Reason for Selection Large areas of intact natural habitat are favorable for conservation of numerous species, including reptiles and amphibians, birds, and large mammals. The Esri Green Infrastructure data covers the entire United States and has been used in other broad-scale conservation planning efforts, so using this existing data helps align the Blueprint with other conservation efforts and reduce duplication of effort. We chose to use “Core Size (acres)” as the metric for this indicator. Other evaluation attributes included in this index, such as the default “Core Score”, were less suitable because they were calculated using inputs that are duplicative of other indicators.Input Data2021 National Land Cover Database (NLCD)Southeast Blueprint 2024 extentEsri’s Intact Habitat Cores 2023, accessed 2-16-2024: Core Size (Acres); to download, select “Open in ArcGIS Desktop” and make a local copy. According to Esri’s data description for the 2023 intact habitat cores update: “This layer represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2019 National Land Cover Data. Cores were derived from all “natural” landcover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons.”Mapping StepsConvert the Esri Intact Habitat Cores 2023 polygons to a 30 m raster using the values in the “Acres” field. We used the feature layer map service as the input in the Polygon to Raster function in the code.Reclassify the above raster into 4 classes, seen in the final indicator values below.Use NLCD to remove zero values in deep marine areas, which are outside the scope of this terrestrial indicator. Use a conditional statement to assign NoData to any area with a pixel value >0 in the NLCD.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 the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:3 = Large core (>10,000 acres)2 = Medium core (>1,000-10,000 acres)1 = Small core (>100–1,000 acres) 0 = Not a coreKnown IssuesThe core analysis for this indicator is based on the 2019 NLCD, not the more recent 2021 NLCD. Esri has shared the scripts and input data used to create this layer, which may also help update this indicator in the future.Even small dirt roads serve as hard boundaries for habitat cores. While this makes sense for some species, this indicator likely underestimates the effective size of the patch for some more mobile animals.Waterbodies like reservoirs are also considered part of habitat cores, so this layer likely overestimates the effective size of the habitat core for most species.Many intact habitat cores have a speckling of small altered areas inside of them. In some cases, like in areas of west TX with concentrated oil wells, there can be many alterations in a gridded pattern across the entire core. This indicator underestimates the cumulative impacts of interior alterations—especially when the small altered footprints are densely packed in a grid within a habitat core.Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedEsri Green Infrastructure Center. Data Description: Detailed Description and Methodology for Intact Habitat Cores. PDF. Last updated June 30, 2023. [https://nation.maps.arcgis.com/home/item.html?id=047d9b05e0c842b1b126bc0767acfd5e]. Esri Green Infrastructure Center, Inc. 2023. Intact Habitat Cores (2023). [https://www.arcgis.com/home/item.html?id=b404b86a079a48049cb50272df23267a].

This GIS dataset represents a reclassification of existing surficial map information for the purpose of portraying the distribution of sand and gravel deposits in Alberta. The surficial geology of Alberta ungeneralised digital mosaic (Alberta Geological Survey DIG2013-0001) represents the primary source of information used in this reclassification. This dataset was updated with more recently published 1:100,000 scale surficial geology maps, and where appropriate new polygon features that were digitized from line features in the Glacial Landforms of Alberta (Alberta Geological Survey Map 604 and DIG2014-0022). The updated surficial geology mosaic was then reclassified using a thematically-based attribute table which categorizes the original surficial geology features based on their sand and gravel component. Attributes within this table comprise: (1) an approximation of the material type (MATERIAL). (2) the aerial proportion that this material represents of the polygon, as a percentage (PROPORTION). (3) an indication of whether the sand and gravel unit is mapped at the land surface or is buried (SRF_BURIED). (4) the depositional environment relating to the sand and gravel unit (GENESIS). (5) the reference source to the original data (SOURCE_MAP). (6) the GIS dataset from which the features were derived (DATASET). and (7) the mapping scale (SCALE). The MATERIAL honours the original surficial geology polygons when sufficiently precise texture/material information was provided. Otherwise MATERIAL is based on the typical range of materials that are associated with each surficial geology unit on a litho-genetic basis, using the standard Alberta Geological Survey surficial geology legend. When multiple surficial geological units that contain sand and gravel are present within a single polygon (i.e. 60% eolian deposits and 40% fluvial deposits), MATERIAL reflects the unit with the greatest proportion. For geological units whose material properties are of marginal significance as a sand and gravel deposit, particularly those that contain a mixture of silt and sand, a hierarchy was used to determine whether they are included as sand and gravel deposits. Fluvial deposits, littoral and nearshore deposits, and eolian deposits with a silt textural modifier in the original mapping data were included as potential sand and/or gravel deposits because these units are often interspersed with sand and/or gravel materials. Glaciolacustrine deposits with a silt textural modifier were not included because this environment generally does not result in the deposition of extensive sand and gravel sediments. After all of the attributes had been updated, all polygons that may contain some component of sand or gravel were extracted from this dataset to create the sand and gravel potential for Alberta digital mosaic. With this dataset, users can view the extent of surficial sand and gravel deposits in the province in a single GIS layer without the need to interpret this information from a variety of legends in the original surficial geology datasets. Users can further highlight polygons that may represent more suitable targets for sand and gravel based on the estimated material type (i.e. by eliminating polygons that typically contain large amounts of silt and fine sand), the estimated proportion of sand and gravel within the polygon, and depositional environment. This dataset best portrays sand and gravel potential that occurs at the land surface or in the very near surface, and does not attempt evaluate the sub-surface distribution of sand and gravel units. This dataset also does not provide any direct assessment of aggregate quality or thickness, and the material information is mostly inferred from the general association between certain surficial material types and their geological, depositional environment. Furthermore, the sand and gravel potential dataset is based on surficial geology maps produced at different scales and using different legends, therefore the detail and amount of information provided by these polygons will exhibit regional variations. The mapping scale for each polygon is provided in the SCALE attribute.

