As of 2023, 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 thousand 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. Blue Ridge Parkway was the most visited NPS park in 2023 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. The Lincoln Memorial made the ranking of the most visited city parks in the U.S., while this may not seem like it comes under the classification of a ‘park’, it is cared for by the National Park Service.

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.

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.

The city park with the highest annual visitation in 2023 was Central Park in New York, accounting for a total of 42 million 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 24 million.

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. The three factors that make up ParkScore all reflect quality: good park systems need adequate acreage, services and investment, and access. For this metric and visual, lower scores are better.

The GIS Section reviews the layer in support of the EOC and the Urban Planning Division. The data is checked for inclusion and geometric placement accuracy. The GIS Section is no longer updating City Parks 100%. We will be reviewing a select handful of parks each year. Generally, we will begin our update schedule with the largest cities first, but a good city parks source may alter that schedule. The years listed below indicate which cities have been updated.

2009: All parks corrected to parcels. Corrected/updated Weston with T. Gates data.

2010: The cities' parks updated included Hollywood, Pompano Beach, and Hollywood.

The review technique was a review and/or incorporation of geodata from Ft. Lauderdale and website information incorporation from Pompano and Hollywood.

2011: Davie Parks updated and corrected based on Irene Degroot's shapefile and aerials. Wilton Manors update complete, city manager’s office said pocket parks are to be expected soon. West Park parks reflect parcels - note Mary Saunders Park is a very irregular shape, made up of many rights-of-way and will be an exception to our parcel-based rule.

2013: Toni Peyton said there were no changes to county parks since Miramar Pinelands. She requested a map of POD parks. Pembroke Pines - reviewed park inventory, park map locator, and spoke with Lori of Chuck Vones' (Dir. Parks and Rec) office. Reviewed Miramar Park Inventory.

2014: Reviewed and updated Coral Springs, Lauderdale By the Sea, Pembroke Park, Pembroke Pines, Lighthouse Point, Fort Lauderdale, Coconut Creek, and West Park.

2015: Reviewed 05/15/15

2021: Updated by BCGIS - Ruth Rothkoph Park and Lieberman Botanical Park in Lauderhill.

Source: BCGIS

Effective Date:

Last Update: 08/03/2021

Update Cycle: As needed.

The Global Public Parks Infrastructure Market was valued at USD 38.76 Billion in 2024 and is expected to reach USD 59.75 Billion by 2030 with a CAGR of 7.32% during the forecast period.

In 2023, the city in the United States with the highest share of parkland was Anchorage, Alaska, where approximately 80 percent of the city was parkland. In second place, with almost half the percentage of parkland was Fremont, California, where 44 percent of the city was parkland.

Description -There polygons represent known public and private parklands throughout the city of Baltimore. Attribute information includes details about park location, acreage, ownership,    managing entity, and park category. Parks are categorized as:Citywide parks: Parks that serve residents across the entire city and host a variety of permitted and non-permitted recreational activities or facilities. They tend to be the larger parks in the city ranging from ten acres to over 990 acres in size. While this represents a wide range in size, several of the smaller parks are part of a larger contiguous park network.Neighborhood parks: These parks serve as basic units of the park system for users within a quarter to half-mile distance. They range between 1 and 28 acres in size and typically offer two or more amenities such as a playground, basketball court, athletic field, and green spaces. Many of the Neighborhood parks are informal in design, are clearly visible and located along well trafficked streets.Mini parks: Small parks that may include one or two amenities such as a pavilion, seating area, playground, or basketball court. Many of the Mini Parks are sited off the beaten path, some are located behind houses or bordered by less trafficked streets. These parks are typically less than 3 acres in size.Green spaces: Open lawn spaces without amenities and of varied size. These spaces serve as flexible spaces for active or passive use. Some include community gardens.Special use: Stand-alone park spaces that have a specific use or role associated with them.Forested spaces: Wooded or forested areas that may or may not be accessible to the public.Civic spaces: Spaces that are significant to the City’s history, host monuments or contain paved plazas that are used for citywide events and gatherings related to recreation and parks or other non BCRP related activities.Data updates on a rolling basis as new parks are constructed or old parks decommissioned. Last updated 01/12/2024. Metadata Contact : jason.chang@baltimorecity.gov To leave feedback or ask a question about this dataset, please fill out the following form: Parks feedback form.

