Here is a brief description of each of the field names

LegalDescr: the legal description of the parcel

Zoning: The zoning code for the parcel. Note: there are slight differences in zoning codes within the Town of Tappahannock compared to the rest of the county.

TotalParc: Total Parcel Value as appraised in the Commissioner of the Revenue

TotalImp: The value of all the improvements in the parcel, as appraised in the Commissioner of the Revenue

TotalLand: The value of the land in the parcel, as appraised in the Commissioner of the Revenue

LegalAcre: Legal AcreageGISAcres: The acreage as calculated in ArcGIS Pro’s Calculate Geometry tool

OwnerName: This is the name of the first owner listed. To see additional owners, navigate to the property card using the web link.

Address: This is the 9-1-1 address for the property. If no 9-1-1 address has been assigned, the property will have an address of 0 Road Name. If multiple addresses are on the property, only one of them is listed here.

PrimaryUse: This is a designation of whether data is Residential (R), Vacant (V), Commercial (C), or has a Mobile Home (T), as well as an indication of the relative size of the property.

TaxJoinGIS: This field enables the parcel data to work with Vision's web map (which you can access from the property card)

TaxLookup: This field has all spaces removed to allow for easy searching of parcels within the webmap

WebLink: This navigates to the property data card in Vision, the data portal for the Commissioner of the Revenue. The home page for that portal is https://gis.vgsi.com/essexva/Search.aspx

PID: This is a number that is one of the ways data can be searched in Vision.GISJoin: This is a field that has the leading and trailing spaces removed to enable the joining of data within GISTaxLabel: This field has only one space between each portion of the tax map to allow for more convenient labelling on the map

Notes: Any special information needed for the parcel, including whether a parcel is split zoned or in the Historic Overlay Zone.

pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. recently discovered, pockmarks along the aquitaine slope within the french eez, were manually mapped although two semi-automated methods were tested without convincing results. in order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition gazcogne1, 2013. bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the kongsberg em302 ship-borne multibeam echosounder of the r/v le suroît at a speed of ~8 knots, operated at a frequency of 30 khz and calibrated with ©sippican shots. precision of seafloor backscatter amplitude is +/- 1 db. multibeam data, processed using caraibes (©ifremer), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. the present table includes 11 morphological attributes extracted from a geographical information system project (mercator 44°n conserved latitude in wgs84 datum) and additional parameters related to seafloor backscatter amplitudes. pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on gazcogne1 and bobgeo2 bathymetric datasets.the pockmark area and its perimeter were calculated with the “calculate geometry” tool of arcmap 10.2 (©esri) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). a first method to calculate pockmark internal depth developed by gafeira et al. was tested (gafeira j, long d, diaz-doce d (2012) semi-automated characterisation of seabed pockmarks in the central north sea. near surface geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). this method is based on the “fill” function from the hydrology toolset in spatial analyst toolbox arcmap 10.2 (©esri), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. the difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. for the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark.latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “zonal geometry as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). pockmark elongation was calculated as the ratio between the major and minor axis length.cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “zonal statistics as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri). slope was calculated from bathymetry with “slope” function from spatial analyst toolbox in arcgis 10.2 (©esri) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

This layer was created by performing a union of tidal marsh vegetation types from the CoastalWetlandPoly3 layer (provided to the County by Friends of the San Juans) and the county's tidal wetlands layer. New classifications were provided by Paul Adamus. Acres were calculated using the calculate geometry tool.

