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.

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

This dataset is a combination of the Broward County Parks, Broward County City Parks, and Broward County State Parks datasets.

Broward County Parks: The locations of all sixty-two county-owned and operated parks, last updated March 2013 when the Lafayette Park boundary changed. This layer was reviewed by the Parks and Recreation Division of Broward County on 5/15/2015 with no updates required. This layer will be updates as park boundaries change or new parks are added to Broward County.

Broward County City Parks: The GIS Section reviews the BCGIS.ParkCity layer annually in support of the EOC and the Comprehensive Plan. 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 managers 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

Broward County State Parks: Two State Parks in Broward County, Fl. Data reviewed 05/15/2015.

Source: Parks and Recreation Division of Broward County

Effective Date: 03/2013

Last Update: 05/15/2015

Update Cycle: As needed

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.

Reason for SelectionProtected 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 DataSoutheast Blueprint 2023 subregions: CaribbeanSoutheast Blueprint 2023 extentNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee EasementPuerto 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 censusOpenStreetMap data “multipolygons” layer, accessed 3-14-2023A 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 3-8-2023U.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 StepsMost 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 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 <10 acre urban park1 = <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.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 MindThis 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 because the landcover data available in the Caribbean does not assess percent impervious in a comparable way.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint

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

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.

Mobile Home Park Locations

Locations and details of mobile home parks in the United States

By Homeland Infrastructure Foundation [source]

About this dataset

The Mobile Home Parks Inventory dataset provides a comprehensive list of mobile home parks across the United States. This dataset is crucial for emergency preparedness and evacuation planning, as mobile home parks are inhabited by a vulnerable population that is particularly susceptible to natural disasters such as hurricanes, tornadoes, and flooding.

The dataset includes detailed information about each mobile home park, including its location coordinates (longitude and latitude), address details (street address, city, state, ZIP code), and additional address information if available. It also provides contact details such as telephone numbers and websites for further information about each park.

Furthermore, the dataset contains essential attributes related to the characteristics of mobile home parks. These attributes include the number of units (mobile homes) within each park, allowing authorities to assess capacity during emergency situations. Additionally, it categorizes the type of each park (e.g., recreational vehicle parks), its status (e.g., operational or closed), and its size classification.

To ensure data accuracy and reliability, various validation methods have been implemented. The validation process includes verifying the data sources from where this information was obtained along with dates when data was sourced or validated.

Moreover, this comprehensive inventory incorporates geographical references with FIPS codes for counties in which these mobile home parks are located. Furthermore,the NAICS code provides an additional industry classification system describing these facilities in greater detail.

Lastly,this Mobile Home Parks Inventory recognizes that reverse geocoding has been employed for gathering precise spatial coordinates.Because vulnerability differs across regions,state boundaries have also been included to facilitate analysis at a higher level.Alongside state boundaries,this dataset acknowledges country-level variations which could be valuable while comparing international mobile homes inventories .

By utilizing this extensive collection of accurate and up-to-date information on mobile home parks in the United States policymakers,government agencies,and emergency responders can effectively plan evacuation strategies,mobile resource allocation,and disaster response efforts for ensuring public safety during natural calamities.This valuable knowledge will ultimately enhance disaster mitigation and the overall resilience of these vulnerable communities

How to use the dataset

• Understanding the Columns:

  • X and Y: These columns represent the longitude and latitude coordinates of each mobile home park. They can be used for geographical analysis and mapping purposes.
  • NAME: This column provides the name of each mobile home park. It is useful for identifying specific parks.
  • ADDRESS: The street address where each mobile home park is located.
  • ADDRESS2: Additional address information (if available) for each mobile home park.
  • CITY: The city where each mobile home park is situated.
  • STATE: The state where each mobile home park is located.
  • ZIP and ZIP4: These columns contain the ZIP code information for each mobile home park, including additional ZIP code details if available.
  • TELEPHONE: The contact telephone number for each mobile home park, which can be useful for making inquiries or gathering more information directly from them.
  • TYPE: This column indicates the type of the mobile home park (e.g., permanent residential, seasonal).
  • STATUS: The status of a particular mobile home park (e.g., open, closed).
  • COUNTY and COUNTYFIPS:The county where each mobile h0me_1park is situated along with its associated FIPS code.

