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State Program Training 2010 Division of Nutrition, Physical Activity and Obesity (DNPAO). Park Access. March 19, 2010. Dee Merriam, FASLA Community Planner National Center for Environmental Health U.S. Centers for Disease Control and Prevention.

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park access

State Program Training 2010

Division of Nutrition, Physical Activity and Obesity (DNPAO)

Park Access

March 19, 2010

Dee Merriam, FASLA

Community Planner

National Center for Environmental Health

U.S. Centers for Disease Control and Prevention

“The findings and conclusions in this presentation have not been formally disseminated by the Centers for Disease Control and Prevention and should not be construed to represent any agency determination or policy.”

F:\# Active Files\CDC\Conferences\2009 Oct 26-28 - NEPH -Atl\PP development

no place to play the trust for public land 2004 study of children s access to parks
“No Place To Play”The Trust for Public Land 2004 study of children’s access to parks.

Los Angeles

Boston 97%

Los Angeles 33%

radial versus network analysis
Radial versusNetwork Analysis

A

X

.25 mile radius

PP slide courtesy of Doug Allen

objective
Objective

Using the City of Atlanta park system examine the difference in estimates of population served resulting from park service areas drawn using radial and network analysis.

methods
Methods
  • Major Tasks:
  • 1. Obtain GIS layer showing park boundaries
  • 2. Identify eligible parks
  • 3. Develop a typology of entrance types
  • 4. Map entrances on air photos
  • 5. Establish service areas
    • - .25 and .5 mile radial and network service areas for each park and the city as a whole
  • 6. Assign population to park service areas
    • - used 2000 census data and proportional weighting
entrance typology

Entrance Typology:

Entrance Typology

All- Primarily automobile but can serve pedestrians and maintenance vehicles

Pedestrian -Serves only pedestrians – no motorized vehicle access

Maintenance - Maintenance entrance only – usually gated

Pedestrian/Maintenance - Maintenance entrance but pedestrian access is possible (no gates)

Potential Pedestrian - No definitive entrance but terrain permits access

mapping entrances
Mapping Entrances
  • Located entrances using air photos
  • Field verified 61 parks

Central

Springvale

potential pedestrian access points
Potential Pedestrian Access Points

- Points along park boundary where entrance to park is possible (permitted by terrain) but no formal entrance exist

- Usually placed at park corners

- Not placed between existing entrances unless entrance fronts a street

create park service areas
Create Park Service Areas
  • Radial Buffer – draw polygon .25 mile from park boundary
  • Network Buffers

Measure .25 mile along street network from entrances

Black dots represent end points .25 mile from closest entrance

create park service areas and mile network buffers
Create Park Service Areas½ and ¼ Mile Network Buffers

One Service Area per entrance is created

(blue outlines)

by connecting end points

create park service areas1
Create Park Service Areas
  • “Dips” are where two Network
  • Service Areas meet
  • Individual Service Areas are merged into one PSA per park
  • Results in oddly shaped Service Area (blue outline)
findings
Findings

154 Parks (out of 345) met the study criteria that there must be a reason to enter the park.

- A park bench,

- A trail,

- An open field, etc.

Parks ranged from .042 acres to 250 acres

18 parks were less than .5 acres

slide18

Park Service Areas for the City as a Whole

Radial Estimate of

Population Served

¼ mile = 44%

½ mile = 79%

Source: Center for Geographic Information Systems, Georgia Institute of Technology

slide19

Park Service Areas for the City as a Whole

Street Network Estimate

of Population Served

¼ mile = 21%

½ mile= 51%

Source: Center for Geographic Information Systems, Georgia Institute of Technology

slide20

51%

44%

21%

79%

City as a WholeEstimates of Population Served

correlation between radial and network population estimates for specific park sites
Correlation between Radial and Network Population Estimates for Specific Park Sites
  • Least Correlation ---- .5%

(Brownsmill)

  • Median ------------------ 44.8%

(Orme)

  • Greatest Correlation - 81.8%

(Hardy Ivy)

