancement costs as indicated by
Public Works Division
85
(19) impact fees and building permits as indicated in Housing Element and
development guidelines
(20) % of land value taxed each year developer has ownership of land;
assumes 2 years
(21) % of development costs charged to cover
developer overhead
(22) % construction costs to buffer against unexpected
increases in costs
(23) borrowed money from joint equity investors; require returns through a higher 'preferred' rate and profit
sharing with developer
(24) lent money developer acquires from a bank or other lending institution; the fees and
interest costs are also financed
(25) as determined in the market survey for vacant land
with similar zoning
(26) the difference between the current land sellin price and the residual land value (i.e. what the
developer is willing to pay for the land)
86
Appendix C: Manual for the Infill Scenario Mapping System
www.igist.com
!
IGIS Technologies, Inc.
10393 San Diego Mission Road • Suite 212
San Diego, California 92108
Phone 619.640.2330 • Fax 619.640.2334
"
About this manual .................................................................... 1
Manual Conventions ....................................................................... 1
Valuable information ....................................................................... 1
Welcome to the Infill Scenario Mapping System....................... 3
Background..................................................................................... 3
Overview ......................................................................................... 3
Getting started.......................................................................... 5
System Requirements..................................................................... 5
Special Notes.................................................................................. 5
Installation....................................................................................... 5
Run SQL Scripts ....................................................................................... 5
Create New Virtual Directory .................................................................... 6
Infill Scenario Mapping System ................................................................ 9
Finalize the Installation ........................................................................... 11
Modify web.config ................................................................................... 11
Interface for Planners ............................................................. 13
Introduction ................................................................................... 13
Create a Scenario ......................................................................... 14
Scenario Wizard............................................................................ 18
Load a Saved Scenario................................................................. 19
View Results ................................................................................. 20
Print............................................................................................... 22
Interface for Administrator ...................................................... 25
Introduction ................................................................................... 25
Glossary................................................................................. 29
Planning Terms............................................................................. 29
Software Application Terms .......................................................... 29
Database Schema and Requirements.................................... 30
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This icon indicates information of special note.
applied to the item.
Special attention should be
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I
n 2005 Solimar Research Group (SRG) developed a prototype of the
Infill Scenario Mapping System for the City of Los Angeles, Department
of City Planning (DCP). This GIS-based software application provides
planners with the ability to locate parcels using a variety of criterion. This
will contribute to the city's long term ability to provide more housing through
identifying infill opportunity parcels. In 2006 SRG was contracted by the
City of Los Angeles to develop the actual Infill Scenario Mapping System,
based on the prototype application. SRG in turn subcontracted with IGIS
Technologies to develop this Infill Scenario Mapping System.
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T
he Infill Scenario Mapping System is an Intranet-based application,
which runs under ArcGIS Server™, ArcSDE™, and Oracle™. It
accesses specific GIS datasets that reside on DCP servers through a
web browser interface. Users of the application are able to specify a variety
of input parameters that are then used to query DCP datasets. First the
user specifies a geographic data layer of interest (for example, Community
Plan Areas, Census Tracts, or MTA Rail Stations). Then the user identifies
the specific CPA, Census Tract, or Rail Station of interest.
Further input parameters specified by the user include zoning categories,
parcel sizes, built capacity ratio, remaining capacity, investment index, and
the year built. This query is then run against the data on the server, and the
results of the query are displayed both graphically (a map) and in terms of
,
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several summary statistics. The user has options to save the parameters,
modify the parameters, define a new query, print the results, or view the
selected parcels.
Once results have been generated the user has access to a variety of tools
to tease out information about infill housing potential. The query can be
modified; the map can be panned or zoomed in to, a polygon can be drawn
to define an area of interest to be analyzed, a parcel can be queried with an
“identify” tool, and individual parcels can be deselected from the results, if
desired.
Note: This application will work with geographic data from anywhere in the
world. However it will only perform effective analysis in areas that have
parcel data (e.g. City of Los Angeles). If the user specifies data that is
beyond the area where the parcel data resides (e.g. beyond Los Angeles),
results may not be as expected.
