File - Michael Wszol

Transcription

(Moutains,2015)
GEOSTATISTICAL ANALYSIS OF SKI RESORTS
ANALYZING QUEBEC, ONTARIO, VERMONT, MICHIGAN, AND NEW YORK
PREPARED BY:
Michael Wszol, GIS Analyst - Niagara Spatial Analysis
Davor Alisic, GIS Analyst - Niagara Spatial Analysis
PREPARED FOR:
Ian Smith
Niagara College GIS-GM Instructor
GISC9308 DELIVERABLE 4B
MARCH 29TH, 2015
March 29th, 2015
Mr. Ian Smith, M.Sc., OLS, OLIP, EP
Professor, GIS-GM Program
Niagara College
135 Taylor Road
Niagara-on-the-Lake, ON
L0S 1J0
Dear Mr. Smith,
RE: GISC9308 Deliverable 4B – Geostatistical Analysis of Ski Resorts
Please accept this letter as our formal submission of Deliverable 4B: Geostatistical Analysis of Ski
Resorts Analyzing Quebec, Ontario, Vermont, Michigan, and New York.
This assignment is comprised of procedures taken in order to create an Inverse Distance
Weighted and Kriging Interpolation. It was shown that neither provided a strong and accurate
prediction when performing the comparison to reality. In addition, weighted overlay analyses
were performed to provide skiers and snowboarders with critical information. The best ski resort
was Mont Orford located in Quebec, while the worst ski hill was Otsego Club located in Michigan.
We look forward to receiving constructive criticism in regards to this assignment in order to
achieve our goals of gaining a stronger familiarity with spatial analysis techniques. Please do not
hesitate to call us at (289) 697-0990, if you experience any problems with the enclosed
documents. Thank you for your time.
Sincerely,
Davor Alisic – BAH
Project Manager, Niagara Spatial Analysis
GIS-GM Certificate Candidate
DA/
Enclosures:
1) Geostatistical Analysis Report and Findings
C.c Michael Wszol
Niagara Spatial Analysis
11 Robertson Road | L0S1J0
Niagara-on-the-Lake | Ontario
[email protected] | (289) 697-0990
i
EXECUTIVE SUMMARY
The southern locations of Canada along with the northern regions of the United States in the eastern side
can get hit with large quantities of snow each and every year. For many people, they take advantage of
these weather conditions by skiing and snowboarding. Since there are many individuals that would like to
know where the best locations to ski are located, studies were undertaken using the Inverse Distance
Weighted and Kriging interpolations to predict these areas by combining them with a weighted overlay
analysis.
The study was completed by collecting data, running two different Interpolation methods, and then
analyzing the results in order to produce valid conclusions. Several data collection parameters were set.
This study used 150 different ski hills to make up the data collection. In addition, these ski resorts ranged
from Michigan, New York, Vermont, Ontario and Quebec. Seeing as this was a large spread of data there
are many positives and negatives to this data collection. Nevertheless, multiple analyses were completed
and results were produced.
In terms of the IDW results, the best ski locations found included Mont Orford, Mont Tremblant and Owls
Head in Quebec. This can be compared to the Kriging interpolation which showed the same result being
Mont Orford as the best ski hill within the data collection. Furthermore, the analysis was completed to
find the overall worst ski resort. The IDW selected the Otsego Club in Michigan to be rated the worst
based on vertical rise, ticket price, and the number of runs. The Kriging interpolation completed the top
four results, which included: Peek’n Peak, Treetops, Otsego Club and Osler Bluff Ski Club.
A comparison to reality analysis was also completed to see how well the two interpolated methods
predicted the three variables that made up the study. Findings showed, based on the average error of
both the Inverse Distance Weighted and the Kringing Interpolations, that there were too many
inconsistencies when predicting vertical rise, ticket prices, and number of runs.
The main issue surrounded the fact that the study area was quite large in comparison to the amount of
sampled locations present in the area. In future studies, it will be imperative that more data is collected
within a smaller location to successfully created as low of an error as possible.
GISC9308 – Spatial Analysis
ii
CONTENTS
Executive Summary ........................................................................................................................................................ i
List of Figures ................................................................................................................................................................ iii
List of Tables ................................................................................................................................................................. iii
1.0 Introduction .............................................................................................................................................................1
2.0 Study Area ...............................................................................................................................................................1
3.0 Methodology ...........................................................................................................................................................3
3.1 Data Collection.....................................................................................................................................................3
3.1.1 Ski Resorts.....................................................................................................................................................3
3.1.2 Vertical Rise ..................................................................................................................................................3
3.1.3 Ticket Prices ..................................................................................................................................................4
3.1.4 Currency........................................................................................................................................................4
3.1.5 Spatial Data ...................................................................................................................................................4
3.2 Data Assessment ..................................................................................................................................................6
3.2.1 Vertical Rise Data ..........................................................................................................................................6
3.2.2 Ticket Price Data ...........................................................................................................................................6
3.2.3 Ski Resort Trails Data ....................................................................................................................................7
3.2.4 Vertical Rise QQ Plot .....................................................................................................................................8
3.3 Trend Analysis ......................................................................................................................................................8
4.0 Interpolation Techniques.........................................................................................................................................9
4.1 Inverse Distance Weighted (IDW) ........................................................................................................................9
4.1.1 IDW Parameters..........................................................................................................................................10
4.2 Kriging Interpolation ..........................................................................................................................................12
4.2.1 Kriging Parameters .....................................................................................................................................12
5.0 Weighted Overlay Analysis Using Kriging and IDW ...............................................................................................17
5.1 Weighted Overlay Criteria and Parameters .......................................................................................................17
5.1.1 Exporting to Rasters....................................................................................................................................17
5.1.2 Reclassification for Best and Worst Ski Resorts ..........................................................................................17
5.1.3 Weighted Overlay Tool ...............................................................................................................................19
5.1.4 Isolating Results ..........................................................................................................................................20
6.0 Comparison to Reality Analysis..............................................................................................................................20
7.0 Findings ..................................................................................................................................................................23
7.1 Interpolation Comparison ..................................................................................................................................23
P a g e | iii
7.2 Best Ski Resorts ..................................................................................................................................................26
7.3 Worst Ski Resorts ...............................................................................................................................................26
8.0 Closure ...................................................................................................................................................................30
9.0 Bibliography ...........................................................................................................................................................31
LIST OF FIGURES
Figure 1: Ski Resort Study Area ......................................................................................................................................2
Figure 2: Vertical Rise ....................................................................................................................................................3
Figure 3: Sampled Location of Ski Resorts .....................................................................................................................5
Figure 4: Vertical Rise Histogram ...................................................................................................................................6
Figure 5: Lift Ticket Prices ..............................................................................................................................................7
Figure 6: Ski Resort Trails ...............................................................................................................................................7
Figure 7: QQ Plot of Vertical Rise ..................................................................................................................................8
Figure 8: Trend Analysis .................................................................................................................................................9
Figure 9: Predicted Graph ............................................................................................................................................11
Figure 10: Error Graph .................................................................................................................................................12
Figure 11: Vertical Rise Histogram ...............................................................................................................................13
Figure 12: Covariance Scatterplot ...............................................................................................................................14
Figure 13: Semivariogram Scatterplot .........................................................................................................................14
Figure 14: Predicted Value Scatterplot ........................................................................................................................16
Figure 15: Error Scatterplot .........................................................................................................................................17
Figure 16: Weighted Overlay Model ............................................................................................................................20
Figure 17: IDW Interpolation Results...........................................................................................................................24
Figure 18: Kriging Interpolation Results ......................................................................................................................25
Figure 19: Best and Worst Ski Resorts - IDW Interpolation .........................................................................................28
Figure 20: Best Overall Ski Resorts - Kriging Interpolation ..........................................................................................29
LIST OF TABLES
Table 1: IDW Parameters .............................................................................................................................................10
Table 2: Prediction Errors ............................................................................................................................................11
Table 3: Search Neighborhood Parameters .................................................................................................................15
Table 4: Reclassification for Best Ski Areas - IDW Interpolation..................................................................................18
Table 5: Reclassification for Best Ski Areas - Kriging Interpolation .............................................................................18
Table 6: Reclassification for Worst Ski Resorts - IDW Interpolation ............................................................................19
Table 7: Reclassification for Worst Ski Resorts - Kriging Interpolation ........................................................................19
P a g e | iv
Table 8: Weighted Overlay Table.................................................................................................................................19
Table 9: Comparison to Reality - IDW Interpolation ....................................................................................................21
Table 10: Comparison to Reality - Kriging Interpolation..............................................................................................22
Table 11: Best Overall Ski Resorts - Kriging Interpolation ...........................................................................................26
Table 12: Best Overall Ski Resorts - IDW Interpolation ...............................................................................................26
Table 13: Worst Overall Ski Resorts - Kriging Interpolation ........................................................................................26
Table 14: Worst Overall Ski Resorts - IDW Interpolation ............................................................................................27
Table 15: Raw Ski Resort Data .....................................................................................................................................34
Table 16: New Ski Resort Data .....................................................................................................................................38
P a g e |1
1.0 INTRODUCTION
This report was created by members of the Niagara Spatial Analysis team for the purpose of creating a geostatistical
analysis. Ski resort data was collected in Ontario, Quebec, Michigan, Vermont, and New York. By using the 150
recorded ski hills, Inverse Distance Weighted and Kriging Interpolations were produced based on variables that were
determined to be most critical to ski resort quality. These were then used to perform several weighted overlay
analyses in order to provide skiers and snowboarders with important information pertaining to the best and worst
ski hills in the area of interest. In addition, a comparison to reality analysis was performed to see how accurately the
data was predicted through the two different interpolation methods.