The following is a data dictionary for fields in the shapefile:PARK: Name of parkADDRESS: Location of park when exact address not availableNEIGHBORHD: Neighborhood park is located inNETNAME: Neighborhood Enhancement Team Area nameNETID: Neighborhood Enhancement Team Area IDCOMDISTID: City Commissioner District IDACRE: Rough acreage of parks (calculated in GIS, not provided by official survey)DREAM: Whether the park is on the City of Miami's Department of Real Estate and Asset Management listCITYPARK: YES for parks that are owned or maintained by the City of Miami, NO for all parks not owned or maintained by City of MiamiCLASS: Park Classification. Under Miami 21, parks greater than 1 acre with amenities catering to a large service area are classified as CITYWIDE. Under Miami 21, parks under 3 acres or parks with no special amenities are classified as NEIGHBORHOOD.TYPE: Specific types of parks. For example, domino, greenway, fields/courts, open space, nature, etc.CITYWIDE: Under Miami 21, Citywide parks can be DESTINATION, COMMUNITY, or LINEAR. Any parks that are not CITYWIDE will be labeled NON-CITYWIDE in this field. DESTINATION parks have specific destination-related amenities. COMMUNITY parks are over 3 acres in size and have active recreation facilities. LINEAR parks are greenways and trails.DEST: Declares park's DESTINATION type: CONSERVATION, WATERFRONT, SPORTS COMPLEX/AQUATIC PARK, SPECIALTY PARK, or NONE. CONSERVATION parks are natural areas protected by conservation (Virginia Key, Simpson, etc.). WATERFRONT parks are over 3 acres in size on waterfront parcels. SPORTS COMPLEX/AQUATIC PARKS are parks with swimming pools or sports parks, like Moore Park. SPECIALTY parks provide unique programs such as Sandra DeLucca Developmental Center or Maximo Gomez Domino Park.DEVELOP: Declares development status of parkOWN: Who owns the parkMAINTAIN: Who maintains the parkM21_ZONE: Zoning under Miami 21, Miami's Form-Based CodeTransect: Which transect the park falls under Miami 21Transect_D: Description of transectFor questions regarding this shapefile, please contact the City of Miami Planning & Zoning Department. For questions on park amenities, please contact the City of Miami Department of Parks and Recreation.

‘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.

Spatial equality of parks is a significant issue in environmental justice studies. In cities with high-density development and limited land resources, this study uses a supply-demand adjusted two-step floating catchment area model (2SFCA), paying attention to residents' subjective preferences and psychological accessibility. It assesses equality of access to urban parks from two dimensions: spatial equality and quantitative equality at a fine scale of 100 × 100 m grid resolution. The spatial equality of urban parks in Chengdu is measured under different transportation modes (walking, cycling, and driving) based on multi-source geospatial big data and machine learning approaches. The results show: (1) There were significant differences in the spatial distribution of park accessibility under different modes of transportation. The spatial distribution under walking was significantly influenced by the park itself, while the distribution of rivers significantly influenced the spatial distribution under cycling and driving; (2) Accessibility to urban parks was almost universally equal in terms of driving, relatively equal in terms of cycling, and seriously unequal in terms of walking; (3) Spatial local autocorrelation analysis shows that park accessibility tended to be significantly clustered, with little spatial variation; and (4) The supply and demand of urban parks were relatively equal. The results can help urban planners to formulate effective strategies to alleviate spatial inequality more reasonably and precisely. The applied research methods can further improve the system of scientific evaluation from a new perspective.

This layer details the location, size, and type of parks/natural areas in the City of Gainesville.