These address data are updated, typically by request, to City of San Marcos Planning and Development Services on a daily to weekly basis. Updates occur as new parcel plats are recorded, as building footprints change, when new service equipment such as cell towers and meters is installed, to bring existing address points into compliance with CAPCOG 911-Addressing guidelines, and as needed for various other circumstances.The 911 addresses (denoted in the Address911 field as “Y”) follow the CAPCOG (Capital Area Council of Governments) Addressing Guidelines (10-28-09) available here: http://www.capcog.org/divisions/emergency-communications/911-technology/(last accessed March 30, 2017).Non-911 addresses (denoted in the Address911 field as “N”) are maintained for location finding, public infrastructure inventory, and for various other circumstances. Location finding address points includes all intersection, 100 block numbers, and mile markers.There are two types of new addresses, In-fill and Subdivisions. In-fill addressing occurs in already developed areas that experience change. The Planning and Development Services Planning Technician updates and maintains the infill addresses, often in coordination with the City of San Marcos Fire Marshal’s office. Planning and Development Services 911 Address Coordinator creates new subdivision addressing. This feature exists in DevServices.sde. Field Information:OBJECTID- System-generated unique identifier for each record within the feature classMAXIMOID- unique identifier tie for public services asset management software; field is auto populated by IT GIS scriptMAXIMOIDPFX- unique identifier with prefix indicating (ADDR) feature tie for public services asset management software; field is auto populated by IT GIS scriptSAN- Site Address Number, assigned based on CAPCOG guidelines; alias: ADDRESSPRD- Prefix Directional (N, S, E, W); alias: PREFIX DIRECTIONSTN- Street Name; alias: STREET NAME; domain: ST_TYPESTS- Street Suffix; alias: STREET TYPEUNIT_NUM- FULLADDR- all caps concatenation of PRD + STN + STS (field calculate with this expression: ucase ([SAN] &" "& [PRD]&" "& [STN]&" "& [STS])UNIT TYPE*- values include: APT, ACSRY, BLDG, CLBHSE, CONDO, DUP, STE- these values , ; domain: ServUnitTypeZIP CODE- Zipcode- currently all 78666 COUNTY- Hays, Caldwell, Comal, or GuadalupeADDINFO*- used to add information about address, such as Business or Complex name or address type SF (single-family), intersection, etc.; alias DESCRIPTIONADDRESS911- yes or no value distinguishes 911 addresses from non-911 addresses; domain: YORNPOINT_X- Calculated geometry for “X Coordinate of Point” in PCS: NAD 1983 StatePlane Texas South Central FIPS 4204 Feet using Decimal DegreesPOINT_Y- Calculated geometry for “X Coordinate of Point” in PCS: NAD 1983 StatePlane Texas South Central FIPS 4204 Feet using Decimal DegreesCREATEDBY- system generated value based on log in ID CREATEDDATE- system generated value in UTMMODIFIEDBY- system generated value based on log in IDMODIFIEDDATE- system generated value in UTMSHAPE System-generated geometry type of the featureADDRESS_TYPE*- used to add information about addressGlobalID-System-generated unique identifier for each record that is required in replicated geodatabases*Indicate field is not consistent. The feature is under audit and overhaul in 2017 and 2018. Project will encompass and establish specific, consistent descriptors, update and add domains, compare and correct, as needed, consistency with these features: AptSteNum, Condo, Apartment, MFHousing, Parcel, Building, Centerline and Street address ranges

Shell Exploration & Production Company (Shell) initiated onshore hydrological studies in northern Alaska starting in 2010. Arctic Hydrologic Consultants and ERM were tasked with executing field surveys related to the following subjects: river hydrology and lake depth. Through Shell’s Prime Contractor Olgoonik Fairweather LLC, field surveys and analysis were carried out by Arctic Hydrologic Consultants and ERM in the year 2012. Please provide credit to Mr. James Aldrich (Arctic Hydrologic Consultants) and Mr. Jon Wolf, Mr. Joe Kmetz and Mr. Mike Cox (ERM) for authorship of the field data and map products. Derivative publications from these data should acknowledge the funding sources as well as original contractor. Funding partners have granted approval for early release and/or release to the public domain of these data. Downloads include final report as well as gage slope calculations, channel geometry data and GIS files.