• Analyzing Park Characteristics: UNITS & SIZE columns provide insights into various aspects: UNITS represents the number of individual dwelling units within a given Mobile Home Park SIZE describes its physical size.

• Demographic Analysis: By referring to NAICS_CODE & NAICS_DESC columns ,you'll get an idea about the associated industries and business activities in the vicinity of each park.

• Geographical Analysis: The LATITUDE and LONGITUDE coordinates allow you to map out mobile home parks on various GIS (Geographic Information System) platforms. You can analyze the distribution of mobile home parks across different states, cities, or counties.

• Emergency Preparedness: ...

Smart Visitor Counters for Parks Market Outlook

According to our latest research, the Global Smart Visitor Counters for Parks market size was valued at $580 million in 2024 and is projected to reach $1.42 billion by 2033, expanding at a robust CAGR of 10.5% during 2024–2033. A major factor driving the growth of this market globally is the increasing emphasis on data-driven park management and visitor experience optimization. As parks and recreational spaces face mounting pressure to balance conservation with accessibility, the deployment of advanced visitor counting solutions has become essential for resource allocation, crowd management, and infrastructure planning. The integration of IoT, AI, and real-time analytics into smart visitor counters is transforming how park authorities understand visitor patterns, enabling them to make informed decisions that enhance both operational efficiency and visitor satisfaction.

Regional Outlook

North America currently holds the largest market share in the smart visitor counters for parks market, accounting for approximately 39% of the global revenue in 2024. The region’s dominance is attributed to its mature technological infrastructure, widespread adoption of smart city initiatives, and significant investments in public park modernization. The United States, in particular, has been at the forefront of integrating advanced visitor management solutions in national and urban parks, driven by strong governmental support, public-private partnerships, and a culture that prioritizes data-driven decision-making. Additionally, the presence of leading technology providers and a high level of awareness among park authorities have contributed to rapid deployment and continuous innovation in this sector. These factors collectively position North America as the benchmark for smart visitor counter adoption and innovation.

The Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR exceeding 13.2% through 2033. This remarkable growth is fueled by rapid urbanization, increasing investments in tourism infrastructure, and government initiatives aimed at enhancing the quality and safety of public recreational spaces. Countries such as China, Japan, Australia, and South Korea are leading the charge, with large-scale deployments of smart visitor counting systems in both urban and nature parks. The proliferation of smart city projects, coupled with rising disposable incomes and growing environmental awareness among the population, is accelerating the adoption of advanced park management technologies. Furthermore, partnerships between international technology vendors and local authorities are fostering knowledge transfer and facilitating the customization of solutions to meet unique regional needs.

In emerging economies across Latin America, the Middle East, and Africa, the adoption of smart visitor counters for parks is gaining momentum, albeit at a slower pace due to infrastructural and budgetary constraints. While there is a clear recognition of the benefits of digital visitor management, challenges such as limited funding, inadequate technical expertise, and fragmented policy frameworks often hinder widespread implementation. Nonetheless, localized demand is rising as governments and non-profit organizations prioritize sustainable tourism and conservation. International development agencies and technology donors are increasingly supporting pilot projects in these regions, laying the groundwork for future market expansion. The gradual rollout of affordable, easy-to-deploy solutions tailored to local conditions is expected to bridge the adoption gap and drive incremental growth over the forecast period.

Report Scope

Park Safety Monitoring Analytics Market Outlook

According to our latest research, the Global Park Safety Monitoring Analytics market size was valued at $2.1 billion in 2024 and is projected to reach $6.8 billion by 2033, expanding at a robust CAGR of 13.6% during the forecast period of 2025–2033. A primary factor fueling this remarkable growth is the increasing emphasis on public safety and security within recreational spaces worldwide. As urbanization accelerates and more people frequent parks for leisure and fitness, authorities and private park operators are prioritizing advanced analytics solutions to proactively monitor, detect, and mitigate safety risks. The integration of artificial intelligence, IoT sensors, and real-time data analytics is revolutionizing how parks manage incidents, crowd flow, and emergency response, thus driving substantial investments in park safety monitoring analytics technologies globally.

