- The Bartlett

Transcription

- The Bartlett

University College London Bartlett School of Graduate Studies M.Sc. Built Environment: Advanced Architectural Studies Final Report Rediscovering the peripheral area of the historic centre of São Paulo Fernanda Lima Sakr September 15th, 2010 I, Fernanda Lima Sakr, confirm that the work presented in this thesis is my own. Where
information has been derived from other sources, I confirm that this has been indicated
in the thesis.
2 Abstract This thesis explores the socio-economic and spatial characteristics of the areas located
in the periphery of the historic core of São Paulo, Brazil. The key objective of the study
is to investigate the different contributions that socio-economic variables and spatial
properties make to the generation of diversity in urban areas. It examines whether the
areas on the periphery of the historic core can, in their current spatial form, play a role
in creating a sustainable urbanism in São Paulo, or whether they are essentially
unsustainable without some radical intervention. Case studies are presented of six
different sub-areas – Brás, Gasômetro, Luz, Oriente, Pari and Santa Cecília. The
theoretical framework and techniques of space syntax are used to identify the
relationship between the spatial properties and the socio-economic variables of these
areas. In addition, the paper discusses previous studies and hypotheses about São Paulo.
It will be suggested here that the current pattern of land use and activities of the six subareas have deficiencies: there is low residential use, low resident density and monofunctionality. The statistical correlations between the socio-economic and spatial
characteristics of each area are weakly related. The specialised retail of the areas plays
an ambiguous role: it is considered to hold economical potential, but at the same time it
impedes the diversification of activities, people and movement. Finally, it concludes
that the theory of centrality could be slightly more flexible in its predictions. One
hypothesis is that the pattern of occupation and activities has to do with the patchwork
layout of the city. The form of the city is neither regular grid structure nor ‘deformed
wheel’, but a patchwork of offset grids in which areas might differ morphologically
from each other depending upon their origins.
Key words: morphology, socio-economic characteristic, periphery of the historic core,
centrality, space syntax.
3 Contents Chapter 1 Introduction........................................................................ 10 Chapter 2 Study Area.......................................................................... 16 Chapter 3 Research methodology........................................................ 44 Chapter 4 Literature Review................................................................ 55 Chapter 5 Analysis and Results............................................................ 62 Chapter 6 Discussion........................................................................... 111 Chapter 7 Conclusion.......................................................................... 117 Bibliography....................................................................... 119 Appendix............................................................................ 124
4 List of Illustrations Figure 1 Map of São Paulo in 1890 showing the firsts settlements of the city.
Figure 2 Map of São Paulo Central Area
Figure 3 Aerial photo showing the areas of study according to EMPLASA classification of UITs.
Table 1 UIT classification according to EMPLASA.
Figure 4 Brazil in the context of South America.
Figure 5 Satellite photo of São Paulo, 2002.
Figure 6 Map of São Paulo and its Central Area.
Figure 7 Map of São Paulo in 1897 showing the railway and the rivers Tamanduateí and Tietê
Figure 8 Victor da Silva Freire diagram (1911).
Figure 9 ‘Plano de Avenidas’ by Prestes Maia (1930).
Figure 10 Map of São Paulo showing the shift of the financial centres over years.
Table 3. Evolution of the resident population: 1991-2000
Table 4. General Information
Table 5. Employment according to activities
Figure 11 Aerial photo highlighting Brás area
Figure 12 Alcantara Machado Ave.
Figure 17 Piratininga street
Figure 20 Brás subway station
Figure 22 Mooca street
Figure 25 Visconde de Parnaíba street
Table 6.General Information
Figure 27 Aerial photo highlighting the Gasômetro area
Figure 29 Largo do Pari (old Pari railway yard)
Figure 31 Estado Avenue
Figure 33 Mercurio – Palacio das Industrias
Figure 34 Santa Rosa street
Figure 39 Gasômetro street
Figure 43 Aerial photo highlighting Luz area
Table 8. General information
Figure 45 São Caetano street
5 Figure 44 Land use map of Luz
Figure 50 25 de Janeiro street
Figure 54 Cel Fernanda Prestes
Figure 61Silva Pinto street
Figure 64 Land use map of Oriente
Figure 71 Rangel Pestana
Table 12.General information
Figure 80 Tiers street
Figure 78 Land use map of Pari
Figure 81 Das Olarias street
Figure 90 Land use map of Santa Cecília
Figure 91 Nothman street
Figure 93 Helvética street
Figure 95 Duque de Caxias street
Figure 98 São João Ave.
Figure 100 Angélica Ave
Figure 105 Predominant land use map (Luz area) – GIS Table
Figure 112 Segment Map of São Paulo showing historic centre and surrounding area and the Y System at
logCH+1 at global radius (n)
Figure 113 Diagram of the Y System: 9 de Julho Ave., 23 de Maio Ave. And Prestes Maia Ave. Extracted
from Meyer (2010)
Figure 114 Segment Map of São Paulo showing logCH+1 at global radius (n)
Figure 115 Segment Map of São Paulo showing historic centre and surrounding area at logCH+1 at local
radius (750m)
Figure 116 Segment Map of São Paulo showing logCH+1 at local radius (750m)
Figure 118 Segment Map of São Paulo showing Patchwork: Metric Mean Depth local radius (2000m)
Figure 119 Segment Map of São Paulo showing local Integration (NC/MD) at radius 1000m.
Figure 120 Land use value
Table 17. Family average income (salary)
Table 18 Number of land use according to activities
Figure 122 Correlation between industrial (%) land use and density
6 Figure 123 Correlation between retail (%) land use and density
Figure 124 Correlation between service (%) land use and density
Figure 125 Correlation between house (number) and housing
Figure 126 Correlation between apartment (number) and housing
Figure 127 Correlation between room (number) and housing
Table 19. Typology of housing
Figure 128 Areas on the periphery of the historic centre of São Paulo. Segment map global Choice logCH+1 radius n
Figure 129 Correlation between global integration (Rn) and local integration (R750)
Figure 130 Correlation between global choice (Rn) and local choice (R750)
Table 20. Integration (NC/MD) of each study area
Figure 131 Correlation between global integration (Rn) and local integration (R3000)
Figure 132 Correlation between global choice (Rn) and local choice (R5000)
Table 21. Summary of comparison: integration (NC/MD) and choice (LogCH+1) at different measures
Figure 134 Correlation between local choice (R750) and local integration (R750)
Table 22. Relationship between integration (NC/MD) and choice (LogCH+1)
Figure 136 Correlation between local integration (R750) and retail (%) land use
Figure 138 Correlation between local choice (R3000) and retail (%) land use
Figure 139 Correlation between global integration (Rn) and service (%) land use
Figure 140 Correlation between local integration (R3000) and service (%) land use
Figure 141 Correlation between global integration (Rn) and industrial (%) land use
Figure 142 Correlation between local integration (R3000) and industrial (%) land use
Figure 144 Correlation between local choice (R3000) and industrial (%) land use
Figure 145 Correlation between global integration (Rn) and house (%)
Figure 147 Correlation between global integration (Rn) and apartment (%)
Figure 148 Correlation between local integration (R3000) and apartment (%)
Figure 149 Block size graph of the area of Brás
Table 24. Comparison between centre and non-centre
Figure 150 Land Use Map of Brás
Figure 151 Segment Map of Brás showing logCH+1 at global radius (n)
Figure 152 Segment Map_logCH+1 at local radius (750m)
Figure 153 Segment Map_Integration (ND/MD) at global radius (n)
Figure 154 Segment Map_Integration (ND/MD) at local radius (750m)
Figure 159 Block size graph of the area of Gasômetro
Figure 160 Land Use Map of Gasômetro
Figure 161 Segment Map of Gasômetro showing logCH+1 at global radius (n)
Figure 166 Global and local choice
Figure 167 Block size graph of the area of Luz
7 Table 26. Comparison between centre and non-centre
Figure 168 Land Use Map of Luz
Figure 169 Segment Map of Luz showing logCH+1 at global radius (n)
Figure 171Segment Map_Integration (ND/MD) at global radius (n)
Figure 177 Block size graph of the area of Oriente
Figure 178 Land Use Map of Oriente
Figure 179 Segment Map of Oriente showing logCH+1 at global radius (n)
Figure 180Segment Map_logCH+1 at local radius (750m)
Figure 188 Land Use Map of Pari
Figure 189 Segment Map of Pari showing logCH+1 at global radius (n)
Figure 193 Synergy
Figure 196 local integration and choice
Figure 198 Land Use Map of Santa Cecília
Figure 199Segment Map of Santa Cecília showing logCH+1 at global radius (n)
Figure 203 Synergy
Figure 205 Global integration and choice
Table 30. Comparison of the shopping streets in each area
8 Acknowledgments I wish to express my gratitude to my supervisors Professor Sam Griffths and Professor
Bill Hillier for their guidance throughout the research.
I would like to thank Dr. Beatriz Campos for her advice and enthusiasm, Professor
Regina Meyer for her valuable insights, João Pinelo for his technical support with the
software used, and Bruno Netto for providing pictures of central São Paulo.
I would also like to thank Dr. Laura Vaughan, Dr. Ruth Conroy-Dalton, Dr. Kerstin
Sailer, and Dr. Alan Penn for their commitment throughout the academic year.
A special thanks to my family and to all my friends at the AAS course, thank you for
making this journey a memorable one.
9 1. INTRODUCTION
10 Chapter 1 1. Introduction The city of São Paulo is a metropolis in flux, growing constantly according to changes
in its economy and society. It has limited physical resources to support these
transformations and rapid expansion. This research looks at settlements on the periphery
of the historic core, exploring the spatiality of their economic activities and its
determinants. The six areas selected for study were the first settlements to emerge along
the north and east border of the historic centre, and along the railway track. Since the
rapid development of the city, and the shift of the financial centre towards to southwest,
these areas have been left behind by the wealthier population, as well as by the public
and private sectors.
An aim of this paper is to “rediscover” the peripheral area of the historic centre of São
Paulo; what can be discovered there from a spatial perspective? It also asks whether the
peripheral areas of the historic core, in their current form, have a role to play in creating
a sustainable urban city, or whether their run-down situation means that they are
essentially unsustainable without some radical intervention. A sustainable city in this
context means a city that is able to support itself socially and economically.
Furthermore, the paper considers whether there are there any socio-economic benefits to
the modern city in preserving the current spatial aspect of these areas; which spatial
factors are of value to the city?
The study analyses the six sub-areas of the city using the theoretical framework of space
syntax since they hold tradition research and techniques on correlations between spatial
configuration and urban centrality. The comparison presents statistical correlations of
spatial-syntactical analyses, based on data from a demographic census, empirical data
and maps, in order to identify the degree of spatial accessibility and influence on the
pattern of land use, activity, and pedestrian and vehicular movement. The analysis will
inform a discussion of the role of spatial configuration in generating diversity and
sustainable urbanism in São Paulo.
11 Chapter 1 Boundary of
the study area
Figure 1 Map of São Paulo in 1890 showing the firsts settlements of the city. Source: SEMPLA
The areas of study were selected according to previous research titled São Paulo Centro
uma nova abordagem (2000), in which Regina Meyer proposes the concept of the
Central Area, which is formed by both the historic core (Sé and República) and the
settlements located on its periphery (Bela Vista, Bom Retiro, Pari, Brás, Santa Cecília,
Campos Elíseos, Barra Funda, Santa Ifigênia, Cambuci, Glicério, Ponte Pequena,
Liberdade, Luz and Consolação) (Meyer 2000:12) (Figures 2, 3). These areas have
individual identities and play a distinctive role in the local and global economy of the
city.
12 Chapter 1 The physical boundaries of the areas are defined precisely according to the
administrative division of district sub-areas developed by EMPLASA1 (Empresa
Paulista de Planejamento Metropolitano SA – Paulista Metropolitan Planning
Organization); classified as UITs (Sistema de Unidades Informarções Territorializadas
–Units System of Territorialized Information) according to the following factors: land
use and pattern of occupation; constructive aspects of the buildings; location of the
relevant land use; urban or rural functionality; morphology and retail streets; poles of
traffic; socio-economic factors and demographic census2. Each UIT is part of one
administrative district (EMPLASA report 2007). It seems relevant to emphasise that this
administrative division (zoning system) is not based on the natural morphological
boundaries.
Bearing in mind the common and different features of each area, the research explores
the areas located to the north and east of the historic centre: Santa Cecilia district –
Santa Cecilia (UIT28); Bom Retiro district – Luz (UIT8); Bras district – Bras (UIT13),
Gasômetro (UIT16), Oriente (UIT15) and Pari district – Pari (UIT 10). The socioeconomic information about these areas was extracted from Regina Meyer literature,
EMPLASA, SEMPLA3 and Viva Centro4 websites.
1
http://www.emplasa.sp.gov.br/portalemplasa/
It is based on information developed by IBGE (Instituto Brasileiro de Geografia e Estatistica – Brazilian
Institute of Geography and Statistics) - http://www.ibge.gov.br/
3
Secretaria Municipal do Planejamento http://www.prefeitura.sp.gov.br/cidade/secretarias/planejamento/
4
Viva Centro Association - http://www.vivaocentro.org.br/vivaocentro/index.htm
2
13 Chapter 1 Figure 2 Map of São Paulo Central Area. Source: Meyer et. al. São Paulo Centro: uma Nova Abordagem
(São Paulo: Associação Viva o Centro, 2000)
D. PEDRO II
PARK
District boundary
UIT boundary
Figure 3 Aerial photo showing the areas of study according to EMPLASA classification of UITs. Source:
EMPLASA report (2009). Map by author
14 Chapter 1 DISTRICT
UIT
DISTRICT
UIT
SÉ
Sé
Parque Dom Pedro
Praca Joao Mendes
República
Santa Ifigenia
Ladeira da
Memoria
Santa Cecilia/
Campos Eliseos
Marechal Deodoro
Várzea da Barra
Funda
BOM RETIRO
Bom Retiro
Armênia
Luz
Brás
Gasômetro
REPÚBLICA
SANTA
CECILIA
BRAS
Oriente
Bresser
PARI
Pari
Canindé
CDC Vigor
Table 1 UIT classification according to EMPLASA. Source: EMPLASA report 2009
The thesis is structured in six sections. Chapter 2 introduces the area of study, providing
a brief description of its historical formation, and outlining important events and criteria
which were used to define the sub-areas. It also discusses the current socio-economic
characteristics of the peripheral area of the historic core of the city of São Paulo.
Chapter 3 is the literature review which presents a background to the current debate
surrounding areas on the periphery of the historic core, including the current situation of
the city of São Paulo and a discussion of sustainable cities as generators of social and
economical diversity. It also establishes connections between space syntax literature and
other theories. Chapter 4 describes the research methodology used. It explains the
available dataset provided by different institutions; the detailed field survey that, when
used alongside existing data, helped to provided an understanding of the current
dynamic of the area of study; the syntactic measures developed by space syntax; the role
of the different software that was utilised; the model of centrality which was applied;
and the implications found in the process. In Chapter 5 interpretations of the findings
are developed in the light of empirical, analytical and syntactic analyses of the data
collected. The performance of each area is discussed and its pattern of centrality; for
instance, how the centre of each area is embedded in the surrounding area, as well as its
spatial shape. In this case, the centres are considered as the busiest shopping streets, and
non-centres the other streets located within the administrative boundary. In addition,
statistical correlations are developed by comparing the socio-economic data and the
syntactic measures extracted from spatial analyses. Chapter 6 discuss the implications of
the analyses relating to the literature previously discussed. Chapter 7 provides a brief
conclusion and propose lines for further study.
15 2. STUDY AREA
16 Chapter 2 2. Study Area This section will give a brief summary of the history of the city of São Paulo, outlining
the relevant periods with regards to this research. It aims to show the important facts
that have influenced the spatial and economic development of the city.
São Paulo is located in the southeast of Brazil at a distance of approximately 70km from
the Atlantic Ocean. It is located on a plateau that is part of the Serra do Mar (Sea
Range), which forms part of the vast region know as the Brazilian Highlands, with an
average elevation of around 800m (2,000ft) above sea level. São Paulo is the most
populous city in the Americas, with approximately 11,037,593 residents within an area
of 1,523 km2 (IBGE).
