ownership_poly_dissolve: This theme portrays information related to surface jurisdiction of lands located in the states of Oregon and Washington.

Note: The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services beginning in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is regularly updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications. PurposeCounty boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use. Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal Buffers (this dataset)Without Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)State BoundaryWith Bay CutsWithout Bay Cuts Working with Coastal Buffers The dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers. Point of ContactCalifornia Department of Technology, Office of Digital Services, gis@state.ca.gov Field and Abbreviation DefinitionsCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead. Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections.Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor. CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information. CDTFA's source data notes the following about accuracy: City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties. In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose. SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon. Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available fire datasets that identify wildfire and prescribed fire burned areas across the United States. However, these datasets are all limited in some way. Their time periods could cover only a couple of decades or they may have stopped collecting data many years ago. Their spatial footprints may be limited to a specific geographic area or agency. Their attribute data may be limited to nothing more than a polygon and a year. None of the existing datasets provides a comprehensive picture of fires that have burned throughout the last few centuries. Our dataset uses these existing layers and utilizes a series of both manual processes and ArcGIS Python (arcpy) scripts to merge these existing datasets into a single dataset that encompasses the known wildfires and prescribed fires within the United States and certain territories. Forty different fire layers were utilized in this dataset. First, these datasets were ranked by order of observed quality (Tiers). The datasets were given a common set of attribute fields and as many of these fields were populated as possible within each dataset. All fire layers were then merged together (the merged dataset) by their common attributes to created a merged dataset containing all fire polygons. Polygons were then processed in order of Tier (1-8) so that overlapping polygons in the same year and Tier were dissolved together. Overlapping polygons in subsequent Tiers were removed from the dataset. Attributes from the original datasets of all intersecting polygons in the same year across all Tiers were also merged so that all attributes from all Tiers were included, but only the polygons from the highest ranking Tier were dissolved to form the fire polygon. The resulting product (the combined dataset) has only one fire per year in a given area with one set of attributes. While it combines wildfire data from 40 wildfire layers and therefore has more complete information on wildfires than the datasets that went into it, this dataset has also has its own set of limitations. Please see the Data Quality attributes within the metadata record for additional information on this dataset's limitations. Overall, we believe this dataset is designed be to a comprehensive collection of fire boundaries within the United States and provides a more thorough and complete picture of fires across the United States when compared to the datasets that went into it.

This data set represents the approximate boundaries of Sales Tax Areas for the State of Utah. Individual Sales Tax Area polygons are formed by each municipality, special sales tax district (in 2006, there were 6 special transit districts) and the unincorporated areas of each county not in a special sales tax district. This dataset also contains an attribute that can be used to dissolve the Sales Tax polygons into the polygons representing tax areas for the proposed Streamlined Sales Tax implementation.Updated Q2 2025

Statewide polygon feature class of seamlines dissolved by date of flight capture for image used in delievery product from USDA Farm Service Agency. Individual shapefiles provided by USDA were appended into one feature class, then dissolved on the IDATE field and re-projected to NAD83 NJ State Plane Feet in order to overlay easily with other state GIS data. The dissolve tool was run with the multipart polygon option.

This Administration feature is the single most valuable feature maintained by the GIS Services staff. It combines the maintenance of many individual polygon features in one main overall feature.It is part of a ArcGIS Topology class maintained with our parcel and zoning features in the Editing Feature Data Set.We use the shared editing capabilities of this topology class to leverage our maintenance procedures as simply as possible. Weekly, the individual features maintained with our Administration feature are created with ArcGIS dissolve function. These include Jurisdiction boundaries, Public Safety Response areas, Voting Precincts, Schools Attendance Zones, Inspections, Library Service Zones, and more.Generally, maintenance of this feature is controlled thru shared editing performed with our parcel/zoning edits with the use of the Topology features in ArcGIS. Changes to features maintained in the Administration feature are caused by a number of issues. Parcel edits, new Public Safety Stations, changes in Voting Precincts, Police Reporting districts and other changes occur often. Most changes can be facilitated by selecting one or more “Administrative” polygons and changing the appropriate attribute value. Use of the “Cut Polygon” task may be necessary in those cases where part of a polygon must be changed from a district to another. The appropriate attribute can be changed in the affected area as necessary.