Regional Outlook

North America currently dominates the Park Safety Monitoring Analytics market, accounting for the largest share of 38% in 2024. This leadership is attributed to the region’s mature infrastructure, high adoption of advanced safety technologies, and robust government regulations mandating public safety in recreational spaces. Cities across the United States and Canada have made significant investments in smart surveillance, AI-driven threat detection, and integrated emergency response systems within urban and national parks. The presence of leading technology vendors and a proactive approach to adopting digital solutions have further propelled North America’s market value, which is forecast to maintain steady growth throughout the next decade. Additionally, strong collaboration between public agencies and private stakeholders has accelerated the deployment of comprehensive park safety analytics platforms, setting a benchmark for other regions.

Asia Pacific is projected to be the fastest-growing region in the Park Safety Monitoring Analytics market, registering a remarkable CAGR of 16.4% from 2025 to 2033. This rapid expansion is fueled by increasing urbanization, a surge in public infrastructure investments, and heightened awareness about safety in public spaces. Countries such as China, Japan, South Korea, and India are investing heavily in smart city initiatives, which include the integration of safety analytics in parks and recreational areas. The growing middle class, rising tourism, and government-led digital transformation programs are further driving the demand for advanced safety monitoring solutions. Local governments are also partnering with technology firms to pilot AI-enabled surveillance and crowd management tools, making Asia Pacific a hotbed for innovation and market growth in this sector.

Emerging economies in Latin America and the Middle East & Africa are experiencing a gradual uptick in the adoption of park safety monitoring analytics, albeit at a slower pace compared to developed regions. These markets face challenges such as limited funding, infrastructural gaps, and varying regulatory standards, which can impede widespread implementation. However, localized demand for safer public spaces, especially in urban centers and tourist destinations, is prompting municipalities and private operators to explore affordable and scalable analytics solutions. Policy reforms and international collaborations are beginning to facilitate technology transfer and capacity building, paving the way for future growth. Nevertheless, the market share of these regions remains modest, and overcoming barriers related to digital literacy and resource allocation will be crucial for unlocking their full potential.

Report Scope

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

In 2024, Anchorage, Alaska led all U.S. cities in parkland per 1,000 residents, offering roughly 3,183 acres. Chesapeake, Virginia followed in second place, with 243 acres per 1,000 residents.

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

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes, and larger open spaces often provide more amenities. To assess the spatial distribution of access to open space for recreation in the southeastern United States, we constructed an index of open space access based on the size of the largest publicly accessible open space of at least 10 acres within 10 miles of each point on the landscape, using three distance categories to represent whether people can reach the open spaces by walking (within 0.5 mile), via a short drive (within 3 miles), or via a longer drive (within 10 miles).

LoRa Sensor Mesh for Parks Security Market Outlook

According to our latest research, the Global LoRa Sensor Mesh for Parks Security market size was valued at $1.2 billion in 2024 and is projected to reach $4.6 billion by 2033, expanding at a CAGR of 16.8% during 2024–2033. This robust growth is primarily fueled by the increasing demand for advanced, scalable, and energy-efficient security solutions in public and private park spaces globally. The rapid proliferation of IoT technologies, combined with the unique long-range, low-power capabilities of LoRa sensor mesh networks, is transforming how urban and natural parks address security, asset tracking, and environmental monitoring. As urbanization accelerates and public safety concerns mount, municipalities and private operators are investing heavily in smart security infrastructures, with LoRa sensor mesh emerging as a preferred backbone due to its reliability, scalability, and cost-effectiveness.

Regional Outlook

North America currently holds the largest share of the LoRa Sensor Mesh for Parks Security market, accounting for approximately 38% of the global revenue in 2024. This dominance is attributed to the region's advanced technological infrastructure, high rate of IoT adoption, and significant investments from both public and private sectors in smart city initiatives. The United States, in particular, has seen widespread integration of LoRa sensor mesh systems in urban parks, recreational areas, and nature reserves, driven by proactive government policies and substantial funding for public safety modernization. Additionally, the presence of leading technology providers and a mature ecosystem for IoT deployment further reinforce North America's market leadership, making it a bellwether for global trends in parks security innovation.