1
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Component
Operating System
.NET Runtime
Oracle Database
Oracle Data Provider for .NET
ArcGIS Server w/ .NET ADF Runtime
ArcSDE
Version
Windows 2000 / Windows 2003
Version 2.0.50727
10.2.0.2
2.102.2.10
9.1 Service Pack 2
ArcSDE 9.1 for Oracle 10g
#
Microsoft .NET 2.0 does not include a Global Assembly Cache Utility
application (gacutil.exe). The Oracle Data Provider for .NET (ODP.NET)
will attempt to utilize the gacutil during its installation process. Ignore the
warning provided by the Oracle installer and manually register the
ODP.NET Dynamic Link Library (DLL) through the .NET 2.0 Configuration
Manager accessed through the system’s control panel. Oracle places the
ODP.NET
DLL
in
the
Oracle
home
directory
under
ODP.NET/2.0/Data.Access.dll.
The ArcGIS Server installation must include both the ArcGIS Server 9.1 for
Windows and the ArcGIS Server .NET Application Developers Framework
(ADF). The ADF installation requires that the Microsoft .NET 1.1 Software
Development Kit (SDK) is installed. If this component is not installed,
download it from Microsoft’s website and install it before attempting the
ArcGIS Server .NET ADF installation. Only install the ArcGIS Server .NET
ADF Runtime on the server. Once the ArcGIS Server .NET ADF
installation is completed, the Microsoft .NET 1.1 SDK can be removed from
the computer.
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4
The SQL
1. Open the command prompt and start SQL*Plus using the following
syntax:
2
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2. C:>sqlplus /nolog
3. Log in to the Oracle database using credentials Infill Housing
parameter tables schema. For example:
4. SQL>connect infill/infill@sde_orcl
5. Run SQL script provided on CD, infill_scripts, to create the
necessary parameter files.
6. SQL>@D:Data\SQL Scripts\infill_scripts.sql
# 0
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Using the Internet Information System (IIS) Microsoft Management Console
(MMC), create a new virtual directory from which this application will run.
1. In the IIS MMC, right click on the Default Web Site header, and
select ‘New | Virtual Directory’ from the context menu.
2. Once the below window appears, click ‘Next’.
5
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3. Enter an alias name for the Virtual Directory.
4. Enter the physical path location where the application will reside.
6
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5. Accept the defaults as displayed below.
6. Click ‘Finish’.
7. Close the MMC.
7
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Now locate the Infill Housing Microsoft Setup Installer (MSI), and double
click the file to begin the installation process.
1. Click ‘Next’ on the welcome screen that appears.
2. Choose the default web site and specify the previously created virtual
directory.
8
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3. Click ‘Next’ to begin the install.
4. The installation will take a couple minutes to complete. Once it has
completed click ‘Next’.
5. Click ‘Close’ on the final screen.
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1. Open the la_infill.mxd provided on the install CD and verify data paths
are set correctly.
2. Once data paths have been verified, save a local copy of the MXD to the
server.
3. In the MXD, the LANDBASE and LANDUSE layers MUST be joined to
the assessor’s data (i.e. TLUPAMS1). The join column for Los Angeles is
LANDBASE.BPP to TLUPAMS1.APN. Please verify these tables are
joined properly before proceeding.
4. Deploy the MXD as an ArcGIS Server pooled service with the name
‘la_infill’.
5. In the “pages” folder of the installation location, open the results.aspx file
in a text editor (e.g. notepad or textpad). Find the following line of code and
update with appropriate ArcGIS Server name and ArcGIS service.
<esri:Map ID="mapResult" runat="server"
DataFrame="Layers" Height="345px" Width="845px"
Host="server_name_here"
ServerObject="service_name_here"
UseMIMEData="true" />
6. Save results.aspx.
7. In the “pages” folder of the install location, open the preview.aspx file in a
text editor. Find the following line of code and update with appropriate
ArcGIS Server name and ArcGIS service.
<esri:Map ID="mapResult" runat="server"
DataFrame="Layers" Height="345px" Width="845px"
Host="server_name_here"
ServerObject="service_name_here"
UseMIMEData="true" />
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0 "
Now modify the web.config file located in the installation location.