Figure 4 Brazil in the context of South America. Source: Guia Geográfico
17 Chapter 2 Figure 5 Satellite photo of São Paulo, 2002. Source: Empresa Brasileira de Pesquisa Agropecuária EMBRAPA.
Figure 6 Map of São Paulo and its Central Area. Source: Guia Geográfico and Prefeitura de São Paulo
In the sixteenth century the city was strategically formed between two valleys plentiful
with water, vegetation, animals and fertile lands, in order to support the Jesuit mission
in the region. The location was chosen for settlement as it guaranteed good visibility
while at the same time being difficult for potential enemies to access. The opening of
the railway in 1867 can be considered the first big change in the history of the city. In
the late nineteenth and early twentieth centuries it enabled the exportation of coffee
beans which attracted international investment and intense immigration from Europe.
The first railroad ran from the seaport of Santos to the western lands beyond the city of
São Paulo. According to Barbosa (2001), the railroad brought about a considerable
transformation of the region; the city became a central point for economic development.
18 Chapter 2 Meyer (1999) suggests that the railway drastically affected the development of the city.
The infrastructure lead an intense process of population growth (from 32,000 to 65,000
inhabitants between 1870 and 1890) and urbanization, especially on land located within
the border of the railway. In terms of urban organisation, the railroad and the rivers
Tamanduateí and Tietê formed an extensive territory to the north of the historic centre
where there was a concentration of industry. Meyer (2010) adds that other factors
influenced the location of industry: the flatlands of the valley, the low cost and facility
of converting farm constructions into factories; and the presence of an immigrant
workforce from the coffee production. Around this region, and particularly in the east,
the boroughs of Brás, Pari, Bom Retiro, Santa Ifigenia and Mooca emerged within the
new urban fabric, generating the firsts ‘vila operárias’ (workers villages).
Tietê river
Tamanduateí river
Railroad
Boundary of the study area
Figure 7 Map of São Paulo in 1897 showing the railway and the rivers Tamanduateí and Tietê
Source: SEMPLA
The rapid process of urbanization resulted in accelerated population growth. In 1886 the
city was a village with 47,697 inhabitants and in 1950 it had 2 198,000 inhabitants
within 420 km2 (Meyer 2010:22) (Table 2).
19 Chapter 2 Table 2. Evolution of the population: 1886-1950
1886
1900
1920
47,697
239,820
579,033
São Paulo
Source: IBGE, Demographic Census 1991 and 2000 – from EMPLASA 2008
1950
2 198,000
In 1911 Victor da Silva Freire developed a street diagram with radial lines emanating
from a central triangle towards new boroughs, emphasising the importance of the links
between neighbourhoods (Figure 8). Since that time vehicular traffic increased
continuously, so in 1930 a new urban model was proposed: the radio-centric ‘Plano de
Avenidas’ (Roads Plan) by Francisco Prestes Maya. Many avenues leading to the
peripheries converge on the centre of this model, with a few ring roads connecting them
at different distances from the centre (Figure 9). Later in 1945 a new system was added:
the Sistema Y (Y System), formed by the avenues 9 de Julho, 23 de Maio and Prestes
Maia, which cross in the centre, intensifying the transversal north-south axis of
vehicular flow. The system particularly affected the central boroughs located to the
north of the historic centre. As a solution it only improved the streets which privileged
vehicular movement. Subsequently, in order to keep traffic controlled, other complexes
were built: 23 de Maio, modifications to the Dom Pedro II park such as the Radial Leste
expressway (1960), and the elevated express west-east axis Elevado Costa e Silva
(1970) (Meyer 1999, 2010).
Figure 8 Victor da Silva Freire diagram (1911). Source: Meyer (2010)
20 Chapter 2 Figure 9 ‘Plano de Avenidas’ by Prestes Maia (1930). Source: Meyer (2010)
The intense expansion of the city from the mid-1940s onwards is mostly explained by
the heavy industrialization, and by the opening of new roadways connecting major
Brazilian cities – a parallel process that simultaneously stimulated remote demands for
new production. This policy was intensified from 1956 under the government of
President Juscelino Kubitschek, who made possible the implementation of heavy
industries throughout the country. But São Paulo was by then both the most attractive
market and the most propitious place for new industries. Under Kubitschek’s guidance,
nearly the entire automobile production industry was implemented in São Paulo’s
region known as ABCD (Santo Andre, São Bernardo, São Caetano, and Diadema). This
industrialization occurred at the fringes of the city, specifically along the new
expressways. The need for easy transportation access at a national scale, for plentiful
electric energy, and for large sites, made the industrial infrastructure from the early
21 Chapter 2 1900s obsolete. Thereafter, the city observed the emptying process of the central
industrial neighborhoods adjacent to the historical centre.
From the mid-1950s, the upper classes turned their attention to Paulista Avenue, with
the transference of many offices and much luxury commerce from downtown to this
region. This process, in which upper class offices and retail activities move away from
stabilized areas to new areas of development, replicates itself continuously in waves
even nowadays. Furthermore, the Paulista Avenue region, so successful in the 1960s,
also suffered from a partial evacuation of offices after the opening of Brigadeiro Faria
Lima Avenue further south, on which São Paulo’s first shopping mall was constructed
in the 1970s. From the late 1980s to the 1990s, some office space was then transferred
to Eng. Luis Carlos Berrini Avenue and, more recently, to Marginal Pinheiros. This fast
shift of the most prestigious office space and, consequentially, the upper class
residential sector, is referred to as the “southwest vector” of development (Rolnik 2001,
Meyer 2004, 2010) (Figure 10).
Paradoxically, while the old centre was being left solely for the economically lower
classes, one of the most expensive known urban elements was introduced there in 1970:
the subway. The existence of both the new subway stations and large bus terminals
within the inner city motivated the movement of pedestrians in the area, making
vehicular flow difficult; however, the majority of pedestrians were from the lower class
population who were not car owners.
Figure 10 Map of São Paulo showing the shift of the financial centres over years. Source: Meyer (2004)
22 Chapter 2 Nevertheless, the fast process of urbanization – the rapid growth of the city facilitated
by the railway, industrial development, the large road system interventions over the
years and the shift of the financial centre to the southwest – may have favoured the
macro accessibility of the system affecting the urban network, with highly accessible
vehicular routes and neighbourhoods juxtaposed with large blocks and mono-functional
uses. Indeed this process directly affected the central area of the city – the historic
centre and boroughs located at its periphery – since this is where the first urban
settlements were located.
Bearing in mind the consequences of this rapid transformation on the urban fabric of the
historic centre’s peripheral areas, it seems relevant to focus on how this development
affects these sub-areas today, in order to better understand the relationship of their
current spatial configuration and populations. For instance, what role do these areas play
for the city and its society? Meyer (2000) argues that the Central Area of São Paulo
reflects s the identity of the city through its urban morphology, socio-economic aspects
and functions. Although the public sector has invested in these areas in transport and
roads, since the 1950s the private sector has concentrated its investments in the more
privileged areas to the southwest, which are occupied by an elite population. As a
consequence, some boroughs with good accessibility, located close to clusters of
services, have suffered from emptiness caused by the shift in the population towards to
the new financial centres in 1950s. By the 1960s the city has a centralized low density
and lower income population (Meyer 2000). The Census shows that density growth has
decreased significantly between 1991 and 2000 (Table 3).
The impact of the current conditions of the Central Area falls not only on this area itself,
but also in the areas located to the north and east side of the city. The rapid process has
left these areas with an undesirable physical and social fabric. As follows, the next
section reports descriptions of each selected sub-area in order to provide a more
comprehensive social and spatial framework for the case studies. Table 3. Evolution of the resident population: 1991-2000
Population 1991
Population 2000
Growth rate
12,359
9,626
-2,733
Brás
5,089
3,240
-1,849
Gasômetro
18,440
13,726
-4,714
Luz
6,343
4,150
-2,193
Oriente
17,626
11,415
-6,211
Pari
40,227
33,305
-6,922
Santa Cecília
Source: IBGE, Demographic Census 1991 and 2000 – from EMPLASA 2008
%
-2.76%
-4.94%
-3.26%
-4.65%
-4.76%
-2.10%
23 Chapter 2 2.1 Case study areas
2.1.1 Brás Figure 11 Aerial photo highlighting Brás area
Source: image: Google earth, map: by author
Brás is a region that in the past was characterized by small farms and country houses
and has grown traditionally as a working class population. It is known as a place that at
the beginning received many Italian immigrants and, afterwards, migrants from the
northeast of the country. The majority of the current population is made up of Korean
and Bolivian immigrants who work in the production of clothes and accessories. It has a
population of 9,626 inhabitants within an area of 1,095km2 (8,791hab/km2) (Table 4).
Its residents are mostly a middleclass and low income population. In terms of the built
environment, many houses (old, horizontal, up to one floor) are considered deteriorated
located in specific streets. There are also mixed-use buildings and squats. There are
some vertical residential buildings which were constructed in 1980s around the railway
track and close to the subway station (Brás station). The busiest street is Alcantara
Machado Avenue, which has a specialized retail offering of machinery and equipment,
as well as electronic devices, clothing and accessories (EMPLASA report and Meyer
2010).
Table 4. General Information
Population
2000 (hab)
Area
(km2)
Housing
2000
Demographic density
(hab/km2)
9,626
1,095
3,476
8,791
Brás
Source: IBGE, Demographic Census 2000 and IGC (Instituto Geografico e Cartografico) – from EMPLASA 2008
Industrial
%
Retail
%
Services
%
Total
2,024
24.84%
2,926
35.92%
3,197
39.24%
8,147
Brás
Source: Ministerio do Trabalho, Rais (Relacao Annual de Informacaoes Sociais) 2005; SEMPLA/Dipro –
from EMPLASA 2008
24 Chapter 2 Figure 12 Alcantara Machado Ave.
Brás subway
station
Figure 16
Land use map of
Brás
25 Chapter 2 Figure 19 Piratininga street
Figure 20 Brás subway station
Source (all pictures): Author
26 Chapter 2 2.1.2 Gasômetro Figure 27 Aerial photo highlighting the
Gasômetro area
Source: image: Google earth, map: by
author
The Gasômetro area, within the same district as Brás, became of note in 1872 when the
presence of a gas power station provided light to the streets of São Paulo. It is one of the
oldest boroughs in the city and its settlements are mainly characterized by storehouses
and warehouses, most of which have converted from industrial into retail and service
uses. It is known for having the first traditional ‘zona cerealista’ (cereal distribution
zone), which supplies the whole city and parts of the country with fruit and vegetables.
Besides this wholesale market, the Largo do Pari (old Pari railway yard) is also used for
this traditional function and it is where the most famous coconut supplier is located. It
has a specialized retail offering of wood, glue, resin and household accessories. The
majority of the shops are located on Gasômetro street. Its demographic density is
4,675hab/km2 (3,240 inhabitants in 0,693km2) (Table 6). Similar to Brás, its residents
are a mainly middleclass and low income population. Its housing is predominantly
horizontal (maximum one or two floors), semi-detached and fronting directly onto the
street. The weaknesses of the area are compounded by its spatial configuration: large
block sizes, a lack of residents and its function as a destination for wholesale and retail
activities (EMPLASA report and Meyer 2010).
Table 6.General Information
Population
2000 (hab)
Area
(km2)
Housing
2000
Demographic density
(hab/km2)
3,240
0.693
1,062
4,675
Gasômetro
Source: IBGE, Demographic Census 2000 and IGC – from EMPLASA 2008
Industrial
%
Retail
%
Services
%
332
6.98%
3,560
74.82%
866
18.20%
Gasômetro
Source: Ministerio do Trabalho, Rais 2005; SEMPLA/Dipro – from EMPLASA 2008
Total
4,758
27 Chapter 2 Figure 28 Largo do Pari (old Pari railway yard)
Largo do Pari
Figure 33 Mercurio – Palacio das Industrias
Figure 32 Land use
map of Gasômetro Figure 34 Santa Rosa street
28 Chapter 2 Figure 35 Coconut supplier (Largo do Pari)
Figure 41 Alfândega sreet
Figure 42 Alfândega street
29 Chapter 2 2.1.3 Luz Figure 43 Aerial photo highlighting Luz
area
author
Different from the previous two areas, Luz is characterized by being a centre of culture
and popular retail in the city. Recently it has received investments in cultural facilities,
with renovated buildings including the Pinacoteca do Estado, Estação da Lingua
Portuguesa, São Paulo Symphony Hall and others. It also has a strong local economy
with concentrated retail and wholesale activities within the clothing/fashion sector
located mainly on José Paulino and Graça streets (popular fashion) and São Caetano
street (wedding clothes and accessories to buy or hire). These shops attract shoppers
from different parts of the city and country during the whole year.
During the nineteenth century the area was formed by small farms and country houses
used by the wealthier population of the city as leisure retreats. It is where the oldest park
in the city is located – the Jardim da Luz (Luz Gardens) – however, this scenery
changed when the earthenware industry settled in the area. Later, with the opening of
the São Paulo Railway in 1867, new warehouses and factories were built and there was
an intensification of the working class population, mainly formed of Italian immigrants.
The area not only underwent spatial transformations (the railway and industrial
occupation), but also social transformations, with the process of immigration. For
instance, José Paulino street was firstly managed by the Portuguese, Turkish, Lebanese
and Arabic, and then by a Jewish population after World War II. Later, a South-Korean
population bought most of the main shops and helped to install a weaving industry
within the fashion sector. Nowadays, within the area, it is possible to find a mix of
ethnicities and cultures, a variety of restaurants and cafés, Jewish theatres, a synagogue,
Christian churches, Presbyterian churches, schools, and so on. Its settlement has a
different typology of both vertical and horizontal housing.
30 Chapter 2 Table 8. General information
Population
2000 (hab)
Area
(km2)
Housing
2000
Demographic density
(hab/km2)
13,726
1,541
4,500
8,907
Luz
Industrial
%
Retail
%
Services
%
Total
11,584
57.42%
4,751
23.55%
3,839
19.03%
20,174
Luz
Oficina Oswald de
Andrade
Cel
Fernando
Tiradentes
subway station
Militar Police
museum
Luz park
Figure 44 Land use
map of Luz
São Paulo
Symphony
Hall
Luz station
Pinacoteca do Estado
31 Chapter 2 Figure 47 São Caetano street
Figure 48 Estado Ave.
Figure 50 25 de Janeiro street
Figure 51 Cantareira street
Figure 52 Cantareira street
Figure 53 Oficina Cultural Oswald de Andrade
Figure 54 Cel Fernanda Prestes
32 Chapter 2 Figure 55 Luz station
Figure 56 Pinacoteca do Estado
Figure 57 José Paulino sreet
Figure 58 José Paulino street
Figure 59 José Paulino sreet
Figure 60 Graça street
Figure 61Silva Pinto street
Figure 62 Aimorés street
33 Chapter 2 2.1.4 Oriente Figure 63 Aerial photo highlighting
Oriente area
author
Oriente is located within the same district as Brás and Gasômetro. It has a density of
3,825hab/km2 (4,150 inhabitants in 1,085km2) (Table 10). The majority of its residents
are considered middleclass and they mostly live in houses (old, horizontal, up to one
floor) and mixed-use buildings. Its local economy is concentrated in the fashion sector –
manufacturing and retail – which brings people from different parts of the city and
country. It is mainly located at Miller street and Orient street, and on the north side of
the Largo do Pari (old Pari railway yard) there is the ‘Feira da Madrugada’ (wee market)
or ‘Shopping Popular da Madrugada’ (wee popular shopping) that covers about
70,000m2, has around 4,000 stalls and is open for work during the hours of 10am to
6pm and from 3am to 10am. This initiative is supported by public and private
institutions.
Population
2000 (hab)
Area
(km2)
Housing
2000
Demographic density
(hab/km2)
4,150
1,085
1,278
3,825
Oriente
Industrial
%
Retail
%
Services
%
Total
11,936
56.79%
7,030
33.45%
2,023
9.77%
21,019
Oriente
Source: Ministerio do Trabalho, Rais 2005 ;SEMPLA/Dipro – from EMPLASA 2008
34 Chapter 2 Mesquita do Brás
Largo da Concórdia
Figure 64 Land use
map of Oriente
Figure 65 Shopping Popular da Madrugada
Figure 66 Mesquita do Brás
Figure 67 Shopping Popular da Madrugada
Figure 68 Oriente street (Sunday)
35 Chapter 2 Figure 69 Shopping Popular da Madrugada-entrance
Figure 75 Railway track
Figure 76 Largo da Concórdia
36 Chapter 2 2.1.5 Pari Figure 77 Aerial photo highlighting Pari
area
author
Pari is known as a traditional working class borough, which grew because of the railway
track and industry. Nowadays it has a middleclass population of 11,415 residents within
1,398km2 (8,165hab/km2) (Table 12). Its buildings – horizontal (one or two floors) and
semi-detached – are considered deteriorated. During the last decade vertical buildings
(more than 5 stories) have been built for middleclass and high income populations in
some points of the borough. Its busiest streets are Silva Teles and Maria Marcolina.
