Chesapeake Conservancy Studies Park Mobility During COVID 19

18 of 25 Metro Areas Experienced Significant Increases in Park Visitation

Chesapeake Bay Watershed: Washington D.C. +93.99%, Baltimore +86.97% 

Annapolis, MD – Today, Chesapeake Conservancy’s Conservation Innovation Center (CIC) released its findings on park mobility during COVID 19. Chesapeake Conservancy’s Geospatial Data Scientist Kumar Mainali and Geospatial Technology Manager Emily Mills used Google’s COVID-19 Mobility Report (see note about Google data at end) to detect change in human mobility in parks in the top 25 United States metro areas by population.

Their study compares mobility (park usage) between Jan 3-Feb 6, 2020 (pre-COVID) and May 16-July 14, 2020 (during COVID) and measured the largest metropolitan areas by population and those within approximately a one-hour-drive.

“The data definitively show what we believed to be true, during this stressful time for our nation, people are turning to parks for health and recreation,” said Chesapeake Conservancy President and CEO Joel Dunn. “Nature is there for us when we need it most. So now more than ever is the time to fund our parks and protect our remaining open spaces. On the federal level, Congress is answering this call through the Great American Outdoors Act, which would fully and permanently fund the Land and Water Conservation Fund and provide billions to address deferred park maintenance. We need to also answer this call on the state and local level, where this huge increase in park visitation more than demonstrates the public’s demand and value for nature. Programs like Maryland’s Program Open Space, Virginia’s Land Conservation Fund and Land Preservation Tax Credit, Pennsylvania’s Keystone Recreation, Park and Conservation Fund, and Delaware’s Open Space Program all help to meet this demand and support local economies through outdoor recreation and tourism, and these programs are an essential part of the solution for our region’s economic recovery.”

Key findings:

  • 18 of 25 metro areas experienced significant increases in park visitation during COVID-19
  • Within the Chesapeake Bay watershed, metro areas experienced a significant increase in park visitation: Washington D.C. +93.99%, Baltimore, MD +86.97%
  • Increases ranged from 5.68 % (San Antonio) to 145% (Detroit)
  • 7 of 25 metro areas, all in Florida or the Southwestern US, experienced a decrease in park visitation; for some of these cities heat is a likely cause of decreased visitation
  • Some of the variability in park visitation is likely due to weather conditions; year-on-year data were not available for comparison
    Decreases ranged from -3.83% (Los Angeles) to -31% (Miami)
  • Outside of the heat variable, the data shows a large increase in park visitation during the COVID-19 pandemic and aligns with on-the-ground reports from park managers

+/- By Market

  • Detroit, MI +145.26%
  • Boston, MA +123.12%
  • Minneapolis-St. Paul, MN +98.41%
  • Washington D.C. +93.99%
  • St. Louis, MO +93.66%
  • New York City, NY +90.01%
  • Philadelphia, PA +88.59%
  • Baltimore, MD +86.97%
  • Chicago, IL +85.25%
  • Seattle-Tacoma-Bellevue WA +78.14%
  • Portland OR +75.33%
  • Denver, CO +72.19%
  • Atlanta, GA +45.08%
  • Dallas, TX +34.29%
  • Charlotte, NC +31.75%
  • San Francisco, CA +23.14%
  • Houston, TX +10.64%
  • San Antonio, TX +5.68%
  • Miami -31.07%
  • Tampa, FL -16.88%
  • Phoenix, AZ -14.97%
  • Orlando, FL -13.78%
  • San Diego, CA -7.53%
  • Riverside-San Bern-Ontario -7.29
  • Los Angeles, CA -3.83%

A note about the Google data: Sourced from Google LLC “Google COVID-19 Community Mobility Reports,” https://www.google.com/covid19/mobility/ Accessed: May 20, 10:36AM. “These datasets show how visits and length of stay at different places change compared to a baseline. We calculate these changes using the same kind of aggregated and anonymized data used to show popular times for places in Google Maps….. We calculate these insights based on data from users who have opted-in to Location History for their Google Account, so the data represents a sample of our users.”

Image credit: Mike Weiss