1. Use the table below to modify the keys at the end of the web.config file.
ags.server
Key
Description
ArcGIS Server Object Manager (SOM) machine name
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ags.service
parcel.name
parcel.apn
zoning.name
zoning.feature.class
zoning.label
zoning.district
lupams.name
lupams.apn
lupams.bldg.units
lupams.landuse
lupams.parcel.area
lupams.improve.value
lupams.land.value
lupams.year.built
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#
(
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ArcGIS service name (Must be a pooled service)
Parcel layer name as it is identified in the MXD
Full path name to the APN field in the parcel feature
class
Zoning layer name as it is identified in the MXD
Full physical name of the zoning feature class
Label column used to identify zoning districts
Zoning identifier field
LUPAMS table name
Full path to LUPAMS apn field
Full path to LUPAMS building units field
Full path to LUPAMS land use field
Full path to LUPAMS parcel area field
Full path to LUPAMS improvement value field
Full path to LUPAMS land value field
Full path to LUPAMS year built field
2. Find
the following section of the web.config file, and update the
connection string information in the web.config to match the database
server the application will use for data queries.
<connectionStrings>
<add name="infill" connectionString="Data
Source=ORACLE_SID;Persist Security
Info=True;User ID=USER;Password=PASSWORD"
providerName="Oracle.DataAccess.Client"/>
</connectionStrings>
3. Verify that all the NHibernate mappings are correct in the web.config
file including the Oracle connection string.
4. Find the following section of the web.config file, and update the
impersonation element in the web.config file to match a user with ArcGIS
Server administrative privileges.
<identity impersonate="true"
userName="AGS_ADMIN_USER"
password="AGS_ADMIN_PW"/>
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*!
T
he main menu (or home page) for the Infill Scenario Mapping System
presents the user with three means of defining input parameters for a
scenario. The most common method for experienced users will be
simply to “Create a scenario”. This option presents the user with an input
form that resides in a single scrollable window. An alternative to this is an
option to “Use the wizard to create a scenario”. This input method
presents the user with a wizard interface to define scenario parameters.
Lastly, the user can select “Load a saved scenario” to browse their
computer to locate a previously saved scenario, then load and run it.
Below is a screen capture of the main menu for the application.
In the lower right corner of this screen there is a link to the
“Administration” tool. This links to a password protected portion of the
application that allows the site administrator to define the environment in
which the application runs, to define various zoning districts and their
attributes, and to define the geographic boundaries used by the application.
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With this interface, the experienced user is able to quickly define all input
parameters from a single, scrollable window.
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Each parameter has a blue
icon after it. By clicking on this icon, the
user can view help information about any given parameter. This help
information can then be removed by clicking the red
icon at the end of
the help information.
When minimum and maximum values are requested, the application will
confirm that the maximum value is greater than the minimum value. Note
that all checks are happening on both the wizard and advanced pages. The
parcel size check is different than the other checks, in that it is handled on
the server-side. This is due to the fact that it is not a straight integer
comparison that can be done on the client side. (For example, 1 acre is
greater than 10,000 square feet. Due to the need for a unit conversion, this
check is performed on the server side.)
Several of the input parameters are defined in the Glossary found near the
back of this manual.
The following input parameters are collected on the Create Scenario
screen:
1. Scenario Boundaries – This selection defines the geographic area that
is being queried. For example, it could be polygonal information like a
Community Plan Area or a Census Tract, line data like bus routes, or point
data like MTA Rail Stations. Once a boundary type has been identified, a
specific feature must be selected from a pick list. For example, if Census
Tract has been chosen, the user must then identify the tract of interest (as
“101400”). If an MTA Rail station is selected, the user must then identify the
specific station from a pick list (as “Metro LA-LB Blue line: GRAND”).
The user can specify a Buffer Distance. This is a required entry for point
data—the user must check the checkbox and then enter a numeric value
and also specify units. For point data, the value defines the length of the
radius of a circle that starts at the point feature. For polygonal data, this
field is optional. When specified for polygonal data, this distance specifies
the width of a buffer that extends out from the polygon boundary. Line data
can also be used to define a scenario boundary. When specifying line data,
the Buffer Distance is an optional field.