They specialise in manufacturing and selling of household goods and popular shops
which attract a variety of buyers from the whole city and country (there are group
excursions to the area of shoppers from different places) (EMPLASA report and
Prefeitura de São Paulo).
Table 12.General information
Population
2000 (hab)
Area
(km2)
Housing
2000
Demographic density
(hab/km2)
11,415
1,398
3,654
8,165
Pari
Industrial
%
Retail
%
Services
%
Total
6,738
43.25%
5,385
34.56%
3,457
22.19%
15,580
Pari
37 Chapter 2 Figure 78 Land use
map of Pari
Figure 79 Vautier street
Figure 80 Tiers street
38 Chapter 2 Figure 81 Das Olarias street
Figure 84 Rio Bonito
Figure 85 Carlos de Campos
Figure 86 Carlos de Campos
Figure 87 Maria Marcolina street
Figure 88 Maria Marcolina
F
39 Chapter 2 2.1.6 Santa Cecília Figure 89 Aerial photo highlighting
Santa Cecília area
Source: image: Google earth, map:
by author
The Santa Cecília area differs from the others in social and economic characteristics. It
has 33,305 inhabitants within 1,441km2 (23,112hab/km2) (Table 14), living mainly in
high rise, high density buildings with different typologies, the majority being mixeduse. In comparison to the other areas, it has both the largest higher income and no
income populations. The economy of the area is based more in the services sector than
retail. Its busiest street is Duque de Caxias, which has diverse retail and activities.
Another point is the presence of an elevated concrete expressway, named Elevado Costa
e Silva and nicknamed ‘Minhocão’. Inaugurated in 197I, it is an iconic symbol that
covers an area of 3.4km at 5.5 metres above the ground, its width varying between 15.5
and 23 metres (Prefeitura de São Paulo). It connects the central areas to the west side of
the city, running from Franklin Roosevelt Square up to the Largo Pericles (Francisco
Matarazzo Avenue). This “overwhelming concrete structure sitting cheek by jowl with
adjacent buildings” (Urban Age Report 2008:154) has contributed to the deterioration
and deprivation of the urban environment of the area (sometimes the distance between
buildings and the expressway is less than 4 metres). During the peak hours it is usually
highly congested, though every night and on Sundays it is closed and ‘converted’ into
an open public space to serve the local population. This built structure has been part of
the different discussions and competitions held by urban planners and governmental
institutions over the years in order to propose new solutions. In May 2010 it was
announced that parts of the expressway will be demolished according to new proposals
that form part of policy interventions developed by the current municipal
administration.
Population
2000 (hab)
Area
(km2)
Housing
2000
Demographic density
(hab/km2)
33,305
1,441
14,204
23,112
Santa Cecília
Industrial
%
Retail
%
Services
%
2,622
10.07%
4,900
18.83%
18,504
71.10%
Santa Cacília
Total
26,026
Elevado Costa e
Silva – ‘Minhocão’
Santa Cecília
subway station
Figure 90 Land use
map of Santa
41 Chapter 2 Figure 91 Nothman street
Figure 92 Guaianazes
42 Chapter 2 Figure 99 São João Ave.
Figure 102 Santa Cecília Subway station
Figure 103 Santa Cecília church
Figure 104 Santa Cecília church
43 3. LITERATURE REVIEW
44 Chapter 3 3. Literature Review 3.1 The city of São Paulo and the areas located on the periphery of its historic centre What can be said about São Paulo today? This chapter investigates how the current
physical layout of the city affects the social and economic aspects of its society, and
what importance the areas on the periphery of its historic centre hold.
The preliminary and most obvious question that shall be addressed in this section is:
what is São Paulo today?
“Today’s São Paulo is a paradigm of a local metropolis in the global world. It is at once a world
city linked to global networks, and a local city, where public spaces manifest social inequality and
urban dereliction.” (Urban Age Report 2008:173)
Sassen (2008) adds São Paulo today is one of the major global financial centres in the
world. It is integrated into the network of global cities. Alongside this, the fragmented
areas left behind by industry, which new services and technological industries
should/could occupy, also play an important role in the city today. With the end of
industrial occupation, the railroad lost its primary function. The decay of industrial areas
brought about the spatial disintegration that generated a fragmented territory: the
abandoned buildings of the inner city and the dormant rail yards. Meyer (2010)
examines the performance of the current physical situation in São Paulo. In the
boroughs adjacent to the historic core, which reflect the changes of the past fifty years,
Meyer argues that the spaces are fragmented and disconnected from the whole network
because of the industrial urban fabric and its physical barriers: railway tracks, elevated
roads and expressways.
According to Meyer (2010), in the past years the change from industry to the production
of knowledge and services has directly impacted São Paulo’s functionality and
spatiality. New technologies based on micro-electronics and communications generate a
different logic for the location of industry. Nowadays, the contemporary scenario
comprehends the generic idea of ‘New Industrial Space’ (Castells, 1989), which is
flexible in its site of production, resulting in an emptying of traditional industrial sectors
and the abandonment of its built complexes to allow for the expansion of service-based
industry and activities in these areas.
“Telecommunications allows work at home in ‘electronic cottages’, while firms become entirely
footloose in their location, freed in their operations by the flexibility of information systems and
by the density and speed of the transportation network.” (Castells 1989:1)
45 Chapter 3 Castells (1989) discussion of ‘new information technologies’ affirms that the high
production of knowledge and technology is the key for new economic growth, and that
this production does have an impressive impact on cities and societies at different
levels: economic, social, political and cultural. Castells’s concepts lead to the
conclusion that the spatial and socio-economic needs of cities and their societies have
changed and are changing constantly. At time of the industrial revolution, the main goal
was to provide more space for people, for machines and for mass production lines.
Nowadays the evolution of technology and information has lead to a division of labour
in society, with people specialising in diverse, independent areas. Also the economies of
cities/ regions/ countries are more competitive in the global network. Instead of
‘industrial revolution’, today there is the ‘technological revolution’, in which the soft
industries, with innovative technologies, extend the productive capacity of working
hours while overcoming spatial distance in all realms of social activities. Technology
has been able to change the basic condition of human life: space and time. Considering
the city of São Paulo and its society, this discussion can be further developed in order to
understand how this contemporary process is occurring there today and how it is
reflected in the urban space and activities of its citizens.
3.2 The importance of the areas located on the periphery of the historic core To understand what role the areas on the periphery of the historic centre play within the
context of the whole city, the idea of centre itself, and its surroundings, needs to be
brought into the discussion. According to Sieverts (2000), the centre is a place in which
all major developments start and it used to be considered a hierarchical tree structure.
As a consequence of contemporary urbanization, the structure of cities has changed
surreptitiously towards to a configuration formed of distributed specialisation, both in
terms of the division of labour and the function of space. Today a single centre no
longer exists within the city (Sieverts 2000).
In São Paulo, with the shift of the financial centre towards to southwest, not only has the
historic core has been left behind, but also the areas immediately to its north and east.
Centres and sub-centres have emerged in different regions, once more making the
historic core and its periphery a forgotten place. A wide range of theories emphasize the
importance of regenerating and preserving the inner areas of the city in order to bring
back social and economic life, and to limit the urban sprawl beyond the city.
46 Chapter 3 Bromley et al (2005) suggest that the implementation of more residential development
within the city centre could help to enhance the ‘vitality and viability’ of the area, by
bringing in more residents, who demand greater facilities, thus increasing the diversity
of uses during day and night. The daytime economy of city centres is dominated by
retail and office activities, with a high density of people and movement. However, the
night-time economy does not attract the same amount of the population; as a result there
are empty areas and fear of crime. The residents of the inner city, who support the local
facilities and activities, contribute to the sustainability of the city centre, its local
economy and transport infrastructure.
“Regeneration through residential development and its contribution to sustainability are key issues
in the city centre. The city centre is an urban area where threats to long-term viability and vitality
have prompted regeneration policies which incorporate housing, and where the rapidity of change
provides an ideal arena for assessing aspects of sustainability in the regeneration strategy”.
(Bromley et al 2005:2411)
So, the idea of ‘residentialisation’ – denser resident areas – is an important element for
the functionality of inner city areas. It helps to intensify the mixed occupations and
activities at all times. As a result, higher urban densities can help to improve the local
infrastructure, public transportation and facilities, diminishing social segregation
(Bromley et al 2005).
In addition, Griffiths et al (2008) in their research on suburbs, argue that sustainable
suburban centres help to control urban sprawl. The ‘sustainability’ within suburban
town centres is interpreted as ‘adaptability’, in the sense of “persistence of socioeconomic activity overtime” (Griffiths et al 2008:2); in other words, the potential of
centres to preserve their vitality and viability over the years rather than running down
towards their complete deterioration. This ability can be facilitated by diverse socioeconomic activities, by mixed-use high streets rather than mono-functional consumer
destinations. The centres are able to offer a variety of employment and social activities.
As it was demonstrated in the research about sustainable suburban high streets, the
layout of the centre and its determinants for land use occupation and people’s
movement is fundamental for its ‘vitality and viability’.
A study about the Central Unplanned Areas in the city of Jeddah, Saudi Arabia,
developed by Karimi et al (2007) presents analyses which demonstrate that the reason
for the decline and deprivation of the central areas lay in the configuration of their urban
fabric: fragmented systems as a result of the rapid development of the city. The layout
of Jeddah’s streets is unable to relate to either the wider context of the city as a whole,
47 Chapter 3 or the historic core. This incompatibility impacts on the socio-economic aspects of the
city. In order to find a solution for the problem, a multi-layered analysis with two
methods was proposed: one brought together a detailed analysis of urban and socioeconomic data (land use, building height, density and so on) with a view to preserving
the integrity of the local structure of the unplanned settlements; the other looked at the
best way to improve the urban configuration and reconnect its core (route structure) to
the wider grid, by identifying weak points in the potential street network and proposing
solutions to fix them. For the improvement of the urban fabric, it is possible to both
propose new route networks and modify the existing ones. This targets the segregation
of the unplanned areas, enhancing the morphology of space and its interplay with socioeconomic variables. From this literature it is possible to retrieve indicators that seem
essential for the discussion of central areas of the city: socio-economic aspects including
land use and activities, resident density, adaptability to change and urban morphology.
Moreover, when dealing with urban research is important to bear in mind what authors
such Willian H. Whyte say about cities. One of the requirements is to learn how the real
city works by observing in depth what is going on within the streets. It is emphasized
that looking at the material world is essential to understanding the interface between the
physical space and people’s behaviour. Indeed, to appreciate the real streets and their
population, for instance, it is recommended to analyse how they act in parks, plazas and
streets by standing there as long as is necessary. After carrying out a variety of
observations, White concludes that streets suffer because of emptiness, for having too
few people, not for being overcrowded (Whyte 2000).
3.3 Urban spatial form The topic of urban spatiality is triggered by Hillier’s studies of urban morphology, in
which the configuration of the urban fabric plays an important role in people’s
movement and has the potential to determine the location of specific land use and
activities shaping the whole city (Hillier et al 1993). The theory of how space itself
influences the socio-economic aspects of a society was considered relevant for this
research since space syntax covers a wide range of empirical studies of urban
environments. It provides a relevant support to the analytical investigations of space in
such a complex city as São Paulo, whose configuration has changed rapidly over the
years. Moreover, it helps us to understand the degree to which the peripheral areas of
the historic centre became fragmented and disconnected from the system as a whole, in
48 Chapter 3 the sense that the pedestrian and vehicular accessibility of these areas was made
difficult because its street network.
Hillier et al (1993) say that the relationship between space and people is through their
behaviour in the space, or their movement in the space; such movement is referred to as
‘natural movement’. The theory of ‘natural movement’ relies on the configuration of the
urban grid itself having the potential to generate more movement in some places than in
others. It means that the proportion of pedestrian movement is determined, other things
being equal, by the structure of the layout itself, rather than by the presence of specific
attractors or magnets (land use). And the grid is affected by both global properties,
which relate each spatial element to every other element in a system, and local
properties: the relation of each space to its neighbours. This process shows that
“movement is fundamentally a morphological issue in urbanism, a functional product of
the intrinsic nature of the grid” (Hillier et al 1993:32). In other words, the spatial form
generates pedestrian flow in the physical city, reflecting the functional formation of the
city and creating probabilistic interfaces between social groups and activities in the
space.
Hillier (1996) then suggests that the previous idea of ‘natural movement’ initiates a
process where areas with high movement rates will attract uses that are high movement
dependent, and this in return will attract more movement. Hillier defines this
phenomenon as “movement economy” (1996:126). It creates a ‘multiplier effect’ – a
social, economical and physical cycle where attractive land uses attract more activities
and pedestrian movement, generating diversity and new enterprises, which in turn
attract further uses and more people. It creates a feedback loop of life potential. So
instead of making initial speculations about the possible problems of the peripheral
areas of São Paulo’s centre being based only on land use and activities (for instance,
because of the shift of the financial centre), the theory suggests that, initially, it is
necessary to investigate the areas’ spatial performance. Indeed, how the physical
properties of the areas facilitate or make difficult the pattern of occupation and
movement in the system.
Subsequently, Hillier (1999) proposed ‘centrality as a process’ in which the movement
economy, acting within the spatial network, generates ‘live centres’; areas with a
concentration of movement and dependent land use. Centrality is based on the interplay
between configuration and attraction, a process in which both spatial properties (town
plan) and functionality (land use and activities) drive people to the centre (pattern of
49 Chapter 3 centre and sub-centres - attraction inequalities). Hillier argues that it is the relationship
of these centres and sub-centres that characterises the evolution of cities in general, and
that they can be understood in terms of the movement economy. He argues that within a
vibrant town centre it should be possible to get from any facility to any other by a quick
and easy route, which stays within the town centre and which itself is lined with town
centre facilities to maximize natural access to all facilities. In short, the network system
in a centre makes itself accessible and obvious. Wherever people go they can easily find
their way back without taking the same route, from all spaces to all others the
orientation facilitates movement. Hillier (2009) also says that the pattern of centre and
sub-centres spread throughout the urban fabric in a more intricate way than the
polycentric form whose hierarchical location can be called ‘pervasive centrality’. This
pattern of linked centres emerges from a consistent self-organising process at the
interface between the grid configuration of space and movement at different scales.
After considering studies about English city centres and Siksna’s resarch The effects of
block size and form in North American and Australian City Centres (1997), Hillier
argues that “successful live centres require both a global position in the settlement, and
a compact and interaccessible local layout condition” (Hillier 1999:21). According to
Hillier, the shape of a ‘live centre’ is formed by a linear and convex process. Firstly it is
linear. As a settlement grows, a convex and compact process of intensification and
metric integration is formed. With linear growth away from the ‘live centre’, local subcentres develop along radial lines, and with further growth smaller scale sub-centres
develop away from the main radials.
Furthermore, cities are formed by a ‘dual process of generic form’, both on a global and
local scale, in which space and movement are explored in different ways (Hillier 2001
and Hillier and Vaughan 2007). The public space process, that brings people together, is
driven by micro-economic factors which are invariant and tend to give cities similar
universal and global structures. The residential spatial process, which structures the
relation between inhabitants and visitors, is driven by socio-cultural factors and thus
tends to make cities locally different to each other. On the one hand, public spaces work
as part of a generative process, maximising pedestrian movement. On the other hand,
residential spaces are conservative in the way that they control the space according to
the needs of the culture. The effects of generative and conservative spaces impact on:
the presence and movement of people, land use distribution, centre and sub-centre
formation, as well as cultural and social features. In later papers, Hillier et al (2007) and
50 Chapter 3 Hillier (2009) reinforce the idea of the duality, suggesting two terms: ‘foreground
network’ – linked centres of all scales, global; and ‘background network’ – primarily
residential space, local.