2.0 STUDY AREA
For this data collection effort, the area of interest was defined on a general location that members were familiar
with. In addition, it was important that the region was large enough to cover a wider spread of data for analysis. This
allowed for a more in-depth analysis on different aspects of ski hills. The study region consisted of Canadian
provinces, Ontario and Quebec. In addition, States in the U.S were included such as Michigan, New York and
Vermont. Another important aspect to the particular study area was the amount of Ski Resorts that were present.
This was an important aspect because the data needed to be clustered so they could be closely compared. The area
of interest is shown in Figure 1.
Figure 1: Ski Resort Study Area
P a g e |2
P a g e |3
3.0 METHODOLOGY
3.1 DATA COLLECTION
3.1.1 SKI RESORTS
The data necessary for the completion of the analysis were gathered using online resources. Ski Central (Ski Central,
2015) provided a list of the ski and snowboarding hills in each of the Canadian provinces, Ontario and Quebec, as
well as the three U.S States, Vermont, Michigan, and New York. In addition, it provided information on lift ticket
prices, vertical rise, and the number of runs for each ski resort. Some of the ski resorts were redirected to their
respective websites where the information was available. These can be found in the bibliography section of the
document.
Excluded from the list of ski resorts were cross country specific hills, mainly due to the fact that it would have caused
too many outliers considering that vertical rise is considerably less than that of a ski and snowboarding resort. Only
ski hills that were open to the public were used. Private ski resorts, or member specific hills were excluded since full
day passes were not applicable. There were also cases where information was available, however upon further
research some ski resorts were temporarily closed due to renovations or permanently closed. In order to keep the
study area as small as possible, while having a suggested total of 150 ski resorts still present, Northern Ontario and
Quebec were excluded as it would have negatively affected the area of interest.
3.1.2 VERTICAL RISE
The vertical rise was measured in feet for the purpose of this study. It gave good indication of the overall scale of
the mountain or hill. Vertical rise is measured by taking the peak elevation point of the ski resort and subtracting it
from the base elevation. Figure 2 illustrates this explanation.
(Ski Vertical Rise, 2015)
Figure 2: Vertical Rise
P a g e |4
3.1.3 TICKET PRICES
For the purpose of this study, the project team avoided using lift ticket prices that gave special rates to students,
children, and seniors. To keep the data consistent, weekday prices were used. Weekend prices tended to be inflated
due to the higher volume of visitors, therefore these were avoided. Resorts offer different lift ticket prices in order
to cater to visitors who do not want to stay for the entire day. The data that was used only consisted of all-day
passes. Some establishments offered low or out of season prices. These were offered during the start and end of the
season since most ski hill runs weren’t fully opened, or were shut down due to the lack of snow. The ticket rates
were taken during the prime season, while excluding tax.
3.1.4 CURRENCY
An important aspect of the data collection process was to determine a common currency for ticket prices. For this
particular study, all monetary values were presented in Canadian dollars. The currency exchange rate was taken on
February 8th, 2015 at a rate of $1.25 per U.S dollar. Therefore, only ski resorts in Michigan, Vermont, and New York
were exchanged at this rate.
3.1.5 SPATIAL DATA
Easting and Northing data were gathered using Google Maps (Google Maps, 2015). By searching the ski area address
and right-clicking on the drop point, it was possible to get the coordinates through the “What’s Here” option. This
data played a crucial role in providing a visual representation of the data through ArcMap 10.2.2. Figure 3 illustrates
the sample locations that were collected within the study area.
Figure 3: Sampled Location of Ski Resorts
P a g e |5
P a g e |6
3.2 DATA ASSESSMENT
3.2.1 VERTICAL RISE DATA
The highest vertical rise appeared to be between the 3300 to the 3600 foot range. In addition majority of the ski
resorts fell below the 1500 foot vertical rise. The mean was calculated at 817.47 feet, while the median equated to
607.5 feet. Lastly, the standard deviation was 648.03 feet based on the data, which suggested that 98% of the time,
a new ski resort’s vertical rise would be between 0 and 2113.53. As seen in Figure 4, it can be stated that the hills in
the 600 foot range are the most frequent.
Vertical Rise Histogram
60
Frequency
50
40
30
20
Frequency
10
0
Vertical Rise(ft)
Figure 4: Vertical Rise Histogram
3.2.2 TICKET PRICE DATA
The ticket price histogram created a unimodel distribution curve where the majority of the data was contained within
the middle. The average ticket price for the sampled data was $56.96, while the median was calculated at $52.5. In
addition, the standard deviation was $23.32 meaning that 98% of the time, a new ski resort’s ticket price would be
between $10.32 and $103.6. The average ticket price of skill hills within our data collection ranged between 40 and
60 Canadian dollars. As shown in Figure 5, these prices also had the largest frequency and occurred at 40-45 of the
hills examined.
P a g e |7
Frequency
Lift Ticket Prices
50
45
40
35
30
25
20
15
10
5
0
Frequency
Ticket Prices(CAD$)
Figure 5: Lift Ticket Prices
3.2.3 SKI RESORT TRAILS DA TA
The most frequent number of trails was 30 and it occurred at 58 ski resorts. The data presented were slightly skewed
to the right, which showed that majority of the data was represented between 15 and 40 trails. The data showed
that on average each ski resort had 32.89 trails and that the median was 25. Lastly, the standard deviation was 25.53,
meaning that 98% of the time, a new ski resort’s trails would be between 0 and 83.95. Figure 6 shows the number
of trails that the sampled data offers to skiers.
Ski Resort Trails
70
60
Frequency
50
40
30
Frequency
20
10
0
15
30
45
60
75
90
105
120
Trails
Figure 6: Ski Resort Trails
135
150 More
P a g e |8
3.2.4 VERTICAL RISE QQ PLOT
The QQ plot in Figure 7 shows the vertical rise data.
Figure 7: QQ Plot of Vertical Rise
Research showed that this plot can be related to a right skewed or a light tailed plot. This dataset relatively
followed the normal distribution line model therefore it was close to a normal distribution with a slight right
skewness. (Stack Exchange, 2015)
3.3 TREND ANALYSIS
This trend analysis investigated the vertical rise at many different ski resort locations. Figure 8 shows the trend
analysis graph.
