Park Accessibility Analysis with ArcGIS Online

The following tutorial describes how to perform simple urban park accessibility analysis using ArcGIS Online with publicly-accessible park location data.

Literature Review

Parks and Public Health

Fewer than half of all Americans meet the national recommendations for moderate to vigorous physical activity - at least 60 minutes/day for youth and 150 minutes/week for adults - and sedentary lifestyles are associated with serious chronic diseases like diabetes, heart disease, and cancer (USDHHS 2008; 2010). Accordingly, physicians have been encouraged to routinely counsel patients about physical activity and to offer "park prescriptions," identifying nearby parks and recommending regular visits (NRPA 2014; Puett et al 2014).

This makes neighborhood parks with large open spaces fundamental infrastructure to support public health (Cohen et al. 2016). Across the United States, over 9,000 local government agencies and organizations manage more than 108,000 public park facilities and 65,000 indoor recreational facilities (Godbey and Mowen 2010).

However, many urban parks were created in a time when work involved strenuous physical activity and fewer labor-saving devices. Accordingly, parks were designed for leisure and recreation rather than physical fitness (Olmstead 1870).

A park is a piece of ground in or near a city or town kept for ornament and recreation (Merriam-Webster 2019). Mertes and Hall (1996), group parks into five categories:

  1. Very small parks (under 2 acres, also called mini-parks, pocket parks, or parklets)
  2. Neighborhood parks
  3. Community and large urban parks
  4. Sports complexes
  5. Natural resource areas

Neighborhood parks represent an especially important category of park from the perspective of public health. Neighborhood parks are usually between two and twenty acres and are intended to serve the routine needs for local residents living within a one-mile radius. Neighborhood parks often contain more facilities than mini-parks: picnic tables, children's playgrounds, basketball courts, ball fields for organized sports, restrooms, green spaces, shade trees, etc. These facilities allow these parks to be useful to a wide variety of people at all stages of life.

Beyond physical health, green infrastructure like parks can also be advantageous for mental health (Sturm and Cohen 2014), heat and emissions mitigation (GIT 2016), reduced greenhouse gas emissions (Groth et al. 2008), and economic development (TPL 2018).

ParkServe and ParkScore

The Trust for Public Land (TPL) is an advocacy organization with a mission to "create parks and protect land for people, ensuring healthy, livable communities for generations to come" (TPL 2019a).

In support of their advocacy, the TPL maintains two products. ParkServe is a a comprehensive database of local parks in the nearly 14,000 cities, towns and communities. ParkScore ® is a more in-depth index that rates the 100 largest cities in the US in how they are meeting the need for parks. ParkScores are an ordinal ranking from zero (worst) to 100 (best) and are based on four groups of characteristics: acerage, investment, amenities, and access (TPL 2019b).

ParkScore ®is similar to WalkScore, which is an index on a scale of zero (bad) to 100 (good) that rates neighborhoods on the extent to which residents can perform their daily activities by walking rather than using automobiles ( 2019).


Open Data

Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike (Open Knowledge Foundation 2019).

Open data is distinguished from proprietary data, which is generated by private companies to allow them to control and safeguard its competitiveness over other companies. This proprietary data is protected by copyright laws, patent laws and secrecy laws (The Law Dictionary 2019). Geospatial data collected by ESRI is often proprietary data. Data from Google Maps is proprietary, although they make it readily available to the you in exchange for showing you advertising and tracking your web activity.

Open data is also distinguished from confidential data, which needs to be kept private or secret for legal and/or business reasons. Confidential data often refers to information about private individuals (such as health or financial records) that organizations are legally or professionaly obligated not to share (Law Insider 2019). Data on individuals collected as a part of medical research is usually confidential data.

Many government entities routinely make non-confidential data available to the public as open data through their websites. Open data portals are web-based interfaces designed to make it easier to find re-usable information (European Commission 2018).

This movement toward open government data has numerous benefits to society as a whole (Open Knowledge Foundation 2019):

All data requires effort to capture and, therefore, costs money. Open data is paid for by the citizens of the city, state, or nation that captures that data, and constraints on public funding limit the amount of data that can be made available.

In contrast, proprietary data is paid for by companies that make money off of that data. Therefore, proprietary data can cover a wider variety of items in more depth than open data if there are businesses or customers willing to pay for the capture, processing, and maintenance of that data. For example, marketing data is very labor-intensive to capture, but businesses are willing to pay for that data because the benefits of that data for guiding business operations outweighs the cost of buying that data from a marketing research company.