In terms of the urban block itself, Siksna (1997), after studying the spatial form of
different cities, suggests a classification for block size: small (under 10,000m2), medium
(10,000-20,000m2) and large (over 20,000m2). Siksna suggests larger blocks size are
more suitable for residential use and smaller for public use And in relation to people’s
movement she classifies 60-70m as very good for pedestrians, c.100m as very
convenient and c.200m as inconvenient for pedestrians. This model helps us to
understand the relation between block sizes, land use and circulation patterns in each
area of study. Bearing in mind that parts of the study areas are old industrial settlements
that emerged rapidly on the periphery of the historic core, they are constituted by blocks
of irregular sizes and form. This results in different patterns of movement. Since the
large blocks of the industrial occupation, one hypothesis to be investigated is if the
configuration of the block makes more difficult the pedestrian flow in the area.
Additionally, from studies of block morphology, Hillier et al (2007) and Hillier (2009)
developed the ‘patchwork theory’. Based on the characteristics of the size and shape of
the block (considering its physical distortion), the measures identify different local areas
with similar values as representing natural partitions of the background. The patchwork
of areas is determined by their spatial differentiation, rather than a well-delimitated
boundary. Yang and Hillier (2007) add that the urban patchwork can be affected by the
external structure of an area, its spatial context acting as a reference against which the
area is outlined, and the boundary is defined as a fuzzy boundary since it depends not
only on how space is formed within the patchwork, but also on how it is embedded
within the whole area. The identified patchwork of areas has no clear boundary, but a
fuzzy boundary defined by its external structures. A fuzzy boundary emerges from the
internal structure of the space and how it relates to the external form. In identifying it, it
helps to look at the different functions of areas, or their spatial characteristics, although
it does not depend on their form, size or specific boundaries. This is about continuity
and discontinuity in the urban grid, and it helps to identify the natural boundary of the
areas according to their spatial form, as well as the nature of the base formation of the
structure of the city. It is to think of the city of São Paulo as a morphological object,
divided by its natural spatial characteristics rather than by administrative and
economical aspects.
51 Chapter 3 3.4 Diversity and Density The issue of diversity and density is discussed by a variety of authors in urban
literature, within different contexts (Jacobs 1961; Penn et al 2009; Marcus 2007). When
looking at these two variables in the city of São Paulo, we should consider the concept
of density in terms of demographic population, and diversity as mixed land use and
activities. To help comprehend these urban indicators and how they relate to the
configuration of the space itself, it seems relevant to bear in mind their different
definitions. Jane Jacobs in The Death and Life of Great American Cities (1961) shows
her social concern for neighbourhood life, demonstrating components that are able to
achieve successful urbanisation and a sustainable society. These are based on the idea
that cities are able to generate diversity and stimulate their economy, motivating new
enterprises, when four spatial and social principles are effectively combined: ‘mixed
primary uses, small blocks, aged buildings and a concentration of people’. For Jacobs,
cities contain the seeds of their own destruction, but lively and diverse cities contain the
seeds of their own regeneration, with enough energy to compensate for problems and
needs outside of themselves.
According to Jacobs (1961), her first principle is that streets need the presence of people
at different times of the day, going in and out, from one place to another, because of
distinct purposes, generating movement and natural encounters within a district. To
achieve this it is essential to combine effective elements that work as anchors, helping
to stimulate the natural economy, such as parks, squares, public buildings and
dwellings. These attractors help to intensify the variety of uses and sub-areas, creating
pools of economy that reach different levels of complexity. Her second principle is
concerned with urban morphology. She emphasizes that the use of small blocks enables
people to turn corners successively; they have more route choices. There are frequent
streets on their way that increase the patterns of movement within the grid. This creates
a fabric of streets as a continuous system throughout the sub-area district, attracting a
mixture of uses along them. Her third principle is to support old and new enterprises, as
well as mixed uses and activities. Cities must have mixed buildings and infrastructure
with different aspects and conditions. In addition, they should have a variety of
buildings with a high enough concentration of residents in order to guarantee the
constant intensification of presence of people. The more these elements are mixed, the
more diverse the economic and social possibilities. Her fourth principle is a high
concentration of people in a district – individuals have to be there at different times of
52 Chapter 3 the day, as residents, shoppers or workers. This dense concentration can be achieved by
the combination of the three principles already described and by adding densities of
dwellings.
The concept of diversity interpreted by Jacobs – mix of land use and activities,
configuration of the urban fabric and movement of people – is directly related to the
idea examined earlier about centrality: form and block size affects pedestrian
movement, which subsequently determines land use and activities.
Penn et al (in Cooper et al 2009) propose a definition for ‘the generation of diversity’
from the combination of Jacobs’ concepts and the theory of ‘movement economy’ from
Hillier, emphasising the human cognitive dimension of intelligibility. The definition of
diversity is the outcome from the relation of physical material, spatial configuration,
social, economic and cultural properties, as well as the communities’ behaviour affected
by their sensory perceptions in urban system.
“Land use and the effects that these have upon the physical detail of the building; human
occupation densities, movement patterns and behaviours; the economic life of land parcels, and the
relation of these to development density and human occupancy – all these seem to be key factors
in our experience of urban diversity.” (Penn et al in Cooper et al 2009:220)
In other words, different aspects of the spatial system are said to play an essential role in
affecting people’s behaviour, but they need to be strategically combined; activities and
uses cannot be mixed randomly. An intelligible spatial structure is required, sensitive to
how humans create their environment. Intelligibility in space syntax theory discusses
the degree to which the immediate environment (local scale structure) conveys
information about the outside immediate perception (global scale structure), thus
encouraging movement flow in the system, the easy navigation by people. (Penn et al in
Cooper et al 2009). So diversity in a neighbourhood lies in having a rich environment in
terms of significant information, which generates socio-economic and cultural relations.
Another way to investigate the determinants of density and diversity was proposed by
Marcus (2007). He argues that density is a geographic variable, and the degree of
‘accessibility to density’ results from the layout of the urban network of streets and
buildings, while diversity relates to the definition stated by Jacobs. The intention of his
research is not only to correlate density and diversity, but to analyse these two variables
within the context of space syntax’s theory of accessibility within the urban system. In
his research on Stockholm, Marcus found correlations: ‘between integration and
people’s movement; accessible building density and population; accessible plots and
53 Chapter 3 diversity indices (amount of age groups and amount of lines of business)’. From these
correlations he proposes that when density, diversity and movement are combined they
are able to capture the ‘use-value and exchange-value’ of the land defined by ‘spatial
capital’. “The exchange-value of spatial capital suggests how the value of the urban
form literally can be translated into economical capital” (Marcus 2007:10). It means that
spatial capital quantifies the effects of the urban fabric on land-value. The urban form is
able to generate variables in accessibility and diversity that it is possible to measure. In
short, once more, it is proposed that the physical city, at an urban design level, affects
urban life in social and economic ways – a relation of cause and effect, which, firstly, is
affected by high accessibility and high density.
54 4. RESEARCH METHODOLOGY
55 Chapter 4 4. Research Methodology This section describes the methodology used to conduct this research, as well as
presenting the limitations encountered during the investigations. The methods applied
consisted of direct site observations, spatial analysis, syntactic analyses and statistical
correlations based on space syntax techniques.
4.1 Available data A land use map (predominant use of the block) and a block size map were provided by
Space Syntax Limited in the form of MapInfo GIS tables. A more detailed map of the
area – plot size – was procured from Viva Centro Association and EMPLASA in the
form of images (jpg) and digital drawings (dwg). All image maps used were converted
into digital drawings, which form the base maps for land use analyses. Figures 105 and
106 show an example of the land use map available and a digital drawing of the Luz
area. In the map, information on predominant block use is divided into residential, retail
and service, industrial, and institutional uses.
The socio-economic data was obtained from SEMPLA (Secretaria Municipal do
Planejamento – Administrative Municipal Planning) and the EMPLASA report (2007)
which were based on the IBGE (Censo Demográfico do Instituto Brasileiro de
Geografia e Estatística – Brazilian Institute of Geography and Statistics), and Rais
(Relação Anual de Informações Sociais - Ministério do Trabalho - Anual Relation of
Social Information).
Figure 105 Predominant land use map (Luz area) – GIS Table
56 Chapter 4 Figure 106 Digital drawing (Luz area) 4.2 Survey data A detailed field survey was conducted in order to qualify the data available with further
information on the occupation of the sites, and also to have a better comprehension of
each area. Observations were carried out from 12th to 21st July 2010 (Monday-Sunday):
-
12th and 19th (1pm-5pm): Santa Cecília;
-
13th (1pm-5pm) and 14th (10am-3pm): Luz;
-
16th (10am-4pm) and 17th (2pm-4pm): Brás;
-
17th and 20th (10am-1pm): Gasômetro;
-
18th (11am-2pm) and 20th (2pm-5pm): Oriente.
Observations examined the existing land use of the built environment at the site scale,
plotting the different kinds of occupation. In addition to residential, retail and/or service,
industrial (industry, storehouse, and warehouse) and institutional (public, religious,
cultural buildings), the categories of mixed-use (retail and/or service on the ground floor
and residential on upper floor(s)) and parking were also recorded (Figure 107). Photos
were taken to register the visual characteristics of the streets (Figure 108-111).
57 Chapter 4 Figure 107 Digital drawing with collected land use information (Luz area) Figure 108, 109, 110, 111 Examples of residential, retail, mixed-use and institutional land use. Source:
author
4.3 Syntactic data The spatial structure data is based on a segment map, which examines the shortest path
in a system. It uses the least number of streets to get to a destination. (Turner, 2008).
The syntactic analysis is based on space syntax theory and techniques. It was found
relevant to consider this methodology given its traditional use in analyses of the urban
environment.
According to Turner (2008), to calculate a syntactic measurement of space there are
three definitions of distance to consider: metric, topological and angular (geometrical).
The metric is the system of shortest mapped paths for integration and choice, the
topological is the one of fewest turns, and the angular is the system of least angle
58 Chapter 4 changes. Within each definition it is possible to extract the measures of integration and
choice using different radii (global and local). For the global scale, radius n (infinity) is
used, which measures each line in the system in relation to all other lines. For the local
scale, the measurement of routes from any line is restricted to only those lines that are
up to the metric distance specified; for instance, the typical radii of 750m, 1000m,
1250m, 1500m, 2000m and so on.
It is known that integration is related to ‘to movement’ in a system. Integration reveals
the streets that have greater potential as destinations. Using segment analysis,
integration is calculated by the degree to which a line is closer to every other segment in
the network, considering the simplest route (using minimum angular depth). “It is a
measure of how accessible each segment is from all the others, and so how much
potential it has as a destination for movement” (Hillier 2009a:2). Turner (2004)
suggests that in segment analysis the measure of integration is obtained by the formula
Node Count/Mean Depth (NC/MD) in which NC is the number of segments
encountered on the route from the current segment to all others, and MD is the mean
depth of the nodes with respect to the root node.
In contrast, the measure of choice is linked to ‘through movement’, with the potential of
each segment providing indications of movement flow, It measures the degree to which
an individual is likely to pass through the segment, from all segments to all others
(Hillier and Iida 2005). According to Hillier (2009a) it is possible to create another measure
by combining the measures of integration and choice. This relationship reveals the combined
potential of a segment as both a destination and a route.
To identify spatial patchworks and area boundaries (Fuzzy boundaries) Hillier and Yang
(2007) developed a method that calculates the metric measures with different metric
radii – to calculate the metric mean depth from segments within the metric radius. When
increasing the radius of any measure, the scale of the patchwork increases
proportionally.
Finally, the analysis of the segment map explores the measures: Choice (logCH+1) and
Integration (NC/MD) at different metric radii: 750m, 1000m, 1250m, 1500m, 2000m,
3000m, 4000m, 5000m, 7500m, 10000m.
59 Chapter 4 4.4 Integrated software Spatial and syntactic analyses were carried out using Depthmap. The software is used to
perform spatial analyses that are designed to understand social processes within the built
environment (VR Centre for the Built Environment). Scattergrams were plotted in Depthmap
after processing, the analyses.. Later, the map was imported into MapInfo to create
graphs of the segment map using the tool ‘Create Thematic Map’- method: Natural
Break, Ranges: 16, Round by: 0.001. It generated maps with a colour range from red
(highest line value) to dark blue (lowest line value). MapInfo also assisted in the
comparison of socio-economic data and spatial data: land use map, block size and
segment map.
AutoCad (software developed by Autodesk) was used to convert the maps from image
format (jpeg) into digital drawings (dwg), as well as to plot the information from the
detailed onsite survey (land use).
The statistical correlations between different variables were developed using Excel and
JMP programs. Firstly, the syntactic measures of the segment map were extracted using
tools in MapInfo and put into a single table. Later the syntactic values were compared
with the socio-economic data available.
4.5 Analytical Model The analytical method applied in the syntactic analyses of the pattern of centre and noncentre areas was based on recent research on centrality, developed in London by Hillier
(2008a). Hillier’s suggested method involves taking the linear extension of the main
high street (centre line), measuring its global and local spatial properties, and comparing
them to the surrounding area (non-centre lines). In order to identify the spatial signature
of centrality, the centres should be significantly and statistically distinguishable from
non-centres. In applying the theory of centrality in São Paulo, either the linear or the
convex extension was taken, depending upon the shape of the centre of each area. Also,
the centre here is defined as the busiest shopping street. The analysis aims to find if: 1)
the centre has shorter segments; 2) if it has higher local choice; 3) if the centre has more
comparable choice as the radius is increased from local to global.
4.6 What the method can help to find The aim of the spatial, syntactic and statistical analyses is to investigate how the
configuration of the grid itself influences pedestrian and vehicular movement flow
60 Chapter 4 within the urban fabric, whether as a route or as a destination; and, furthermore, how
this impacts on land use occupation and activities. Finally, the analyses can help us to
understand how the relationship between spatial properties and socio-economic
characteristics work within the peripheral areas of São Paulo’s historic core. Research
developed by Space Syntax using this methodology shows that the urban form is not
only directly associated to people’s movement, but also it shapes the pattern of land use
within the system.
4.7 Implications One challenge for the research was the on-site observation and the development of the
detailed land use map. Due to the lack of time available and the size of the selected area,
and also for reasons of personal security, a few blocks were not completely observed.
Also, weather conditions (cold and rain) during the first week (12th -16th) may have
impacted on the understanding of pedestrian movement in the area.
61 5. ANALYSIS AND RESULTS
62 Chapter 5 5. Analysis and Results As follows, the findings will be divided into two categories: descriptive, which includes
the syntactic, geometric, geographic and demographic analyses of the whole São Paulo
and its sub areas; and statistical, which tests for correlations between the socioeconomic and spatial measures.
5.1 Descriptive: spatial configuration of São Paulo This section reveals the outcomes of the analysis. The segment map presented in Figure
114 at measure LogCH+1 global radius (Rn) clearly show the macro situation
described, with a high value in terms of road systems: the radio-centric model, Radial
Leste, and the Y System – 9 de Julho, 23 de Maio and Prestes Maia (Figure 112).
Moreover, at this radius the map picks up other main roads that privilege vehicular
movement – the Costa e Silva elevated expressway – those constructed on the border of
important rivers – Estado Avenue (Tamanduateí river), Marginal Tietê (Tietê river) and
Marginal Pinheiros (Pinheiros river) – as well as arterial roads that connect the historic
core and the non-central areas. Interestingly, it also outlines the streets where the shift of
the financial centre towards to southwest occurred: Paulista, Brigadeiro Faria Lima
Avenue and Marginal Pinheiros. So this change of land use and activities might be
related to the spatial form of the city. One hypothesis is that the financial centres, where
the wealthier populations are located and which have received more private investment,
are also the areas that have the potential for routes at the global scale.
Within the map, the configuration as a whole presents a relatively fragmented spatial
system. There are both several clusters of regular grids settled within different
directions where original main routes may be linked to new settlements, and a
patchwork pattern. A recent article about Brazilian cities concludes that the
configurational features of Brazilian cities indicate a “labyrinthine structure derived
from a ubiquitous patchwork layout. The exception is the oasis in the labyrinth – the old
urban core preserving the positive configurational features from the city of the past.”
(Medeiros and Holanda 2010:88) In the map (Figure 114) it can be seen that the areas
located immediately to the northeast and east of the historic core do not have concise
structures. There are a few clusters of grid formations but they become weak in the
context. A strong grid structure appears further east, within a more peripheral, noncentral area, although it has less potential than the grids in the west and southwest where
the financial centres are found.