P a g e |9
Figure 8: Trend Analysis
The trend analysis showed that the x and y axis are based on spatial location while the z axis value represented the
vertical rise. Furthermore, the data was widely spread out over a large spatial distance. However, there were strong
clusters in some areas which ultimately indicated that there were multiple ski hills in the area. Looking at the larger
cluster with the highest elevations, when relating this to the study location it could be said that this location was
either Vermont or Quebec. Both of these locations followed the trend and it could also be seen how these locations
were further east from the smaller hills in the west. By focusing on the red trend line, it showed that the more east
it travelled, the higher elevation will rise. As the elevation rose, there were also many more ski hills in the area which
resulted in the creation of the clusters.
4.0 INTERPOLATION TECHNIQUES
4.1 INVERSE DISTANCE WEIGHTED (IDW)
The Inverse Distance Weighted interpolation technique was used to predict values in areas that had not been
sampled. The tool used values that had already been measured and assumed that things that were close to one
another were more likely to be in common than those that were further away. The IDW was limited in terms of the
range of values and would not create mountains or valleys if the data hadn’t been provided. This was due in large
part because the tool was a weighted distance average and fell between the highest and lowest points; it would not
go above or below the data provided. (ArcGIS Desktop Help 10.2, 2015)
For the purpose of this study, the project team created three different IDW interpolations by using the variables that
were collected. These include vertical rise, ticket prices, and runs.
P a g e | 10
4.1.1 IDW PARAMETERS
Setting up the parameters for the Inverse Distance Weighted was completed using the Geostatistical wizard. This
procedure started by inputting the dataset and selecting the designated fields that were required to be analyzed.
This process was not as intensive and manipulative compared to the Kriging analysis, however it provided results
that could be related in multiple different ways.
After inputting the data, the next step was to change the values of the parameters. For this particular IDW, most of
the parameters were left to the default setting since these produced the best results. In the general properties, the
power was left at a default value of 2. The reason for this was largely due to the fact that the data was dispersed and
the influence of the data points worked best at a power of 2. After considering different parameters, they were
ultimately changed back to the best produced surface. However, the main change to the parameters was the
adjustment of the major and minor semiaxis. This option changed the size of the circle that selected the predicted
location. In addition, it was changed to pin point the best location and get the most important data points. Table 1
illustrates the parameters used.
General Properties
Power
2
Search Neighborhood
Neighborhood type
Standard
Maximum Neighbors
15
Minimum Neighbors
10
Sector type
1 Sector
Angle
0
Major semiaxis
2
Minor semiaxis
2
Anisotropy factor
1
Table 1: IDW Parameters
Once the parameters were set up, this led to the cross validation which was the next and final step. From this, the
predicted and error scatterplot could be viewed along with the table for each plot. The predicted graph can be seen
in Figure 9 consisting of the IDW vertical rise.
P a g e | 11
Figure 9: Predicted Graph
Along with this graph the Regression Function was presented with an equation of 0.62507073467062 *x
+306.079404914536. Furthermore, Table 2 shows the prediction errors created from the IDW parameters.
Prediction Errors
Samples
150 of 150
Mean
51.6674
Root-Mean-Square
525.6529
Table 2: Prediction Errors
Another result produced from the IDW process was the error graph. These values were predicted again by the
parameters set within the previous steps. The Regression function was -0.37492926532938 *x +306.079404914537.
The error graph was shown in Figure 10.
P a g e | 12
Figure 10: Error Graph
4.2 KRIGING INTERPOL ATION
Kriging was a geostatistical analysis method that produced an estimated surface using z values within a dataset.
Kriging was another interpolation method that allowed the user to be more manipulative of the data and allowed
for more change in terms of parameters that influenced the results. This method used neighboring values to create
the predicted values and the trend lines of the predicted values. Additionally, the Kriging function used a circular
radius and took into consideration the distance of the various neighboring data in order to create an influence on
each predicted point.
For this particular study, three different Kriging interpolations were created. The method was run on the vertical
rise, the number of runs and lastly, the rates of the ski hill. The Kriging analysis was completed on multiple factors.
The same parameters were used to keep the results consistent and in turn provide better comparison.
Similarly to the IDW, the Kriging interpolation was found within the Geostatistical Analyst extension as well. There
were many different tools and features to the extension, which could all be manipulated to find the desired results.
To complete the Kriging interpolation, this method was done through the Geostatistical Wizard and then completed
in five steps within the dialog box to produce a result. The parameters were manipulated within these steps.
4.2.1 KRIGING PARAMETERS
The Kriging function had the ability to input multiple data sets in order to compare them at the same time. However
for this particular study, each of the data sets were done separately and compared on their own. As a result, the
source data was the chosen dataset which was always examined since it contained all of the data. The data field was
the option that was changed based on the interest of the Kriging interpolation. The three data fields studied
included; vertical rise, rates, and runs.
P a g e | 13
Once the desired data was selected, the type of Kriging was chosen. For all three different Kriging interpolations
produced for this study, the ‘Simple’ type was chosen. This option was also chosen because it produced the best
results for the study and made most sense. This feature also provided different aspects of the Kriging steps, which
other Kriging types don’t offer. For example, ‘Simple’ produced a histogram which was also step 3.
As a result from the previous step, a histogram was created and produced to show the data field. For this step, all
the setting were left to default. This particular histogram produced the vertical rise data field seen below in Figure
11.
Figure 11: Vertical Rise Histogram
The next step was to determine multiple parameters that the Kriging method would produce. However, within this
step there were also different images that could be analyzed and manipulated. It was possible to optimize the model
when completing this step. This option produced the best results for the data, which showed the lowest RMS error
and the overall surface displayed. However, with this option it was very important to set all parameters before
optimizing the model. This is because the different parameters changed how the model was optimized. The result
would change and different results would be provided. Furthermore, the model type was chosen to be ‘stable’ as
this type provided the best model influence for the predicted values over others. Apart from this parameter being
changed, all other parameters within this step were left to default. From these optimized parameters, Figure 12 and
Figure 13, illustrate the covariance and semivariogram scatterplots.
P a g e | 14
Figure 12: Covariance Scatterplot
Figure 13: Semivariogram Scatterplot
P a g e | 15
The Kriging process was performed to use the neighboring data points to predict a certain point and trend in an
unknown location. As a result, certain neighborhood parameters were set to gather the data and produce the result.
Therefore, the maximum and minimum neighbor values were important as they should have been set depending on
the dataset. For this particular dataset there were multiple areas that were clustered, therefore the maximum
neighbors did not have to be such as large number. However if these data points were not as clustered, it would
have been necessary to increase the number. Ultimately, this method used distance and influence from the data to
produce the predicted point and trend. The next parameter was the sector type. For this parameter 4 sectors were
chosen since it was the best that fit the data and as the cross hairs were on even 90 degree angles, this allowed the
data to fall into the different sections. When the 4 sections with the 45 degree offset were chosen, the parameter
did not fit the data, which resulted in more points being assigned to one section. As a result, the 4 sector type was
selected. Overall, these parameters can be seen below in Table 3.
Search Neighborhood
Neighborhood Type
Standard
Maximum Neighbors
6
Minimum Neighbors
2
Sector type
4 Sectors
Copy from Variogram
True
Table 3: Search Neighborhood Parameters
In addition, when the location to select the predicted point was chosen, the same area was used for the other Kriging
interpolations that were created. This created consistency amongst all the variables. By doing this, it also selected a
location in which the most data points in a clustered area were. This was done so that the best prediction result was
possible. There were large gaps that consisted of no data within the study area. Therefore, by selecting a clustered
area, there was a possibility for more accurate results for the predicted point.
The final step within this method was to view the cross validation and see the various results and figures presented.
This step produced various scatterplots such as the predicted values, error values, standardized error, and normal
QQ plot. Additionally, this step produced a table of the dataset of all the points within this data for each plot. Lastly
a view at the bottom produced results of predicted errors and included different values. Figure 14 shows the
predicted values of the vertical rise with the parameters set throughout this methodology.