The dataset shows the map of the areas where the speedà Vehicles are restricted to promote safe forms of mobility. light, like the cycle path ## Additional information No information provided ## Dataset fields * id (Area Code): Unique area code * via_id (via code): Code via toponymy * street_name (street name): Name via * stretch (street): Description of the section or portion of the route affected by the measure * Ordinance (Order): Order reference * date_ord (Order Date): Date of order * type_ord (Order Type): Type of order: Synthetic type code (Zone, Road or Residential Zone), speed limit and ordinance information * info_ord (Order Information): Further codified information on the Ordinance * A (provisional measure * C (Controvial) * area (City Hall): Town hall code(s) * Limit (Limit): Speed limit in km/h * Residence (Residential area): Boolean: the Ordinance establishes a residential area * year (Year): Year of Establishment * aggiorn (Update date): Record update date * sq.m. (Sq.m. area): Area calculated from GIS geometry * Notes (Notes): Notes (if any) * Geom (Geometry): Polygonal geometry This dataset has been issued by the Municipality of Milan.

Detailed calculations of ground-ice volumes in permafrost deposits are necessary to understand and quantify the response of permafrost landscapes to thermal disturbance and thawing. Ice wedges with their polygonal surface expression are a widespread ground-ice component of permafrost lowlands. Therefore, the wedge-ice volume (WIV) is one of the major factors to be considered, both for assessing permafrost vulnerability and for quantifying deep permafrost soil carbon inventories. Here, a straightforward tool for calculating the WIV is presented. This GIS and satellite image-based method provides an interesting approach for various research disciplines where WIV is an important input parameter, including landscape and ecosystem modeling of permafrost thaw or organic carbon assessments in deep permafrost deposits. By using basic data on subsurface ice-wedge geometry, our tool can be applied to other permafrost region where polygonal-patterned ground occurs. One is able to include individual polygon geomorphometry at a specific site and the shape and size of epigenetic and/or syngenetic ice wedges in three dimensions. Exemplarily, the WIV in late Pleistocene Yedoma deposits and Holocene thermokarst deposits is calculated at four case study areas in Siberia and Alaska. Therefore, we mapped ice-wedge polygons and thermokarst mounds (baydzherakhs) patters on different landscape units by using very-high-resolution satellite data. Thiessen polygons were automatically created in a geographic information system (GIS) environment to reconstruct relict ice-wedge polygonal networks from baydzherakh center-point patterns. This information was combined with literature or own field data of individual ice-wedge sizes, to generate three-dimensional subsurface models that distinguish between epi- and syngenetic ice-wedge geometry. We demonstrate that the WIV can vary considerably, not only between different permafrost regions, but also within a certain study site. Detailed information about methods and results can be found in the publication to which this dataset is a supplement. This dataset is part of the data collection "mapped ice wedge polygon patterns from geoeye-1, worldview-1"

The 2001 Madera County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. The finalized data include a shapefile of western Madera County (land use vector data), and JPEG files (raster data from aerial imagery). In May 2013, errors in acreage calculations were found in the original finalized data. The “Calculated Geometry” function of ArcGIS was used to correct the errors. The name of the original shapefile was 01ma.shp. The name of the revised shapefile is 01ma_v2.shp. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using developed photoquads. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. If the data is compared to the previous digital survey (i.e. the two coverages intersected for change detection determination), there will be land use changes that may be unexpected. The linework was created independently, so even if a field’s physical boundary hasn’t changed between surveys, the lines may differ due to difference in digitizing. Numerous thin polygons (with very little area) will result. A result could be UV1 (paved roads) to F1 (cotton). In reality, paved roads are not converted to cotton fields, but these small polygons would be created due to the differences in digitizing the linework for each survey. Additionally, this kind of comparison may yield polygons of significant size with unexpected changes. These changes will almost always involve non-cropped land, mainly U (urban), UR1 (single family homes on 1 – 5 acres), UV (urban vacant), NV (native vegetation), and I1 (land not cropped that year, but cropped within the past three years). The unexpected results (such as U to NV, or UR1 to NV) occur mainly because of interpretation of those non-cropped land uses with aerial imagery. Newer surveys or well funded surveys have had the advantage of using improved quality (higher resolution) imagery or additional labor, where more accurate identification of land use is possible, and more accurate linework is created. For example, an older survey may have a large polygon identified as UR, where the actual land use was a mixture of houses and vacant land. A newer survey may have, for that same area, delineated separately those land uses into smaller polygons. The result of an intersection would include changes from UR to UV (which is normally an unlikely change). It is important to understand that the main purpose of DWR performing land use surveys is to aid in development of agricultural water use data. Thus, given our goals and budget, our emphasis is on obtaining accurate agricultural land uses with less emphasis on obtaining accurate non-agricultural land uses (urban and native areas). 5. Water source information was not collected for this survey. 6. Not all land use codes will be represented in the survey.