63 Chapter 5 The presence of the Marginal Tietê considerably affects the structure of the north part of
the city. It looks like a labyrinth network, but it seems that there is logic in its formation.
There are strong roads (similar to a tree’s roots) which connect the expressway to the
peripheral settlements. It can be justified by the topological aspect of the area – it is
close to the natural border ‘Serra da Cantareira’.
64 Prestes Maia Ave.
9 de Julho Ave.
23 de Maio Ave.
Figure 112 Segment Map of São Paulo showing historic centre and
surrounding area and the Y System at logCH+1 at global radius (n)
Border study area
Figure 113 Diagram of the Y System: 9 de Julho Ave., 23 de Maio Ave.
And Prestes Maia Ave. Extracted from Meyer (2010)
Figure 114 Segment Map of São Paulo showing logCH+1 at global radius (n)
high
low
Prestes Maia Ave.
9 de Julho Ave.
23 de Maio Ave.
Figure 115 Segment Map of São Paulo showing historic centre and
surrounding area at logCH+1 at local radius (750m)
Border study area
Figure 116 Segment Map of São Paulo showing logCH+1 at local radius (750m)
high
low
Figure 118 Segment Map of São Paulo showing Patchwork: Metric Mean Depth local
radius (2000m)
Border study area
high
low
Chapter 5 Looking at the maps at a local scale, radius 750m (Figure 115 and 116), they display a
different picture: the main roads that privilege vehicular flow lose their potential for
movement, for instance Estado Avenue, which at radius n has an average of 7.585,
decreases to 2.596 at radius 750m, and 9 de Julho Avenue changes from 7.682 to 2.793.
The segments that maintain higher values are the ones closer to the historic core, a few
on Paulista Avenue and Brigadeiro Faria Lima Avenue. In this case, the settlements
located to the north and east of the historic centre considerably lose their values. The
map also picks up some streets that will be discussed in more depth when describing
each area. Another point to draw attention to is the different line values of the
settlements located along the railway track, from the southeast to the northwest.
Locally, these are considered very segregated compared with the global scale. This
reveals the effect of the large blocks from the industrial occupation on the area: fewer
streets and less potential for through-movement within the streets. Similarly, this
happens with segments of Estado Avenue located within Dom Pedro II Park. They have
a high value at the global scale, but low at the local. The low potential for routes
matches what was observed in the area: high vehicular movement, traffic in peak hours
and very low pedestrian flow.
Furthermore, at this radius the whole area to the north of Marginal Tietê sees an
increase in values. Its potential for movement flow is found not only in the roads that
link the Marginal to the periphery, but also on others more localised streets.
Looking at the map of Land use value (Figure 120), it reveals the economic effect of the
shift of the financial centre and higher income population to southwest. Spatially
speaking, there is a better correlation between land value and integration (NC/MD)
rather than choice measures. At the local scale, radius 1000m (Figure 119), Paulista
Avenue and its surrounding area, Brigadeiro Faria Lima Avenue and Eng. Luis Carlos
Berinni Avenue are shown to have very high values as destinations, while the areas to
the north and east of the historic centre have low values, especially the streets located
along the railway track.
So far there has been a descriptive review of the whole São Paulo, the central and noncentral areas. The initial findings show that, at a global scale, the roads highlighted with
high values match the main roads of the city. On the other hand, at the local scale those
roads lose their potential as routes and destinations. This allows us to hypothesise that
most of these roads prioritise vehicular movement rather than pedestrian flow – there is
an intense use of cars and buses by the population due to the lack of subway and train
68 Chapter 5 infrastructure in the city. Moreover, not only the railway track itself and the settlements
along it, but also Dom Pedro II Park might intensify the physical separation between the
historic core and its peripheral areas, acting as physical barriers in the inner city.
69 Financial centre
Study area
Figure 119 Segment Map of São Paulo showing local Integration (NC/MD) at radius 1000m
Figure 120 Land use value. It shows the effect in land use value resulted by the shift of the financial
centre and residential population to southwest
high
low
Chapter 5 5.2 Descriptive socio‐economic characteristics of the areas located on the periphery of the historic centre of São Paulo In addition to the social information about the city and its sub areas previously
discussed, as follows is a socio-economic and spatial correlation in order to find the
relevant relationships between the areas. According to the Demographic Census
developed in 2000 by IBGE, it seems that there is a direct relationship between the size
and the population of the areas, r² = 0.4239 – the smaller the area the less populated, and
vice versa, but with one exception, the area of Santa Cecília which is 1,441.00km2. The
geographical order from the biggest to the smallest area is: Luz, Santa Cecília, Pari,
Brás, Oriente and Gasômetro.
Santa Cecília is the most densely populated among the areas since the majority of its
housing is based on high-density vertical buildings. It is where both the higher income
and no income population live together, as well as the place that has the highest number
of jobs within the service sector, 18,504 – more than five times greater than the other
regions (Table 16). This can be explained by its location close to more costly, noncentral boroughs – Higienópolis and Pacaembu – and its proximity to the historic centre,
the first financial centre of the city.
The relationship between density (x-axis) and the total employed population (y-axis) in
the areas is positive and direct (r² = 0.4501). Figure 121 shows that Santa Cecília has the
highest value in both variables; Oriente, Luz and Pari have a high value of employed
population but low density, while Brás and Gasômetro are both low density and have
low employment figures. The least dense area is Oriente, where the lack of residents
gives over space to retail, service and industrial uses. Also it is the region that offers the
greatest amount of employment and has the greatest amount of established industry and
retail. Similar to Oriente, Gasômetro has some common features: lack of residents, low
density, a mainly middleclass population and employment within the retail sector, but it
offers more service than industrial activities Brás and Pari also have established retail
sectors which offer employment.
Population
2000 (hab)
Area (km2) Housing 2000
Demographic density
(hab/km2)
9 626
1 095
3 476
8 791
Brás
3 240
0.693
1 062
4 675
Gasômetro
13 726
1 541
4 500
8 907
Luz
4 150
1 085
1 278
3 825
Oriente
11 415
1 398
3 654
8 165
Pari
33 305
1 441
14 204
23 112
Santa Cecília
Source: IBGE, Demographic Census 2000 and IGC (Instituto Geografico e Cartografico) – from
EMPLASA 2008
Table 17. Family average income (salary)
no
More than
%
5 to 10
%
%
income
20
130
3.82
1022
30.07
330
9.71
Brás
63
6.1
288
27.88
34
3.29
Gasômetro
204
4.78
1,154
27.01
393
9.2
Luz
67
5.36
348
27.82
40
3.2
Oriente
210
5.91
968
27.23
281
7.9
Pari
702
5.54
4,232
33.4
1,201
9.48
Santa Cecília
Source: IBGE, Demographic Census 2000, Setores Censitarios – from EMPLASA 2008
Table 18. Number of land use according to activities
Bras
Gasometro
Luz
Oriente
Pari
Santa Cecilia
Industrial
105
43
1,153
1,431
547
147
%
17.95%
6.90%
42.19%
51.25%
39.72%
8.91%
Retail
313
449
945
1,089
552
562
%
53.50%
72.07%
34.58%
39.00%
40.09%
34.08%
Services
167
131
635
272
278
940
%
28.55%
21.03%
23.23%
9.74%
20.19%
57.00%
Total
585
623
2,733
2,792
1,377
1,649
Source: Ministerio do Trabalho, Rais -Relacao Annual de Informacaoes Sociais 2005;
Taking the demographic density (Table 16) on the x-axis and the proportional
percentage of industry, retail and service activities respectively on the y-axis (Table 18),
the results are as follows:
-
Industry (Figure 122): Oriente, Pari and Luz have a high percentage of industrial
land use but low density. Santa Cecília is high density but has low industrial
land use. Brás and Gasômentro have neither a high industrial percentage nor
high demographic density.
-
Retail (Figure 123): Gasômetro and Brás have a high percentage within retail
activities but low density, although with Santa Cecília the opposite occurs. Luz,
Pari and Oriente have both low percentages of retail activities and low density.
-
Services (Figure 124): Santa Cecília has a higher proportion of service activities
and is a high density area. On the contrary, Oriente has the lowest industrial
72 Chapter 5 percentage and demographic density. Brás, Gasômetro, Luz and Pari have higher
percentages and densities than Oriente, but they are still weakly correlated.
Finally it shows that the areas with a higher percentage of industrial activity are the ones
with lower densities. The same finding is taken in places with a high percentage of
retail. On the other hand, the areas where the majority of uses fall within the service
sector are the ones with higher densities.
Figure 121 Correlation between total employed
population and density
Figure 123 Correlation between retail (%) land
use and density
Figure 122 Correlation between industrial
(%) land use and density Figure 124 Correlation between service (%) land
use and density
Another point to consider when dealing with social and economic characteristics is the
typology of housing (its historic and economic formation). When the total number of
residential properties and the different typologies – house, apartment and rooms (Table
73 Chapter 5 19) is compared - it is found r² = 0.0002 (Figure 125), r² = 0.9728 (Figure 126) and r² =
0.3644 (Figure 127) respectively. This impressively illustrates the culture of high-rise,
high density buildings in São Paulo. Despite the general characteristics of these areas –
old industrial sites with working class villages, and the existence of one-two floor
housing – the number of residents in apartments is still much higher than the
predominant typology of the areas.
Figure 125 Correlation between house (number)
and housing
Figure 126 Correlation between apartment
(number) and housing
Figure 127 Correlation between room (number) and housing
74 Chapter 5 Table 19. Typology of housing
Brás
Gasômetro
Luz
Oriente
Pari
Santa Cecília
House
640
219
814
313
2,289
515
%
18.83%
21.20%
19.05%
25.02%
64.39%
4.06%
Apartment
2,634
772
3,122
865
1,131
11,903
%
77.49%
74.73%
73.08%
69.14%
31.81%
93.94%
Room
125
42
336
73
135
253
%
3.68%
4.07%
7.87%
5.84%
3.80%
2.00%
Total
3,399
1,033
4,272
1,251
3,555
12,671
Source: IBGE, Demographic Census 2000 – Setores Censitarios – from EMPLASA 2008
These comparisons were established in order to give us a better understanding of the
relationships between the areas selected, and to help in the further spatial, syntactic and
statistical analysis developed below.
5.3 Descriptive spatial configuration of the areas on the periphery of the historic centre Pari Luz Santa Cecília Oriente Gasômetro
historic centre Brás Figure 128 Areas on the periphery of the historic centre of São Paulo. Segment map showing global
Choice - logCH+1 radius n
75 Chapter 5 Figure 129 Correlation between global
integration (Rn) and local integration (R750)
Figure 130 Correlation between global choice
(Rn) and local choice (R750)
Taking the integration measure (NC/MD), with radius 750m (the smallest radius
considered due to the size of the city) as the local variable and radius n as the global,
and with global integration on the x-axis and local on the y-axis, a weak result of r² =
0.0111 is generated (Figure 129). It suggests that: Santa Cecília is both globally and
locally integrated, Pari is locally but not globally integrated, Brás, Luz and Gasômetro
are globally but not locally integrated, and Oriente is neither globally nor locally
integrated. When it briefly consider the different local measures analysed, for instance,
when the radius is increased from 750m to 10,000m, Santa Cecília remains the most
locally integrated area until the radius 4000m (1,052.17), while from the radius 5000m
it changes and Brás becomes the most locally integrated area ( Table 20).
Taking the measure logCH+1 radius n on the x-axis and logCH+1 radius 750m on the yaxis, it shows a stronger, but still weak r² = 0.2135 (Figure 130), suggesting that Santa
Cecília and Gasômetro are both global and local, Pari is globally but not locally, Brás is
locally but not globally and Oriente and Luz are neither globally nor locally.
Table 20. Integration (NC/MD) of each study area
Int Rn
Brás
Gasômetro
Luz
Oriente
Pari
Santa Cecilia
6,570.72
6,598.82
6,636.26
6,366.28
6,415.20
6,748.55
Int
R750
65.169
74.434
71.191
78.926
93.541
90.545
Int
R1000
101.784
119.195
110.817
123.336
141.408
141.802
Int
R2000
316.268
387.255
349.273
365.862
360.576
412.391
Int
R3000
641.43
702.205
654
664.058
614.056
727.155
Int
R4000
1,041.78
1,042.51
957.402
988.326
917.309
1,052.17
Int
R5000
1,449.98
1,413.28
1,286.42
1,366.97
1,278.78
1,404.97
Int
R10000
4,377.44
4,362.66
4,052.15
4,044.59
3,945.30
4,098.91
integration (Rn) and local integration (R3000)
(Rn) and local choice (R5000)
Table 21. Summary of comparison: integration (NC/MD) and choice (LogCH+1) at
different measures
Int Rn vs R750
Choice Rn vs R750 Int Rn vs R3000 Choice Rn vs R5000
Global
Global
Local
Local
Global
Local
Global
Local
Brás
Gasômetro
Luz
Oriente
Pari
Santa Cecília
significant
non-significant
However, it is very important to make clear that in this case, considering the lowest
radius used – 750m – both correlations, integration (NC/MD) and choice (logCH+1),
have very low r² values. Looking to other radii it is found that on the integration
measures the highest correlation is between radius n and radius 3000m, r² = 0.4499
(Figure 131), which indicates that Santa Cecília and Gasômetro are both globally and
locally integrated, Oriente is locally but not globally integrated, Brás and Luz are
globally but not locally integrated, and Pari is neither globally nor locally integrated.
Also, the choice correlation on the radius 5000m is r² = 0.5589 (Figure 132) which
suggests that Brás and Santa Cecília are both globally and locally, Gasômetro, Oriente
and Pari are locally but not globally, and Luz is neither globally nor locally. The
intelligibility, the correlation between connectivity and integration, of all areas selected
77 Chapter 5 is very weak, r² = 0.0324; and so is synergy, the relation of local and global integration,
within the radius 750m, r² = 0.0111 (Figure 129), but when considering the radius
3000m it increases to r² = 0.4499 (Figure 131).
Finally, by considering the correlation between integration (R3000 X Rn) (Figure 131)
and choice (R 5000 X Rn) (Figure 132) it can be argued that the areas of Santa Cecília
and Gasômetro have good potential as destinations, while Brás and Santa Cecília are
the places with higher potential for movement flow within local and global scales.
(Rn) and global integration (Rn)
Figure 134 Correlation between local choice
(R750) and local integration (R750)
Table 22. Relationship between integration (NC/MD) and choice (LogCH+1)
NC/MD int
Global (n)
Brás
Gasômetro
Luz
Oriente
Pari
Santa Cecília
6,570.72
6,598.82
6,636.26
6,366.28
6,415.20
6,748.55
LogCH+1
Local
(750m)
65.169
74.434
71.191
78.926
93.541
90.545
Global (n)
6.066
5.688
5.444
5.535
5.507
5.873
Local
(750m)
2.473
2.594
2.514
2.613
2.737
2.614
High
Mediun
Low
In addition, it is possible to create another measure by combining the integration and the
choice measures in order to see which areas have potential as both destinations and
routes. Taking the global (n) choice on the x-axis and global integration on the y-axis,
the result is (Figure 133): Santa Cecília has the potential for both through and to
movement. Brás has high through movement but lower to movement. Luz and
Gasômetro are strong destinations but weak routes. Pari and Oriente are weak on both
78 Chapter 5 through and to movement in the global scale. At the local scale radius 750m the
correlation between both is high, r² = 0.8140 (Figure 134). This result indicates: Pari has
potential as both a route and destination; Santa Cecília has higher to movement than
through movement; Oriente and Gasômetro have equal potential for both but with lower
values; and Luz and Brás have neither to nor through movement.
In summary, when comparing all areas, one hypothesis to be suggested is that the area
of Santa Cecília is better embedded in the context of the whole city, locally and
globally. It means that Santa Cecília’s system allows people to change easily from
global to local scales, facilitating the movement within the area. As a result, this spatial
potential for movement attracts more people and activities, which in turn attract further
uses and more people. It can be argued that this area is more successful in terms of
urban fabric than the others. On the other side, Oriente and Luz are the areas with very
low correlation between scales, and so less spatially successful.
5.4 Descriptive statistical correlations of the areas on the periphery of the historic centre An exploration of the syntactic and socio-economic data shows that the correlation
between the size (km2) of the areas and their global integration (n) is very low, r² =
0.0007, but that local integration (750m), considering the highest relation, is strong, r² =
0.8518. This strongly indicates that there is no relationship at all between size and
global integration, only local integration.