Data Formats

Although most large city and county governments make some open data available, the amount, variety, and quality of this data can vary widely.

Of equal importance, there are no universal standards for how this geospatial data is made available, meaning an GIS analyst that uses open data needs to be prepared to process data that is made available through a variety of web apps and in a variety of formats. Some of the common formats include:

Download and Import

Data Portal Download

Many cities make their geospatial data available through data portals. If data has been uploaded to the portal with location information, it can usually be downloaded as a shapefile, GeoJSON file, and/or KML file.

Socrata is a web app used by many government agencies for disseminating open data, including geospatial data. The video below demonstrates how to download a shapefile from a Socrata data portal, in this case, parks in Chicago:

  1. Locate the data portal
  2. Find the data set in the data catalog
  3. Select Export and choose Shapefile
  4. Save the file to your local hard drive
  5. In ArcGIS Online, go to Content
  6. Select Add item -> From my computer
  7. Select the file on your hard drive
  8. Give the file a meaningful name and some tags
  9. When Creating Service completes, open the layer in a New map viewer
  10. If the layer has an attribute for the type of park, use it to style the layer
  11. Save the map under a meaningful name and share it to get a link
Downloading a Shapefile From a Socrata Data Portal

Feature Services

Government agencies often use ArcGIS servers as part of their enterprise GIS infrastructure and some even use ESRI's ArcGIS Hub software to power their public open data portals. These servers can make feature services available that allow you to connect your GIS software directly to the servers rather than having to download and import geospatial data files.

The video below shows how to add a map layer from a service using Portland's GIS data portal as an example.

  1. Find the data portal
  2. Find the data set in the portal
  3. Select APIs and copy the GeoService URL
  4. Create a new map in ArcGIS Online
  5. Add Layer From Web
  6. Select An ArcGIS Server Web Service
  7. Paste the server URL into the URL box
  8. Remove any extra text after the MapServer and number part of the URL
  9. Click OK and the layer should appear in your map
Adding a Map Layer From an ArcGIS REST Map Service

Static Shapefiles

Some cities still disseminate data as shapefiles that you can select from a list of static files. These files often require some searching to file.

For example, even though the City of Orlando has an open data portal, they still distribute the shapefile of park boundaries as static shapefiles. The video below demonstrates how to find such files if you can't find them in the data portal.

Once you have downloaded a zipped shapefile, follow the instructions above to import it into ArcGIS Online.

Downloading a Static Shapefile


City park data will often contain facilities like mini-parks and unimproved areas that are not particularly useful for recreation, and you will want to filter out those facilities from your data.

Optimally, the data will contain a field indicating the type of park, and you can use the Filter function to select features to ignore. The video example below uses park data from Chicago as an example, and the exact name and values in the fields will vary by city.

  1. View the table of attributes to see if there is a type field
  2. Click the Filter icon under the layer of parks
  3. Add a set of filters on the type field for all the types of parks associated with recreation
  4. Style the layer by the type field so you can see what you've got
  5. Save As the map under a meaningful name and Share to get a link
Filtering Parks Based On a Type Field

If there is no type field but there is a size field, you can filter out small parks. The video example below uses park data from Portland as an example, filtering in only parks of two acres or greater, which will catch most neighborhood parks.

  1. Click the Filter icon under the layer of parks
  2. Select the size field - in this case Acres
  3. Choose is at least and enter the minimum size - in this case two acres
  4. Apply Filter
  5. Save As the map under a meaningful name and Share to get a link
Filtering Parks Based On Size


To be optimally useful and convenient, a park needs to be within a ten-minute walk of a person's home. Buffers are areas of a specific distance around features. We can use a 1/4 mile buffer as a rough estimate of the area around parks that are within a ten-minute walk.

The example below creates buffers around parks in Portland, OR.

