Crawley using r to evaluate street stress on park use

Using R to Evaluate the

Affects of Street Stress on

Park Use Elizabeth Crawley Rental Manager

CompassTools, Inc.

The Plan

Reasons for this study

Background

Data

Methods

R

Results

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.

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

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

Hypotheses

Higher road stress surrounding parks will result in

less use.

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

Chen, et al, 2010

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.

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.

http://www.census.gov/geo/maps-data/data/tiger.html



http://www.drcog.org/index.cfm?page=regionaldataandmaps

http://data.denvergov.org/dataset/city-and-county-of-denver-hud-income-levels-census-tract



















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

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

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

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

LTS networks

Bike stress levels Pedestrian stress levels

Service Areas

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

“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.

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

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


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


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

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


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


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


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

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

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.

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
























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.

Documents

Crawley using r to evaluate street stress on park use