Figure 135 Correlation between global integration
(Rn) and retail (%) land use
Figure 136 Correlation between local integration
(R750) and retail (%) land use
79 Chapter 5 Figure 137 Correlation between global choice
(Rn) and retail (%) land use
(R3000) and retail (%) land use
Bearing in mind that the spatial variables which predict retail distribution are likely to
be associated to the global and local integration measure, in this case it seems to be
weakly relevant, since the correlation between the percentage of retail within each area
and the global (Rn r² = 0.0002) (Figure 135) and local integration (R750m r² = 0.1887
and 3000m r² = 0.0260) is very low (Figure 136). What seems interesting is that the
relationship value between retail (%) and the choice (logCH+1) measure is higher than
integration, though still weak (Rn r² = 0.0964) (Figure 137) and (R750m r² = 0.0322
3000m r² = 0.233) (Figure 138). It seems that the pattern of retail occupation is more
associated with streets that have potential as routes rather than destinations. For
instance, in Brás the location of retail seems to follow the logic of space at the global
scale for route movement, but not at the local scale. Gasômetro in all correlations has a
high percentage of retail in the area, but it is not located in streets that have potential as
either routes or destinations.
integration (Rn) and service (%) land use
(R3000) and service (%) land use
Looking to other variables, it was found that there is a good correlation between
services (%) and global integration (r² = 0.6833) (Figure 139) in which Santa Cecília is
on top with maximum correlations, Brás, Gasômetro and Luz are in the middle with
considerable correlations, and Pari and Oriente are on the bottom with very low
correlations. It means that in Santa Cecília the location of services follows the logic of
the most integrated spaces at the global scale. Although in Pari and Oriente the location
of services seems random. At the local scale the radius 3000m was considered (r² =
0.3691) (Figure 140) since it has better correlations than the radius 750m (r² = 0.100),
but it is still weak. The results are as follows: Santa Cecília and Pari maintain the same
position compared to the others; Brás, Gasômetro and Luz are still in the middle; and
Oriente’s integration value increases.
integration (Rn) and industrial (%) land use
(R3000) and industrial (%) land use
(Rn) and industrial (%) land use
(R3000) and industrial (%) land use
Surprisingly, industrial occupation seems to locate according to the urban system. There
are considerable correlations between industrial location, integration and choice
measures. The integration correlations at the global scale (Rn r² = 0.5131) (Figure 141)
and at the local scale (R3000m r² = 0.4136) (Figure 142) can be considered relevant. In
both cases Santa Cecília and Gasômetro are shown to have low percentages of industrial
occupation but they are relatively highly integrated, while Pari has a high percentage of
industrial occupation but is less integrated. Compared to integration, choice reveals a
better correlation at the global radius (Rn r² = 0.5179) (Figure 143) and at the local
(R3000m r² = 0.5768) (Figure 144). Globally, Luz, Oriente and Pari have a high
82 Chapter 5 percentage of industries, but they are not located on streets with high potential as routes,
but in Santa Cecília and Brás the industries are located in more potential streets.
Locally, Oriente and Pari have a higher percentage of industrial area and choice value
while Santa Cecília and Brás have a lower percentage of industries but higher choice
value. In short, the findings reveal that industrial occupation in the area over the years
has tended to follow the systematic logic of streets that have potential as routes rather
than destinations.
Figure 145 Correlation between global
integration (Rn) and house (%)
(R3000) and house (%)
Figure 147 Correlation between global
integration (Rn) and apartment (%)
(R3000) and apartment (%)
Observing the typology of housing of the area – houses and apartments – and
correlating it with the integration measure – the variable that predicts if the area is more
83 Chapter 5 segregated or integrated within the whole system – the results are as follows: in Pari
both houses and apartments are found to be located on more segregated streets in both
measures integration and choice. This indicates that they might be located in more
private and conservative spaces where it is easier to control the public – either residents
or visitors. While in Santa Cecília residential occupation is located in more integrated
areas with easier accessibility for the public. This reveals that these areas might be
culturally distinguishable from each other since they have different spatial formations.
Table 23. Summary of correlations and r² values
Correlations
r²
Int (NC/MD) Rn vs Retail (%)
Int (NC/MD) R750 vs Retail (%)
Int (NC/MD) R3000 vs Retail (%)
logCH+1 Rn vs Retail (%)
logCH+1 R750 vs Retail (%)
logCH+1 R3000 vs Retail (%)
0.0002
0.1880
0.0260
0.0964
0.0322
0.2330
Int (NC/MD) Rn vs Service (%)
Int (NC/MD) R750 vs Service (%)
Int (NC/MD) R3000 vs Service (%)
logCH+1 Rn vs Service (%)
logCH+1 R750 vs Service (%)
logCH+1 R3000 vs Service (%)
0.6833
0.1000
0.3691
0.3145
0.0065
0.2015
Int (NC/MD) Rn vs Industry (%)
Int (NC/MD) R750 vs Industry (%)
Int (NC/MD) R3000 vs Industry (%)
logCH+1 Rn vs Industry (%)
logCH+1 R750 vs Industry (%)
logCH+1 R3000 vs Industry (%)
0.5131
0.0048
0.4136
0.5179
0.0435
0.5768
significant
Finally, it can be argued that, in general, the statistical correlations between the socioeconomic and spatial characteristics of each area are weakly related. Results have
shown that the location of retail is hardly predicted by the configuration of the space –
shops might not be located in streets that are well embedded in the whole system with
high potential for movement. The location of services is organized slightly differently.
They might be located in streets more integrated within the whole city at the global
scale. On the other hand, results have shown that the location of industry has a higher
relationship with the spatial form of the city. Industrial uses are likely to be located in
streets with better potential for movement. This pattern might have a connection with
the historical occupation of the areas. They were originally developed by the heavy
industries located along the railway track and adjacent to the historic core, which in turn
84 Chapter 5 attracted more people and infrastructure. So it can be said that this attraction occurred
not only because of the land use, but also because of the spatial properties. 5.5 Descriptive statistical correlations of each study area As follows, a comparison between the centre and non-centre areas is developed. The
micro-structure of each area is considered in order to find out how the internal-toexternal spatial pattern relates to the different kind of activities – pedestrian and
vehicular movement as well as land use distribution. While doing this, Hillier (2008)
suggests to keep in mind some variables that will index the spatial value of centrality:
“shorter segments, higher local choice and more comparable choice as we increase
radius from local to global” (Hillier 2008:01).
5.5.1 Brás The spatial structure of the area appears disorganised, without a concise grid form. The
land use pattern is mixed throughout the area, with clusters of residences located in the
inner area and industrial settlements located closer to the railway track (Figure 150). Its
block size varies from 20m to 350m, so some are optimal for pedestrians while others
can be very inconvenient (Figure 149). According to observations, it was found that the
main vehicular and pedestrian movement occurs along Alcantara Machado Avenue and
it is where the majority of shops are located. It is considered the busiest street of the
area. Also high pedestrian movement was observed in parallel streets such as Moóca
and Visconde de Parnaíba.
Rangel Pestana Alcantara Machado Da Figueira Mooca
Figure 149 Block size graph of the area of Brás
85 Chapter 5 Looking the segment map of the area and at global choice (logCH+1 radius n) (Figure
151) it can be seem that both Alcantara Machado Avenue and Mooca street have high
potential for through movement at the global scale. At the local scale (logCH+1 radius
750m) (Figure 152) these roads lose potential, but the segments located closer to Da
Figueira street can still be considered to have relatively high potential for through
movement. When looking at global integration (NC/MD radius n) (Figure 153) both
Alcantara Machado Avenue and Mooca street appear to have high values. At the local
radius (750m) (Figure 154) both Alcantara Machado Avenue and Mooca street maintain
high values in the segments closer to Da Figueira street and they lose potential when
closer to the railway track. This reveals that the presence of the railway track
considerably affects the system. It induces the ‘edge effect’, that is the tendency for the
streets/segments on the edges of the spatial network to be distinct from interior areas
since they are close to the edge (Hillier 1996).
The correlation between logCH+1Rn and integration (Rn r² = 0.5744) (Figure 157)
shows that Alcantara Machado Avenue and Mooca street have higher potential for both
‘to and through’ movement at the global radius. At the local radius (750m, r² = 0.5866)
(Figure 158) some segments of Da Figueira street are differentiated. Synergy (r² =
0.5478) (Figure 155) and the relation between global and local (1000m) choice
(logCH+1) (Figure 156) reveals Mooca and Da Figueira streets. This means that these
streets are the ones better embedded within the system, and so have better potential for
movement.
It is not a coincidence that the centrality of the area located along Alcantara Machado
Avenue is linear. The relationship between centre and non-centre reveals that the centre
has shorter segments, as the theory predicts, by about 56%, although it has higher local
choice only in its average, not in its maxima. The difference diminishes with increase
radius in the maxima only up to radius 4000m, after that it increases, but in its average
the difference increases rather than decreases; for instance, the radius 2000m is 14% and
4000m is 20%. At the global radius its maxima is more or less indistinguishable with
only 2% of difference (Table 24).
Area
Segment
L_Max
Bras
Centre
184.622
Non-centre 416.358
%difference
-56%
LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R
n_Max
n_Av
1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av
8.484
8.352
2%
8.468
5.898
44%
3.514
4.052
-13%
3.139
2.823
11%
4.399
4.948
-11%
4.172
3.661
14%
5.013
5.422
-8%
4.853
4.110
18%
5.452
5.796
-6%
5.318
4.416
20%
1.205
6.186
-81%
1.127
4.640
-76%
86 8.467
8.484
Figure 151 Segment Map of Brás showing logCH+1 at global radius (n)
Linear
3.106
Figure 150 Land Use Map of Brás
2.462
7231.35
97.056
7120.81
49.062
high
low
Chapter 5 Mooca Da Figueira Figure 155 Synergy
Moóca Da Figueira Figure 156 Global and local choice
Moóca R² = 0.574481
Alcantara Machado Figure 157 Global integration and choice
Da Figueira Figure 158 Local integration and choice
88 Chapter 5 5.5.2 Gasômetro Gasômetro has no concise grid structure in its system. It has the biggest block size of all
the areas – 460m – while its smallest block size is 20m (Figure 159). According to
Siksna (1997:24) 200m or more is ‘very coarse meshed: inconvenient for pedestrians’.
This biggest block is where the old Pari railway yard is located (today it serves as a fruit
and vegetable market). Its industrial settlements are mainly located along the railway
track and close to the railway yard. Its residential buildings are located sprawling
throughout the area, but there are clusters located closer to Rangel Pestana Avenue and
Gasômetro street (Figure 160). The main shopping street is Gasômetro and, according to
observations, is where there is both high pedestrian and vehicular movement. At the
same time, Mercúrio Avenue and Santa Rosa street (especially at the intersection with
Mendes Caldeira street) have intense pedestrian and vehicular movement due to their
proximity to Estado Avenue. Many people from the west side of the avenue take this
way (by car) to go into the area.
Old Pari railway yard Figure 159 Block size graph of the area of Gasômetro
Taking the segment map and both measures global choice (LogCH+1 radius n) (Figure
161) and global integration (NC/MD radius n) (Figure 162) Mercúrio Avenue and
Estado Avenue are highlighted as having higher values. Gasômetro street and Rangel
Pestana Avenue have lower values, but still considerable. At the local choice (logCH+1
89 Chapter 5 radius 750m) (Figure 164) Mercúrio Avenue, Rangel Pestana Avenue and Gasômetro
street have high values, but Estado Avenue loses considerable local potential for
movement. As said before, this avenue prioritises fast vehicular flow. At the local
integration radius 750m (Figure 163) Mercurio Avenue and Gasômetro street have
higher values than Rangel Pestana Avenue. Also, the analyses reveal that Mercúrio
Avenue, which marks the limit between the historic core and the Gasômetro area, is
well embedded in the system, locally and globally.
Comparing residential locations and the spatial system shows that the cluster of housing
is mainly located in a more segregated area with less movement and less potential as a
route or destination at the local scale (750m), repelling the accessibility to the area.
The spatial structure of Gasômeto has high synergy (correlation of global and local
integration) using radius 1000m (r² = 0.7820) (Figure 165), and so does the correlation
global and local choice (logCH+1) (r² = 0.8062) (Figure 166). Taking the choice
correlation Gasômetro street, which is the busiest shopping street of the area, has a high
correlation but lower than Mercúrio Avenue.
It can be argued that the centrality of the area is distributed linearly along Gasômetro
street, since this is where the majority of shops are located. As the theory predicts, the
centre has shorter segments by about 60%. However, as increase the local radius, the
difference at its maxima is negative and increases (radius 2000m is -8% and 4000m is 10%). The non-centre has higher values than the centre. At its average, the centre has
higher local choice and this diminishes as you increase the radius (2000m is 19% and
4000m is 16%). Also at global radius (n) the difference is distinguishable by about 22%
(Table 25).
Area
Segment
L_Max
Gasometro
Centre
170.782
Non-centre 424.557
%difference
-60%
n_Max
n_Av
6.462
8.263
-22%
6.241
5.620
11%
3.845
4.001
-4%
3.471
2.927
19%
4.644
5.033
-8%
4.472
3.756
19%
5.018
5.534
-9%
4.863
4.168
17%
5.292
5.867
-10%
5.144
4.430
16%
5.486
6.087
-10%
5.357
4.613
16%
90 high
5.994
low
6.452
Linear
Figure 161 Segment Map of Gasômetro showing logCH+1 at global radius (n)
Figure 160 Land Use Map of Gasômetro
6636.07
6790.13
2.635
62.927
103.51
3.452
Chapter 5 Mercúrio Figure 165 Synergy R² = 0.8062
Mercúrio Gasômetro Figure 166 Global and local choice
92 Chapter 5 5.5.3 Luz Physically speaking, Luz is divided in two by Tiradentes Avenue – the east side
between Tiradentes Avenue and Estado Avenue, and the west between Tiradentes
Avenue and Tenente Pena street. Both Tiradentes Avenue and Estado Avenue have high
vehicular movement and low pedestrian movement, but they are spatially and
functionally differentiated. Figure 168 shows that the predominant land use in
Tiradentes Avenue is institutional (important museums and cultural buildings), retail
and services, while in Estado Avenue it is abandoned buildings, squatters and
storehouses. The local fashion economy is also separated. On the east side retail is
linearly concentrated along São Caetano street and the pedestrian movement falls
considerably in João Teodoro and 25 de Janeiro street. According to observations, João
Teodoro has very low pedestrian movement with high vehicular flow. The existing land
uses do not seem to work as attractors – there are local restaurants, a gas station, parking
spaces and the Police Museum. What seems to help the high values is the size and the
number of streets: on the south side of the João Teodoro block size varies from 20m to
115m and on the north side from 118m to 287m (Figure 167). The number of frequent
streets increases the potential of the grid for movement. In João Teodoro street it helps
the vehicular flow, while in São Caetano street it helps the pedestrian flow, which may
explain its shopping function.
Tiradentes Ave.
João Teodoro
Figure 167 Block size graph of the area of Luz
On the west side centrality occurs convexly along José Paulino, Silva Pinto, Aimorés
and Graça streets (Figure 168). Pedestrian movement decreases towards the north of the
area where mixed land use and spots of housing are found. In the case of José Paulino, it
93 Chapter 5 seems that block size does not influence the intensification of movement, but rather the
shops themselves – the blocks vary from 55m to 370m (Luz Park) (Figure 167).
Looking at the map and at global choice (logCH+1 radius n) (Figure 169) shows that
José Paulino, São Caetano and João Teodoro streets have high potential as routes. At the
local scale (logCH+1 radius 750m) (Figure 170) they considerably lose their values, as
they do at the local integration measure (NC/MD 750m) (Figure 172). Looking at global
integration (NC/MD radius n) (Figure 171) the map reveals significant changes: José
Paulino does not appear to have a high level of global integration. Only São Caetano
and João Teodoro streets show potential as destinations.