Detailed calculations of ground-ice volumes in permafrost deposits are necessary to understand and quantify the response of permafrost landscapes to thermal disturbance and thawing. Ice wedges with their polygonal surface expression are a widespread ground-ice component of permafrost lowlands. Therefore, the wedge-ice volume (WIV) is one of the major factors to be considered, both for assessing permafrost vulnerability and for quantifying deep permafrost soil carbon inventories. Here, a straightforward tool for calculating the WIV is presented. This GIS and satellite image-based method provides an interesting approach for various research disciplines where WIV is an important input parameter, including landscape and ecosystem modeling of permafrost thaw or organic carbon assessments in deep permafrost deposits. By using basic data on subsurface ice-wedge geometry, our tool can be applied to other permafrost region where polygonal-patterned ground occurs. One is able to include individual polygon geomorphometry at a specific site and the shape and size of epigenetic and/or syngenetic ice wedges in three dimensions. Exemplarily, the WIV in late Pleistocene Yedoma deposits and Holocene thermokarst deposits is calculated at four case study areas in Siberia and Alaska. Therefore, we mapped ice-wedge polygons and thermokarst mounds (baydzherakhs) patters on different landscape units by using very-high-resolution satellite data. Thiessen polygons were automatically created in a geographic information system (GIS) environment to reconstruct relict ice-wedge polygonal networks from baydzherakh center-point patterns. This information was combined with literature or own field data of individual ice-wedge sizes, to generate three-dimensional subsurface models that distinguish between epi- and syngenetic ice-wedge geometry. We demonstrate that the WIV can vary considerably, not only between different permafrost regions, but also within a certain study site. Detailed information about methods and results can be found in the publication to which this dataset is a supplement. This dataset is part of the data collection "mapped ice wedge polygon patterns from geoeye-1, worldview-1"

Field descriptions/definitions for street light utility ownership data as follows:

ObjectID: GIS auto-generated unique identifier Utility: utility name Shape: GIS geometry type Shape.STArea(): GIS calculated area Shape.STLength(): GIS calculated total length of perimeter

Detailed calculations of ground-ice volumes in permafrost deposits are necessary to understand and quantify the response of permafrost landscapes to thermal disturbance and thawing. Ice wedges with their polygonal surface expression are a widespread ground-ice component of permafrost lowlands. Therefore, the wedge-ice volume (WIV) is one of the major factors to be considered, both for assessing permafrost vulnerability and for quantifying deep permafrost soil carbon inventories. Here, a straightforward tool for calculating the WIV is presented. This GIS and satellite image-based method provides an interesting approach for various research disciplines where WIV is an important input parameter, including landscape and ecosystem modeling of permafrost thaw or organic carbon assessments in deep permafrost deposits. By using basic data on subsurface ice-wedge geometry, our tool can be applied to other permafrost region where polygonal-patterned ground occurs. One is able to include individual polygon geomorphometry at a specific site and the shape and size of epigenetic and/or syngenetic ice wedges in three dimensions. Exemplarily, the WIV in late Pleistocene Yedoma deposits and Holocene thermokarst deposits is calculated at four case study areas in Siberia and Alaska. Therefore, we mapped ice-wedge polygons and thermokarst mounds (baydzherakhs) patters on different landscape units by using very-high-resolution satellite data. Thiessen polygons were automatically created in a geographic information system (GIS) environment to reconstruct relict ice-wedge polygonal networks from baydzherakh center-point patterns. This information was combined with literature or own field data of individual ice-wedge sizes, to generate three-dimensional subsurface models that distinguish between epi- and syngenetic ice-wedge geometry. We demonstrate that the WIV can vary considerably, not only between different permafrost regions, but also within a certain study site. Detailed information about methods and results can be found in the publication to which this dataset is a supplement (https://doi.org/10.1002/ppp.1810).