(Link to Metadata) ONSITE is a pre-selected subset of SSURGO certified soil data depicting onsite sewage disposal ratings of Vermont soils. The NRCS Top20 table was joined to SSURGO polygons. The joined data set was then DISSOLVED on the ONSITE attribute in order to merge polygons with the same ONSITE classification code. VCGI HAS NOT PERFORMED QAQC ON THE RESULTS. AS A RESULT, THIS DATASET SHOULD BE USED WITH CAUTION. NOTICE: This information identifies the new onsite sewage disposal class. This new system replaces the old classification system. Ratings are based on Vermont Environmental Protection Rules, August 16, 2002, based on 20% maximum slope - for lots created on or after June 14, 2002. It doesn't replace onsite investigation. These are the five major classes. Class I - WELL SUITED Class II - MODERATELY SUITED Class III - MARGINALLY SUITED Class IV - NOT SUITED Class V - NOT RATED Refer to documentation bundled with the SOILATTR product--AKA VT DATA - NRCS TOP20 SOILS ATTRIBUTES AND DOCUMENTATION (which is a stand-alone item in Vermont Open Geodata Portal); SOILATTR can be directly downloaded via https://drive.google.com/uc?export=download&id=13a68oimr0sVu_D4jXrNKAMNBIbVE9GN7 . Survey Dates - https://www.nrcs.usda.gov/wps/portal/nrcs/surveylist/soils/survey/state/?stateId=VT

This dataset visualizes approximate size and locations of herring spawn events from 1941 to 2001. Notes (Fisheries and Oceans Canada, 2015):Polygons were digitized over a 1:20,000 scale, TRIM base map.Available data is NOT a complete set. Many historical herring spawn records could not be digitized.The initial digitized set of Strait of Georgia and Barkley Sound shape files were subsequently re-digitized to the same standards as those of Haida Gwaii, Prince Rupert District and the West Coast of Vancouver Island.Spawning areas (m2) cannot be correctly calculated using these shape files. Actual areas may be significantly lower or higher than those represented by some of the digitized polygons. Consistent protocols, however, were used to digitize a historical spatial dataset that varied considerably in accuracy, precision and completeness over a 70 year time period.Further information:Spawning Areas of BC (Hay et al. 2014) or PDF (2013). Available from: http://www.pac.dfo-mpo.gc.ca/science/species-especes/pelagic-pelagique/herring-hareng/hertags/pdf/project-eng.pdfMapping Herring Spawn (McCarter et al. 2005) or PDF. Available from: http://www.dfo-mpo.gc.ca/Library/315704.pdfCopyright and citation guidelines apply for all downloads.Data Source: Fisheries and Oceans Canada - https://www.pac.dfo-mpo.gc.ca/science/species-especes/pelagic-pelagique/herring-hareng/herspawn/pages/default6-eng.html Lineage: Original polygons clipped to Strait of Georgia Stock Assessment Region.The dataset was altered so that from the original polygons the features from 1950 to 1959 were merged in order to show information for the whole decade as opposed to by each year. This process was then repeated for the features from 1990 to 1999. The two resulting datasets were then analyzed to determine where they intersected and differed, with the result being a dataset that shows areas where spawn presence was recorded in the 1950s, areas where spawn presence was recorded in the 1990s, and areas where spawn presence occurred in both decades. Known issue(s): 1954 was not present in the original dataset and is therefore not included in the decadal information for the 1950s. For more information contact: Ben Skinner, GIS Specialist, Pacific Salmon Foundation, bskinner@psf.ca

