1. Using R to Evaluate the Affects of Street Stress on Park Use
Elizabeth Crawley
Rental Manager
CompassTools, Inc.

2. The Plan
Reasons for this study
Background
Data
Methods
R
Results

3. Urban Green Space: Any undeveloped land in urban areas that is partially covered by vegetation, such as parks, cemeteries, forests, river corridors, playing fields, etc.

4. Benefits of Urban Green Spaces
Environmental Services
Removal of pollution
Oxygen generation
Noise reduction
Mitigation of urban heat island effects
Regulation of microclimates
Soil stabilization
Recharging ground water
Carbon sequestration
Erosion control
Biodiversity conservation
And more…
Health Affects
Exercise
Weight control
Reduces stress levels
Reduces blood pressure
Reduces BMI z-scores
Reduces risks of certain diseases
Improves mental health
Improves recovery rates

5. Standards and recommendations
The World Health Organization: 9 m2 per person
European Environment Agency: people live within 900 m
English Nature: people live within 300 m of 2 ha

6. Hypotheses
Higher road stress surrounding parks will result in less use.

7. Denver, CO
Pop = 610,000
Population density = 4,000 people per m2
Administrative area = 154.9 mi2
GDP per person = 49,200 US$
Average Temperature = 50oF

8. Chen, et al, 2010

9. Over 4000 acres of parks, trails, gardens and other green spaces
4% of the total area is green space
Includes private parks, golf courses, cemeteries, etc.

10. Data Sources
US census: http://www.census.gov/geo/maps-data/data/tiger.html
Denver Regional Council of Governments (DRCOG): http://www.drcog.org/index.cfm?page=regionaldataandmaps
Denver Open Data Catalog: http://data.denvergov.org/dataset/city-and- county-of-denver-hud-income-levels-census-tract
Bronson, R. Alternative and adaptive transportation: What household and neighborhood factors support recovery from a drastic increase in gas price? Thesis. University of Denver, 2013.

11. Methods
Park selection
Randomly selected 11 parks
Data collection
October 2013
Sampled 4 entrances at each park
Sampled entrances 3 times for 20 minutes
Converted Level of Traffic Stress (LTS) shapefile to network
Calculated half-mile and 1 mile service area for park entrances
Calculated LTS averages
Poisson’s Regression

13. Park Selection
ID
Park
Acres
Trails
Field
Playground
1
Barnum Park
34.04
Y
Y
Y
2
City Park
314.4
Y
Y
Y
3
Eisenhower (Mamie D.) Park
27.7
Y
Y
Y
4
Grant Frontier Park
16.6
Y
Y
Y
5
James A. Bible Park
83.6
Y
Y
Y
6
Montbello Central Park
36.8
Y
Y
Y
7
Pinehurst Park
13.7
Y
Y
Y
8
Rocky Mountain Lake Park
54.9
Y
Y
Y
9
Rosamond Park
35.6
Y
Y
Y
10
Swansea Park
10.8
Y
Y
Y
11
Washington Park
157.5
Y
Y
Y

14. Bicycle LTS Scoring
≤25 mph
=30 mph
≥35 mph
2-3 lanes
LTS 2
LTS 3
LTS 4
4-5 lanes
LTS 3
LTS 4
LTS 4
6+ lanes
LTS 4
LTS 4
LTS 4
LTS 1
LTS 2
LTS 3
LTS 4
Physically separated bike path
X
Most local
X
Collector urban (17), collector
X
LTS 4 street with a bike lane
X
Interstate urban (11), freeway urban (12), other primary arterial urban (14), Minor arterial urban (16); volume classification (arterial); type (ramp)
X
Bronson, 2013

15. Pedestrian LTS Scoring
Bike LTS 1
Bike LTS 2
Bike LTS 3
Bike LTS 4
Sidewalk ≥5ft
LTS 1
LTS 1
LTS 1
LTS 3
Sidewalk 4ft
n/a
LTS 1
LTS 2
LTS 3
Sidewalk 3ft
n/a
LTS 2
LTS 3
LTS 4
Sidewalk ≤2ft
n/a
LTS 3
LTS 4
LTS 4
Bronson, 2013

16. LTS networks
Bike stress levels
Pedestrian stress levels

17. Service Areas

19. Pearson’s Coefficient Matrix
Acres
% LowMod
Pop_SQMI
Bike_LTS_1
Ped_LTS_1m
Acres
1
-0.1448755
-0.009307279
0.30994987
-0.135930051
% LowMod
-0.14488
1
-0.143675778
0.38367136
0.123655126
Pop_SQMI
-0.00931
-0.1436758
1
-0.5271868
-0.522569574
Bike_LTS_1m
0.30995
0.3836714
-0.527186832
1
0.592874976
Ped_LTS_1m
-0.13593
0.1236551
-0.522569574
0.59287498
1

20. “ggplot2”: plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.
“sandwich”: Model-robust standard error estimators for cross-sectional, time series, and longitudinal data
“msm”: Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.