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

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

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

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE. A spatial representation of land use. The polygons contained in this feature class were derived from the Oakland County Tax Parcel feature class. Each parcel was categorized by its land use. When a parcel has multiple land uses, the dominant land use is shown. Assessing records and orthophotography were the main sources used to attribute each tax parcel with land use information. The data was collected in August 2010. Key attributes are the land use and key pin (Sidwell number). Land Use stores the Land Use description for each parcel. The Key Pin is the unique Parcel Identification Number (Pin) used to link the parcel to the parcel attributes which are stored and maintained in Oakland County Land Records.The 2010 version of land use was created using the 2009 version as a primary source. It was assumed that if the Parcel Identification Number and the Use Code were equal in both years, then the land use did not change. Thus, only the parcels where a change occured had to be assigned a land use. To ensure the quality of the land use data, however, several types of use codes were manually checked again for the 2010 update to enusre the assigned use is still accurate. Also, reference data such as the Adult Care, Public Beach, and Recreation Land feature classes the were used to locate misrepresented parcels.The development process consists of three basic steps. First, parcels with use codes that are assumed to relate to a single land use are categorized as such. Second, parcels with a use code that is assumed to relate to more than one land use are manually classified using orthophotography and ownership as a reference. Lastly, tax parcels, right-of-way, and hydrography are unioned to create a single land use dataset.Assumptions:In 2005, Oakland County Equalization and many of the local assessors have changed from using the use codes described below to property class codes.
These codes were interpreted to simliar use codes for the purpose of this data. One major change is that tax exempt property is no longer recorded using the "ME, Miscellaneous Exempt" use code, but are instead usually classifed as "RV, Residential Vacant." The "Taxable" field was referenced to locate tax exempt property such as schools, churches, and governemnt-owned property during this land use update.Any parcel classified as vacant may be an accessory use to an adjacent, commonly owned, improved parcel. In this event, the vacant parcel is reclassified as the use of the adjacent, commonly owned, improved parcel. It should be noted, however, that parcels with use codes of RV, SV, and LV, that represent a single family vacant use, are exempt from this assumption.Parcels with an Equalization use code of RV, SV, or LV may include uses to be reclassified as Vacant or Recreation & Conservation (due to subdivision open space). These parcels are not manually checked. Queries are conducted to search for those parcels that are subdivision open space. The following strings are queried from the Owner1 and Owner2 fields: "*own*" (unknown, homeowner), "ass*" (association, ass'n, etc), and "park."The Equalization use code BI, Business Improved includes uses that are reclassified as Commercial/Office. Uses may also fall into Public/Institutional, however, all BI parcels are not manually checked for reclassification.The Equalization use code MM, Miscellaneous Business includes uses that are reclassified as Recreation and Conservation, Multiple Family, Commercial/Office, or Mobile Home Park.The Equalization use code ME, Miscellaneous Exempt includes uses that are reclassified as Recreation & Conservation, Public/Institutional, Transportation, Utility & Communication, Industrial (municipal landfills), or any Single Family classification.The Equalization use code KI, Condominium Improved includes uses that are reclassified as Multiple Family, Single Family, or Commercial/Office. Any parcel with an Equalization use code of FI, Farm Improved or FV, Farm Vacant is considered to be an active agricultural use.Any parcel with an Equalization use code of II, Industrial Improved is considered to be an industrial use.Any parcel with an Equalization use code of FC, Farm Conservation is considered to be a recreational/conservation use.Any parcel with an Equalization use code of AI, Apartment Improved is considered to be a multiple family residential use.Any parcel with an Equalization use code of UI, Utility Improved is considered to be a transportation, utility, or communication related use.Parcels with an Equalization use code of DI, Developmental Improved are reclassified as Single Family or Vacant.Any parcel with an Equalization use code of DV for Developmental Vacant may be reclassified as Vacant, Recreation & Conservation, (golf courses) or Industrial (mining or extractive).Polygons in the ROW region of the parcel coverage will be classified as Recreation and Conservation, and Commercial/Office, Vacant, Road ROW, and Railroad ROW.Because of inconsistencies in Use Code data, unique uses, and the goal of creating an accurate coverage that is not limited by its metadata, there may be exceptions from these assumptions.Exceptions:There are nine isolated cases where the land use would be tremendously overstated if the whole parcel was shown in a single use. In these cases, the polygon was split to show the use of the rest of the parcel vacant. These parcels are listed below: 01-29-451-001 01-35-300-014 04-08-200-002 04-24-100-004 07-13-301-006 14-04-376-002 18-19-476-015 21-10-200-001 21-10-200-002