% of radial population within network service area (network est/radial est)

slide22

Least correlation – Brownsmill .5%

Population within the .25 mile radial buffer = 2,613

Population within the .25 mile street network = 13

slide23

Greatest Correlation- Hardy Ivy 81.8%

Population within the .25 mile radial buffer = 482

Population within the .25 mile street network = 394

slide24

Most Population Served- Freedom Park

Population within the .25 mile radial buffer = 10,524

Population within the .25 mile street network = 6,851

limitations
Limitations
  • Assumes that population is equally distributed within census block groups
  • May miss access points (undercount) or identify entrances that are not usable (overcount.)
  • Assumes all streets are walkable.
  • Assumes that all parks provide equal service
find service gaps
Find Service Gaps

Radial

Network

slide30

Illustrates where access points can be added.

Population within .25 Radial Service Area = 4,311

Population within .25 mile Street Network Service Area = 2,439

slide31

Illustrates where access points can be added.

Population within .25 Radial Service Area = 4,311

Population within .25 mile Street Network Service Area = 2,439

slide32

Illustrates impact of adding a street.

Population within .25 Radial Service Area = 743

Population within .25 mile Street Network Service Area = 25

slide33

Illustrates impact of adding a street.

Population within .25 Radial Service Area = 743

Population within .25 mile Street Network Service Area = 25

conclusions
Conclusions
  • In all cases radial analysis overstated service
  • At the system scale more precise maps provide clearer representation of service gaps
  • Distance tended to reduce the discrepancy between the radial and network estimates
  • At the site scale graphics illustrate where entrances can be added to expand service
  • Graphics show where street connectivity can be added to expand service
slide35

Placing parks within a walkable distance

Make Active Transportation --Easy

slide36

Park

Park

Access to parks--- Destinations

3

2

1

Geographic Location

Location in relation to the street

  • Have a park– some place to go
  • Give the park street frontage
  • Make the park easy to get to from many points in the neighborhood.
open space located in the back of lots is not visible or accessible
Open Space located in the back of lots is not visible or accessible.

Street House Back Yards House Street

Public Realm

Public Realm

slide40

Open Space that is commonly owned and highly visible and accessible.

House Street Park Street House

Public Realm

slide42

Place park for maximum visibility and access.

  • Open Space fronts street
  • Lots are across the street which gives community access.
  • Connecting streets direct views to open space
  • Narrow lots front the greenspace.
  • Highest premium for lots with greatest access.

Open space fronts street. It is highly visible and accessible.

putting the principles to work
Putting the Principles to Work

Original Site Concept

  • Access is through access alleys and likely around dumpsters.
  • Limited points to view the natural area.
  • Few of the residents have access.
  • The site is isolated.
putting the principles to work1
Putting the Principles to Work

Revised Site Concept

  • Main Road fronts the open space.
  • Maximum natural surveillance
  • All residents have equal access.
  • The open space becomes a significant amenity.
slide46

Walk route distance is important !

A

X

Dee Merriam

770-488-3981

dmerriam@cdc.gov

www.cdc.gov/healthyplaces

.25 mile radius

PP slide courtesy of Doug Allen

slide50

F:\# Active Files\Photos\Research Photos\12-06 for Atlanta flyer/McKinley-Wilson Triangle – 5-05

putting the principles to work2
Putting the Principles to Work

Original Site Concept

Revised Site Concept

neighborhood parks and active living npal

Neighborhood Parks and Active Living (NPAL)

Candace Rutt, PH.D.

Centers for Disease Control and Prevention

key research questions
Key Research Questions
  • Who uses parks and for what purposes?
  • To what extent do parks serve as venues for physical activity?
  • What factors predict park use and physical activity?

Individual factors

Parks attributes

Park connectivity

Neighborhood attributes

Other factors??