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2. Zoning Selection – The user is presented with a pick list of available
zoning types. The user can select one or many zones from “Available
zones”, and then “Add” them to the “Included Zones”. All zones can be
selected by clicking “Add All”. An individual zone can be selected by
clicking on it. “Ctrl-Click” selects or deselects individual zones. “ClickDrag” selects multiple zones. Clicking on a zone and then “Shift-Clicking”
on another zone selects the two zones as well as all zones between them.
3. Parcel Size – The user is able to specify a minimum and maximum
parcel size to be queried. There are a variety of units that the user can use
to define the search. The minimum parcel size default is 7,000 square feet,
although the user can specify another value if desired. If no maximum is
specified, the maximum parcel size is open-ended.
4. Built Capacity Ratio – The user is able to specify a minimum and
maximum built capacity ratio. The default minimum is zero, and if the
maximum is left blank, it is open-ended.
5. Remaining Capacity – The user is able to specify a minimum and
maximum remaining capacity. The default minimum is one, and if the
6. Investment Index – The user is able to specify a minimum and
maximum investment index. The default minimum is zero, and if the
7. Year Built – The user can specify a start and end date for the year a
building is built. The default start date is 1803 (the first year that has
records), and the end date is set to the default of 1990. The user can
change either of these dates from a pick list.
8. Vacant Lots – The user can check the “Include Vacant Parcels”
checkbox. If this is checked, vacant parcels will be selected based on the
geographic area and the requested zones. The vacant parcels will be
selected regardless of parcel size and year built.
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Once the scenario has been defined, the user can either “View Results” or
“Save” the scenario to disk. Saving the scenario allows the user to save
off the parameters used to create a scenario as an XML file. Then the XML
file can be passed to a colleague for them to use, it can be reloaded at a
later time and used to generate a scenario, or it can be reloaded, modified,
and then used to generate a scenario.
No matter which parameter input method has been used, once all
parameters have been defined, the user can select “View Results.” This
causes a query to be run against the Los Angeles DCP GIS databases,
based on the parameters entered. Once the query is completed, the user is
presented a results page that shows a map and summary statistics that
characterize the query results. This is described after the following
sections, which describe the two other methods of parameter input.
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- 9 *
The Scenario Wizard provides more guidance to the user when entering the
parameters for a scenario. The help information is automatically displayed,
and a separate screen is provided for each of the parameters that are input.
On any given screen, the user has the option to move back to a previous
wizard screen or forward to the next screen. The user can view results
whenever the user chooses.
Below is a sample screen from the wizard.
The user then simply moves forward through the wizard screens by clicking
“Next”, and can “View Results” from any of the wizard screens.
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From the main menu, the user also has the option to “Load a saved
scenario” from disk. When the user selects this option, the following
interface is presented.
The user clicks the “Browse” button, and then is presented with a dialog
box to allow navigation to a file, which can then be selected. The user must
select a file that ends in the extension XML, otherwise the selection will not
be allowed. Once selected, the user clicks “Load”. The application reads
the XML file and populates the “Create a Scenario” screen with the
parameters from the XML file. Then the user can simply scroll to the bottom
of the page, and click on “View Results”.
8
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Once all input parameters have been entered to define a scenario, the user
can select “View Results”. This activates a query that is run against the
Los Angeles DCP geographic databases, and produces results that are
depicted on the computer screen. These results include a map showing the
selected polygons, several summary statistics on the selected parcels, and
a listing of all the parameters that the query was based on.
Below is an example of the “View Results” screen.
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Across the top of the above screen are a series of menu items. They allow
the user to take a variety of actions as required.
Modify – selecting this item gives the user the option to review existing
parameters, make changes as required, and then review the new results.
Save – this gives the user the opportunity to save the input parameters off
to disk, stored in an XML file.
Create new – selecting this item clears out any existing parameters, and
gives the user the chance to define a new scenario.
Print – selecting this menu item sends the results to a printer.
Export APNs – this exports all selected APNs into a comma separated text
file.
Notice that there are two options for defining the “Active Background”,
namely “Zoning” and “Land Use”. This particular Results page shows
that the user selected the Zoning radio button. The user also opened up
the legend, which shows the color scheme that depicts Zoning on the upper
right hand side of the map. The user can select “Close” to close out the
legend.