This county boundary polygon is based off of the parcel polygons, which are available from our Open Data Portal. This boundary polygon was generated by a Dissolve GP process. It is not to be used for legal purposes or border disputes, and is shared with the sole purpose of adding a reasonable estimate of the County Boundary in the spirit of general mapping purposes.The geometry of the parcel polygons, upon which this layer is based, are derived by a manual process of entering coordinate geometry as defined by a parcel's Deed. As not all Deeds agree with eachother, and some of the older definitions weren't perfectly accurate, this creates conflicts between parcels that inevitably lead to slight distortions. Again, these distortions are small enough to make the data useful for mapping purposes; to create a "best available" view of the state of the metes and bounds of the County and its subsequent parcels.This particular view has the CDN enabled in order for it to perform better for viral maps and apps.

Status: UPDATED occasionally using ArcMap Contact: Michael Liberti, Tucson Water, 520-837-2226, Michael.Liberti@tucsonaz.gov Intended Use: Primary data record of service area boundaries. Not intended for map display unless approved by data owner. Known errors/qualifications This is a rough approximation of the obligated areas served by Tucson Water and some errors may exist especially in those areas outside the City of Tucson limits. Does not include the non-potable service area boundaries. The non-potable water system includes all water types that have not been treated to potable standards: reclaimed water lines, raw CAP mains from the canal, secondary effluent piped from the County treatment plant to the Roger Road reclaimed plan, TARP water in the mains between the recovery wells and the treatment plant and the CAVSARP recovery wellfields. Boundaries may overlap other water company service area boundaries because their definition of the boundaries may vary.***SERVED = The parcel has a TW water meter and is consuming water.***OBLIGATED = Either 1) a vacant parcel inside the City of Tucson that does not have a TW meter, or 2) a vacant/abandoned parcel that has a meter, but is not consuming water. ***COMMITTED = a parcel outside the City of Tucson that 1) is in an area of intergovernmental contractual agreement (e.g. Dove Mountain/Continental Ranch...) 2) is a master planned development for which TW previously granted a service agreement (will have a corresponding GREENLINE feature) 3) is under 20 acres and is located adjacent to a delivery pipe and is served on three sides 4) a Tucson Unified School District parcel ***NOT OBLIGATED = Call Tucson Water Development Services for clarification. ALSO NOTE THAT PIPELINE RIGHT-OF-WAYS OUTSIDE TUCSON ARE ALSO COMMITTED (NO PARCEL) So, a parcel can be committed by contract, by prior approval or by location Individuals that live outside Tucson who are not currently served by TW fall under #3. Unless directed otherwise by Tucson Water, by default any parcel outside the City of Tucson that does not have a Tucson Water meter will NOT be served unless they annex. Replaces ObligatedServiceArea feature class formerly maintained in VMDB. The name ObligatedServiceArea is still used, but now is just a filtered view of ServiceArea. September 2010 - loaded ServiceArea shapefile into EDITSDE geodatabase to be maintained as feature class instead of shapefile. Renamed to ServiceArea. ObligatedServiceArea becomes a view of the ServiceArea feature class. sdetable -o create_view -T OBLIGATEDSERVICEAREA -t SERVICEAREA -w "SA_TYPE in ('SERVED','OBLIGATED','COMMITTED')" -c "OBJECTID,SHAPE,SA_TYPE,SA_SUBTYPE,REC_DATE,INSTALLYEAR" -i sde:sqlserver:pw-sql2005 -D editsdeAugust 2010 - ObligatedServiceArea criteria is redefined based on decisions by Mayor and Council. New shapefile called ServiceArea becomes the data of record.10/10/2008 Bryn Enright Copy of most recent Obligated Service Area provided to John Regan from Pima County to post in the GIS Library and MapGuide site.10/10/2008 Bryn Enright Renamed the feature class from admin.ObligatedServiceArea to admin.ObligatedServiceAreaHistory. The most current area will be made available in a new database view called admin.ObligatedServiceArea.10/10/2008 Bryn Enright "Appended the most recent obligated service area created by Michael Liberti which was created by:1. Dissolving the current service area (req_SA_current.shp) with committed areas outside the city (dove mtn, larry's marana, diamond bell, corona de tucson...)2. Erased the city polygon (LIMJURIS) from the dissolve.3. Added the entire city polygon back in (i.e. obligated area) but then erased all of the ""water providers"" with CCNs ('56' water right)." In = req_SA_obligated.shp Out = ADMIN.ObligatedServiceAreaJune 2008 Bryn Enright Shapefile imported into Geodatabase (EMAPDB) In = Req_ServiceArea_v1.shp Out = ObligatedServiceAreaJune 2008 Michael Liberti Layer dissolved to show only 1 polygon for obligated area. In = ServiceArea_v1.shp Out = Req_ServiceArea_v1.shpJune 2008 Michael Liberti Layer updated with new services and existing remote services that were not previously included.February 2008 Michael Liberti Layer updated with new services for the 2008 Update to the Water Plan 2000-2050November 2004 Michael Liberti "Modified PCLIS WATERCOS as collection of PARCELS intersecting SERVICES by M Liberti. Layer created for use in the Water Plan 2000-2050" In=PCLIS WATERCOS Out=ServiceArea_v1.shp Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet consectetur adipiscing elit. Massa enim nec dui nunc. Quis commodo odio aenean sed adipiscing diam donec adipiscing. Nulla pellentesque dignissim enim sit amet venenatis urna. Sit amet volutpat consequat mauris nunc congue nisi vitae. Fames ac turpis egestas maecenas pharetra convallis posuere morbi leo. Morbi tristique senectus et netus et malesuada fames ac turpis. Eget lorem dolor sed viverra ipsum nunc. Id ornare arcu odio ut sem. Morbi leo urna molestie at elementum eu. In metus vulputate eu scelerisque. Lobortis mattis aliquam faucibus purus in massa tempor nec feugiat. Ut sem viverra aliquet eget sit amet tellus cras adipiscing. Lobortis mattis aliquam faucibus purus in massa tempor. Donec massa sapien faucibus et molestie ac feugiat. Et odio pellentesque diam volutpat commodo sed egestas egestas. Pharetra magna ac placerat vestibulum lectus. Fermentum leo vel orci porta non pulvinar neque laoreet suspendissePurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Update FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