The Asia Pacific region is expected to be the fastest-growing market, with a projected CAGR of 20.5% during the forecast period. This rapid growth is underpinned by increasing urbanization, rising investments in smart city infrastructure, and heightened focus on public safety across countries such as China, Japan, South Korea, and India. Governments in these nations are actively promoting the adoption of IoT-enabled security solutions to address growing challenges related to park safety, environmental monitoring, and asset management. The region’s strong manufacturing base and burgeoning technology sector also contribute to lower hardware costs and faster deployment cycles, making LoRa sensor mesh solutions more accessible to a broader range of end-users.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing LoRa sensor mesh technology, although adoption rates remain modest due to infrastructure constraints and budgetary limitations. However, localized demand for affordable, low-maintenance security systems in public parks and nature reserves is rising, driven by increasing awareness of safety issues and the need to protect valuable natural resources. Policy initiatives aimed at digital transformation and public safety improvement are beginning to gain traction, but challenges such as limited technical expertise, inconsistent regulatory frameworks, and funding gaps continue to hinder widespread implementation. Nevertheless, these regions represent significant untapped potential, especially as technology costs decline and international development agencies prioritize smart infrastructure projects.

Report Scope

Spray Park Market Outlook

According to our latest research, the global spray park market size reached USD 2.05 billion in 2024, reflecting a robust expansion driven by increasing demand for recreational water play areas across urban and suburban landscapes. The market is expected to register a compound annual growth rate (CAGR) of 7.9% from 2025 to 2033, propelling the market value to an estimated USD 4.10 billion by 2033. This surge is primarily attributed to the growing emphasis on family-oriented outdoor entertainment, heightened urbanization, and a rising focus on community wellness initiatives. As per our in-depth analysis, the integration of innovative water play features and sustainable materials is significantly shaping the market’s trajectory.

One of the primary growth factors for the spray park market is the escalating urbanization across both developed and emerging economies. Urban planners and municipal authorities are increasingly investing in public recreational infrastructure to enhance the quality of life in cities. Spray parks, with their water-efficient designs and ability to cater to diverse age groups, are becoming a staple in community parks and urban plazas. The integration of interactive water features and sensory play elements not only attracts families but also supports child development and inclusive play. Furthermore, these parks are often seen as safer alternatives to traditional swimming pools, as they eliminate the risk of drowning, thus appealing to parents and caregivers. The trend of revitalizing urban spaces with multipurpose recreational facilities continues to bolster the demand for spray parks worldwide.

Another significant driver is the increasing adoption of spray parks by the private sector, particularly in the hospitality and amusement industries. Hotels, resorts, and amusement parks are leveraging spray parks as value-added amenities to attract families and enhance guest experiences. The competitive landscape in hospitality is prompting operators to differentiate their offerings, and water play areas serve as a compelling draw, especially in regions with warm climates or high tourist footfall. In addition, the growing popularity of themed spray parks—integrating unique designs, interactive features, and advanced water management systems—has further fueled market growth. These developments are supported by advancements in construction materials and water recirculation technologies, which have improved the durability, safety, and sustainability of spray parks.

The spray park market also benefits from increasing awareness about the importance of outdoor physical activity and community engagement. Public health campaigns and government initiatives promoting active lifestyles have encouraged the development of accessible recreational spaces. Spray parks offer an inclusive environment where children, adults, and individuals of all abilities can engage in physical activity, socialize, and cool off during hot weather. This aligns with broader societal goals of fostering community cohesion, reducing screen time, and combating sedentary lifestyles. Moreover, the relatively low maintenance and operational costs of spray parks compared to traditional aquatic centers make them an attractive investment for municipalities and private operators alike.

Regionally, North America continues to dominate the spray park market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States and Canada have witnessed significant investments in public spray parks, driven by favorable government policies and high consumer demand. Meanwhile, the Asia Pacific region is experiencing rapid growth, fueled by rising disposable incomes, urban expansion, and increasing adoption of Western recreational trends. Latin America and the Middle East & Africa are also emerging as promising markets, supported by tourism development and a growing focus on family entertainment infrastructure. These regional dynamics underscore the global appeal and adaptability of spray parks across diverse cultural and economic contexts.

Product Type Analysis

The spray park market is segmented by product type into standalone spray parks, integrated spray parks, and portable spray parks. Standalone spray parks, often found in public parks and urban spaces, are designed as independent facilities dedicated solely to water play. These parks are typically larger in scale, offerin