Ecological

Framework

slide56

Study design

  • Phase I: Descriptive study
    • In-park surveys
    • Direct observation of physical activity
    • GIS distances from home to parks
study parks
Study parks
  • 12 parks selected from Atlanta metro county: DeKalb County
  • Park selection goal: maximize variability on key factors
    • Size
    • Facilities
    • Programming
    • SES
    • Ethnicity of neighborhood
    • Neighborhood configuration
phase i data collection
Phase I: Data Collection
  • June – August 2004
  • 8 days per park
    • 4 weekdays
    • 4 weekend days
  • 14 hours per day (6:30AM – 8:30PM)
  • Intercept Surveys and Direct Observation
intercept surveys
Intercept surveys
  • Stationed at entrances and
  • exits
  • 13 intercept questions
  • 6+years old
  • Spanish survey
slide60

Direct Observation of Physical Activity

Divided parks into activity scanning areas

Recorded number

crossing line on trail

slide61

GIS Measures

  • Mapped address data from respondents
  • Indicated mode
  • Calculated closest distance to park from home via street network
  • Created ½ mile street network and “crow fly” buffers to conduct walkability measures
demographics direct observation

9% Teen

37% Female

63% Male

50% Adult

39% Children

3% Older Adult

Total: 15,917 park users

Demographics Direct Observation

physical activity direct observation

9% Vigorous

26% Sedentary

29% Moderate

35% Light

Physical Activity Direct Observation
binary logistic regression

Outcome variables

% Exercise

Visits/week

Active Activities

(25% or less; greater

(fewer than 2 d/wk;

than 25%)

2 or more d/wk)

(active versus sed)

Demographic

Age

Race

White (base)

1.9 (1.5,2.5)

0.29(0.23,0.38)

Black

Other

1.9 (1.3,2.8)

1.8 (1.1,2.8)

1.7 (1.1,2.5)

Ethnicity

Gender

1.2 (1.0,1.5)

Visits/week

6.6 (5.4,8.1)

Travel behaviors

1.3 (1.001,1.7)

5.2 (4.1,6.6)

2.5 (1.9,3.3)

Mode

GIS travel dist.

0.95 (0.94,0.97)

Environmental

1.5(1.02,2.15)

Walkable Neigh

Pool

0.40 (0.22,0.73)

0.38 (.21,.70)

2.2 (1.3,3.7)

Rec Center

Trail

1.3 (1.03,1.6)

0.52 (0.41,0.67)

Restrooms

0.62 (0.44,0.88)

Dog Area

Binary Logistic Regression
slide67

Greater car use related to neighborhood characteristics and connectivity?

Hairston Park

Tobie Grant Park

cluster analysis
Cluster Analysis
  • Goal: identify homogenous subgroups of urban park users that are as different as possible from each other
  • 2 step cluster analysis
    • No a-prior number of groups
    • Large sample size (N=2,930)
    • Continuous and categorical data
cluster analysis1
Cluster Analysis
  • 19.1% of sample in Cluster 1
    • High intensity neighborhood exercisers
  • 23.0% of sample in Cluster 2
    • Moderate intensity neighborhood exercisers
  • 36.7% of sample in Cluster 3
    • Non-neighborhood sedentary child caregivers
  • 13.3% of sample in Cluster 4
    • Non-neighborhood sports enthusiasts
cluster analysis2
Cluster Analysis
  • Cluster 1: High intensity neighborhood exercisers
    • Lived closer
    • Younger
    • Male
    • White
    • Non-motorized transportation
    • Got more of their total pa at the park
    • Visit more frequently
    • More likely to engage in fitness/exercise, run/jog, bike, walk, pass through the park, and commute through the park
slide73

Cluster Analysis

  • Cluster 2: Moderate intensity neighborhood exercisers
    • Lived closer
    • Older
    • Female
    • White
    • Non-Hispanic
    • Non-motorized transportation
    • Got more of their total pa at the park
    • Visit more frequently
    • More likely to engage in fitness/exercise, walk their dog, and walk
cluster analysis3
Cluster Analysis
  • Cluster 3: Non-neighborhood sedentary child caregivers
    • Lived further from the park
    • Use motorized transportation
    • Younger
    • Female
    • Non-white
    • Went less frequently
    • Got less of their total pa at the park
    • Were more likely to take their children to the park and playground, watch sports, swim, fish, camp, picnic, engage in community events, or other activities
slide75