P a g e | 16
Figure 14: Predicted Value Scatterplot
The equation for this scatterplot was 0.409565482997915 * x + 480.452078227821.
In Figure 15, the error scatterplot produced an equation of -0.590434517002086 * x + 480.452078227821.
P a g e | 17
Figure 15: Error Scatterplot
Once this last step was completed, ArcGIS was able to produce a surface of the end result. The symbology could be
changed based on desired appeal and use of the image.
5.0 WEIGHTED OVERLAY ANALYSIS USING KRIGING AND IDW
The IDW and Kriging interpolations allowed the project team to create several weighted overlay analyses. The goal
of this was to provide skiers and snowboarders with critical information pertaining to ski resorts in the study area.
In addition, it was important to see if the results would vary at all based on the two different techniques use.
Therefore, the same parameters were used for each of the interpolation methods. The project team wanted to make
skiers and snowboarders alike knowledgeable about these places in case they were basing their decision on these
factors.
5.1 WEIGHTED OVERLAY CRI TERIA AND PARAMETERS
5.1.1 EXPORTING TO RASTERS
It was necessary to export each individual variable vector file into a raster image. These vector files were created
by using the IDW and Kriging methods.
5.1.2 RECLASSIFICATI ON FOR BEST AND WORST SKI RESORTS
The parameters for the best ski resorts were based on three criteria, which included vertical rise, ticket cost, and
runs. The rasters created by the IDW and Kriging methods were used in order to show the ski hill with the highest
runs, lowest ticket prices, and highest vertical rise based on the data set. The higher the vertical rise, the faster and
more enjoyable the trails tend to be. Trails play a crucial part in offering variety to the skier as it keeps them
entertained for majority of the day. Lastly, being able to enjoy a ski resort for an affordable price is key to many as
P a g e | 18
well, since skiing tends to be quite costly, especially with rental equipment not included in the price. Table 4 shows
the reclassifications that were used to show the best overall ski resort for the IDW interpolation.
Vertical Rise
New Values
Ticket Prices
New Values
Runs
New Values
100.80 – 424.31
1
18.84 – 40.28
9
4-20
1
424.32 – 670.18
2
40.29 – 47.28
8
21-26
2
670.19 – 941.92
3
47.29 – 54.05
7
27-33
3
941.93 – 1213.67
4
54.06 – 60.82
6
34-39
4
1213.68 – 1498.36
5
60.83 – 68.04
5
40-48
5
1498.37 – 1757.16
6
68.05 – 75.72
4
49-58
6
1757.17 – 2041.85
7
75.73 – 85.20
3
59-69
7
2041.86 – 2572.40
8
85.21 – 96.48
2
70-86
8
2572.41 – 3400.59
9
96.48 – 133.95
1
87-136
9
Table 4: Reclassification for Best Ski Areas - IDW Interpolation
The Kriging interpolation technique created different ranges than the IDW. The values still ranged from 1-9 similarily
to the IDW, however the vertical rise, ticket prices, and runs used figures based on trends rather than the sampled
data. For example, vertical rise only went up till 1492.17 in vertical rise even though the highest vertical rise was
closer to the 3400 vertical feet mark. Table 5 represented the reclassification process in order to retrieve the best
ski resorts according to the Kriging interpolation.
Vertical Rise
New Values
Ticket Prices
New Values
Runs
New Values
286.87 – 423.94
1
25.95 – 42.11
9
6 – 23
1
423.95 – 537.38
2
42.12 – 49.55
8
24 – 29
2
537.39 – 660.28
3
49.56 – 54.72
7
30 – 33
3
660.29 – 792.62
4
54.73 – 58.92
6
34 – 37
4
792.63 – 906.06
5
58.93 – 63.12
5
38 – 43
5
906.07 – 1005.32
6
63.13 – 69.26
4
44 – 50
6
1005.33 – 1114.03
7
69.27 – 77.34
3
51 – 59
7
1114.04 – 1255.83
8
77.35 – 88.01
2
60 – 73
8
1255.84 – 1492.17
9
88.02 – 108.37
1
74 - 95
9
Table 5: Reclassification for Best Ski Areas - Kriging Interpolation
In order to find the worst ski resorts according to the IDW method, the same amount of values were used for the
reclassification. The major difference was in the way they were ordered. For example, higher values were assigned
to the lower vertical rise. Higher ticket prices were assigned higher values, while ski resorts with lower run count
were assigned higher values. Table 6 reflects these changes in order to find the worst ski resorts based on these
three variables.
P a g e | 19
Vertical Rise
New Values
Ticket Prices
New Values
Runs
New Values
100.80 – 424.31
9
18.84 – 40.28
1
4-20
9
424.32 – 670.18
8
40.29 – 47.28
2
21-26
8
670.19 – 941.92
7
47.29 – 54.05
3
27-33
7
941.93 – 1213.67
6
54.06 – 60.82
4
34-39
6
1213.68 – 1498.36
5
60.83 – 68.04
5
40-48
5
1498.37 – 1757.16
4
68.05 – 75.72
6
49-58
4
1757.17 – 2041.85
3
75.73 – 85.20
7
59-69
3
2041.86 – 2572.40
2
85.21 – 96.48
8
70-86
2
2572.41 – 3400.59
1
96.48 – 133.95
9
87-136
1
Table 6: Reclassification for Worst Ski Resorts - IDW Interpolation
The Kriging method used the same reclassification technique as was previously explained for the Inverse Distance
Weighted interpolation. Table 7 illustrates these parameters.
Vertical Rise
New Values
Ticket Prices
New Values
Runs
New Values
286.87 – 423.94
9
25.95 – 42.11
1
6 – 23
9
423.95 – 537.38
8
42.12 – 49.55
2
24 – 29
8
537.39 – 660.28
7
49.56 – 54.72
3
30 – 33
7
660.29 – 792.62
6
54.73 – 58.92
4
34 – 37
6
792.63 – 906.06
5
58.93 – 63.12
5
38 – 43
5
906.07 – 1005.32
4
63.13 – 69.26
6
44 – 50
4
1005.33 – 1114.03
3
69.27 – 77.34
7
51 – 59
3
1114.04 – 1255.83
2
77.35 – 88.01
8
60 – 73
2
1255.84 – 1492.17
1
88.02 – 108.37
9
74 - 95
1
Table 7: Reclassification for Worst Ski Resorts - Kriging Interpolation
5.1.3 WEIGHTED OVERLAY TOOL
The weighted overlay tool was used to bring in the three reclassified outputs for each respective interpolation. The
overlay tool set weights based on the importance of each criteria. Table 8 outlined the weights that were given for
all four weighted overlays that were performed.
Weighted Overlay Table
Raster
% Influence
Rates
34
Vertical Rise
33
Runs
33
Table 8: Weighted Overlay Table
P a g e | 20
5.1.4 ISOLATING RESULTS
Once the weighted overlay was performed, it was necessary to isolate the results into point features so that only
the best and worst ski results would be shown in the data. This was accomplished through several individual tools.
The raster calculator allowed the project team to display only the highest value output through the ‘Map Algebra
Expression’. Once this was was performed, the raster was converted into a polygon so that it could eventually be
clipped with the ski areas. The clip tool was used to isolate all results other than what fell under the parameters set
in the raster calculator. Figure 16 illustrates the model that was used for the overlay analysis, excluding the clip tool.
Figure 16: Weighted Overlay Model
6.0 COMPARISON TO REALITY ANALYSIS
In order to gain a better sense of the quality and accuracy of the IDW and Krigging interpolations, a comparison to
reality analysis was performed. Upon creating both interpolations, the project team noticed that both techniques
covered the entire state of New Hampshire. This made it the perfect area to perform an analysis since no ski areas
were used from this State. The same data collection parameters were used as previously described.
The Validation/Prediction tool was used in order to compare the actual value to the predicted value based off the
IDW and Krigging analysis. The project team decided to show the predicted value for all three variables including
vertical rise, ticket prices, and number of runs. The results for the IDW comparison is located in Table 9.