Along the left side of the results page are a series of icons that allow the
user to interact with the map in a variety of ways. Hover the cursor over
each icon, and you can see a context message that summarizes what the
icon does. Below is a short description of the icons, as they appear on the
results page, starting with the top icon.
Clicking on the “Zoom In” icon allows the user to zoom in on the map.
Clicking the “Zoom Out” icon allows the user to zoom out on the map.
Clicking the “Slider Bar” between these two icons allows the user to
incrementally zoom in or out by moving the slider bar.
Clicking the “Pan” icon allows the user to pan around in the map.
Note when using the pan tool, user must left-click on the map, and then
icon. Release the cursor
drag to pan. The cursor will briefly display the
and pan to where you want to be, then left-click to redraw the map.
Clicking the “Draw Polygon” icon allows the user to draw a polygon on the
map. To draw a polygon, user selects this tool. The cursor becomes a
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cross-hair, and the vertices of the polygon are digitized by left-clicking the
mouse. User continues digitizing vertices to define the polygon, and an arc
is defined by rubber-banding. To close the polygon, user selects “Ctrl +
Left-Click”. Once the polygon has been closed, the current query is rerun, based on the geographic area defined by the polygon.
Clicking the “Deselect Parcel” icon removes the selected polygon from the
current scenario. Once the parcel has been deselected, the current query
is re-run, with the deselected parcel being left out.
Clicking on the “Parcel” Information icon allows the user to select a parcel
to review certain attributes of the parcel based on its APN.
From the “Results” page, the user can select “Print” from the menu, and
will be presented with the following screen.
On this screen, the user uses a radio button to select the “Paper Size”.
The “Page Orientation” is set to Landscape, and cannot be changed. The
user can also customize two of the “Page Components”, so that a “Map
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Title” and an “Authored By” field can be defined. These two text fields
will be included in the print out.
The user can select the “View Results” page to return to the previous
screen, or, by selecting “Print Preview”, the results can be printed. The
user is then presented with a web page that is print-ready. The user will be
reminded to set the browser orientation to landscape. Click “OK” on this
reminder, then from the browser menu, select “File > Page Setup”, set the
“Orientation” to “Landscape”, and click “OK”. Then on the browser
menu select “File > Print,” select the desired printer, and click “Print.”
To continue using the Infill Scenario Mapping System, press the browser’s
“Back” button, and then select “View Results”. This returns the user to
the Results page, where additional queries can be initiated.
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Access to this portion of the application is from the main menu. By clicking
“Administrator” at the lower right side of the “Main Menu”, the user will
be prompted to enter a username and password, and then will be
presented with the main “Administration” screen. This portion of the
application provides an interface for the site administrator to define the
environment in which the application runs, to define various zoning districts
and their attributes, and to define the geographic boundaries used by the
application.
Note that each of the “Administraion” screens has a Logout menu item in
the top right hand side of the screen, and this will take the user back to the
“Main Menu”.
Following are the “Administration Tool” screens.
The “Boundary Administration” screen allows the user to add, edit, or
delete datasets from the ArcSDE database that are available to the Infill
Scenario Mapping System to define geographic areas for scenarios. This
data can be point data, line data, or polygon data.
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To add a new boundary, click on “New Boundary,” and enter the required
information in the dialog box. This includes 1) the Physical Name of the
feature class in the ArcSDE database 2) the Display Name, 3) the Shape
Column (this is the name of the geometry column in the feature class table
)and 4) the content and format of the Label Column.
Note that this last column uses the syntax of Oracle’s PL/SQL. Refer to
http://downloadwest.oracle.com/docs/cd/A91202_01/901_doc/appdev.901/a89856/toc.htm
– or to –
http://www.oracle.com/technology/tech/pl_sql/index.html
for further information.
Note for the MTA Rail Stations, under Label Column, the entry is
RAILNAME||'
:'
||STATION This defines this field so that it would read like
this: “Metro LA-LB Blue line: GRAND”.
Existing boundaries can be edited or deleted by selecting either the “Edit”
or the “Delete” button.
