As of 2024, Chugach State Park in Anchorage, Alaska, was the largest city park in the United States by a long shot, spanning 464,318 acres. Second in the ranking was the Great Dismal Swamp in the Coastal Plain Region of southeastern Virginia and northeastern North Carolina, at 113,000 acres. A wide variety of park authorities Most parks in the U.S. are owned by the municipality, state, county, regional agency, or the federal government. Both McDowell Sonoran Preserve and South Mountain Preserve are part of the state park system along with most of the parks in the ranking. One of the more well-known park authorities is the National Park Service (NPS) – an agency of the federal government. The Golden Gate National Recreation Area was the most visited NPS park in 2024 alongside many other well-known U.S. parks. What defines a park? Parks in the U.S. are often called a variety of names, just a few of which are: forest, reserve, preserve and wildlife management area. Sometimes the differences between parks in the U.S. can vary massively from monuments to expansive woodland. In 2024, Central Park in New York, topped the ranking of the most visited city parks in the U.S.

The city park with the highest annual visitation in 2023 was Central Park in New York, accounting for a total of ********** visitors. The second most visited city park in that year was Golden Gate Park in San Francisco, with nearly half the visitation of Central Park, at **********.

This graph depicts the size of county-owned city parks in the U.S. in 2010. The Bear Creek Pioneers Park in Houston has an area of 2,168 acres.

This graph depicts the size of municipally owned city parks in the U.S. in 2010. The Cullen Park in Houston has a size of 9270 acres.

In 2024, the city in the United States with the highest share of parkland was Anchorage, Alaska, where approximately 84 percent of the city was parkland. In second place, with almost half the percentage of parkland was Fremont, California, where 43 percent of the city was parkland.

The Trust for Public Land built a comprehensive database of local parks in the nearly 14,000 cities, towns, and communities. We used census-defined urban areas to define where to collect and create local data. For each municipality, geographic boundaries were obtained from the U.S. Census 2010 Places geospatial dataset. Associated population estimates are derived from ESRI’s 2018 Demographic Forecasts. We attempted to contact each municipality with a request for their parks data. If no GIS data was provided, we created GIS data for the place based on available resources, such as park information from municipal websites, GIS data available from counties and states, and satellite imagery.

This dataset identifies property managed partially or solely by NYC Parks. This data has been produced in whole or part using secondary data. Data accuracy is limited by the scale and accuracy of the original sources. Site-specific conditions should be field-verified.

Records are added as more land is designated under NYC Parks’ jurisdiction. Each record represents an acquisition.

User Guide: https://docs.google.com/document/d/1NExNJF5YKID04oOopi0fHainRuGG3Pz_jKSrMujPsPk/edit?usp=sharing

Data Dictionary: https://docs.google.com/spreadsheets/d/1Q4DBWu7riNFxWvy1vnTJHoOI3r2L9oW6eCN56jCNyCw/edit?usp=sharing

‘Total tweets’ enumerates all public tweets posted from a GPS latitude/longitude inside that city. ‘Park tweets’ is the total number of tweets posted from inside parks. The ‘% tweets in park’ column calculates Park tweets / total Tweets. ‘Park visitors’ is the number of unique users who tweeted inside one of that city’s municipal park locations as defined by Trust for Public Land’s ParkServe. ‘Parks visited’ is the number of unique facilities from which a tweet was posted within that city. ‘Tweets per capita’ is number of total messages for the entire period divided by the city’s population in 2012.

In 2024, New York City had the highest public park and recreation spending of any city in the United States at approximately *** billion U.S. dollars. Second in the ranking was Chicago, Illinois, which spent around *** million U.S. dollars on parks and rec.

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0 national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