This feature class represents combined 200ft and 1000ft beach buffers derived from Intertidal_PL. The process for creating this combined buffer is as follows: Select from Intertidal_PL where Description in ( 'CHK' , 'INT', 'EST', 'UIT' ) and buffer it 1000 ft, then dissolve all, so it's just a big ol' blob. Use the same selected set from Intertidal_PL and buffer it 200 feet, then dissolve all.Then select from Intertidal_PL where Description in ( 'CHK' , 'SW', 'EST' ) and erase those areas from the big buffered blobs. (I do not erase the INT areas because that might leave gaps and slivers along the shoreline above the water.)Add a field called Buff1000ft to the 1000 foot buffer and populate it with 'Y'. Add a field called Buff200ft to the 200 foot buffer and populate it with 'Y'. Identity the 200 ft buffer onto the 1000 ft buffer.Select where the field Buff200ft is blank and populate it with 'N'. (This would be the area within the 1000 ft buffer that falls outside of the 200 ft buffer.)The resulting buffer polygons can end up being wider than 1000ft or 200ft respectively in some places (beach…etc), but the intent of the polygons is to go landward 1000ft/200ft – the additional width is due to the beach/island/peninsula type stuff. The reason I originally started choosing to buffer CHK, INT, EST, and UIT was because I was concerned that buffering only INT might lead to odd gaps.