Cluster Analysis

  • Cluster 4: Non-neighborhood sports enthusiasts Lived further from the park
    • Lived furthest from the park
    • Use motorized transportation
    • Younger
    • Male
    • Hispanic
    • Went less frequently
    • Got more of their total pa at the park
    • Were more likely to play organized and non-organized sports and disk golf.
cluster analysis4
Cluster Analysis
  • Somewhat similar to study done by Emory and GT students
    • Neighborhood exercisers (68.2%)
    • Sports enthusiasts (26.0%)
    • Community center aficionados (2.8%)
    • Leisure lovers (2.8%)
conclusions1
Conclusions
  • Activities engaged in and distance to the park were more important than most of the demographic variables
  • People tend to travel further to engage in certain types of activities (Giles-Corti & Donovan, 2002)
  • Can be used with social marketing to increase park use in different segments of the population
conclusions2
Conclusions
  • Parks are an important venue for physical activity
  • People who live closer to parks use them more frequently and get more of their total physical activity at the park
  • Should encourage planners to place parks within neighborhoods with amenities than encourage physical activity, such as trails
multi institutional collaborative team
Multi-Institutional Collaborative Team

Centers for Disease Control and Prevention

Dr. Howard Frumkin, CDC

Dr. Candace Rutt, CDC

Laura Biazzo, CDC

Shauntrelle Chappell, CDC

Emory University

Dr. Karen Mumford

Dr. Lance Waller

Georgia State University

Dr.Amy Helling

Georgia Institute of Technology

Dr. Cheryl Contant

Dr. Steve French

Tony Giarrusso

University of Georgia

Dr. Steve Dempsey

We gratefully acknowledge support from the Robert Wood Johnson Foundation Active Living Research Program

neighborhood parks and active living npal1

Neighborhood Parks and Active Living (NPAL)

Candace Rutt, PH.D.

Centers for Disease Control and Prevention

key research questions1
Key Research Questions
  • Who uses parks and for what purposes?
  • To what extent do parks serve as venues for physical activity?
  • What factors predict park use and physical activity?

Individual factors

Parks attributes

Park connectivity

Neighborhood attributes

Other factors??

Ecological

Framework

slide82

Study design

  • Phase I: Descriptive study
    • In-park surveys
    • Direct observation of physical activity
    • GIS distances from home to parks
study parks1
Study parks
  • 12 parks selected from Atlanta metro county: DeKalb County
  • Park selection goal: maximize variability on key factors
    • Size
    • Facilities
    • Programming
    • SES
    • Ethnicity of neighborhood
    • Neighborhood configuration
phase i data collection1
Phase I: Data Collection
  • June – August 2004
  • 8 days per park
    • 4 weekdays
    • 4 weekend days
  • 14 hours per day (6:30AM – 8:30PM)
  • Intercept Surveys and Direct Observation
intercept surveys1
Intercept surveys
  • Stationed at entrances and
  • exits
  • 13 intercept questions
  • 6+years old
  • Spanish survey
slide86

Direct Observation of Physical Activity

Divided parks into activity scanning areas

Recorded number

crossing line on trail

slide87

GIS Measures

  • Mapped address data from respondents
  • Indicated mode
  • Calculated closest distance to park from home via street network
  • Created ½ mile street network and “crow fly” buffers to conduct walkability measures
demographics direct observation1

9% Teen

37% Female

63% Male

50% Adult

39% Children

3% Older Adult

Total: 15,917 park users

Demographics Direct Observation

physical activity direct observation1

9% Vigorous

26% Sedentary

29% Moderate

35% Light

Physical Activity Direct Observation
binary logistic regression1

Outcome variables

% Exercise

Visits/week

Active Activities

(25% or less; greater

(fewer than 2 d/wk;

than 25%)

2 or more d/wk)

(active versus sed)

Demographic

Age

Race

White (base)

1.9 (1.5,2.5)

0.29(0.23,0.38)

Black

Other

1.9 (1.3,2.8)

1.8 (1.1,2.8)

1.7 (1.1,2.5)

Ethnicity

Gender

1.2 (1.0,1.5)

Visits/week

6.6 (5.4,8.1)

Travel behaviors

1.3 (1.001,1.7)

5.2 (4.1,6.6)

2.5 (1.9,3.3)

Mode

GIS travel dist.