22. Poisson’s Test: Total Park Use
Estimate
Std.Error
z-score
Pr(>|z|)
(Intercept)
3.32E+00
1.44E-01
23.11
<2.00E-16
Pop_SQMI
6.63E-05
1.51E-05
4.394
1.11E-05
% LowMod
-8.98E-03
2.13E-03
-4.216
2.49E-05
Acres
3.58E-03
2.41E-04
14.846
<2.00E-16

23. Poisson’s Test: Pedestrian LTS scores
Total Vehicle Use
Total Use
Total Pedestrian Use
Total Bicycle Use
Variable
Coefficient
StdError
z-value
P-value
Intercept
4.2503658
0.1654938
25.683
<2e-16
Acres
0.0042109
0.0002042
20.619
< 2e-16
LowModAvg
-0.0277047
0.0014290
-19.388
<2e-16
Ped_LTS_1m
0.2623475
0.0882020
2.974
0.00294
Variable
Coefficient
StdError
Z value
P-value
Intercept
3.1861761
0.2547435
12.507
< 2e-16
Acres
0.0036333
0.0003205
11.337
< 2e-16
LowModAvg
-0.0251740
0.002173
-11.582
< 2e-16
Ped_LTS_1m
0.3546953
0.1341913
2.643
0.00821
Variable
Coefficient
StdError
Z value
P-value
Intercept
3.9318751
0.2605735
15.089
<2e-16
Acres
0.0038760
0.0003421
11.331
<2e-16
LowModAv
-0.0281268
0.0023434
-12.002
<2e-16
Ped_LTS_1m
-0.0896872
0.1393243
-0.644
0.52
Variable
Coefficient
StdError
Z value
P-value
Intercept
1.7235420
0.3994641
4.315
1.6e-05
Acres
0.0060014
0.0004335
13.844
< 2e-16
LowModAvg
-0.0335790
0.0032862
-10.218
<2e-16
Ped_LTS_1m
0.8036480
0.2157909
3.724
0.000196

24. Poisson’s Test: Bicycle LTS scores
Total Vehicle Use
Total Use
Total Pedestrian Use
Total Bicycle Use
Variable
Coefficient
StdError
z-value
P-value
Intercept
2.2812952
0.2341904
9.741
<2e-16
Acres
0.0026295
0.0002434
10.803
<2e-16
LowModAvg
-0.0324837
0.0014891
-21.815
<2e-16
Bike_LTS_1m
1.0513087
0.0975706
10.775
<2e-16
Variable
Coefficient
StdError
Z value
P-value
Intercept
2.4756370
0.3586037
6.904
5.07e-12
Acres
0.0026813
0.0003836
6.991
2.74e-12
LowModAvg
-0.0274803
0.0022799
-12.053
< 2e-16
Bike_LTS_1m
0.5788709
0.1503070
3.851
0.000118
Variable
Coefficient
StdError
Z value
P-value
Intercept
3.0494100
0.3956062
7.708
1.28e-14
Acres
0.003431
0.0004171
8.227
< 2e-16
LowModAv
-0.0297508
0.0024441
-12.173
< 2e-16
Bike_LTS_3m
0.3179075
0.1656562
1.919
0.055
Variable
Coefficient
StdError
Z value
P-value
Intercept
-5.0540748
0.5641783
-8.958
< 2e-16
Acres
0.0013987
0.0004813
2.906
0.00366
LowModAvg
-0.0523074
0.0035455
-14.753
<2e-16
Bike_LTS_1m
3.4756722
0.230154
15.101
< 2e-16

25. Summary of Results
755 pedestrians (37%)
419 cyclist (21%)
844 vehicles (42%)
Larger parks results in more users
Parks in neighborhoods with higher percentages of low- to moderate- income houses lower number of users
Parks in high population density areas have more users

26. Summary of Results: LTS
Higher percent of low- to moderate-income houses resulted in lower park use regardless of transportation method.
Pedestrian LTS averages were only significant for pedestrian park use and negatively correlated.
Bicycle LTS averages were significant and positive for all transportation methods.

27. Resources
http://www.statmethods.net/stats/correlations.html
http://cran.r-project.org/doc/manuals/R-intro.html
http://www.rstudio.com/
https://support.rstudio.com/hc/en-us/articles/200552336- Getting-Help-with-R

28. References
Bronson, R. Alternative and adaptive transportation: What household and neighborhood factors support recovery from a drastic increase in gas price? Thesis. University of Denver, 2013.
Chen, D; G. Doherty, A. Georgoulias, M.A. Hughes, R. Kassel, T. Wright, and R. Zimmerman. US and Canada Green City Index. Munich, Germany. Siemens AG Economist Intelligence Unit.2011.
Giles-Corti, B.; M.H. Broomhall; M. Knuiman; C. Collins; K. Douglas; K. Ng; A. Lange; and R.J. Donovan. “Increasing Walking: How Important is Distance To, Attractiveness; and Size of Public Open Space?” Am. Journal of Preventive Medicine. 28(2S2) 2005: 169-176.
Heynen, N.; H.A. Perkins; P. Roy. “The Political Ecology of Uneven Green Space: The impact of political economy on race and ethnicity in producing environmental inequality in Milwaukee.” Urban Affairs Review. 42 (1) 2006: 3-25.
Mennis J. “Socioeconomic-Vegetation Relationships in Urban, Residential Land: The Case of Denver, Colorado.” Photogrammetric Engineering & Remote Sensing. 72(8) 2006: 911- 921.
Sotoudehnia, F and A Comber. “Measuring Perceived Accessibility to Urban Green Spaces: An Integration of GIS and Participatory Map.” AGILE, 2011.