P a g e | 21
New
Hampshire
Resorts
Vertical
Rise(ft)
Predicted
Vertical
Rise(ft)
Vertical
Rise
Error(ft)
Ticket
Prices
($CAD)
Predicted
Ticket
Prices
($CAD)
Ticket
Prices
Error
($CAD)
Runs
Predicted
Runs
Runs
Error
Attitash
1750
1730
20
$93.75
$75.31
-$18.44
67
60
-7
Black
Mountain
1100
1730
630
$68.75
$74.76
$6.01
45
59
14
Bretton
Woods
1500
1650
150
$102.50
$70.48
-$32.02
97
54
-43
Cannon
Mountain
2180
1601
-579
$92.50
$69.27
-$23.23
72
53
-19
Cranmore
1200
1750
550
$80.00
$76.25
-$3.75
57
61
4
Loon
Mountain
2100
1648
-452
$103.75
$73.69
-$30.06
61
55
-6
Mount
Sunapee
1510
1727
217
$98.75
$94.16
-$4.59
66
68
2
Waterville
Valley
2020
1680
-340
$93.75
$77.06
-$16.69
52
57
5
Wildcat
Mountain
2112
1706
-406
$93.75
$73.05
-$20.70
50
57
7
Crotched
Mountain
1000
1699
699
$77.50
$91.96
$14.46
25
65
40
Table 9: Comparison to Reality - IDW Interpolation
The average vertical rise error was 404.3 feet, therefore anything over this amount was poorly predicted by the IDW
in terms of the results. To put the size of this error into perspective, some of the sampled data that was included in
the list of 150 ski hills only had a 400 foot vertical rise. This gives good indication when attempting to understand
the size of the average error. Attitash and Bretton woods were most accurately predicted with an error of only 20
and 150 feet respectively, while Crotched Mountain and Black Mountain were the most poorly predicted ski areas.
When observing the ticket price error for the IDW, results show that the method predicted the cost to be less than
what they actually were. Therefore, it can be determined that based on the data that surrounded the newly added
ski resorts, New Hampshire had much higher ticket prices. Cranmore and Mount Sunapee were the only two ski
resorts that fell under $5.00 of the actual value.
The average run error was skewed based on two very poor predictions from Bretton Woods and Crotched Mountain.
For this reason, the average error was 14.7. However, if those two ski resorts were not included in the equation, the
average error for runs would have only been 8.
P a g e | 22
Table 10 illustrates the error results for the Kriging interpolation in order to see whether there were any similarities
and differences between the two methods.
New
Hampshire
Resorts
Vertical
Rise(ft)
Predicted
Vertical
Rise(ft)
Vertical
Rise
Error(ft)
Ticket
Prices
($CAD)
Predicted
Ticket
Prices
($CAD)
Ticket
Prices
Error
($CAD)
Runs
Predicted
Runs
Runs
Error
Attitash
1750
1526
-224
$93.75
$57.58
-$36.17
67
35
-32
Black
Mountain
1100
1564
464
$68.75
$57.88
-$10.87
45
35
-10
Bretton
Woods
1500
1628
128
$102.50
$55.80
-$46.70
97
35
-62
Cannon
Mountain
2180
1622
-558
$92.50
$52.79
-$39.71
72
34
-38
Cranmore
1200
1508
308
$80.00
$58.14
-$21.86
57
34
-23
Loon
Mountain
2100
1577
-523
$103.75
$54.74
-$49.01
61
36
-25
Mount
Sunapee
1510
1408
-102
$98.75
$68.58
-$30.17
66
36
-30
Waterville
Valley
2020
1573
-447
$93.75
$56.47
-$37.28
52
35
-17
Wildcat
Mountain
2112
1578
-534
$93.75
$57.71
-$36.04
50
35
-15
Crotched
Mountain
1000
1496
496
$77.50
$65.78
-$11.72
25
35
10
Table 10: Comparison to Reality - Kriging Interpolation
The vertical rise error appeared to be slightly better than the IDW method since the average was calculated at 378.4
feet. While this method shows improvement, the error is still too large given the complexity of the Kriging
interpolation. Unlike the IDW, the Kriging technique did not have any vertical rise error under 100 feet.
The ticket price error followed the same trend as the IDW. It showed that the prediction should be less than the
actual ticket prices, meaning that New Hampshire’s rates were higher than the sampled data. The Kriging technique
had an average error of $31.95, which was almost two times worse than the IDW. This is worrying since the Kriging
interpolation is supposed to be a much more intricate technique.
The runs prediction showed that New Hampshire had more ski trails than the sample data, since majority of the
predicted run errors were below the actual figures. In addition, the IDW was more accurate than the Kriging
interpolation since it only had an average error of 14.7 comparing to the Kriging’s 26.2.
P a g e | 23
To sum up, the location of the new points could have had a lot to do with the prediction errors being so high. The
points were outside the cluster of data, which made it harder for even the most intricate interpolation techniques.
If the data was located more centrally to the cluster of ski areas that had already been sampled, perhaps the results
would have been more accurate. Overall, by performing this comparison, it showed that neither technique precisely
showed reality.
7.0 FINDINGS
7.1 INTERPOLATION CO MPARISON
When comparing the IDW and Kriging interpolations, it was important to look at all three variables that were created
using each of these methods. Several generalizations could be made based off the results. Firstly, all three Inverse
Distance Weighted techniques that were created appeared to show a higher variation in values than the Kriging
interpolation. This could have been caused by the parameters that were set by each. In addition, the values that
were created for the IDW method appeared to stay true to the data presented whereas the Kriging technique had
changed these values for all three of the variables. This was due to the conversation from a vector to raster file.
Lower values also tended to cover a larger area in the Inverse Distance Weighted interpolation as opposed to the
Kriging technique, which appeared to display values that consisted mainly in the middle of the data range. This in
turn resulted in a larger spread of the data in regards the IDW interpolation.
The IDW and Kriging interpolations are most effective when there is a large dataset. The problem with the data
presented for this study was that there were only 150 ski resorts that were within a large geographical area. There
is was a lot of room for vertical rise to differentiate from each point. As a result, this had a negative impact on the
root-mean-square error. Many parameters were attempted in order to make the error as low as possible. The best
that the project team was able to accomplish was an error of 525.65 for vertical rise based on these chosen
parameters. This negatively affected the prediction process that was analysed earlier in the comparison to reality
section as well.
When comparing the vertical rise IDW to the Kriging, it is apparent that the State of Michigan has a larger area of
small vertical rise data in the Inverse Distance Weighted than the Kriging. In addition, Vermont appeared to have a
higher vertical rise in the IDW interpolation than the Kriging. The blue trend in the IDW runs interpolation appears
to be more prominent than the Kriging method. The Kriging technique largely consisted of runs between 32 and 57,
without much variation. Lastly, the Kriging interpolation for price appeared very similar to the runs interpolation.
There wasn’t much variance present, whereas the IDW showed high rates in the southeast corner near New
Hampshire and Vermont.
The results of both the Kriging and IDW interpolation can be seen in Figure 17 and Figure 18.
P a g e | 24
Figure 17: IDW Interpolation Results
P a g e | 25
Figure 18: Kriging Interpolation Results
P a g e | 26
7.2 BEST SKI RESORTS
The weighted overlay analysis produced different results based on the type of interpolation techniques that were
used. The best overall ski resort for the Kriging method is shown in Table 11.
Best Overall Ski Resort – Kriging Interpolation
Ski Resort
Province/State
Vertical Rise(ft)
Lift Ticket Price ($CAD)
Runs
Mont Orford
Quebec
1933
59
61
Table 11: Best Overall Ski Resorts - Kriging Interpolation
Mont Orford seemed to fit the parameters of a great ski resort since it boasted a vertical rise of 1933 feet, while
comprising of 61 runs. In addition, the lift ticket price was considerably lower given the first two variables. The
Inverse Distance Weighted interpolation offered different results, however Mont Orford was one of three that were
chosen based on the parameters. The IDW results were good in terms of providing more than one option for the
best ski hills. Table 12 illustrates these results below.