0.95 (0.94,0.97)

Environmental

1.5(1.02,2.15)

Walkable Neigh

Pool

0.40 (0.22,0.73)

0.38 (.21,.70)

2.2 (1.3,3.7)

Rec Center

Trail

1.3 (1.03,1.6)

0.52 (0.41,0.67)

Restrooms

0.62 (0.44,0.88)

Dog Area

Binary Logistic Regression
slide93

Greater car use related to neighborhood characteristics and connectivity?

Hairston Park

Tobie Grant Park

cluster analysis5
Cluster Analysis
  • Goal: identify homogenous subgroups of urban park users that are as different as possible from each other
  • 2 step cluster analysis
    • No a-prior number of groups
    • Large sample size (N=2,930)
    • Continuous and categorical data
cluster analysis6
Cluster Analysis
  • 19.1% of sample in Cluster 1
    • High intensity neighborhood exercisers
  • 23.0% of sample in Cluster 2
    • Moderate intensity neighborhood exercisers
  • 36.7% of sample in Cluster 3
    • Non-neighborhood sedentary child caregivers
  • 13.3% of sample in Cluster 4
    • Non-neighborhood sports enthusiasts
cluster analysis7
Cluster Analysis
  • Cluster 1: High intensity neighborhood exercisers
    • Lived closer
    • Younger
    • Male
    • White
    • Non-motorized transportation
    • Got more of their total pa at the park
    • Visit more frequently
    • More likely to engage in fitness/exercise, run/jog, bike, walk, pass through the park, and commute through the park
slide99

Cluster Analysis

  • Cluster 2: Moderate intensity neighborhood exercisers
    • Lived closer
    • Older
    • Female
    • White
    • Non-Hispanic
    • Non-motorized transportation
    • Got more of their total pa at the park
    • Visit more frequently
    • More likely to engage in fitness/exercise, walk their dog, and walk
cluster analysis8
Cluster Analysis
  • Cluster 3: Non-neighborhood sedentary child caregivers
    • Lived further from the park
    • Use motorized transportation
    • Younger
    • Female
    • Non-white
    • Went less frequently
    • Got less of their total pa at the park
    • Were more likely to take their children to the park and playground, watch sports, swim, fish, camp, picnic, engage in community events, or other activities
slide101

Cluster Analysis

  • Cluster 4: Non-neighborhood sports enthusiasts Lived further from the park
    • Lived furthest from the park
    • Use motorized transportation
    • Younger
    • Male
    • Hispanic
    • Went less frequently
    • Got more of their total pa at the park
    • Were more likely to play organized and non-organized sports and disk golf.
cluster analysis9
Cluster Analysis
  • Somewhat similar to study done by Emory and GT students
    • Neighborhood exercisers (68.2%)
    • Sports enthusiasts (26.0%)
    • Community center aficionados (2.8%)
    • Leisure lovers (2.8%)
conclusions3
Conclusions
  • Activities engaged in and distance to the park were more important than most of the demographic variables
  • People tend to travel further to engage in certain types of activities (Giles-Corti & Donovan, 2002)
  • Can be used with social marketing to increase park use in different segments of the population
conclusions4
Conclusions
  • Parks are an important venue for physical activity
  • People who live closer to parks use them more frequently and get more of their total physical activity at the park
  • Should encourage planners to place parks within neighborhoods with amenities than encourage physical activity, such as trails
multi institutional collaborative team1
Multi-Institutional Collaborative Team

Centers for Disease Control and Prevention

Dr. Howard Frumkin, CDC

Dr. Candace Rutt, CDC

Laura Biazzo, CDC

Shauntrelle Chappell, CDC

Emory University

Dr. Karen Mumford

Dr. Lance Waller

Georgia State University

Dr.Amy Helling

Georgia Institute of Technology

Dr. Cheryl Contant

Dr. Steve French

Tony Giarrusso

University of Georgia

Dr. Steve Dempsey

We gratefully acknowledge support from the Robert Wood Johnson Foundation Active Living Research Program