DCGIS is an interactive map that provides increased functionality for advanced users as well as access to about 150 layers of GIS data, including parcel information, contour lines, aerial photography, county park amenities, park trails, bikeways, county road construction, roundabouts, floodplains and more. It allows you to create a map at any scale you wish.
The Interactive GIS Map is intended for use on any device - mobile or desktop - with high speed access.

These data were automated to provide an accurate high-resolution historical shoreline of Mobile Bay, Alabama suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attri...

Pitt County parcel data in tabular format and updated nightly. This data is maintained by Pitt County Tax Administration, for specific questions regarding the data please contact the Tax department at 252-902-3400. This data can also be searched and visualized online using the Online Parcel Information System (OPIS) here: https://gis.pittcountync.gov/opis/ or try Mobile OPIS here: https://gis.pittcountync.gov/opis/-mobile/.

This is a connection to the Crawford County Government public platform for exploring and downloading open data, discovering and building apps, and engaging to solve important local issues. You can analyze and combine Open Datasets using maps, as well as develop new web and mobile applications. Let's make our great community even better, together!

Web/Data Disclaimer: Click here for Crawford County, PA GIS Web/Data Disclaimer

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr. This metadata record describes the Texas South 1 digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications. This DEM includes data for Calhoun, Jackson, Matagorda, and Victoria Counties. The DEM was produced from the following lidar data sets: 1. 2017 25 County AL Lidar 2. 2014 Mobile County, AL Lidar 3. 2014 NRCS/USGS/NPS Choctaw and Washington Co., AL Lidar The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88, Geoid12B) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

This data consists of a composite inundation hazards layer for counties from TX to ME, which have a flood exposure snapshot. The dataset was developed from a union of FEMA flood hazard, USACE hurricane evacuation study, NOAA sea level rise of 3-ft above MHHW, and NOAA Shallow Coastal flooding georeferenced digital data. The source and date have been preserved for each source dataset in the attribution. A unique identifier for each hazard, a total number of hazards and hazard description list in the attribution provide coastal risk exposure for each polygon

This layer is sourced from maps.coast.noaa.gov.

This map service presents spatial information developed as part of the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management’s Coastal Flood Exposure Mapper. The purpose of the online mapping tool is to provide coastal managers, planners, and stakeholders a preliminary look at exposures to coastal flooding hazards. The Mapper is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help communities initiate resilience planning efforts. Currently the extent of the Coastal Flood Exposure Mapper covers U.S. coastal areas along the Gulf of Mexico and Atlantic Ocean. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© NOAA Office for Coastal Management

These data were automated to provide an accurate high-resolution composite shoreline of GRAND BAY TO PENSACOLA MOBILE BAY, AL suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies. This metadata describes information for both the line and point shapefiles. The NGS's attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST) was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so that the data would be more accurately translated into S-57.

Layout of mobile home lots in parks where the lots are not individually owned. The lot boundaries were derived from plot plans obtained from Lee County DCD and E-911/Addressing. When available, the street number from the E-911 plans were used to create a site address. Otherwise, the lot number was concatenated with SiteStreet to calculate the SiteAddress.

Data in this layer is compiled from a variety of sources. Attributes have been added to distinguish the sources."LeePA Building Footprints" are created and maintained by the Lee County Property Appraiser's GIS. The geometry and attributes are extracted from their databases and combined based on the unique building key."LeePA Condo Buildings" are created from features in the Lee County Property Appraiser's parcel fabric. The geometry and attributes are extracted from their databases and combined using a variety of methods.Other buildings have been added by Lee County GIS. These are typically mobile/manufactured homes or time shares. Most mobile/manufactured homes were created using Esri's Building Footprint Extraction deep learning package and Regularize Building Footprint geoprocessing tool from 2024 aerial imagery. Additional attributes were added by Lee County GIS.