  1. Click the Perform Analysis icon under the layer of parks
  2. Select Use Proximity -> Create Buffers
  3. Enter buffer size - in this case 0.25 for 1/4 of a mile
  4. Select Options and Dissolve so that overlapping buffers are combined
  5. Give the buffer layer a meaningful name - Using your name and the name of the city can help uniquely identify your layer in your organization
  6. Unclick Use current map extent so that parks outside what you can see on the current map get buffered too
  7. Click Run Analysis
  8. Style the resulting layer so that it clearly stands out over the map
  9. Save As the map under a meaningful name and Share to get a link
Quarter-Mile Buffers Around Parks

ArcGIS Online does have a tool (Create Drive-Time Areas) that can calculate walking distance in a way that considers the street network and obstacles to walking (like rivers and freeways), although that tool is very credit-intensive and should only be used with small data sets or when you have access to a large number of credits.

Walking-Dependent Neighborhood

In areas where people have access to automobiles, it is possible for them to drive to a park for recreation, although that added step can be a disincentive. However, areas where a significant percent of the population does not have access to either an automobile or a park in convenient walking distance are candidates for adding parks. Low automobile access often (but not always) correlates with income, making this a social justice issue.

To find area with low automobile access, you can use American Community Survey data for auto ownership. Such information is in Table S2504: PHYSICAL HOUSING CHARACTERISTICS FOR OCCUPIED HOUSING UNITS.

In the example video below, this data has been included in a ZIP Code layer available from the FSC ArcGIS Online organization.

  1. Add and Search for Layers in My Organization for Minn 2017 American Community Survey Zip Code Tabulation Areas
  2. Style the layer based on the % No Vehicle variable
  3. Move the auto ownership layer under the park buffer layer so you can see the areas with low park access and low auto ownership
  4. Use the Measure Area and Distance tool to find the lat/long for a location in a prospective neighborhood and look around in Google Maps Street View for a possible park location
Finding Neighborhoods with Low Automobile Ownership and Walking Access to Parks


Cohen, Deborah A., Bing Han, Catherine J. Nagel, Peter Harnik, Thomas L. McKenzie, Kelly R. Evenson, Terry Marsh, Stephanie Williamson, Christine Vaughan, and Sweatha Katta. 2016. "The First National Study of Neighborhood Parks: Implications for Physical Activity." American Journal of Preventative Medicine 51 (4), 419–426.

European Commission. 2018. "Open Data Portals." Accessed 3 July 2019.

Godbey, Geoffrey and Andrew Mowen. 2010. "The benefits of physical activity provided by park and recreation services: the scientific evidence." Accessed 3 July 2019.

Groth, Philip, Rawlings Miller, Nikhil Nadkarni, Marybeth Riley, and Lilly Shoup. 2008. "Quantifying the greenhouse gas benefits of urban parks." Accessed 3 July 2019.

The Law Dictionary. 2019. "What is proprietary data?" Accessed 3 July 2019.

Law Insider. 2019. "Definition of Confidential Information." Accessed 3 July 2019.

Merriam-Webster. 2019. "Park." Accessed 3 July 2019.

Mertes, James David and James R. Hall. 1996. Park, Recreation, Open Space and Greenway Guidelines. Ashburn, VA: National Recreation and Park Association.

National Recreation and Park Association (NRPA). 2014. "Prescribing parks for better health: Success stories."

Olmsted, Frederick Law. 1870. "Public parks and the enlargement of towns." Larice, Michael and Elizabeth Macdonald (editors), "The Urban Design Reader," 2nd edition. London: Routledge.

Open Knowledge Foundation. 2019. "What is Open Data?" Accessed 3 July 2019.

Puett, Robin, Jane Teas, Vanesa España-Romero, Enrique Garcia Artero, Duck-chul Lee, Meghan Baruth, Xuemei Sui, Jessica Montresor-López, and Steven N. Blair. 2014. "Physical activity: Does environment make a difference for tension, stress, emotional outlook, and perceptions of health status?" Journal of Physical Activity and Health 11 (8), 1503–1511.

Sturm, Roland, and Deborah Cohen. 2014. "Proximity to urban parks and mental health." The Journal of Mental Health Policy and Economics 17 (1): 19-24.

Trust for Public Lands (TPL). 2018. "The economic benefits of Cleveland Metroparks." Accessed 3 July 2019.

Trust for Public Lands (TPL). 2019a. "About us." Accessed 3 July 2019.

Trust for Public Lands (TPL). 2019b. "Parkscore® and Parkserve - About, Methodology, and FAQ." Accessed 3 July 2019.

United States Department of Health and Human Services (USDHHS). 2008. "Physical Activity Guidelines for Americans." Washington, DC: United States Department of Health and Human Services.

