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The population density of University Park, MD was 5,283 in 2018. The population density of County Center, VA was 1,667 in 2018.

Population Density

Population Density is computed by dividing the total population by Land Area Per Square Mile.

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Geographic and Population Datasets Involving County Center, VA or University Park, MD

  • API

    MTA Transit Oriented Development (TOD) Data

    opendata.maryland.gov | Last Updated 2024-03-25T15:38:10.000Z

    *** DISCLAIMER - This web page is a public resource of general information. The Maryland Mass Transit Administration (MTA) makes no warranty, representation, or guarantee as to the content, sequence, accuracy, timeliness, or completeness of any of the spatial data or database information provided herein. MTA and partner state, local, and other agencies shall assume no liability for errors, omissions, or inaccuracies in the information provided regardless of how caused; or any decision made or action taken or not taken by any person relying on any information or data furnished within. *** This dataset assesses rail station potential for different forms of transit oriented development (TOD). A key driver of increased transit ridership in Maryland, TOD capitalizes on existing rapid transit infrastructure. The online tool focuses on the MTA’s existing MARC Commuter Rail, Metro Subway, and Central Light Rail lines and includes information specific to each station. The goal of this dataset is to give MTA planning staff, developers, local governments, and transit riders a picture of how each MTA rail station could attract TOD investment. In order to make this assessment, MTA staff gathered data on characteristics that are likely to influence TOD potential. The station-specific data is organized into 6 different categories referring to transit activity; station facilities; parking provision and utilization; bicycle and pedestrian access; and local zoning and land availability around each station. As a publicly shared resource, this dataset can be used by local communities to identify and prioritize area improvements in coordination with the MTA that can help attract investment around rail stations. You can view an interactive version of this dataset at geodata.md.gov/tod. ** Ridership is calculated the following ways: Metro Rail ridership is based on Metro gate exit counts. Light Rail ridership is estimated using a statistical sampling process in line with FTA established guidelines, and approved by the FTA. MARC ridership is calculated using two (2) independent methods: Monthly Line level ridership is estimated using a statistical sampling process in line with FTA established guidelines, and approved by the FTA. This method of ridership calculation is used by the MTA for official reporting purposes to State level and Federal level reporting. Station level ridership is estimated by using person counts completed by the third party vendor. This method of calculation has not been verified by the FTA for statistical reporting and is used for scheduling purposes only. However, because of the granularity of detail, this information is useful for TOD applications. *Please note that the monthly level ridership and the station level ridership are calculated using two (2) independent methods that are not interchangeable and should not be compared for analysis purposes.

  • API

    Energy and Water Data Disclosure for Local Law 84 2018 (Data for Calendar Year 2017)

    data.cityofnewyork.us | Last Updated 2024-10-01T19:33:28.000Z

    Data and metrics on water and energy consumption in privately owned buildings over 25,000 ft2 and in City-owned buildings over 10,000 ft2.

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    Energy and Water Data Disclosure for Local Law 84 2019 (Data for Calendar Year 2018)

    data.cityofnewyork.us | Last Updated 2024-10-01T19:33:43.000Z

    Data and metrics on water and energy consumption in privately owned buildings over 25,000 ft2 and in City-owned buildings over 10,000 ft2.

  • API

    Energy and Water Data Disclosure for Local Law 84 2017 (Data for Calendar Year 2016)

    data.cityofnewyork.us | Last Updated 2024-10-01T19:33:13.000Z

    Data and metrics on water and energy consumption in privately owned buildings over 25,000 ft2 and in City-owned buildings over 10,000 ft2.

  • API

    Energy and Water Data Disclosure for Local Law 84 2016 (Data for Calendar Year 2015)

    data.cityofnewyork.us | Last Updated 2024-10-01T19:33:01.000Z

    Data and metrics on water and energy consumption in privately owned buildings over 25,000 ft2 and in City-owned buildings over 10,000 ft2.

  • API

    Energy and Water Data Disclosure for Local Law 84 2015 (Data for Calendar Year 2014)

    data.cityofnewyork.us | Last Updated 2024-10-01T19:32:47.000Z

    Data and metrics on water and energy consumption in privately owned buildings over 25,000 ft2 and in City-owned buildings over 10,000 ft2.

  • API

    Energy and Water Data Disclosure for Local Law 84 2014 (Data for Calendar Year 2013)

    data.cityofnewyork.us | Last Updated 2024-10-01T19:32:33.000Z

    Data and metrics on water and energy consumption in privately owned buildings over 25,000 ft2 and in City-owned buildings over 10,000 ft2.

  • API

    National Community Based Survey of Supports for Healthy Eating and Active Living (CBS HEAL)

    data.cdc.gov | Last Updated 2023-08-25T15:08:48.000Z

    Community-Based Survey of Supports for Healthy Eating and Active Living (CBS HEAL) is a CDC survey of a nationally representative sample of U.S. municipalities to better understand existing community-level policies and practices that support healthy eating and active living. The survey collects information about policies such as nutrition standards, incentives for healthy food retail, bike/pedestrian-friendly design, and Complete Streets. About 2,000 municipalities respond to the survey. Participating municipalities receive a report that allows them to compare their policies and practices with other municipalities of similar geography, population size, and urban status. The CBS HEAL survey was first administered in 2014 and was administered again in 2021. Data is provided in multiple formats for download including as a SAS file. A methods report and a SAS program for formatting the data are also provided.

  • API

    NYC Building Energy and Water Data Disclosure for Local Law 84 (2022-Present)

    data.cityofnewyork.us | Last Updated 2024-10-01T19:56:35.000Z

    Local Law 84 of 2009 (LL84) requires annual energy and water benchmarking data to be submitted by owners of buildings with more than 50,000 square feet. This data is collected via the Environmental Protection Agency's (EPA) <a href="https://www.energystar.gov/buildings/tools-and-resources/portfolio-manager-0">Portfolio Manager website</a> Each property is identified by it's EPA assigned property ID, and can contain one or more tax lots identified by one or more BBLs (Borough, Block, Lot) or one or more buildings identified by one or more building identification numbers (BIN) Please visit <a href="https://www1.nyc.gov/site/buildings/codes/benchmarking.page">DOB's Benchmarking and Energy Efficiency Rating page</a> for additional information.