The “Zoning Administration” screen allows the user to add, edit, or delete
zoning districts. The parameters currently set were provided to the
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development team by the DCP based on current zoning ordinances and
rules.
To add a new boundary, click on “New Zone”, and enter the required
information in the dialog box. This includes 1) the Zone, 2) the
Description, 3) the Minimum Lot Size, and 4) the Units per Acre.
This information should only be changed if there is a legal change to the
zoning ordinances by the City Council. This tool is not meant as a way to
arbitrarily manipulate the outputs of the application.
Existing zone districts can be edited or deleted by selecting either the
“Edit” or the “Delete” button.
The “Server Administration” screen allows the user to change the
database server used to access data. The Department of City Planning
currently uses two servers on a rotating basis to support data based web
applications. This screen allows application administrators to modify the
data connection parameters of the application without having to manually
update application code.
Oracle Connection String: A standard connection string that identifies which database to
connect. More information about connection strings can be found at
http://www.connectionstrings.com. Only Oracle database servers are supported at this
time.
ArcGIS Server Object Manager Name: The name of the machine where the ArcGIS Server
service is installed and accessed.
ArcGIS Service Name: The name of the ArcGIS Server service used in the application.
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Built Capacity Ratio: A ratio derived by dividing current number of
residential units on a parcel by the maximum units allowed based on the
parcel’s zoning category. Parcels can have Built Capacity Ratios greater
than 100% due to variances and other special zoning situations.
Demographic Overlays: Overlaying census data to identify demographic
trends in the areas of study.
Economic Pro Forma: A detailed financial analysis of development
potential of a given parcel.
Investment Index: A ratio that is derived by dividing the improvement value
of a parcel by the land value. References the amount of investment that
has been made on a given parcel. Values of 0.9 and under are often
identified as redevelopment opportunities and those that are 2 and over are
not.
Maximum Units: Maximum units allowed on a parcel, derived by
multiplying zoning density by acreage of parcel.
Population Forecast: Projection of population growth associated with
potential residential development.
Remaining Capacity: The number of units that can still be developed on a
parcel in addition to existing units. Derived by subtracting existing units
from ‘Maximum Units’.
0
Configuration File: This file stores all of the user’s settings from the
current scenario. This allows the user to stop the analysis and return to the
scenario later without needing to recreate the scenario from scratch. This
also provides an easy way to share scenarios between users.
Scenario: An infill analysis based upon parameters described or inputted
by the user. Scenarios are the primary unit of the application.
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*& 3!
1. Required Tables
Table Name
TLUPAMS1
Schema Changes
None
LANDBASE (DCP
Parcels)
None
Indices
1. APN
2. LANDVAL
3. IMPRVAL
4. LANDUSE
5. BLD1YRBL
6. BLD1UNIT
7. PRCLAREA
1. BPPP
ZONING
AREA_UNIT
None
New Table
1. ZONE_CODE
None
BOUNDARY
New Table
None
CITY_ZONE
New Table
None
GEOMETRY_TYPE
New Table
None
LANDUSE_CODE
New Table
None
MEASURE_UNIT
New Table
None
Notes
Participates in a table join with LANDBASE setup in the
ArcMap MXD. The join is based on LANDBASE.BPP =
TLUPAMS1.APN.
Participates in a table join with LANDBASE setup in the
ArcMap MXD. The join is based on LANDBASE.BPP =
TLUPAMS1.APN.
None
This table is used to convert area measurements (sq. miles,
acres, etc.) into square feet for area calculations.
Keeps track of the application defined boundary layers used
in step one of the wizard.
Defines the maximum units per acre for each zoning district in
the city. This list of districts needs to be updated if new
zoning districts are added to the city’s zoning ordinance.
Lookup table for the BOUNDARY table identifying the
geometry type of layers defined in the BOUNDARY table.
Manages the high level land use types (i.e. Residential,
Commercial, Industrial, etc) for each of the individual land use
codes possible in the TLUPAMS1 table. This table is used for
filtering out non-residential units that may appear in
BLD1UNIT field of the TLUPAMS1 table.
This table is used to convert linear measurements (i.e. miles,
yards, meters, etc.) into feet.