Best Overall Ski Resorts – IDW Interpolation
Ski Resort
Province/State
Vertical Rise (ft)
Runs
Mont Orford
Quebec
1933
59
61
Mont Tremblant
Quebec
2116
82
95
Owls Head
Quebec
1772
45
45
Table 12: Best Overall Ski Resorts - IDW Interpolation
Mont Tremblant and Owls Head were the two resorts that were not present in the Kriging interpolation overlay
results. However, a case can be made for these two as well. While Mont Tremblant is more expensive than all the
other resorts, it offers twice as many runs in some cases while consisting of a 2116 foot vertical rise. This is also the
highest out of all the chosen ski resorts. Owls Head was very affordable at $45.00 and offered 45 runs along with
1772 feet in vertical rise. This ski resort outlines great affordability and variety.
7.3 WORST SKI RESORT S
The worst ski resorts also varied depending on the type of interpolation that was used. Table 13 showed the results
for the worst overall ski hills based on the Kriging interpolation.
Worst Overall Ski Resorts –Kriging Interpolation
Ski Resort
Province/State
Vertical Rise (ft)
Runs
Peek’n Peak Resort
New York
400
$70.00
27
Treetops Ski Resort
Michigan
225
$62.50
23
Otsego Club
Michigan
358
$102.50
31
Osler Bluff Ski Club
Ontario
743
$68.00
26
Table 13: Worst Overall Ski Resorts - Kriging Interpolation
P a g e | 27
Four ski hills were deemed to be the worst overall based on the vertical rise, ticket price, and the amount of runs
offered. The most costly ski resort was situated in Michigan at a cost of $102.50, while the ski hill with the lowest
vertical was Treetops Ski Resort, also located in Michigan. All four show characteristics that are indicative of a poor
ski resort. The IDW interpolation resulted in only one ski resort, which was also found by the Kirging method to be
one of the worst overall ski areas. Table 14 shows the result of the IDW weighted overlay results.
Worst Overall Ski Resorts – IDW Interpolation
Ski Resort
Province/State
Vertical Rise(ft)
Runs
Otsego Club
Michigan
358
$102.50
31
Table 14: Worst Overall Ski Resorts - IDW Interpolation
In order to get a visualization of both the best and worst ski areas in the selected study area. Figure 19 and Figure
20 were provided.
Figure 19: Best and Worst Ski Resorts - IDW Interpolation
P a g e | 28
Figure 20: Best Overall Ski Resorts - Kriging Interpolation
P a g e | 29
P a g e | 30
8.0 CLOSURE
This study involved two different interpolation methods to analyze ski data. This data collection sampled 150 ski
resorts in the provinces of Ontario and Quebec, as well as, the States of Michigan, New York and Vermont. One of
the changes that would have been made if done differently would be to get a larger dataset and a smaller study
area. This would allow the predicted data to be better represented. As for this study, there were large distances
between some ski hills, this lead to a misinterpretation of the vertical rise since there could be drastic changes
present within these gaps. However with these methods being prediction interpolations, the data is skewed due to
estimates.
While working with the data there were also a few assumptions made about certain data features. Particularly,
discussing the different age restrictions for prices. They tended to differ based on each ski resort and it wasn’t till
after that the project team noticed this slight change. Some resorts had made adult prices ranging from ages 18 to
64, while other ski hills chose to make adult regular prices 19 to 65. These differing age restrictions might hamper
someone who is 18 and would like to go to one of the best ski resorts that was selected based off the overlay analysis.
Therefore, it is important to pay attention to even the finest details as it may affect someone or something. When
dealing with data that is collected off the internet, there are bound to be some mistakes whether it is on the user’s
end or the provider’s.
P a g e | 31
9.0 BIBLIOGRAPHY
Cazenovia Ski Club. (2015). Retrieved from http://www.skicaz.com/cazskiclub/
Le Relais Centre De Ski Lac-Beauport. (2015). Retrieved from http://www.skirelais.com/le-relais-skisnow.php
Woods Valley. (2015). Retrieved from http://www.woodsvalleyskiarea.com/
Alpine Ski. (2015). Retrieved from http://www.alpineskiclub.com/
ArcGis Desktop Help 10.2. (2015, ). Retrieved from ESRI: http://support.esri.com/en/
Batawa Ski Hill. (2015). Retrieved from http://www.batawaskihill.com/
Beartown Ski Area. (2015). Retrieved from http://www.skibeartown.com/
Beaver Valley Ski Club. (2015). Retrieved from http://www.beavervalley.ca/
Bolar Mountain . (2014). Retrieved from http://www.bolermountain.com/
Caledon Ski Club. (2015). Retrieved from http://caledonskiclub.com/
Centre de ski Vallee Bleue. (2015). Retrieved from http://www.vallee-bleue.com/
Cochran's Ski Area. (2015). Retrieved from http://www.cochranskiarea.com/
Craigleith Ski Club. (2015). Retrieved from http://www.craigleith.com/
Devil's Glen Country Club. (2015). Retrieved from http://www.devilsglen.com/
Gallix Station Recreotouritique. (2015). Retrieved from http://skigallix.com/
Google Maps. (2015). Retrieved from https://www.google.ca/maps
Hunt Hollow Ski Club. (2015). Retrieved from http://www.hunthollow.com/
Kamiskotia Snow Resort. (2015). Retrieved from http://www.kamiskotia.com/
Larder Lake Ski Club. (2015). Retrieved from http://www.larderlakeskihill.com/
Laurentian Ski Hill. (2105). Retrieved from http://www.laurentianskihill.com/
Le Valinouet. (2015). Retrieved from http://www.valinouet.qc.ca/
Lydon Outing Club. (2015). Retrieved from http://www.skilyndon.com/
Madawaska Valley. (2015). Retrieved from http://www.madawaskavalley.ca/
Mansfield Ski Club. (2015). Retrieved from http://www.mansfieldskiclub.com/
Mont Blanc Ski Venture. (2015). Retrieved from http://www.montblanc-skiventure.com/en/
Mont Lac Vert. (2015). Retrieved from http://www.montlacvert.qc.ca/index
P a g e | 32
Mount Chinguacousy. (2015). Retrieved from http://www.brampton.ca/en/residents/CommunityCentres/DMG-Chinguacousy-Park/Mount-Chinguacousy/Pages/Welcome.aspx
Mount Dufour Ski Area. (2015). Retrieved from http://www.mountdufour.com/home.html
Mountains. (2015). Retrieved from
https://c402277.ssl.cf1.rackcdn.com/photos/2325/images/hero_small/mountainshero.jpg?1345838509
Mountains. (2015). Retrieved from
https://c402277.ssl.cf1.rackcdn.com/photos/2325/images/hero_small/mountainshero.jpg?1345838509
On the Snow. (2015). Retrieved from http://www.onthesnow.ca/new-york/royal-mountain-ski-area/skiresort.html
Osler Bluff Ski Club. (2015). Retrieved from http://www.oslerbluff.com/
Pando Ski Center. (2015). Retrieved from http://www.pandopark.com/
Parc Du Mont-Comi. (2015). Retrieved from http://www.mont-comi.ca/en
Pine Ridge Ski Club. (2015). Retrieved from http://pineridgeskiclub.ca/
Porcupine Mountain Ski Area. (2015). Retrieved from http://porkiesfun.com/mobile/
Remi Ski Club. (2015). Retrieved from http://www.remiskiclub.com/indexEn.html
Ski Chantecler. (2015). Retrieved from https://www.skichantecler.com/en/
Ski Garceau. (2015). Retrieved from http://www.skigarceau.com/
Ski Mont Gabriel . (2015). Retrieved from http://www.skimontgabriel.com/
Ski Montcalm. (2015). Retrieved from http://www.skimontcalm.com/
Ski Saint-Bruno. (2015). Retrieved from http://skisaintbruno.ca/
Ski the Legend Hickory. (2015). Retrieved from http://www.hickoryskicenter.com/
Ski Vertical Rise. (2015). Retrieved from
https://www.google.ca/search?q=ski+vertical+rise&es_sm=93&source=lnms&tbm=isch&sa=X&e
i=8VMZVfn7AsOZyASNwoLoBA&ved=0CAcQ_AUoAQ&biw=1680&bih=949#imgdii=_&imgrc=nta
gAWZFqXTw5M%253A%3BwdhmO1nldfekM%3Bhttps%253A%252F%252Fhakuba.files.wordpress.com%252F2011%25
Smith, I. (2015). GISC9308 Spatial Analysis: Deliverable D4B - Geostatistical Analysis of Student Collected
Spatial Data. Niagara-on-the-Lake,ON, Canada.