The map provided represents the various parks and areas of open space that are within and neighboring the Salinas city limits. The parks are classified by Type: Small, Community, Neighborhood, Special Recreation, and Large Urban. The parks seen on the map include:Cesar Chavez Community Park Acacia Court El Dorado Community Park Central Community Park Mo Co Sheriff's Posse GroundsTwin Creeks Golf CourseCarmel CornerSalinas Fairways Golf CourseMission Neighborhood ParkMyrtle Court Play LotVeterans Memorial ParkRossi Rico Linear ParkwaySalinas Golf & County ClubConstitution Soccer ComplexCreekbridge Neighborhood ParkNatividad Neighborhood ParkMcKinnon Neighborhood ParkSalinas Fairways Golf CourseNatividad Creek Community ParkWoodside Neighborhood ParkFirehouse Recreation CenterWilliams Ranch Neighborhood ParkBataan Memorial ParkLos Padres Neighborhood ParkLa Paz Neighborhood ParkSalinas Municipal StadiumHartnell Neighborhood ParkSanta Rita Neighborhood ParkNorthgate Tot LotBread Box Recreational CenterLaurelwood Neighborhood ParkGabilan Play LotGene Robinson ParkSteinbeck Neighborhood ParkNorthgate Neighborhood ParkCloster Community ParkLaurel Neighborhood ParkJaycee Tot LotSoberanes Neighborhood ParkLaurel Heights Neighborhood ParkMonte Bella Community ParkSoto SquareSanborn Neighborhood ParkSanta Lucia PlaygroundMaple Play LotFerrasci ParkClay Street Play LotAzahel Cruz ParkSalinas Recreation CenterClaremont Manor Neighborhood ParkHarden Neighborhood ParkExposition Grounds (Expo Site)Frank Paul School ParkSherwood Park

The Austin Parks and Recreation System's ranking on the Trust for Public Land ParkScore Index. This index ranks the park systems of the 100 largest cities in the U.S. based on park acreage, park size, park funding, park access, and a variety of other factors.

This data set supports HE.C.2 of SD23.

Data Source: Trust for Public Land (TPL) ParkScore Analysis

Calculation Method: The ParkScore is assessed by the Trust for Public Land Survey and Analysis. The areas that make up the ParkScore categories include adequate acreage in proportion to a city’s population, services/amenities and investment, and parkland access. The ParkScore Ranking is based on a comparison of 100 participating cities in the United States, and a city’s ranking is based on a composite of their raw score in each category. The higher the score, the lower the ranking number you are on the list of 100 cities. The most desirable ranking on the list is one, currently Austin is ranked 43 out of 100.

Measure Time Period: Annually (Calendar Year)

Automated: No

Date of last description update: 4/1/2020

About this dataset

Context

The dataset tabulates the population of Parks by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Parks across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.97% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Parks is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Parks total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Parks Population by Gender. You can refer the same here

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. Because beaches in Puerto Rico and the U.S. Virgin Islands are open to the public, beaches also provide important outdoor recreation opportunities for urban residents, so we include beaches as parks in this indicator. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022
Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee Easement
Puerto Rico Protected Natural Areas 2018 (December 2018 update): Terrestrial and marine protected areas (PACAT2018_areas_protegidasPR_TERRESTRES_07052019.shp, PACAT2018_areas_protegidasPR_MARINAS_07052019.shp) 
2020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 census
OpenStreetMap data “multipolygons” layer, accessed 3-14-2023

A polygon from this dataset is considered a park if the “leisure” tag attribute is either “park” or “nature_reserve”, and considered a beach if the value in the “natural” tag attribute is “beach”. OpenStreetMap describes leisure areas as “places people go in their spare time” and natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format and translated ton an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.

TNC Lands - Public Layer, accessed 3-8-2023
U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)

Mapping Steps

Most mapping steps were completed using QGIS (v 3.22) Graphical Modeler.
Fix geometry errors in the PAD-US PR data using Fix Geometry. This must be done before any analysis is possible.
Merge the terrestrial PR and VI PAD-US layers.
Use the NOAA coastal relief model to restrict marine parks (marine polygons from PAD-US and Puerto Rico Protected Natural Areas) to areas shallower than 10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature.
Merge into one layer the resulting shallow marine parks from marine PAD-US and the Puerto Rico Protected Natural Areas along with the combined terrestrial PAD-US parks, OpenStreetMap, TNC Lands, and USVI beaches. Omit from the Puerto Rico Protected Areas layer the “Zona de Conservación del Carso”, which has some policy protections and conservation incentives but is not formally protected.
Fix geometry errors in the resulting merged layer using Fix Geometry.
Intersect the resulting fixed file with the Caribbean Blueprint subregion.
Process all multipart polygons to single parts (referred to in Arc software as an “explode”). This helps the indicator capture, as much as possible, the discrete units of a protected area that serve urban residents.
Clip the Census urban area to the Caribbean Blueprint subregion.
Select all polygons that intersect the Census urban extent within 1.2 miles (1,931 m). The 1.2 mi threshold is consistent with the average walking trip on a summer day (U.S. DOT 2002) used to define the walking distance threshold used in the greenways and trails indicator. Note: this is further than the 0.5 mi distance used in the continental version of the indicator. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation.
Dissolve all the park polygons that were selected in the previous step.
Process all multipart polygons to single parts (“explode”) again.
Add a unique ID to the selected parks. This value will be used to join the parks to their buffers.
Create a 1.2 mi (1,931 m) buffer ring around each park using the multiring buffer plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 1.2 mi buffer is created for each park.
Assess the amount of overlap between the buffered park and the Census urban area using overlap analysis. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix. This step creates a table that is joined back to the park polygons using the UniqueID.
Remove parks that had ≤2% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: In the continental version of this indicator, we used a threshold of 10%. In the Caribbean version, we lowered this to 2% in order to capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles.
Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.
Join the buffer attribute table to the previously selected parks, retaining only the parks that exceeded the 2% urban area overlap threshold while buffered. 
Buffer the selected parks by 15 m. Buffering prevents very small parks and narrow beaches from being left out of the indicator when the polygons are converted to raster.
Reclassify the polygons into 7 classes, seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).
Export the final vector file to a shapefile and import to ArcGIS Pro.
Convert the resulting polygons to raster using the ArcPy Polygon to Raster function. Assign values to the pixels in the resulting raster based on the polygon class sizes of the contiguous park areas.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 6 = 75+ acre urban park 5 = >50 to <75 acre urban park 4 = 30 to <50 acre urban park 3 = 10 to <30 acre urban park 2 = 5 to <10 acre urban park 1 = <5 acre urban park 0 = Not identified as an urban park Known Issues

This indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources. 
This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.
This indicator includes parks and beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a park) or incorrect tags (e.g., labelling an area as a park that is not actually a park). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future.

Other Things to Keep in Mind

This indicator calculates the area of each park using the park polygons from the source data. However, simply converting those park polygons to raster results in some small parks and narrow beaches being left out of the indicator. To capture those areas, we buffered parks and beaches by 15 m and applied the original area calculation to the larger buffered polygon, so as not to inflate the area by including the buffer. As a result, when the buffered polygons are rasterized, the final indicator has some areas of adjacent pixels that receive different scores. While these pixels may appear to be part of one contiguous park or suite of parks, they are scored differently because the park polygons themselves are not actually contiguous. 
The Caribbean version of this indicator uses a slightly different methodology than the continental Southeast version. It includes parks within a 1.2 mi distance from the Census urban area, compared to 0.5 mi in the continental Southeast. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation. Similarly, this indicator uses a 2% threshold of overlap between buffered parks and the Census urban areas, compared to a 10% threshold in the continental Southeast. This helped capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles. Finally, the Caribbean version does not use the impervious surface cutoff applied in the continental Southeast

The statewide composite of parcels (cadastral) data for New Jersey was developed during the Parcels Normalization Project in 2008-2014 by the NJ Office of Information Technology, Office of GIS (NJOGIS.) The normalized parcels data are compatible with the NJ Department of the Treasury system currently used by Tax Assessors, and those records have been joined in this dataset. This composite of parcels data serves as one of the framework GIS datasets for New Jersey. Stewardship and maintenance of the data will continue to be the purview of county and municipal governments, but the statewide composite will be maintained by NJOGIS. Parcel attributes were normalized to a standard structure, specified in the NJ GIS Parcel Mapping Standard, to store parcel information and provide a PIN (parcel identification number) field that can be used to match records with suitably-processed property tax data. The standard is available for viewing and download at https://njgin.state.nj.us/oit/gis/NJ_NJGINExplorer/docs/NJGIS_ParcelMappingStandardv3.2.pdf. The PIN also can be constructed from attributes available in the MOD-IV Tax List Search table (see below). This feature class includes a large number of additional attributes from matched MOD-IV records; however, not all MOD-IV records match to a parcel, for reasons explained elsewhere in this metadata record. The statewide property tax table, including all MOD-IV records, is available as a separate download "MOD-IV Tax List Search Plus Database of New Jersey." Users who need only the parcel boundaries with limited attributes may obtain those from a separate download "Parcels Composite of New Jersey". Also available separately are countywide parcels and tables of property ownership and tax information extracted from the NJ Division of Taxation database. The polygons delineated in this dataset do not represent legal boundaries and should not be used to provide a legal determination of land ownership. Parcels are not survey data and should not be used as such. Please note that these parcel datasets are not intended for use as tax maps. They are intended to provide reasonable representations of parcel boundaries for planning and other purposes. Please see Data Quality / Process Steps for details about updates to this composite since its first publication. NOTE For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

In 2024, there were almost seven park playgrounds for every 10,000 residents in Cincinnati, Ohio, making it the city with the most playgrounds per 10,000 residents. Madison, Wisconsin followed behind, with roughly 6.5 park playgrounds for every 10,000 residents.