pcgpraqM is one of several Pima County Government Property Rights (PCGPR) layers. pcgpraqM displays Pima County Acquired Property Rights data, which include property, various easements, and other miscellaneous conveyances. The areas in question were either acquired by recorded deed or lease. Polygon features and attributes are based on recorded instruments. A nightly batch process appends all section shapefiles to create pcgpracq. The shape pcgpracq is further processed to join all area (polygon) values to related sql table attributes. If a duplicate/triplicate exists the polygon makes a copy of itself.The maintenance of this layer is handled by Pima County. For more detailed information, please refer to the original metadata, found here. PurposeShows information about property rights in Pima County.Dataset ClassificationLevel 0 - OpenKnown UsesUsed in the HP Dashboard Map.Known ErrorsThis layer has overlapping polygons that are not represented in the coverage format. Do not use a coverage format version of this layer. This layer is built from acquisition section drawings and related information stored in sql tables. This layer represents all document and/or classcode records related to an acquisition area (polygon). If an area (polygon) references more than one document and/or classcode, it creates a duplicate of itself and references the additional data. In that way all data is represented in the shapefile. In addition, the areas (polygons) are dissolved on common document data information. If a right of way area (polygon) was split during initial data entry along a section line, it is no longer represented by multiple polygons; the pieces are dissolved into one common area (polygon). As a result of the dissolve any BB_NO (unique identifier of the polygon disappears). In some cases, Pima County owned road Rights-Of-Way (ROW) do not encompass the entire portion of the overall road ROW.Data ContactPima County Information Technology Department - Geographic Information Systems201 N Stone Ave., 9th FloorTucson, AZ 85701GISDdata@pima.govUpdate FrequencyThe update of this layer is handled by Pima County. The last known update was 2014 but that date may not be accurate.

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

Beech Bark Disease (BBD) is affecting forest ecology within the upper Great Lakes region and at UMBS. This dataset includes GIS layers of mapped BBD locations and hotspots that were derived from aerial NAIP imagery biennially 2012-2018. For more information please see Barnett et al 2022 (https://www.sciencedirect.com/science/article/pii/S0378112722004807?)Locating and tracking outbreaks of pests and pathogens enables forest ecologists and managers to assess impact or develop interventions. The purpose of this project was to explore and prototype an open-source framework for identification of BBD-affected tree crowns and for assessment of their spatial-temporal distribution over heterogeneous forested landscapes. Results located hotspots of BBD on the UMBS landscape over a time series 2012-2018 and confirmed its spatial relationships with different cover types based on a local UMBS Landsat-derived classification and the NLCD classification (National Land Cover Dataset). Limitations include the fact that BBD can only be observed via remote sensing at certain stage of its development, and limitations in the radiometric consistency of the input NAIP imagery upon which automated algorithms must rely and affecting accuracy. Thus, data is appropriate to describe hotspots of BBD and its expansion over time plus relationships to different cover types; it is not an exhaustive map of BBD-affected trees.

Simplified, smoothed, vector version of storm surge areas from NOAA SLOSH MEOW for Florida, Category 5 hurricane. This data is prebaked and NOT reflective of current storm path, strength, etc. The SLOSH MEOW data looks at the worst case scenario for all areas if a storm were to hit directly at any location. This dataset is intended for use with DEP apps and scripts which locate facilities and points of interest within or near storm-surge areas. Source data based on 30-meter (1 arc-second) resolution DEM data. Smoothed using Polynomial Approximation with Exponential Kernel (PAEK) and 100 meter tolerance. Donut holes of less than 1000 meters sq filled to reduce chance of points/facilities being missed during intersection due to arbitrary gaps. Most of these donuts are inland ponds and other small water features that were masked in original data. Raster to Poly, Pairwise Dissolve, Select for FL, Smooth Polygon, Eliminate Polygon Part.Source DataSLOSH InfoNOAA Storm Surge Hazard App

The Tampa Bay Regional Resiliency Coalition Boundary is a dissolve of Citrus, Hernando, Pasco, Pinellas, Hillsborough, Manatee, and Sarasota county boundary polygons derived from a Census TIGER/Line 2022 shapefile.You can learn more about the Resiliency Coalition by clicking here.