United States Department of Health and Human Services (USDHHS). 2010. "The Surgeon General’s Vision for a healthy and fit nation." Accessed December 12, 2015.

Urban Climate Lab at the Georgia Institute of Technology (GIT). 2016. "The benefits of green infrastructure for heat mitigation and emissions reductions in cities." Accessed 3 July 2019. 2019. "Walk Score Methodology." Accessed 3 July 2019.

Appendix: Local Health Departments and Open Data Portals

1. New York, NY

NYC Department of Health and Mental Hygiene:

NYC Open Data (Socrata):

Parks (Shapefile, GeoJSON, KML):

2. Los Angeles, CA

Los Angeles County Department of Public Health:

Los Angeles Open Data (Socrata):

Parks (Shapefile, GeoJSON, KML):

3. Chicago, IL

Department of Public Health:

Chicago Data Portal (Socrata):

Parks (Shapefile, GeoJSON, KML):

4. Dallas, TX

Dallas County Health and Human Services:

Dallas Open Data (Socrata):

City of Dallas Park Polygons (Shapefile):

5. Houston, TX

Houston Health Department:

City of Housting Open GIS Data (ArcGIS Hub):

Park Boundaries (Shapefile, KML, Feature Service):

6. Washington, DC

DC Health:

Open Data DC (ArcGIS Hub):

Parks and Recreation Areas (Shapefile, KML, Feature Service):

7. Philadelphia, PA

City of Philadelphia Department of Public Health:


Parks & Recreation Assets (Shapefile, GeoService, GeoJSON):

8. Miami, FL

Florida Department of Health in Miami-Dade County:

Miami-Date Open Data Hub (ArcGIS Hub):

County Park Boundary:

9. Atlanta, GA

Fulton County Board of Health:

Atlanta Regional Commission Open Data and Mapping Hub (ArcGIS Hub):

Parks (Shapefile, KML, Feature Service):

10. Boston, MA

Boston Public Health Commission:

Boston Maps Open Data (ArcGIS Hub):

Open Space (Shapefile, KML, Feature Service):

11. San Fransisco, CA

San Francisco Department of Public Health:

DataSF (Socrata):

Park Lands (Shapefile, KML, GeoJSON - use the original link to get a zipped shapefile):

12. Phoenix, AZ

Maricopa County Department of Public Health:

Maricopa Association of Governments Open GIS:

Park data only available in a web map

13. Riverside, CA

Riverside County Public Health:

City of Riverside Open Data GIS Portal (ArcGIS Hub):

City Parks (KML, Shapefile, Feature Service):

14. Detroit, MI

Detroit Health Department:

Detroit's Open Data Portal (Socrata):

Parks 2016 (KML, Shapefile, GeoJSON):

15. Seattle, WA

Public Health — Seattle & King County:

King County GIS Open Data (ArcGIS Hub):

Parks in King County / park area (Shapefile, KML, Feature Service):

16. Minneapolis, MN

City of Minneapolis Health Department

opendataminneapolis (ArcGIS Hub):

Parks (Shapefile, KML, Feature Service):

17. San Diego, CA

Public Health Services - County of San Diego:

SanGIS/SANDAG GIS Data Warehouse (Requires free login):

18. Tampa, FL

Florida Department of Health in Hillsborough County:

City of Tampa GeoHub (ArcGIS Hub):

Park Polygons (Shapefile, KML, Feature Service):

19. Denver, CO

Denver Department of Public Health and the Environment:

Denver Open Data:

Denver Parks:

20. St. Louis, MO

City of St. Louis Department of Health:

City of St. Louis Open Data (CommonSpot):

Park Data (Shapefile):

21. Baltimore, MD

Baltimore City Health Department:

Open Baltimore:

Parks (CSV addresses only):

22. Charlotte, NC

Mecklenburg County Health Department:

Open Mapping (proprietary portal):

Park Locations (Shapefile):

23. Orlando, FL

Florida Department of Health in Orange:

Orlando Open Data (Socrata):

Parks (Point Shapefile):

24. San Antonio, TX

City of San Antonio Metropolitan Health District:

Open Data SA (Comprehensive Knowledge Archive Network - CKAN):

Park Boundaries (Shapefile, KML, GeoJSON, API):

25. Portland, OR

Multnomah County Health Department:

Portland Maps - Open Data (ArcGIS Hub):

Parks (Shapefile, KML, GeoJSON, or API):