Reason for SelectionProtected 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 3.0national 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

Human-nature connection (HNC) is a concept derived from investigating the formulation and extent of an individual’s identification with the natural world. This relationship is often characterized as an emotional bond to nature that develops from the contextualized, physical interactions of an individual, beginning in childhood. This outcome presents complexity in evaluating the development of HNC but suggests optimism in the pathways for enhancing lifelong HNC. As urban populations increase, there is a growing recognition worldwide of the potential for urban green space to cultivate HNC and thus shape the environmental identity of urban residents. The results of an online survey of 560 visitors to three community parks (managed primarily to provide a variety of physical, social and cultural opportunities) and three conservation parks (managed primarily to protect native plants and wildlife) in Madison, Wisconsin, USA, were used to investigate HNC. Linear mixed effects models evaluated visitors’ HNC as a function of their (1) literacy and sentiment about wildlife species, (2) park experience, (3) number and frequency of nine childhood and adult recreation experiences, and (4) demographics. Across the park response groups, the number and frequency of childhood and adult recreation experiences was significantly associated with HNC, and this positive association persisted in multiple recreation activities. Furthermore, species literacy and sentiment, visiting a park for 'Nature', and frequent and extended visitation also was significantly associated with HNC by park type. Our research demonstrates the importance of lifelong recreation experiences in the development and enhancement of HNC and provides evidence for differences in the expression of HNC associated with particular attributes of urban park visitors and their views of wildlife. Methods Methodology Study Area Madison has a population of approximately 270,000 residents, covers approximately 260 km2, and is located in south central Wisconsin, USA (US Census Bureau, 2022). Madison is currently the fastest growing city in Wisconsin and is home to the state capital and the University of Wisconsin-Madison (US Census Bureau, 2022). The study area is within the Yahara Watershed, now largely dominated by agricultural and urban land cover, and experiences four distinct seasons (Carpenter et al., 2007, Wisconsin State Climatology Office, 2010).
The six selected parks were based on their classification as a community or conservation park; an estimated visitation rate; a central, western, or eastern location in Madison; and approval from the Madison Parks Division of the City of Madison (Figure 1). The size of the community parks ranged from 19.07 ha to 101.50 ha, and the size of the conservation parks ranged from 24.39 ha to 39.17 ha. The parks can be broadly described as mixed forest ecosystems with open grass areas and low levels of pavement and structural development. Conservation parks contain native grasslands whereas community parks may contain native grasslands and/or mowed turf. By definition, conservation parks are managed to protect native plant and wildlife species, resulting in the inclusion of vegetation and management practices supporting that objective (City of Madison Parks Division, 2022). As a result of their conservation status, recreation therein is limited to physical activities such as hiking and snowshoeing and nature-based activities such as watching birds / wildlife and photography. Dogs are not allowed in conservation parks. Community parks are designed to provide a variety of physical, social, and cultural opportunities, including athletic fields and courts, playgrounds, and picnic shelters. Community parks allow dogs that are leashed and licensed (City of Madison Parks Division, 2022).
Study Population and Survey We conducted an online survey to park visitors in three conservation parks and three community parks in Madison. Our research design was approved by the University of Wisconsin Education and Social/Behavioral Science Institutional Review Board as exempted research. We developed the survey in Google Forms and administered it in the parks using a park-specific quick response (QR) code printed either (1) on posters that were statically accessible to park visitors throughout the study period or (2) on postcards dynamically handed to park visitors at selected times during the study period. The posters were visible outdoors in all six parks from 2021-09-04 through 2021-10-24 (high-use fall period) and from 2022-06-09 through 2022-08-24 (high-use summer period). Postcards were distributed in the six parks on four Saturdays in both September and July from 10.00 to 12.00. These dates and times were selected to coincide with the days and times with the highest number of park visitors, the availability of surveyors, and the approval of the Madison City Parks Division. Each postcard had a unique three-digit number required to access the online survey. Adults (18 years or older) were approached by the surveyor (lead author and/or student assistants trained in research ethics and project specifics) and invited to participate. After verbally agreeing to participate (standard approach for exempted research), each potential respondent was asked three questions to check for nonresponse bias: (1) zip code, (2) year of birth, and (3) main reason for visitation. For poster and postcard respondents who continued on to take the online survey, the first question was a screening for informed consent, with only those who actively acknowledged consent continuing into the study’s content questions.
The online survey consisted of 30 questions, grouped into four categories: (1) literacy and sentiment about wildlife species, (2) recreation and park experience, (3) HNC, and (4) demographics. For species literacy and sentiment, respondents were asked questions evaluating (1) the correct photographic identification of six mammal species, each considered a generalist and likely present in the study parks, and (2) visitor sentiment about each species (Figure 2). For recreation activity, respondents were asked questions about (1) the number and frequency of childhood and adult experiences with bird / wildlife watching, camping, canoeing / kayaking, fishing, gardening, hiking, hunting, nature photography, and picnicking; (2) the main reason for visitation; (3) prior visitation; (4) length of visit; and (5) distance of residence to the park. For HNC, the abbreviated six-item short form of the Nature Relatedness Scale (NR-6) was used, with four statements from NR-Self (1-4) and two statements from NR-Experience (5 and 6):

My connection to nature and the environment is a part of my spirituality. My relationship to nature is an important part of who I am. I feel very connected to all living things and the earth. I always think about how my actions affect the environment. My ideal vacation spot would be a remote, wilderness area. I take notice of wildlife wherever I am.

Demographic questions included age group, educational level, and gender. The survey responses were in the form of a short answer (only identification of species), exclusionary checkboxes, or a 5-point Likert scale response (“Never” to “Very Often” or “Disagree Strongly” to “Agree Strongly”). Wildlife literacy and sentiment questions were accompanied by a corresponding species-specific color photo (Figure 2). Species sentiment was measured by species-specific exclusionary responses: 'I am happy they live at the park’, ‘I think they are important for the park ecosystem', 'I am concerned about their impact on human safety', 'I am concerned that they bring disease', 'I think they are a nuisance', or 'I am unsure how I feel or do not care’. We piloted the survey with a focus group before administering it in the six parks to identify possible issues such as unclear language or challenges in viewing on mobile devices and adjusted our final survey accordingly. All survey responses were anonymous.
Analysis Initial exploratory analysis included a random effect for park type (community and conservation) and a random effect and interaction term for survey type (postcard and poster). The type of park was a significant factor, and the models afterwards were separated into two model sets, one for community park visitors and one for conservation park visitors. A random effect was included for the parks sampled (3 community parks or 3 conservation parks) within the corresponding model set. The type of survey was not a significant random effect, and the data of each type of survey were combined based on the type of park. No differences were found between the potential and actual respondents by postcard with respect to zip code, year of birth, and main reason for visitation. This suggests that nonresponse bias was unlikely.
Mixed-effects linear models were applied using the ‘lme’ function in the 'nlme' package (v3. 1-152; Pinheiro et al., 2021) of the R software, version 4.2.1 (R Core Team, 2019). As our work forwards investigation on the specific factors associated with HNC (using the mean NR-6 score of a respondent) rather than the conventional application of NR-6 as a predictor of pro-environmental behavior or self-assessed well-being, we evaluated factors independently rather than collectively. Separate models were developed for community and conservation park survey data to evaluate HNC as a function of factors within four categories: (1) species literacy and positive species sentiment; (2) number, frequency, and type of outdoor recreation activities of childhood and adulthood; (3) main reason for visitation, prior visitation, length of visit, and distance of residence to the park; and (4) demographic factors (age category, educational level, and gender). Species literacy was calculated as the average of responses recorded in six

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

Analysis of ‘Strategic Measure_Austin's ParkScore ranking (absolute score and ranking among U.S. cities)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d5c4f877-f0ae-4de5-9219-2a35c6aed08d on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/Austin-s-ParkScore-Ranking-absolute-score-and-rank/rnwr-4s4u/

--- Original source retains full ownership of the source dataset ---

As of March 2024, Chiba was the prefecture in Japan with the highest number of city parks, amounting to approximately 6.47 thousand. Hokkaido Prefecture followed with around 4.99 thousand city parks. There were over 84.15 thousand city parks scattered throughout Japan in that year. City parks refer to parks that were built as a part of city planning, such as parks in residential areas, urban parks, large-scale parks, green buffer zones, and city parks run by the county.

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 boroughs of the City of Montreal benefit from more than 1,495 parks that extend over an area of more than 6,412 ha. This data set represents all the parks and public spaces identified and illustrates their surface polygons in the context of the urban fabric of the territory. The data is not representative of the parks of the linked cities, which are only partially represented. The boundaries of parks and public spaces generally follow the cadastre, but cannot be used as a legal reference to accurately locate the location of a park. The data provided in this data set is for representation purposes only. Interact with visualization data View of Montreal's parks and sports facilities.This third party metadata element was translated using an automated translation tool (Amazon Translate).