This shapefile was derived from a set of Historic Vegetation Maps for the Palau Islands. The creation of this shapefile was performed in two parts. The first part was the creation of a shapefile to represent the areal extent and vegetation polygons. The second part was the transfer of the Vegetation Legend into a database. Process for digitizing Palau Vegetation Maps -- 1. Scan hardcopy maps using a large format scanner at a 400dpi resolution -- 2. Using Adobe Photoshop the Scanned maps were cropped into two separate images, each saved as a *.TIF. One image contained the graphical map and the second containing just the Vegetation Legend. -- 3. The TIF images were then imported into ERDAS Imagine, assigned projection information, and georeferenced. The actual projection of the maps was not clearly available. The following projection information was obtained from the United States Geologic Survey but lacked a horizontal datum. (The Guam 1963 was selected because it gave the closest reprojection into UTM.) Projection: Modified Azimuthal Equidistant Spheroid: Clarke 1866 Datum: Guam 1963 Longitude of center of projection: 134° 27' 1.6015" Latitude of center of projection: 7° 21' 4.3996" False easting: 50,000 meters False northing: 150,000 meters -- 4. Images were then reprojected to: Projection: UTM Spheroid: WGS84 Datum: WGS84 UTM Zone: 53 N -- 5. Because the direct reprojection to UTM was not accurate enough the images were then directly referenced, using ArcGIS, to the IKONOS Image of Palau that was provided by the USDA Pacific Northwest Research Station. For the purposes of this explanation process this file will be called "Rectified Image" -- 6. Each Rectified Image was then recoded (within ERDAS Imagine) from a grayscale image to a black/white binary image (a binary image is necessary for later steps). -- 7. Using ArcGIS a GRID file was created from a Rectified Image using the reclassify option within Spatial Analyst (the reclassification maintained the binary characteristic if the Rectified Image). This file will henceforth be referred to as the "Editable Image." -- 8. The Editable Image was cleaned using the Raster Claeanup tool within the ArcScan extension. Isolated pixels were removed and polygon outlines were smoothed and refined. -- 9. Using the Generate Features option within the ArcScan extenstion The Editable Image was vectorized into a shapefile. -- 10. The following attributes were added to the shapefile. Poly_Desc Island Area Perimeter MapSheet MapPolyNum DB_Join_ID -- 11. The values of the MapPolyNum were added manually for each polygon by using the Rectified image as a reference. Process for transferring the Vegetation Legends into a Database -- 1. The legend from each map were scanned was processed through an Optical Character Recognition program (ABBYY FineReader 7.0). -- 2. The processed legend was exported into an Excel Spreadsheet. -- 3. The vegetation labels were then decomposed and categorized into their component parts. -- 4. The spreadsheets concatenated and then imported into Microsoft Access. Now that both the Graphical Map and the Vegetation Legend were processed they were joined together.Geographical Coordinate System: WGS 1984 DatumData Projection: UTM Zone 53 North