2020 Oregon Labor Day fires represents a list of fires that were on the landscape during the historic wildfire event that began on September 7, 2020. Acreage information was generated October 19th and are approximations. Discrepancies in acreage numbers may be the result of time of calculation or map projection used for geometry calculations. Contact:Steve TimbrookGIS Data AdministratorAdministrative BranchInformation Technology Program - GIS UnitOregon Department of Forestrysteve.timbrook@odf.oregon.gov503.931.2755

Line Name: Name and/or basic description, if applicable.Trail System Name: Only applicable to trails. Some smaller trail segments within a particular park, may be a part of a larger trail system (ex. a trail in Barkley Meadows Park may be a part of the larger Gilleland Creek Greenway Trail System).Status: This field uses a domain in the database called, Status, which explains the current physical state of the trail. Although there are several domain values for this domain, for this particular feature class, only a few are likely to be used: Open, Closed, Planned & Proposed.Domain Values Value Definitions Open Trail is open to the public, free of charge Open_Fee Trail is open to the public, access fee is charged Open_Restricted Trail is open to the public, but access is restricted – ex by permit or reservation only Closed Trail exists but is not open to the public Decommissioned Trail has been removed from service or transferred to another jurisdiction Planned Trail planned for the future Proposed Trail ROW acquired or agreed to by all owning/managing partners Unknown Status of the trail unknown Difficulty Rating: This field uses a domain in the database called, Difficulty Rating, which defines the difficulty rating of the trail.Domain Values Value Definitions Easiest A trail requiring limited skill with little challenge to travel - mostly level; adequate width; minor crossings, well marked More Difficult A trail requiring some skill and challenge to travel. Minor ascent/descent; narrow passages; heights; natural crossings; length Most Difficult A trail requiring a high degree of skill and challenge to travel. Significant ascent/descents; scrambling crossings; some bouldering, extensive length from trail head to trail access Special/Technical Capability Rapids above level 2 for canoes or 3 for Kayaks, climbing gear, cold weather gear suggested Varies Difficulty depends on environmental conditions, floods, ice, snow etc. Accessibility Status:This field uses a domain in the database called, Accessibility Status. Accessibility guideline compliance status for trail or segment that is designed for pedestrian use.Domain Values Value Definitions Accessible Trail meets current accessibility guidelines (ADA and Access Board Interpretation/Guidelines) Not Accessible Trail does not meet accessibility guidelines Ineligible Trail determined ineligible to meet current trail accessibility guidelines Not Evaluated Trail not evaluated for accessibility GIS Length Ft: Geometry calculated with GIS. May need to be re-calculated to account for any changes in geometry.Width Ft: Manually input by user, since the linear feature contains no width to calculate with GIS.Park Name & Park ID: Identifies which park the feature falls within.

The truck routes displayed within City limits are derived from City of Madison truck route signs and Madison General Ordinance 12.89. All truck routes shown outside of the City limits should be verified with the municipality they are shown in. Field descriptions/definitions for truck route data as follows:

ObjectID: GIS auto-generated unique identifier

speed_limi: speed limitMGO: note field for ordinance changes/updatesRodwycateg: roadway category designationFunct_clas: 1-Principal Arterial; 2-Primary Arterial; 3-Standard Arterial; 4-Collector; 5-Local streetSTATION: volume count station numberSOURCE: volume count station or linked segmentOrdRef: line item number from City of Madison Ordinace 12.89(1)TruckRoute: 1-City of Madison street; 2-County Highway; 3-State/US Highway; 4-Other Jurisdiction (Implied)Shape: GIS geometry typeShape.STLength(): GIS calculated segment length

US Census Bureau 2020 Tracts, with total population, Hispanic and Not Hispanic population, race counts and Household counts. Used for granular inspection of population changes. Dissolved from tabulation blocks. There are multi-part polygons because of non-contiguous geometry.Change columns were calculated using this general approach:2020 count minus 2010 countNegative value in change column indicates lower count in 2020 than 2010. Upon data review it was found that the 2010 Total Population count summarized from the calculated change table is 286 fewer than the total reported for Dallas in 2010. This is because of 104 2010 tabulation block centroids that did not intersect the 2020 Dallas tabulation blocks. Of these, 16 tabulation blocks had population counts greater than 0 and none appear to have been correctly assigned to Dallas. For the sake of population change by block, it seems safe to exclude these blocks from the calculations.