The synergy in Luz (local radius 1000m) (r² = 0.6983) (Figure 173) reveals that the
street with the highest correlation is Cantareira, that is, one of the streets that crosses the
railway track, connecting both sides. This street is also highlighted on the correlation
between global and local (750m) choice (r² = 0.7778) (Figure 174). The relationship
between choice and integration at the global scale shows that Cantareira Avenue has
potential both as routes and destinations. In short, it is suggested that Cantareira is the
street with the highest potential for movement attraction, whether ‘to’ or ‘through’
movement. Once more, the street that seems to have the most potential for movement
and, consequently, for location of the land use, is not the one considered to be the main
shopping street of the area.
Considering the parameters suggested, the measures of centre (busiest streets) and noncentre say that the shopping streets have shorter segments by about 28%. Looking at the
different measures, in the maxima the non-centre has higher values than the centre, but
in the means it is the other way round, the centre has higher values. Also, as the radius
is increased the difference between centre and non-centre does not diminish. (Table 26)
Area
Segment
L_Max
Luz
Centre
282.314
Non-centre 394.824
%difference
-28%
n_Max
n_Av
6.486
8.550
-24%
5.812
5.368
8%
3.613
3.812
-5%
3.077
2.787
10%
4.657
4.772
-2%
3.984
3.547
12%
5.150
5.414
-5%
4.442
3.929
13%
5.392
5.867
-8%
4.710
4.194
12%
5.537
6.215
-11%
4.888
4.387
11%
94 5.924
6.051
6.352
6.208
Cantareira
Convex
Figure 169 Segment Map of Luz showing logCH+1 at global radius (n)
2.649
2.720
Figure 168 Land Use Map of Luz
Linear
2.761
3.071
Cantareira
76.510
6609.87
68.881
7101.44
high
6643.47
67.740
7114.56
Cantareira
100.017
Cantareira
low
Chapter 5 Cantareira José Paulino São Caetano Figure 173 Synergy
Cantareira Figure 174 Global and local choice
Cantareira Figure 175 Global integration and choice
Cantareira Figure 176 Local integration and choice
96 Chapter 5 5.5.4 Oriente Oriente is the area with the highest amount of land use in retail (Brás-313, Gasômetro449, Luz-945, Oriente-1,089, Pati-552 and Santa Cecília-562), and according to
observations during the day there is intense pedestrian movement not only in the main
shopping streets – Oriente street and Miller street – but also around Largo do Pari (old
Pari railway yard) where there are thousands of shopping stalls (Figure 178). However,
during the weekend (after 3:00pm on Saturday when the shops close) the area becomes
empty, with no pedestrians, especially the main streets where the uses are basically
retail. The area lacks residents – they are sprawling located in mixed-use buildings, so
very low density. Also there are few institutional buildings.
Looking at the segment map, it reveals that for global choice (logCH+1) radius (n)
(Figure 179) movement is more intense on the periphery and the main streets – Oriente
and Miller – and has low potential for through movement. Actually, it matches with the
high vehicular flow of the area. People arrive by car and leave the area mainly taking
São Caetano and João Teodoro streets, since they cross Estado Avenue (towards the
west) and Rangel Pestana further south. At the local radius 750m (Figure 180), it
highlights with high value the Oriente street that correspond with the real situation on
week days. Although the Miller street maintains with low value. Interestingly, there is a
significant differentiation between the global and local scales in the area. The
integration (NC/MD) map global radius (n) (Figure 181) shows that Miller street is
highly integrated while Oriente is very poorly integrated. At the radius 750m (Figure
182) Miller street has low values while Oriente is very integrated. In this case, it seems
that block size does not have much influence on the intensification of pedestrian
movement. The size of the blocks in the whole area varies from 25m to 385m. But the
majority of the blocks along Oriente are more than 100m, which is very convenient for
pedestrians according to Siksna (1997) (Figure 177).
97 Chapter 5 Oriente Figure 177 Block size graph of the area of Oriente
Synergy (r² = 0.6040) (Figure 183) and the relationship between integration and choice
at global and local (1000m) scales shows that segments of João Teodoro have higher
values. Global and local choice correlation (r² = 0.8479) (Figure 184) outlines João
Teodoro and Rangel Pestana streets. It suggests that João Teodoro has high potential for
movement attraction at both global and local scales. It allows people to change scales
easily. Also, the uses that are high movement dependent, for example retail, would
benefit from this location.
It can be argued that the centrality of the area expands convexly not only along Oriente
street and Miller street, but also around Mns. Andrade street, Elisa Witacker street and
others (Figure 178). Within the area, the analysis between centre and non-centre shows
that the centre has significantly shorter segments by about 55%, as the theory predicts.
At the global radius (n) the difference is distinguishable by about 27%. As the radius is
increased the difference at the maxima rises, but from radius 750m to 1000m it
decreases. Considering the average, the difference increases up to radius 3000m,
afterwards it diminishes (Table 27).
Area
Segment
L_Max
O riente
Centre
190.307
Non-centre 424.557
%difference
-55%
n_Max
n_Av
6.007
8.241
-27%
5.934
5.480
8%
3.669
3.787
-3%
3.371
2.907
16%
4.500
4.847
-7%
4.312
3.675
17%
4.809
5.476
-12%
4.707
4.085
15%
4.981
5.896
-16%
4.919
4.374
12%
5.127
6.171
-17%
5.081
4.588
11%
98 6.007
5.920
Figure 179 Segment Map of Oriente showing logCH+1 at global radius (n)
Convex
2.636
2.761
Figure 178 Land Use Map of Oriente
85.065
6635.63
6350.19
79.413
high
low
Chapter 5 João Teodoro Figure 183 Synergy João Teodoro Rangel Pestana Figure 184 Global and local choice
João Teodoro Figure 185 Global integration and choice João Teodoro Figure 186 Local integration and choice 100 Chapter 5 5.5.5 Pari Pari is also an area with a high amount of retail compared to other uses and, differing
from Oriente, the area has a higher number of residents and blocks in which the
predominant use is housing. The locations of the residences are better defined and are
located mainly in the centre and further north. There is fewer numbers of institutional
and industrial buildings (Figure 188).
As observed, there is high pedestrian movement in Silva Teles and Maria Marcolina
streets (both main streets for retail). Also, the streets perpendicular to João Teodoro,
such Carnot, Vautier, Tiers, Rodrigo dos Santos and Barão de Ladário have
considerably high pedestrian movement within the blocks close to João Teodoro, but
they change when moving towards the north, where residences are found (Figure 188).
Looking at the segment map at global choice (logCH+1 radius n) (Figure 189) it
highlights João Teodoro and Silva Teles streets, as well as Carlos de Campos street,
which is characterized by high vehicular flow. Maria Marcolina, at this radius, has
lower values than other streets but it is still strong. The areas where there is more
residential use are also more segregated, they have lower global values. At the radius
750m (Figure 190) Silva Teles street has segments with both high and low values, so
does Maria Marcolina. Carlos de Campos maintains higher choice measure. Similar to
global choice, global integration (NC/MD radius n) (Figure 191) reveals that João
Teodoro, Silva Teles and Carlos de Campos streets are highly integrated. At the local
radius (750m) (Figure 192) they maintain high values. In this case it is suggested that
the centre has expanded linearly along Silva Teles and Maria Marcolina streets (Figure
188). Moreover, the concise grid structure of the blocks – grids with different
orientation – intensifies the potential for movement in the area. The size of the blocks
varies from 25m to 285m (Figure 187).
101 Chapter 5 Figure 187 Block size graph of the area of Pari
Pari has a low synergy correlation (r² = 0.3971) (Figure 193), which highlights Carlos
de Campos and João Teodoro streets. The global and local choice relation is high (r² =
0.7985) (Figure 194) and shows Carlos de Campos as having higher values. So does the
relationship between integration and choice at the local radius (1000m) (Figure 196). In
this case it suggests that Carlos de Campos street is the one best embedded in the
network, with high potential as both a route and destination.
The comparison between centre and non-centre streets reveals that the centre has shorter
segments than the non-centre (by about 39%). The centre has high local choice only in
its means not in its maxima. As the radius is increased, its maxima decreases and its
mean increases the difference; for example, maxima radius 2000m is 6% difference
while 4000m is 5%, and means radius 2000m is 18% difference while 4000m is 22%.
At the global radius (n) the maxima difference is 39% and means is 3% (Table 28).
Area
Segment
L_Max
Pari
Centre
187.810
Non-centre 308.046
%difference
-39%
n_Max
n_Av
7.051
8.779
-20%
6.701
5.438
23%
3.554
3.897
-9%
3.404
3.035
12%
4.572
4.874
-6%
4.408
3.726
18%
5.175
5.476
-5%
4.936
4.100
20%
5.602
5.896
-5%
5.310
4.365
22%
5.909
6.235
-5%
5.601
4.580
22%
102 6.6190
5.869
Figure 189 Segment Map of Pari showing logCH+1 at global radius (n)
2.905
2.734
Linear
Figure 188 Land Use Map of Pari
109.591
6738.84
6633.45
89.312
high
low
Carlos de Campos Chapter 5 João Teodoro Figure 193 Synergy
Carlos de Campos Figure 194 Global and local choice
Estado Figure 195 Global integration and choice
Carlos de Campos João Teodoro Figure 196 local integration and choice
104 Chapter 5 5.5.6 Santa Cecília According to observations, the area of Santa Cecília is predominantly mixed use. It has
higher pedestrian and vehicular movement at its north and south boundaries – Angelica
Avenue, Eduardo Prado street and Duque de Caxias Avenue – and on the avenues that
cross the area – Rio Branco and São João (Costa and Silva). From these avenues the
pedestrian and vehicular movement tends to decrease when going towards the inner
area. Within the area a clear differentiation between the right and left side of Rio Branco
Avenue was observed. In this context, the avenue seems to be a barrier to the area in
terms of movement and functionality. On its right side, between the avenue and the
railway track, the area has more empty, abandoned and degenerated buildings than the
left side. Also, there are more parking places because of the proximity to the São Paulo
Concert Hall and other cultural buildings, resulting in emptier streets without natural
surveillance by residents and retailers (Figure 198).
Another physical and visual barrier in the area is the elevated expressway –
‘Minhocão’– which at ground floor level has considerably high pedestrian movement,
(despite being dark and degraded) due to the presence of bus stops and its proximity to
the tube stations Marechal Deodoro and Santa Cecília. Indeed, the street where Santa
Cecília station is located has high pedestrian flow. This might be explained not only by
the presence of the station, which works as attractor, but also by the fact that the blocks
around it are less organised (since this area has a concise grid structure) and are smaller
in size compared to the area as a whole. The size of the blocks varies from about 15m to
250m. The majority of blocks in which the predominant use is residential are located at
the north border – Angelica Avenue, Eduardo Prado street – where there is higher
movement(Figure 197).
The segment maps show that in all measures – global and local choice (logCH+1 radius
n and radius 750m) (Figure 199, 200) and global and local integration (NC/MD radius n
and 750m) (Figure 201, 202) – Duque de Caxias street has high potential as both a
destination and route. Also, this street is considered the busiest street of the area; the
shops are located linearly along it.
105 Chapter 5 Figure 197 Block size graph of the area of Santa Cecília
The synergy (Figure 203) is low and highlights Duque de Caxias street and São João
Avenue. Also the relationship between choice and integration in the global radius (n)
picks these streets, but with higher correlations (r² = 0.6038) (Figure 205).
Both
correlations – choice (logCH+1) (global n vs local 1000m) and choice vs integration
(1000m) (Figure 204, 206) – highlight Jaguaribe street, the street located at the border
of the area close to Santa Cecília subway station. It can be argued that in this area the
main shopping street matches with the spatial system. Duque de Caxias has potential as
both a route and destination on the global scale.
An examination of the difference between centre and non-centre shows that centre has
significantly shorter segments than non centre by about 38%, as the theory predicts. The
maxima difference at radius n is indistinguishable by about 0.44%. By increasing the
local radius, the maxima difference decreases up to radius 4000m (9%), and then
increases (5000m – 10%). Similarly, the average difference diminishes and at radius
4000m is 11% but at 5000m is 12% (Table 29).
Area
Segment
L_Max
Santa Ce cilia
Centre
207.891
Non-centre 335.602
%difference
-38%
n_Max
n_Av
7.727
7.761
-0.438%
7.722
5.767
34%
3.490
3.923
-11%
3.347
2.965
13%
4.287
4.696
-9%
4.220
3.784
12%
4.779
5.237
-9%
4.663
4.222
10%
5.143
5.650
-9%
5.027
4.516
11%
5.424
6.030
-10%
5.316
4.738
12%
106 Jaguaribe
7.723
Figure 199 Segment Map of Santa Cecília showing logCH+1 at global radius (n)
ão
sti
ba
Se
Linear
ira
re
Pe
Figure 198 Land Use Map of Santa Cecília
Jaguaribe
3.112
high
Jaguaribe
7189.11
Jaguaribe
121.409
low
Chapter 5 São João
Duque de Caxias Figure 203 Synergy
Jaguaribe
Sebastião Pereira Figure 204 Global and local choice
São João R² = 0.603844 Duque de Caxias Figure 205 Global integration and choice
Jaguaribe
Figure 206 Local integration and choice
108 Chapter 5 Table 30. Comparison of the shopping streets in each area
Area
Segment
L_Av
LogCH+1Rn LogCH+1 LogCH+1
_Av
R750_Av R1000_Av
LogCH+1
R2000_Av
LogCH+1
R3000_Av
LogCH+1 LogCH+1
R4000_Av R5000_Av
NC/MD
Integration
Rn_Av
NC/MD
Integration
R750_Av
NC/MD
Integration
R1000_Av
NC/MD
Integration
R2000_Av
NC/MD
Integration
R3000_Av
NC/MD
Integration
R4000_Av
NC/MD
Integration
R5000_Av
Bras
Alcantara Machado
119.291
8.468
2.716
3.139
4.172
4.853
5.318
1.127
7189.700
71.493
107.908
316.633
687.785
1162.690
1610.790
Gasometro
Gasômetro
82.343
6.241
3.020
3.471
4.472
4.863
5.144
5.357
6693.820
82.721
128.724
403.854
721.032
1075.230
1454.600
Luz
José Paulino
São Caetano
142.936
69.213
6.242
6.119
2.705
2.992
3.201
3.321
4.327
4.142
4.888
4.575
5.147
4.783
5.287
4.937
6638.550
7117.390
71.020
87.418
119.298
135.793
381.181
455.529
693.316
820.042
1009.240
1115.520
1316.330
1485.000
Oriente
Oriente
Miller
120.065
104.462
5.945
5.919
3.044
2.834
3.481
3.217
4.416
4.166
4.731
4.674
4.910
4.932
5.063
5.106
6344.040
6627.140
96.575
78.047
144.588
125.954
384.979
383.576
686.567
714.164
994.718
1044.840
1352.440
1415.960
Pari
Silva Teles
Maria Marcolina
96.244
95.827
6.911
6.238
2.991
2.891
3.422
3.365
4.449
4.318
5.042
4.703
5.450
5.003
5.760
5.251
6756.800
6725.300
108.930
93.021
172.577
158.330
438.358
398.873
721.065
724.887
1086.350
1042.480
1482.310
1417.080
Santa Cecilia
Duque de Caxias
84.817
7.722
2.970
3.347
4.220
4.663
5.027
5.316
7157.430
116.824
180.809
486.804
836.458
1198.920
1587.110
Highest values
Chapter 5 In short, after looking at the micro structure of each area and taking their busiest
shopping streets into consideration, (Table 30) it is revealed that the street that seems to
be best integrated in the whole system at different local scales is Duque de Caxias
(Santa Cecilia). It is located within the area which is also the one best embedded within
the whole system. Although on a global scale Alcantara Machado is slightly more
integrated than Duque de Caxias. This means that Duque de Caxias is easy accessible
and has higher potential as a destination compared to the other streets. As a result, land
uses such as retail can benefit from being located along it. Considering the potential for
routes between spaces, Oriente and Silva Teles streets are the ones with highest values
at different local scales, while Alcantara Machado has the highest value at the global.
As explained before, the analyses of the areas were developed using the determined
administrative boundaries, rather than the natural limits defined by their spatial
morphology. One hypothesis is that this might affect the relation between centre and
non-centre of each area. As it was shown, the outcome didn’t always follow the
conjecture predicted by the theory. At the same time, Hillier (2008) argues that in some
cases centres cannot be distinguished from their context, and there is a way to test this:
by considering the spatial values of the centre segments as a ratio against the average
for the 800 metric pedestrian context (Hillier 2008). By doing this, the typological
difference of the centre would be powerfully distinct from its context. To investigate
this contrast, it would be necessary to repeat the process of analyses adopting the natural
boundary of each area (within 800m) as a further research study that could be compared
to the one developed here.