About this dataset

Context

This list ranks the 1 cities in the Manassas Park city, VA by American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Native American Population: This column displays the rank of cities in the Manassas Park city, VA by their American Indian and Alaska Native (AIAN) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Native American Population: The Native American population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Native American. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Manassas Park city Native American Population: This tells us how much of the entire Manassas Park city, VA Native American population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Data Download: The Secured Areas 2024 dataset is also available as an ESRI polygon geodatabase dataset.The secured areas dataset shows public and private lands that are permanently secured against conversion to development, GAP 1-3, through fee ownership, easements, or permanent conservation restrictions. It also includes a set of more temporary easement and GAP 4 open space lands not permanently secured for nature conservation. TNC compiled these data from state, federal, and private sources and assigned a GAP Status and other standard attribute fields to the best of our ability. The Secured Areas dataset is a TNC product created primarily for estimating current extent and status of secured lands with a conservation focus, GAP 1-3. The non GAP 1-3 lands are less comprehensively mapped given the lack of their inclusion in some primary source datasets, but they are included as available in our source datasets. Any updates, corrections, or discrepancies with respect to official versions of source federal, state, or local protected areas databases should be viewed as provisional until such time as such changes have been reviewed and accepted by the official data stewards for those other protected areas databases.GAP STATUS GAP status is a classification developed by the US Fish and Wildlife Service, to reflect the intent of the landowner or easement holder. GAP 1 and 2 are commonly thought of as “protected” for nature", while GAP 3 are “multiple-use” lands. Other temporary conservation easement lands and/or protected open space without a conservation value or intent are assigned GAP 4. (Citation: Crist, P.J., B. Thompson, T. C. Edwards, C. G. Homer, S. D. Bassett. 1998. Mapping and Categorizing Land Stewardship. A Handbook for Conducting Gap Analysis.) In addition to GAP 1-3 lands, in our TNC secured areas product we classified six additional classes of open space lands (permanent agricultural easements, temporary conservation easements, temporary agricultural easements, urban parks, state board lands, other GAP 4 lands). The following definitions guided our assignment of lands into the following nine classes:TNC CLASS CODE (fields TNCCLASS, TNCCLASS_D)1 = GAP 1: Permanently Secured for Nature and Natural Processes. Managed for biodiversity with all natural processes, little to no human intervention. Primary intention of the owner or easement holder is for biodiversity, nature protection, natural diversity, and natural processes. Land and water managed through natural processes including disturbances with little or no human intervention.Examples: wilderness area, some national parks2 = GAP 2 = Permanently Secured for Nature with Management: Managed for biodiversity, with hands on management or interventions. Primary intention of the owner or easement holder is for biodiversity conservation, nature protection, and natural diversity. Land and water managed for natural biodiversity conservation, but may include some hands on manipulation or suppression of disturbance and natural processes. Examples: national wildlife refuges, areas of critical environmental concern, inventoried roadless areas, some natural areas and preserves3 = GAP 3: Permanently Secured for Multiple Uses, including nature: Primary intention of the owner or easement holder for multiple uses. Strong focus on recreational use, game species production, timber production, grazing and other uses in additional to these lands providing some biodiversity value. May include extractive uses of a broad, low-intensity type (e.g. some logging. grazing) or of a localized intense type (e.g. mining, military artillery testing area, public access beach area within large natural state park). Examples: recreation focused protected areas such as state parks, state recreation areas, wildlife management areas, gamelands, state and national forests, local conservation lands with primary focus on recreational use.38 = State Board Lands and State Trust Lands: Lands in western and some southern states that are owned by the state and prevented from being developed, but which are managed to produce long term sustained revenue for the state’s educational system. These lands were separated from other protected multiple use lands in GAP 3. Most of these lands are subject to timber extraction and management for profitable forest product production. Some also have agricultural use and revenue generated from grazing and/or agricultural production leasing. These lands are not specifically managed for biodiversity values, and some are occasionally sold in periodic auctions by the state for revenue generation. Note this type of land is most commonly assigned GAP 3 in the PAD-US GAP classification.39 = Permanent Agricultural Easements: Conservation land where the primary intent is the preservation of farmland. Land is in a permanent agricultural easement or an easement to maintain grass cover. The land will not be converted to a built or paved development. Example: vegetable farm with permanent easement to prevent development. Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.4 = GAP 4: Areas with no known mandate for permanent biodiversity protection. Municipal lands and other protected open space (e.g. town commons, historic parks) where the intention in management and the use of the open space is not for permanent biodiversity values. It was beyond our capacity to comprehensively compile these GAP 4 lands, and as such, they are included only where source data made it feasible to easily incorporate them. 5 = Temporary Natural Easements: Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.6 = Temporary Agricultural Easements: Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.9 = Urban Parks: While unlikely to have biodiversity value, urban parks provide important places for recreation and open space for people. We went through and identified parks whose name is recreation based (i.e. Playground, Community garden, Golf, fields, baseball, soccer, Mini, school, elementary, Triangle, Pool, Aquatic, Sports, Pool, Athletic, Pocket, Splash, Skate, Dog, Cemetery, Boat). Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.OWNERSHIP DEFINITIONSThe type of owner or interest holder for each polygon was assigned to a set of simple reporting categories as follows (see fields = Fee_Own_T and InterstH_T )TVA -Tennessee Valley Authority, BLM -Bureau of Land Management, , BOR- Bureau of Reclamation, FWS - U.S. Fish & Wildlife Service, UFS - Forest Service, DOD - Department of Defense, ACE - Army Corps of Engineers , DOE - Department of Energy, NPS - National Park Service, NRC - Natural Resources Conservation Service, FED – Federal Other, TRB - American Indian Lands, SPR - State Park and Recreation , SDC - State Department of Conservation, SLB - State Land Board , SFW - State Fish and Wildlife, SNR - State Department of Natural Resources, STL -State Department of Land, STA - Other or Unknown State Land, REG - Regional Agency Land, LOC – Local Government (City, County), NGO - Non-Governmental Organization, PVT- Private, JNT - Joint , OTH- Other , UNK - UnknownPROTECTION TYPE DEFINITIONS: (see field PRO_TYPE_D)DesignationEasementEasement and DesignationFeeFee and DesignationFee and EasementFee, Easement, and DesignationDATA SOURCES: The 2024 CONUS Secured Areas dataset was compiled by TNC from multiple sources. These include state, federal, and other non-profit and land trust data. The primarily datasets are listed below. 1. U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022. Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B.) Downloaded 1/10/2024 Note this dataset was used as the primary source outside of the Northeast 13 states. For the Northeast states, please see more detailed source information below.2. National Conservation Easement Database (NCED) https://www.conservationeasement.us/ Downloaded 1/12/2024. Note this dataset was used outside the Northeast 13 states. For Northeast states, please see more detailed source information below. 3. Natural Resources Conservation Service (NRCS) Easements. 2024. Downloaded 1/12/2024 https://datagateway.nrcs.usda.gov/4. Conservation Science Partners, Inc. 2024. Wild and Scenic River corridor areas. Dataset compiled by Conservation Science Partners, Inc. for American Rivers as of 2/14/2024 (per. Communication Lise Comte , Conservation Science Partners, Inc. 2/14/2024)5. The Nature Conservancy. 2024. TNC Lands. Downloaded 3/1/2024.6. The Nature Conservancy Center for Resilient Conservation Science. 2021. Military Lands of the Southeast United States. Extracted from Secured areas spatial database (CONUS) 2021. https://tnc.maps.arcgis.com/home/item.html?id=e033e6bf6069459592903a04797b8b07.7. The Nature Conservancy Center for Resilient Conservation Science. 2022. Northeast States Secured Areas. https://tnc.maps.arcgis.com/home/item.html?id=fb80d71d5aa74a91a25e55b6f1810574

When prioritizing regions for conservation protection, decisions are often based on the principle that a single large (SL) reserve should support more species than several small (SS) reserves of the same total area (SLOSS). This principle remains a central paradigm in conservation planning despite conflicting empirical evidence and methodological concerns. In urban areas where small parks tend to dominate and policies to promote biodiversity are becoming increasingly popular, determining the most appropriate prioritization method is critical. Here, we document the role of SLOSS in defining the seasonal diversity of birds in 475 parks in 21 US cities. Collections of small parks were consistently associated with higher species richness, spatial turnover, and rarity. Collections of both small and large parks were associated with higher phylogenetic and functional diversity whose patterns varied across seasons and cities. Thus, collections of small parks are a reliable source of species richness driven by higher spatial turnover and rarity, whereas collections of both small and large parks contain the potential to support higher phylogenetic and functional diversity. The presence of strong intra-annual and geographic variation emphasizes the need for regional prioritization strategies where multiple diversity metrics are examined across parks and seasons.

This graph shows the cities with the most acres of parkland per 1,000 residents in the United States in 2023. In that year, Anchorage, Alaska, had the most parkland per 1,000 residents with approximately 3,022 acres of land.