,
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2. Optional Tables
Any number of optional tables can be used for both querying and cartographic purposes. Any table used in this category is unchanged
from the schema in which it was received.
a. Custom Boundaries
The custom query layers must be added to the BOUNDARY table to be used in the application.
Special Note: At least one boundary layer must be defined to perform a successful query. If no boundaries were selected, the
queries could potentially query the entire city and take an inordinate amount of time and resources on the server.
Table Name
Area Planning Commissions
Census Tracts
Community Planning Areas
City Council Districts
Historic Preservation Overlay Zone (HPOZ)
Neighborhood Council Districts
MTA Rail Stations
Source
Los Angeles Department of City Planning
Solimar Research Group
b. Custom Cartography
The custom cartography is defined in the la_infill.mxd file that is used by the ArcGIS Server application. The only layers required in
the MXD file are the LANDBASE, TLUPAMS1, and ZONING where LANDBASE and TLUPAMS1 are joined as defined in the required
tables table.
Custom Cartography layers used in the testing environment
Table
California County Boundaries
California Incorporated Places
Los Angeles Street Centerlines
Los Angeles Area Planning Commissions
Source
State of California (UC – Davis)
State of California (UC – Davis)
City of Los Angeles
City of Los Angeles
,
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3. New Table Schemas
a. AREA_UNIT
Column Name
ID
ABBREVIATION
NAME
SQFT_MULTIPLIER
Type
INTEGER
VARCHAR2(10)
VARCHAR2(20)
FLOAT
Definition
Primary Key
Display abbreviation used in the GUI
Full display name used in the GUI
Conversion factor to square feet
Type
INTEGER
VARCHAR2(50)
VARCHAR2(50)
VARCHAR2(25)
VARCHAR2(100)
VARCHAR2(75)
NUMBER
Definition
Primary key
Schema name + ‘.’ + layer name in the ArcSDE database
Display name used in the GUI
Geometry column name
Display label SQL for each feature in the GUI
Deprecated
Geometry type (Foreign Key to GEOMETRY_TYPE)
Type
VARCHAR2(10)
VARCHAR2(50)
INTEGER
FLOAT
Definition
Primary key (implicit key to ZONE_CODE in ZONING)
Zoning district description
Minimum lot size of the zone
Maximum number of units allowed per acre in zone
b. BOUNDARY
Column Name
ID
PHYSICAL_NAME
BUSINESS_NAME
GEOM_COLUMN
LABEL_COLUMN
DESCRIPTION
GEOM_TYPE
c. CITY_ZONE
Column Name
ZONE
DESCRIPTION
MIN_LOT_SIZE
UNITS_ACRE
,
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d. GEOMETRY_TYPE
Column Name
GEOMETRY_ID
GEOMETRY_TYPE
Type
NUMBER
VARCHAR2(25)
Definition
Primary Key
Geometry type (i.e Point, Line, Polygon)
Type
VARCHAR2(5)
VARCHAR2(40)
VARCHAR2(15)
VARCHAR2(15)
Definition
Primary Key (implicit key to LANDUSE in LANDBASE)
Fine grain land use description
Secondary land use description
Generic land use description
Type
INTEGER
VARCHAR2(10)
VARCHAR2(30)
FLOAT
Definition
Primary Key
Measurement abbreviation for display in the GUI
Measurement name for display in the GUI
Conversion factor to convert linear units to feet
e. LANDUSE_CODE
Column Name
LU_CODE
LU_TYPE
LU_GROUP
LU_CATEGORY
f. MEASURE_UNIT
Column Name
ID
ABBREVIATION
NAME
FEET_MULTIPLIER
The following sequence needs to be loaded into the Oracle schema for certain queries to operate successfully:
"The Infill Scenario Mapping System requires the following sequence be loaded into the Oracle schema for certain queries to operate:
CREATE SEQUENCE INFILL.NHIBERNATE_SEQ
START WITH 27
MAXVALUE 999999999999999999999999999
MINVALUE 10
NOCYCLE
NOCACHE
NOORDER;
"
Note that the table schemas can be automatically generated by running the script called “infill_scripts_v1.sql”, which can be found on the application
CD.
,,

California Infill Estimation Methodology Project Phase II

Transcription

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