110 Chapter 6 6. DISCUSSION
111 Chapter 6 6. Discussion The previous chapters have suggested how the issues of sustainable urbanisation – in
the sense of cities that are self-sufficient – and urban diversity can be approached.
Thinking about São Paulo and the areas located on the periphery of its historic core,
what factors motivate or impede the generation of diversity in these areas? According to
Hillier (1996, 2009), the streets of the urban system can be subtle and complex. Both
have universal characteristics and cultural properties, and reflect the economic and
social forces of the city and its society. These variables are expressed spatially in
different ways, depending upon how they are embedded in the system globally and
locally. The configuration of space is an essential driving factor in the location of land
use and people’s movement in the system. So the structure of the urban fabric plays a
vital role in the existence of a diverse and dense population. That is why spatial form is
the most powerful indicator of the presence of people, and of successful urbanization.
Moreover, to generate diversity all elements need to be combined – mixed land use and
activities, configuration of space and population density. Even for Jacobs (1961), spatial
form is, in itself, an essential factor. She suggests careful observation of the potentiality
of block configuration (size and form) to create movement and an intensification of
people presence. So far, it has been possible to analyse these variables in all areas. To
summarise the results there follows here a table with the findings from the social, spatial
and syntactic analyses.
112 Chapter 6 Table 31. Summary of the areas
With regards to the mixed land use column of the table, it shows that Santa Cecilia has
the highest amount of services. The socio economic factors reveal information about
residential populations. With the exception of the Santa Cecília area, the whole central
area is lacking in residential use. As discussed before, the presence of people is the most
important factor in keeping a place alive. During the day all areas are busy and liveable,
but during the night they become empty. This lack of natural surveillance generates
dangerous areas. People presence is so essential for the vitality and viability of the city
that even within Santa Cecilia the streets closer to the railway track, where there are
fewer residents, are emptier. In this case, public investment could work in partnership
with the real estate market, by developing incentive policies to encourage residential
development in the areas. As suggested by Bromley et al (2005), a process of
113 Chapter 6 ‘residentialisation’ needs to be introduced, in which houses replace other land uses. In
the case of São Paulo, parking space and other infrastructure could be replaced, while
abandoned and deteriorated buildings could be revitalised. The implementation of new
residential areas will change not only the density of residents, but also land use in
general. New uses and activities will be created in order to support the local population.
The areas should avoid ‘mono-functionality’, where one kind of activity is dominant.
For instance, in Santa Cecília the proportion of land use of services is much higher than
other areas, 57%; Gasômetro has 72.07% in retail and Oriente 51.25% industrial use. In
urban studies there is no fixed formula to quantify the ideal land use pattern to achieve
successful urbanisation, but what is recommended is a mixed balance of activities. This
generates diversification of people and activities within the area: workers, shoppers and
residents. As found, the study areas are not diversified in their land use or their
activities. In terms of retail activities, the areas slightly differ from each other, but
within each area there is mono-specialisation. Brás, Luz and Oriente specialise in
fashion and accessories, Gasômetro in wood and Pari in household goods. Santa Cecília
is the only area that has no specialized retail. In theory it could be argued that this
specialisation actively prevents the ‘generation of diversity’; however, since Brazilian
retail culture is based on shopping malls, not shopping streets, specialisation is
considered to be the potential of these areas located on the periphery of the city’s
historic centre. People from all over the country come to these areas in search of
products at better prices, and they can find them as they have different options of places
to shop. In other words, what makes these areas important for the city is their local
economy, the specialised retail and activities along the streets. But taking into
consideration their lack of residential areas and mono-functionality it must be said that
they are neither self-contained nor self-supporting.
Another factor to be considered is how the pattern of this land use is distributed in the
system. We should not only think of the areas’ functions and activities, but also how
they are affected by their spatial configuration. The findings suggest that within these
areas there is no logical pattern of occupation to be followed:
-
The area with the highest density, Santa Cecília, is also the one that is best
spatially embedded in the whole city. It has lower industrial and retail activities but they
are located on streets that have good potential as routes and destinations. Santa Cecília’s
service activities also track the logic of space: they are located on streets with good
choice and integration values.
114 Chapter 6 -
The two areas with the lowest densities do not have the same pattern of
occupation or spatiality. Gasômetro has a high number of retail activities located on
more globally integrated streets, but they are not located on streets with either local
integration or high through movement. Oriente has more industrial land use that is
globally located in more segregated areas with low choice; although locally it is
integrated with higher choice. Its retail and services activities are lower and located in
less integrated places.
-
Brás, Luz and Pari have similar demographic densities. In Brás retail activities
are located in streets with good potential for ‘to and through’ movement at the global
scale, but not the local. So it goes for Brás’ industrial and services land use. Land use in
Luz (industrial, retail and services) is located globally in well-integrated, high choice
streets, but this is not the case locally. Pari has a similar amount of industrial and retail
activities. Its industries and services are located in more segregated, lower choice
streets. Its retail is globally segregated with lower choice, but locally it has potential as
both route and destination.
One reason which could be suggested for the above findings, that does not seem to be so
evident, is the patchiness of the grid system. As argued by Ortiz-Chao (2007), many
Latin American cities have ended up as patchworks of areas with different spatial
characteristics because of their rapid growth over the last few decades. They are not
similar to western cities, which grew organically with a spatial hierarchy that shapes the
pattern of land use, such as London with its ‘deformed wheel’ structure: “an integration
core and a number of integrated spikes that radiate from it to the limit of the settlement”
(Ortiz-Chao 2007:12). Indeed, the analysis of São Paulo has shown that the structure of
the city could be considered as a patchwork system and a ‘labyrinthine structure’.
The results suggest that the theory of centrality could be adapted to the context of São
Paulo, since the performance aspects of the space match some of the variables required.
The analysis of the relationship between centre and non-centre has as a result:
-
Centre has shorter segments;
-
Higher local choice, but on the average not on the maxima;
-
The difference does not necessarily diminish with an increase in radius;
-
At radius n the difference is not necessarily indistinguishable.
115 Chapter 6 Indeed, it was found that the area with the best spatial performance, as predicted by the
analytical model applied, is the one that is embedded in a regular grid structure. Also, it
is the place where there is higher demographic density, more services than industries
and no specialised retail.
Incorporating the relationships between integration and movement, accessibility and
diversity that can capture the variables of the social performance of urban form
discussed by Marcus (2007), how does spatial capital occur in the study areas?
Considering the analyses developed by measuring the spatial properties of each area and
comparing them to the socio-economical variables, it was shown that density, diversity
and movement are not always combined. Each area was found to have its own pattern of
configuration and occupation, i.e. they are weakly related. The areas lacking in
demographic density and a mix of functionality might have a role to play within the
configuration of the urban fabric. As argued, the higher the global and local integration,
the more potential an area has for attracting people and activities, and the higher the
accessibility and diversity, the higher the spatial capital. But this does not mean that
higher spatial capital is always better. Spatial capital helps to translate the value of the
urban form in distinct ways for everyday life; socially, economically and culturally.
Indeed, not all needs require high spatial capital, since in each society people have
different requirements (Marcus 2007). Different spatial capital outcomes can emerge
from diverse societies according to their cultural and social aspects. It could be
suggested that the peripheral areas of São Paulo’s historic core have ‘lower spatial
capital’ because of their statistically weak socio-economic-spatial relationship.
Finally, in the case of the study areas in São Paulo, special attention should be given to
the spatial structure of each one; and not only to the micro-structure of each individual
area and its socio-economic interfaces, but to each area within the city as a whole, in
order to improve the socio-economic relationship of the city. Strategic intervention in
the street network could be proposed in order to improve the accessibility of all areas.
The areas located on the periphery of the historic centre have the potential to become a
more successful urban network embedded in the city. They could benefit the city and, in
turn, they could benefit from the city and its contemporary society.
116 7. CONCLUSION
117 Chapter 7 7. Conclusion This thesis has examined the socio-economic and spatial characteristics of six areas
located on the periphery of the historic centre of São Paulo. The key objective of the
investigation was to explore the socio-economic variables and the spatial properties in
the theoretical context of sustainable urbanisation (cities that are socially and
economically self-sufficient) and the generation of diversity. The framework and
techniques of space syntax were used in order to identify these relationships.
The study has shown that the socio-economic and spatial components of each area have
deficiencies:
1) they lack residential areas and, therefore, have a low density of residents and
pedestrian movement;
2) the areas tend to be mono-functional, with high numbers of one specific activity and
land use. As a consequence, their retail offering is specialised;
3) the statistical correlations between the socio-economic and spatial characteristics of
each area are weakly related;
4) they have ‘lower spatial capital’ because of this weak statistical socio-economicspatial relationship.
These findings lead to an ambiguous correlation: the areas are considered important for
the city because of their specialised retail and local economy, but at the same time this
impedes the diversification of activities, people and movement.
Finally, when analysing the spatial performance of the areas considering the
administrative boundaries, it has been found that the centres (busiest shopping streets)
do not follow a fixed rule in terms of distribution. The centre of each area has its own
logic of occupation. Analyses have shown that the outcomes did not always follow the
conjecture predicted by the theory. This suggests that the theory of centrality could be
slightly more flexible in its predictions. One hypothesis is that this pattern has to do
with the patchwork layout of the city. It was shown that the form of the city is neither
regular grid structure nor ‘deformed wheel’, but a patchwork of offset grids in which
areas might differ morphologically from each other depending upon their origins. This
patchwork form could be attributed to the rapid process of development which the city
has undergone in its formation.
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123 APPENDIX 124 Appendix Table 32. Employment according to activities
Industrial
%
Retail
%
Services
%
Total
2,024
24.84%
2,926
35.92%
3,197
39.24%
8,147
Brás
332
6.98%
3,560
74.82%
866
18.20%
4,758
Gasômetro
11,584
57.42%
4,751
23.55%
3,839
19.03%
20,174
Luz
11,936
56.79%
7,030
33.45%
2,053
9.77%
21,019
Oriente
6,738
43.25%
5,385
34.56%
3,457
22.19%
15,580
Pari
2,622
10.07%
4,900
18.83%
18,504
71.10%
26,026
Santa Cecília
Source: Ministerio do Trabalho, Rais (Relacao Annual de Informacaoes Sociais) 2005; SEMPLA/Dipro –
from EMPLASA 2009
125 Table 33. Different measures
Mean
Angular
Integration
Connectivity Connectivity
Rn
4.509
3.064
6,570.72
Brás
4.633
3.154
6,598.82
Gasômetro
4.557
3.074
6,636.26
Luz
4.707
3.164
6,366.28
Oriente
4.781
3.244
6,415.20
Pari
4.966
3.277
6,748.55
Santa Cecilia
Integration
R750
65.169
74.434
71.191
78.926
93.541
90.545
Integration
R1000
101.784
119.195
110.817
123.336
141.408
141.802
Integration
R1250
145.938
174.011
158.26
175.21
192.334
201.674
Integration
R1500
196.019
238.996
214.645
232.535
245.942
267.047
Integration
R2000
316.268
387.255
349.273
365.862
360.576
412.391
Integration Integration
R2500
R3000
463.817
641.43
542.046
702.205
502.631
654
511.311
664.058
481.799
614.056
569.425
727.155
Integration
R4000
1,041.78
1,042.51
957.402
988.326
917.309
1,052.17
Integration
R5000
1,449.98
1,413.28
1,286.42
1,366.97
1,278.78
1,404.97
Integration
R7500
2,716.84
2,703.86
2,490.32
2,567.65
2,492.54
2,591.23
Integration
R10000
4,377.44
4,362.66
4,052.15
4,044.59
3,945.30
4,098.91
Segment
Length
115.968
108.47
100.65
100.119
93.185
109.325
Metric
Step Depth
1,381.59
1,124.59
1,401.35
2,151.02
2.526.72
1,220.94
Table 34. Choice measures
logCH+1
Rn
6.066
Brás
5.688
Gasômetro
5.444
Luz
5.535
Oriente
5.507
Pari
5.873
Santa Cecilia
logCH+1
R750
2.473
2.594
2.514
2.613
2.737
2.614
logCH+1
R1000
2.844
2.987
2.837
2.946
3.056
2.986
logCH+1
R1250
3.134
3.265
3.087
3.206
3.287
3.261
logCH+1
R1500
3.353
3.491
3.289
3.411
3.478
3.477
logCH+1
R2000
3.694
3.835
3.621
3.730
3.763
3.808
logCH+1
R2500
3.944
4.070
3.849
3.956
3.974
4.050
logCH+1
R3000
4.159
4.244
4.017
4.141
4.145
4.246
logCH+1
R4000
4.475
4.509
4.282
4.427
4.417
4.544
logCH+1
R5000
4.709
4.695
4.472
4.640
4.636
4.769
logCH+1
R7500
5.161
5.110
4.876
5.023
5.022
5.191
logCH+1
R10000
5.529
5.425
5.158
5.300
5.266
5.492
Table 35. Relation Centre and Non-centre
Segment
L_Max
Segment
L_Av
LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R
n_Max
n_Av
750_Max
750_Av
1000_Max
1000_Av
2000_Max
2000_Av
3000_Max
3000_Av
4000_Max
4000_Av
5000_Max
5000_Av
Brás
Centre
Non-centre
%difference
184.622
416.358
-56%
119.291
115.735
3%
8.484
8.352
2%
8.468
5.898
44%
3.106
3.564
-13%
2.716
2.456
11%
3.514
4.052
-13%
3.139
2.823
11%
4.399
4.948
-11%
4.172
3.661
14%
5.013
5.422
-8%
4.853
4.110
18%
5.452
5.796
-6%
5.318
4.416
20%
1.205
6.186
-81%
1.127
4.640
-76%
Gasômetro
Centre
Non-centre
%difference
170.782
424.557
-60%
82.343
111.702
-26%
6.462
8.263
-22%
6.241
5.620
11%
3.452
3.599
-4%
3.020
2.541
19%
3.845
4.001
-4%
3.471
2.927
19%
4.644
5.033
-8%
4.472
3.756
19%
5.018
5.534
-9%
4.863
4.168
17%
5.292
5.867
-10%
5.144
4.430
16%
5.486
6.087
-10%
5.357
4.613
16%
Luz
Centre
Non-centre
%difference
282.314
394.824
-28%
103.763
100.010
4%
6.486
8.550
-24%
5.812
5.368
8%
3.322
3.454
-4%
2.687
2.478
8%
3.613
3.812
-5%
3.077
2.787
10%
4.657
4.772
-2%
3.984
3.547
12%
5.150
5.414
-5%
4.442
3.929
13%
5.392
5.867
-8%
4.710
4.194
12%
5.537
6.215
-11%
4.888
4.387
11%
Oriente
Centre
Non-centre
%difference
190.307
424.557
-55%
113.564
100.306
13%
6.007
8.241
-27%
5.934
5.480
8%
3.201
3.407
-6%
2.957
2.582
15%
3.669
3.787
-3%
3.371
2.907
16%
4.500
4.847
-7%
4.312
3.675
17%
4.809
5.476
-12%
4.707
4.085
15%
4.981
5.896
-16%
4.919
4.374
12%
5.127
6.171
-17%
5.081
4.588
11%
Pari
Centre
Non-centre
%difference
187.810
308.046
-39%
96.114
93.016
3%
7.051
8.779
-20%
6.701
5.438
23%
3.117
3.582
-13%
2.960
2.725
9%
3.554
3.897
-9%
3.404
3.035
12%
4.572
4.874
-6%
4.408
3.726
18%
5.175
5.476
-5%
4.936
4.100
20%
5.602
5.896
-5%
5.310
4.365
22%
5.909
6.235
-5%
5.601
4.580
22%
Santa Cecilia
Centre
Non-centre
%difference
207.891
335.602
-38%
84.817
110.729
-23%
7.727
7.761
-0.438%
7.722
5.767
34%
3.146
3.614
-13%
2.970
2.593
15%
3.490
3.923
-11%
3.347
2.965
13%
4.287
4.696
-9%
4.220
3.784
12%
4.779
5.237
-9%
4.663
4.222
10%
5.143
5.650
-9%
5.027
4.516
11%
5.424
6.030
-10%
5.316
4.738
12%
126

- The Bartlett

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