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

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

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

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

Mapping Steps

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

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

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

Other Things to Keep in Mind

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

This data represents the graphic portrayal of land parcels and their spatial relationships throughout York County, South Carolina. Land parcel boundaries are also the basis for and define coincident boundaries for other layers, such as zoning, subdivisions, public safety response (ORI -Police, Fire, EMS) and Jurisdiction.Boundaries are established from a variety of sources including cadastral plats, subdivision plats, deeds, land contracts, right-of-way plats, and others. Each feature represents a parcel of land that is inventoried by a unique identifier, referred to as a “Tax Map Id” number. This dataset also includes multi-unit structures which have separate tax accounts for each unit, such as condominium units, represented as stacked polygon features. The parent parcel number [ParentTaxID] for the land parcel is distinguished from the child parcel [TaxMapID] for the condo unit. This data does not include mobile home data. Attributes include data stored within the Esri Fabric data model combined with those from the CAMA data. Examples of relevant attributes include:the [TaxMapID], [ParcelID] and [AprAccNum] can be used to uniquely identify each parcel. the [MailAddr1], [MailAddr2], [MailApt], [MailCity], [MailState], [MailZip] can be used as the full tax billing address for the owner.The [Owner1], [Owner2], [Owner3] describe the owner.the [YearBuilt] offers the oldest year a building was built on the property, reference this web map for info on potential lead pipes on premises;the area of the parcel in acres [GISSizeAC] as calculated from the parcel geometry and also the [deededAcres] from recorded documents, and ;the date that the parcel boundary was last edited [DATE_MODIFIED].How were parcels compiled? This layer was initially developed as an ink-on-mylar property maps maintained by the County from the early 1970's through around 2001.In the 1990s, the county procured services to convert parcels from source documents, however the product delivered in 2000 used a methodology which lost fidelity of source documents. Since then, county staff adhered to this same methodology in their daily work. Between 2001 and 2015 staff used an Esri topology to maintain parcel data in ArcMap. In 2015 the county migrated to Parcel Fabric (ArcMap) and then in 2021 to Pro (2.6/10.8.1 Enterprise) Parcel Fabric. In May of 2021 the county began outsourcing maintenance of parcel edits. This has worked well and was initiated in part to ensure a higher standard of editing practice was adhered to, but also to fulfil a shortage of skilled staff in the job market. County parcel mapping staff remain responsible for simple transactions (merge, split), compilation of materials to create vendor edit request task, and QC or review of vendor work. In Q4 2021, County Staff performed a needs assessment to review alignment issues between parcels and other layers and the internal business requirements for data alignment to parcels. They determined boundary layers must remain coincident with parcels, which are used in decision making by citizens and across many areas of government. Also, it was determined that our parcels had many errors from 20 years of edits in a non-Fabric data model and the previous editing practices. The county will be remapping parcels using ARP grant funding in the 2023-2024 timeframe. Upon delivery in 2024, data maintenance practices will ensure ongoing alignment with parcels.Year BuiltTo obtain the year built for structures on a property, use the 'Buildings' table available through our open data portal.Once you have downloaded the 'Buildings' table and this parcels layer, consider processing the building records in some way to join or perform a relate as there could be many buildings on one parcel, using the following fields:Parcel.AprAccNum = BuildingTable.PropertyID(Note: 98,227 parcels have 1 building, 647 parcels have 2 buildings, 272 have 3 or more)Data SchemaReview the Parcel schema document (PDF) to gain a better understand of the data fields. Access the file geodatabase source data in SC State Plane coordinate system