Input Data

NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)
Southeast Blueprint 2023 subregions: Caribbean

Mapping Steps

Make a copy of the Southeast Caribbean CUSP feature line dataset and reproject it to ESPG 5070.
For the big island of Puerto Rico, special steps were required to deal with CUSP shorelines that did not connect across large rivers.
  Add and calculate a field to use to dissolve the lines.
    Dissolve the lines using the dissolve function, which reveals where there are gaps in the shoreline.
    Use the integrate tool to snap together nearby nodes, using a tolerance of 8 m. This connects the disconnected lines on the big island of Puerto Rico.
    Convert these modified shorelines to a polygon.
    Add and calculate a dissolve field, then dissolve using the dissolve tool. This is necessary because interior waterbodies on the big island of Puerto Rico also have shorelines in the CUSP data. This step produces a layer where inland waterbodies are included as a part of the island where they occur.
    From the resulting layer, select the big island of Puerto Rico and create a separate polygon feature layer from it. This extracts a modified shoreline boundary for the big island of Puerto Rico only. We don’t want to use the modified shorelines created above for other islands that didn’t have an issue of disconnected shoreline segments near large rivers.

Go back to the original Caribbean CUSP lines and convert them to polygons.
Add a dissolve field and dissolve using the dissolve tool. This produces a layer where all inland waterbodies are included as a part of the island where they occur.
From the island boundaries derived from the original CUSP data, remove the polygons that overlap with the big island of Puerto Rico derived from the modified CUSP data. This produces a layer representing all U.S. Caribbean islands except the big island of Puerto Rico.
Merge the modified big island of Puerto Rico layer with the layer for all other islands.
Create and populate a field that has unique IDs for all islands.
Convert the island polygon to a raster using the ArcPy Feature to Raster function. This makes a raster that correctly represents the interior of the islands. However, because the Feature to Raster function for polygons works differently than the Line to Raster function, the shoreline doesn’t perfectly match the result we get when we convert the CUSP lines to a raster. 
Because the Caribbean coastal shoreline condition indicator is created from the CUSP lines, we need the shorelines to match exactly. To reconcile this, go back to the original Caribbean CUSP line data and use the Feature to Raster function again, this time converting the lines to a raster. 
Use the ArcPy Cell Statistics “MAXIMUM” function to combine the two rasters above (one created from the CUSP lines and one created from the CUSP-derived polygons).
Export the raster that represents the extent of Caribbean islands.
Use the Region Group function to give unique values to each island.
Reclassify to make 3 island size classes. The big island of Puerto Rico is the only island in the highest class. The medium island class contains the following islands: Isla Mona, Isla de Vieques, Isla de Culebra, St. Thomas, St. John, and St. Croix. All other islands were put in the smaller class. All other non-island pixels in the Caribbean were given a value of marine.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2023 Data Download or Caribbean-only Southeast Blueprint 2023 Data Download under > 6_Code. Literature Cited National Oceanic and Atmospheric Administration (NOAA), National Ocean Service, National Geodetic Survey. NOAA Continually Updated Shoreline Product (CUSP): Southeast Caribbean. [https://coast.noaa.gov/digitalcoast/data/cusp.html].

Thirty-one Wildlife Management Areas located in seven Fish and Game regions have been established to protect wildlife habitat and are available for hunting, fishing and other public enjoyment of wildlife. Varying from 275 to 85,000 acres, each area is dedicated to primary purposes such as big game, waterfowl and upland game. The lands encompassed by the WMAs vary from backcountry forested habitats for big game animals to agricultural areas with important pheasant and waterfowl habitat. Thousands of big game animals winter on some WMAs and tens of thousands of waterfowl winter on others. Nongame wildlife use all the WMAs. These lands are diverse and critical habitats that provide great opportunities to enjoy both game and nongame wildlife. Various private, state, and federal entities have entered into agreements with or sold lands to IDFG for the management of the state's fish and wildlife. Each property agreement/lease/deed/easement/permit in the IDFG Lands database was digitized based on their legal descriptions. Various GIS datasets were used to help digitize each property: township range section, geographic names, ITD roads, FS roads, hydrography, land ownership, county boundaries, etc. Properties were combined (via dissolve) based on property name to create the polygons for each WMA.