This is SCAG 2019 Regional Land Use dataset developed for the final 2024 Connect SoCal, the 2024-2050 Regional Transportation Plan/Sustainable Communities Strategy (RTP/SCS), including general plan land use, specific plan land use, zoning code, and existing land use at parcel-level (approximately five million parcels) for 197 local jurisdictions in the SCAG region.The regional land use dataset is developed (1) to aid in SCAG’s regional transportation planning, scenario planning and growth forecasting, (2) facilitate policy discussion on various planning issues, and (3) enhance information database to better serve SCAG member jurisdictions, research institutes, universities, developers, general public, etc. It is the most frequently and widely utilized SCAG geospatial data. From late 2019 to early 2020, SCAG staff obtained the 2019 parcel boundary GIS file and tax roll property information from county assessor’s offices. After months of data standardization and clean-up process, SCAG staff released the 2019 parcel boundary GIS files along with the 2019 Annual Land Use dataset in February 2021. In December 2021, SCAG staff successfully developed the preliminary dataset of the 2019 regional land use data and released the draft SCAG Data/Map Book in May 2022. The preliminary land use data was reviewed by local jurisdictions during the Local Data Exchange (LDX) process for Connect SoCal 2024. As a part of the final 2019 regional land use data development process, SCAG staff made every effort to review the local jurisdictions’ inputs and comments and incorporated any updates to the regional land use datasets. The products of this project has been used as one of the key elements for Connect SoCal 2024 plan development, growth forecasting, scenario planning, and SCAG’s policy discussion on various planning issues, as well as Connect SoCal key growth strategy analysis.Note: This dataset is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Users should consult with each local jurisdiction directly to obtain the official land use information.2019 SCAG Land Use Codes: LegendLand Use Description Single Family Residential1110 Single Family Residential 1111 High Density Single Family Residential (9 or more DUs/ac) 1112 Medium Density Single Family Residential (3-8 DUs/ac) 1113 Low Density Single Family Residential (2 or less DUs/ac)Multi-Family Residential1120 Multi-Family Residential 1121 Mixed Multi-Family Residential1122 Duplexes, Triplexes and 2- or 3-Unit Condominiums and Townhouses1123 Low-Rise Apartments, Condominiums, and Townhouses1124 Medium-Rise Apartments and Condominiums1125 High-Rise Apartments and CondominiumsMobile Homes and Trailer Parks1130 Mobile Homes and Trailer Parks1131 Trailer Parks and Mobile Home Courts, High-Density1132 Mobile Home Courts and Subdivisions, Low-DensityMixed Residential1140 Mixed Residential1100 ResidentialRural Residential 1150 Rural ResidentialGeneral Office1210 General Office Use 1211 Low- and Medium-Rise Major Office Use 1212 High-Rise Major Office Use 1213 SkyscrapersCommercial and Services1200 Commercial and Services1220 Retail Stores and Commercial Services 1221 Regional Shopping Center 1222 Retail Centers (Non-Strip With Contiguous Interconnected Off-Street Parking) 1223 Retail Strip Development1230 Other Commercial 1231 Commercial Storage 1232 Commercial Recreation 1233 Hotels and MotelsFacilities1240 Public Facilities1241 Government Offices1242 Police and Sheriff Stations1243 Fire Stations1244 Major Medical Health Care Facilities1245 Religious Facilities1246 Other Public Facilities1247 Public Parking Facilities1250 Special Use Facilities1251 Correctional Facilities1252 Special Care Facilities1253 Other Special Use FacilitiesEducation1260 Educational Institutions1261 Pre-Schools/Day Care Centers1262 Elementary Schools1263 Junior or Intermediate High Schools1264 Senior High Schools1265 Colleges and Universities1266 Trade Schools and Professional Training FacilitiesMilitary Installations1270 Military Installations1271 Base (Built-up Area)1272 Vacant Area1273 Air Field1274 Former Base (Built-up Area)1275 Former Base Vacant Area1276 Former Base Air FieldIndustrial1300 Industrial 1310 Light Industrial1311 Manufacturing, Assembly, and Industrial Services1312 Motion Picture and Television Studio Lots1313 Packing Houses and Grain Elevators1314 Research and Development1320 Heavy Industrial1321 Manufacturing1322 Petroleum Refining and Processing1323 Open Storage1324 Major Metal Processing1325 Chemical Processing1330 Extraction1331 Mineral Extraction - Other Than Oil and Gas1332 Mineral Extraction - Oil and Gas1340 Wholesaling and WarehousingTransportation, Communications, and Utilities1400 Transportation, Communications, and Utilities 1410 Transportation1411 Airports1412 Railroads1413 Freeways and Major Roads1414 Park-and-Ride Lots1415 Bus Terminals and Yards1416 Truck Terminals1417 Harbor Facilities1418 Navigation Aids1420 Communication Facilities1430 Utility Facilities1431 Electrical Power Facilities1432 Solid Waste Disposal Facilities1433 Liquid Waste Disposal Facilities1434 Water Storage Facilities1435 Natural Gas and Petroleum Facilities1436 Water Transfer Facilities 1437 Improved Flood Waterways and Structures1438 Mixed Utilities1440 Maintenance Yards1441 Bus Yards1442 Rail Yards1450 Mixed Transportation1460 Mixed Transportation and UtilityMixed Commercial and Industrial1500 Mixed Commercial and IndustrialMixed Residential and Commercial1600 Mixed Residential and Commercial 1610 Residential-Oriented Residential/Commercial Mixed Use 1620 Commercial-Oriented Residential/Commercial Mixed UseOpen Space and Recreation1800 Open Space and Recreation 1810 Golf Courses 1820 Local Parks and Recreation 1830 Regional Parks and Recreation 1840 Cemeteries 1850 Wildlife Preserves and Sanctuaries 1860 Specimen Gardens and Arboreta 1870 Beach Parks 1880 Other Open Space and Recreation 1890 Off-Street TrailsAgriculture2000 Agriculture2100 Cropland and Improved Pasture Land2110 Irrigated Cropland and Improved Pasture Land2120 Non-Irrigated Cropland and Improved Pasture Land2200 Orchards and Vineyards2300 Nurseries2400 Dairy, Intensive Livestock, and Associated Facilities2500 Poultry Operations2600 Other Agriculture2700 Horse RanchesVacant3000 Vacant3100 Vacant Undifferentiated3200 Abandoned Orchards and Vineyards3300 Vacant With Limited Improvements3400 Beaches (Vacant)1900 Urban VacantWater4000 Water4100 Water, Undifferentiated4200 Harbor Water Facilities4300 Marina Water Facilities4400 Water Within a Military Installation4500 Area of Inundation (High Water)Specific Plan7777 Specific PlanUnder Construction1700 Under ConstructionUndevelopable or Protected Land8888 Undevelopable or Protected LandUnknown9999 Unknown