Stack Exchange. (2015). Retrieved from How to interpret a QQ plot:
http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot
Station De Ski Mont Original . (2015). Retrieved from http://www.montorignal.com/
P a g e | 33
Station Mont Cascades Resport. (2015). Retrieved from http://www.montcascades.ca/
Val Neiget Ma Montag. (2015). Retrieved from http://www.skivalneigette.com/
Val Saint-Come. (2015). Retrieved from http://www.valsaintcome.com/fr/ski
Vivez Vallee du Parc. (2015). Retrieved from http://www.valleeduparc.com/
P a g e | 34
APPENDIX A
Table 15: Raw Ski Resort Data
Resort
Province_State
Country
Northing
Easting
Vertical
Rise
Rate
Run
Searchmont
Ontario
Canada
46.77031
-84.049814
750
50
18
Mount Dufour
Ontario
Canada
46.385829
-82.636247
320
41
7
Laurentian Ski Hill
Boler Mountain
(London Ski Club)
Hidden Valley
Highlands
Ontario
Canada
46.338032
-79.432665
350
38
10
Ontario
Canada
42.94495
81.339061
220
38
15
Ontario
Canada
45.357139
-79.130919
333
48
13
Sire Sam's Ski Area
Madawaska Valley
Ski Area
Ontario
Canada
45.125768
-78.490228
331
50
12
Ontario
Canada
45.436469
-77.662923
450
32
12
Calabogie Peaks
Ontario
Canada
45.27495
-76.781478
780
41
29
Mount Pekenham
Mount St. Louis
Moonstone
Ontario
Canada
45.325394
-76.329116
280
33
10
Ontario
Canada
44.627867
-79.668552
550
56
40
Pine Ridge Ski Club
Ontario
Canada
44.397818
-79.632036
320
35
10
Horseshoe Resort
Ontario
Canada
44.555904
-79.66858
304
52
26
Craigleith Ski Club
Ontario
Canada
44.526525
-80.327351
700
70
33
Alpine
Ontario
Canada
44.523789
-80.344579
710
70
36
Blue Mountain
Beaver Valley Ski
Club
Ontario
Canada
44.503711
-80.310316
720
64
36
Ontario
Canada
44.358656
-80.545529
508
68
29
Devil's Glen Club
Ontario
Canada
44.354724
-80.199955
510
63
23
Ski Snow Valley
Ontario
Canada
44.41009
-79.789261
279
37
19
Mansfield Ski Club
Ontario
Canada
44.197686
-80.053245
440
48
15
Hockley Valley
Ontario
Canada
43.977807
-80.046677
300
47
15
Devils Elbow
Skyloft Ski & Country
Club
Ontario
Canada
44.220566
-78.576896
350
51
11
Ontario
Canada
44.028198
-79.076987
400
60
22
Ski Lakeridge
Ontario
Canada
44.028124
-79.063892
400
55
23
Dagmar
Ontario
Canada
44.0117
-79.060198
200
51
12
Brimacombe
Ontario
Canada
44.022574
-78.574969
300
50
21
Batawa Skil Hill
Ontario
Canada
44.166955
-77.596888
571
35
9
Caledon
Ontario
Canada
43.801549
-80.01264
275
55
23
Uplands Ski Centre
Ontario
Canada
43.826614
-79.437197
100
21
4
Chicopee
Ontario
Canada
43.429894
-80.42149
200
40
15
Glen Eden
Ontario
Canada
43.510648
-79.942918
245
37
12
Osler Bluff Ski Club
Ontario
Canada
44.45812
-80.285169
743
68
26
Camp Fortune
Quebec
Canada
45.511235
-75.851651
590
37
23
Edelweiss Valley
Quebec
Canada
45.646202
-75.849704
656
42
18
Le Chantecler
Quebec
Canada
45.950609
-74.147211
600
42
24
Le Massif
Quebec
Canada
47.297525
-70.648447
2526
66
52
P a g e | 35
Le Relais
Quebec
Canada
46.942255
-71.29965
735
45
29
Le Valinouet
Quebec
Canada
48.654439
-70.894755
1148
41
27
Mount Avila
Quebec
Canada
45.886299
-74.131619
615
43
13
Mont Blanc
Quebec
Canada
46.10844
-74.481924
1000
46
41
Mont Cascades
Quebec
Canada
45.593741
-75.848777
541
39
20
Mont Garceau
Quebec
Canada
46.338906
-74.219008
1000
30
25
Mont Gleason
Quebec
Canada
45.928718
-71.958665
627
31
18
Mont Habitant
Quebec
Canada
45.885594
-74.150079
551
39
11
Mont La Reserve
Quebec
Canada
46.286789
-74.181549
1000
36
37
Mont Lac Vert
Quebec
Canada
48.356241
-71.612175
787
36
20
Mont Olympia
Quebec
Canada
45.916316
-74.123791
656
43
24
Mont Orford
Quebec
Canada
45.318874
-72.217685
1933
59
61
Mount Original
Quebec
Canada
46.409956
-70.582626
984
38
23
Mont Rigaud
Quebec
Canada
45.46838
-74.339392
393
38
9
Mont Saint- Bruno
Quebec
Canada
45.558384
-73.334046
440
65
15
Mont Saint-Sauveur
Quebec
Canada
45.885767
-74.150279
700
54
38
Mont Sainte-Anne
Quebec
Canada
47.075325
-70.902992
2050
75
66
Mont Sainte-Marie
Quebec
Canada
45.944548
-75.879195
1251
44
20
Mont Sutton
Quebec
Canada
45.103131
-72.561634
1500
60
60
Mont Tremblant
Quebec
Canada
46.213397
-74.58512
2116
82
95
Owls Head
Quebec
Canada
45.077543
-72.295168
1772
45
45
Parc Du Mont-Comi
Quebec
Canada
48.467409
-68.194599
1000
31
29
Ski Bromont
Quebec
Canada
45.301433
-72.63695
790
62
42
Ski Mont Gabriel
Quebec
Canada
45.922798
-74.153556
656
35
18
Ski Montcalm
Quebec
Canada
46.041882
-73.830924
278
35
24
Ski Morin Heights
Quebec
Canada
45.902371
-74.266125
656
43
24
Ski Vorlage
Quebec
Canada
45.644883
-75.934797
450
35
17
Stoneham
Quebec
Canada
47.02649
-71.381546
1132
59
49
Val Neigette
Quebec
Canada
48.366626
-68.481571
623
26
25
Val St-Come
Quebec
Canada
46.272785
-73.869516
984
51
39
Val D'Irene
Quebec
Canada
48.471824
-67.572474
899
33
26
Vallee Bleue
Quebec
Canada
46.026354
-74.21781
364
36
19
Vallee Du Parc
Quebec
Canada
46.615557
-72.795416
551
33
20
Beartown Ski Area
New York
United States
44.768845
-73.582752
150
26.25
4
Belleayre Ski Resort
New York
United States
42.142214
-74.510778
1404
80
50
Brantling
New York
United States
43.150011
-77.065356
240
37.5
9
Bristol Mountain
New York
United States
42.745
-77.404444
1200
82.5
34
Buffalo Ski Club
New York
United States
42.681007
-78.691504
500
50
43
Catamount Ski Area
New York
United States
42.171456
-73.477764
1000
78.75
35
Dry Hill Ski Area
New York
United States
43.931184
-75.901247
300
8.75
35
Gore Mountain
New York
United States
43.672222
-74.006944
2537
102.5
107
Greek Peak
New York
United States
42.505
-76.147222
952
80
38
P a g e | 36
Hickory Ski Center
New York
United States
43.47027
-73.811227
1200
56.25
18
Holiday Mountain
New York
United States
41.626342
-74.609288
400
52.5
7
Holiday Valley
New York
United States
42.2625
-78.668056
750
85
58
HoliMont
New York
United States
42.273192
-78.68922
700
77.5
52
Hunt Hollow Ski Club
New York
United States
42.644967
-77.471021
825
56.25
20
Hunter Mountain
New York
United States
42.200278
-74.230278
1600
95
57
Kissing Bridge
New York
United States
42.601057
-78.651823
600
65
39
Labrador Mountain
New York
United States
42.741797
-76.029727
700
62.5
22
Maple Ski Ridge
New York
United States
42.817844
-74.031555
1200
47.5
8
McCauley Mountain
New York
United States
43.696111
-74.961389
633
26.25
30
Mount Pisgah
New York
United States
44.345602
-74.125068
329
18.75
5
Mt. Peter Ski Area
New York
United States
41.247626
-74.295475
400
56.25
12
Oak Mountain
New York
United States
43.518133
-74.362194
650
17.5
34
Peek'n Peak Resort
New York
United States
42.0625
-79.7366667
400
70
27
Plattekill
New York
United States
42.289337
-74.653086
1100
72.5
35
Royal Mountain
New York
United States
43.081368
-74.504825
550
50
14
Big Tupper
New York
United States
44.170674
-74.477906
1151
31.25
25
Snow Ridge
New York
United States
43.639705
-75.419683
500
48.75
22
Song Mountain
New York
United States
42.774167
-76.15823
700
62.5
24
Swain
New York
United States
42.47678
-77.854061
650
62.5
30
Thunder Ridge
New York
United States
41.507222
-73.581111
600
62.5
30
Titus
New York
United States
44.763333
-74.235278
1200
56.25
42
Toggenburg
New York
United States
42.826078
-75.958962
700
62.5
21
Tuxedo Ridge
New York
United States
41.246111
-74.227222
400
52.5
8
West Mtn
New York
United States
43.285556
-73.728333
460
56.25
40
Whiteface
New York
United States
44.365833
-73.902778
3430
111.25
87
Willard Mountain
New York
United States
43.022472
-73.516702
505
50
14
Windham Mountain
New York
United States
42.291667
-74.259444
1600
97.5
49
Woods Valley
New York
United States
43.301587
-75.382328
500
43.75
10
Bolton Valley
Vermont
United States
44.415833
-72.869722
1704
86.25
71
Bromley
Vermont
United States
43.227778
-72.938611
1334
88.75
45
Cochran's Ski Area
Vermont
United States
44.396
-72.982
91
25
4
Jay Peak
Vermont
United States
44.929444
-72.532222
2153
90
76
Killington
Vermont
United States
43.625833
-72.797778
3050
115
140
Lyndon Outing Club
Vermont
United States
44.53305
-71.98719
430
12.5
10
Mad River Glen
Vermont
United States
44.200833
-72.924444
2037
93.75
45
Magic Mountain
Middlebury Snow
Bowl
Vermont
United States
43.192778
-72.76
1700
78.75
40
Vermont
United States
43.939167
-72.9575
1050
68.75
17
Mount Snow
Vermont
United States
42.958889
-72.923611
1700
112.5
80
Okemo
Vermont
United States
43.401607
-72.715807
2200
115
120
Pico
Vermont
United States
43.662575
-72.842907
1967
86.25
52
P a g e | 37
Q Burke Mtn
Vermont
United States
44.587865
-71.91585
2011
80
50
Smugglers
Vermont
United States
44.572778
-72.776111
2610
87.5
78
Stowe
Vermont
United States
44.531
-72.787
2160
135
116
Stratton
Vermont
United States
43.114167
-72.90667
2003
122.5
92
Sugarbush
Vermont
United States
44.137222
-72.906667
2600
111.25
111
Suicide Six
Vermont
United States
43.663889
-72.544444
650
80
23
Alpine Valley
Michigan
United States
42.65388
-83.52275
300
52.5
25
Apple Mountain
Michigan
United States
43.473011
-84.101854
220
43.75
12
Big Powderhorn
Michigan
United States
46.504167
-90.096111
622
65
33
Blackjack
Michigan
United States
46.5021
-90.0046
465
58.75
19
Boyne Mountain
Michigan
United States
45.163889
-84.932778
500
86.25
115
Caberfae Peaks
Cannonsburg Ski
Area
Michigan
United States
44.249722
-85.725
485
57.5
34
Michigan
United States
43.054414
-85.501013
250
46.25
15
Crystal Mtn
Michigan
United States
44.52
-85.992222
375
83.75
45
Indianhead
Michigan
United States
46.5
-89.970833
638
67.5
30
Marquette Mountain
Michigan
United States
46.508
-87.42
600
56.25
25
Mont Ripley
Michigan
United States
47.129167
-88.559444
440
52.5
24
Mount Bohemia
Michigan
United States
47.391697
-88.013578
900
71.25
81
Mt. Brighton
Michigan
United States
42.538056
-83.806944
230
61.25
26
Norway Mountain
Michigan
United States
45.789524
-87.869615
500
48.75
17
Nub's Nob
Michigan
United States
45.468333
-84.903611
427
83.75
52
Otsego Club
Michigan
United States
45.027409
-84.655152
358
102.5
31
Pine Knob
Michigan
United States
42.74722
-83.372829
300
58.75
17
Pine Mountain
Michigan
United States
45.839261
-88.088386
500
25
27
Porcupine Mountains
Michigan
United States
46.819676
-89.648362
641
43.75
28
Shanty Creek
Michigan
United States
44.948699
-85.185239
450
75
53
Ski Buttersweet
Michigan
United States
42.467187
-85.758676
350
51.25
20
Ski Brule
Michigan
United States
46.028894
-88.700223
500
57.5
43
Ski Mt. Holly
Michigan
United States
42.827985
-83.56436
250
52.5
19
Snow Snake
Michigan
United States
43.959264
-84.780789
210
33.75
12
Swiss Valley
Michigan
United States
41.954207
-85.826898
225
46.25
11
Treetops
Michigan
United States
45.033165
-84.589124
225
62.5
23
P a g e | 38
Table 16: New Ski Resort Data
Resort
Province_State
Country
Northing
Easting
Attitash
New Hampshire
United States
44.082902
-71.229486
Black Mountain
New Hampshire
United States
44.166595
Bretton Woods
New Hampshire
United States
Cannon Mountain
New Hampshire
Cranmore
Rates
Runs
1750
75
67
-71.164124
1100
55
45
44.259262
-71.460335
1500
82
97
United States
44.170237
-71.688918
2180
74
72
New Hampshire
United States
44.056505
-71.110079
1200
64
57
Loon Mountain
New Hampshire
United States
44.056535
-71.625795
2100
83
61
Mount Sunapee
New Hampshire
United States
43.313759
-72.074178
1510
79
66
Waterville Valley
New Hampshire
United States
43.965052
-71.528206
2020
75
52
Wildcat Mountain
New Hampshire
United States
44.267668
-71.239429
2112
75
50
Crotched Mountain
New Hampshire
United States
43.007613
-71.878669
1000
62
25
Vertical Rise

File - Michael Wszol

Transcription

Similar documents

Roy Parker - New Mexico Ski Hall of Fame

PDF - Blandford Ski Area

THE APRÉ - Double Diamond Ski Club

Aramón (Spain)

26527 membership plus exclusive offer - feb 14

Craigleith Ski Club - Collingwood, ON February 1st, 2013

Vail 2010 WSA

DECEMBER 2011 General membership meetings

APPENDIX 1b - De La Salle College