A point feature class containing locations of the Miami-Dade County Public Library System's Bookmobile stops.Updated: Quarterly The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

This layer shows computer ownership and internet access by age and race. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population age 18 to 64 in households with no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28005, B28003, B28009B, B28009C, B28009D, B28009E, B28009F, B28009G, B28009H, B28009I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Mobile Home Locations in Palm Beach County.

Description Cellphone tower extract is a copy of the data found at https://ised-isde.canada.ca/site/spectrum-management-system/en/spectrum-management-system-data. Data is organized into a point layer of tower locations grouped by provider. All the providers transmitters are located in a table related by a unique TowerID.Dataset Usage General data layer for use when needed, for example to identify shortfalls in cell service for field work.Data Source Modified version of Innovation, Science and Economic Development Canada datasetData Criticality 1Sensitive Data NoCurator Greg SpiridonovCurator Job Title GIS SpecialistCurator Email greg.spiridonov@grey.caCurator Department IT / GISCurator Responsibilities Maintain,Oversight_Control_AccessMaintenance and Update Frequency MonthlyUpdate History New ZIP downloaded Nov 14 2023Published Map Service(s) https://gis.grey.ca/portal/home/item.html?id=718f08a731924856827f85178bb649cbPublicly Available Publicly availableOpen Data Published to Open DataOffline (sync) Not sure at this timeOther Comments Dataset relates to table GC_CellTower_TransmittersPermissionsAssign permissions to map service if published.Group PermissionsCurator Department ViewGrey County Staff ViewPublic View

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Pitt County city limits in spatial format. Data is updated nightly. For questions regarding this data please contact Pitt County Tax Administration at 252-902-3400. This data can also be visualized online using the Online Parcel Information System (OPIS) here: http://gis.pittcountync.gov/opis/ or using Mobile OPIS here: http://gis.pittcountync.gov/opis/-mobile/.

Pitt County parcels in spatial format. For questions regarding this data please contact Pitt County Tax Administration at 252-902-3400. This data can also be searched and visualized online using the Online Parcel Information System (OPIS) here: http://gis.pittcountync.gov/opis/ or try Mobile OPIS here: http://gis.pittcountync.gov/opis/-mobile/.

A polygon feature class of Mobile Home Parks and Recreational vehicles within Miami-Dade County.Updated: Annually The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

Point feature class of Mobile Home Parks and Recreational vehicles within Miami-Dade County.Updated: Annually The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere