Data from various sources, including 2018 and 2019 multibeam bathymetry data collected by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS) were combined to create a composite 30-m resolution multibeam bathymetry surface of central Cascadia Margin offshore Oregon. These metadata describe the polygon shapefile that outlines and identifies each publicly available bathymetric dataset. The data are available as a polygon shapefile.

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

The Global Urban Polygons and Points Dataset (GUPPD), Version 1 is a global data set of 123,034 urban settlements with place names and population for the years 1975-2030 in five-year increments. The data set builds on and expands the European Commission, Joint Research Centre's (JRC) 2015 Global Human Settlement (GHS) Urban Centre Database (UCDB). The JRC Settlement Model (GHS-SMOD) data set includes a hierarchy of urban settlements, from urban centre (level 30), to dense urban cluster (level 23), to semi-dense urban cluster (level 22). The UCDB only includes level 30, whereas the GUPPDv1 adds levels 23 and 22, and uses open data sources to both check and validate the names that JRC assigned to its UCDB polygons and to label the newly added settlements. The methodology described in the documentation was able to consistently label a greater percentage of UCDB polygons than were previously labeled by JRC.

Polygons - Interests is one of a suite of feature classes (5 in total) contained within Landgate's Tenure-by-Polygon SLIP service and provides a processed "flattened" data structure for cadastral polygons with land tenure type and ownership details. This layer compliments the Polygon - Master layer and contains all polygons captured within the Spatial Cadastral Database (SCDB) that is considered an "interest". _ NOTE: This product is for information purposes only and is not guaranteed. The information may be out of date and should not be relied upon without further verification from the original documents. Where the information is being used for legal purposes then the original documents must be searched for all legal requirements. Strict access criteria applies, due to sensitivity of information contained in this data service, please contact customerexperience@landgate.wa.gov.au for further information. _

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

In June 2022, Thailand legalized recreational cannabis. Currently, cannabis is now the most consumed drug. Cannabis usage can increase inflammatory responses in the respiratory tract. Sharing of cannabis waterpipes has been linked to increased tuberculosis risks. Using a national in-patient databank, we aimed to 1) describe the spatiotemporal correlation between cannabis-related and tuberculosis hospital admissions, and 2) compare the rate of subsequent pulmonary tuberculosis admission between those with prior admissions for cannabis-related causes and those without. Both admission types were aggregated to the number of admissions in monthly and provincial units. Temporal and spatial patterns were visualized using line plots and choropleth maps, respectively. A matched cohort analysis was conducted to compare the incidence density rate of subsequent tuberculosis admission and the hazard ratio. Throughout 2017–2022, we observed a gradual decline in tuberculosis admissions, in contrast to the increase in cannabis-related admissions. Both admissions shared a hotspot in Northeastern Thailand. Between matched cohorts of 6,773 in-patients, the incidence density rate per 100,000 person–years of subsequent tuberculosis admissions was 267.6 and 165.9 in in-patients with and without past cannabis-admission, respectively. After adjusting for covariates, we found that a cannabis-related admission history was associated with a hazard ratio of 1.48 (P = 0.268) for subsequent tuberculosis admission. Our findings failed to support the evidence that cannabis consumption increased pulmonary tuberculosis risk. Other study types are needed to further assess the association between cannabis consumption and pulmonary tuberculosis.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

This cadastral polygon dataset is a digital representation of all land parcel boundaries within Western Australia. It represents all crown land (land owned by the State) and freehold land (land held in fee simple) and is sourced from the Spatial Cadastral Database (SCDB) which is the official digital cadastral map base of all crown and freehold land parcels within the State of Western Australia. The dataset covers the State of Western Australia and the Commonwealth jurisdictions of Cocos Keeling Island and Christmas Island. NOTE: This product is for information purposes only and is not guaranteed. The information may be out of date and should not be relied upon without further verification from the original documents. Where the information is being used for legal purposes then the original documents must be searched for all legal requirements. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. For further information please contact your Landgate Customer Experience Consultant or email customerexperience@landgate.wa.gov.au.

BodyParts3D organ model data with the polygon reduction rate of 99%. The zip-compressed download files contain multiple files of ELEMENT file ID-specific polygon data in Wavefront OBJ format.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover postal divisions for the whole world. The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (Geospatial data, Map data, Polygon daa)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

CAP Geofences: Precision & Accuracy for Business Success

Unmatched Geofencing Accuracy

CAP geofences are meticulously hand-drawn to provide superior accuracy, surpassing automated, machine-generated polygons that only cover building footprints. Our approach considers the entire shopping center ecosystem, including parking lots, out -parcels, and surrounding structures, ensuring a more comprehensive and precise representation.

Commitment to Accuracy

Unlike conventional geofencing solutions, CAP continuously refines its geofences through ground-truthing, eliminating inaccuracies such as drift and leakage. While this process takes longer than automated methods, it results in the highest level of reliability, minimizing errors and maximizing actionable insights.

Enhancing Business Operations

CAP geofences empower businesses by offering deep insights into foot traffic patterns. Instead of just counting visitors, businesses can track movement across different areas, such as parking lots, walkways, and specific stores. This level of granularity helps optimize operations, refine marketing strategies, and better understand customer behavior.

Precision in Mobile Advertising

For advertisers, CAP’s geofences enable accurate location-based targeting, ensuring messages reach the right audience without the risk of geofence drift or leakage. This precision leads to higher engagement rates, improved ROI, and more effective campaigns.

Setting a New Standard

By prioritizing accuracy over speed, CAP geofences redefine industry standards, providing reliable data that businesses can trust. Whether for analyzing foot traffic, optimizing ad strategies, or understanding consumer behavior, CAP delivers results that drive success.

Download In State Plane Projection Here. Landmark (Facility Site) point and polygons utilize the Esri Local Government Information model to structure the attribute data. Polygon features are mapped by tax parcel with updates occurring on a weekly basis as either parcel or feature changes are detected.

Update Frequency: This dataset is updated on a weekly basis.

This toolbox contains two tools. The first tool turns x,y point data (with metadata) within a defined sampling area into Thiessen polygons, then dissolves those polygons based on a designated categorical variable. The second tool performs geometry manipulations to modify and reclassify the polygons into meaningful zones within the sample area; primarily, this involves splitting and merging polygons that do not contain any original sampling points.Contains example data (x,y point data and shapefile of sample area).The tools in this toolbox are written in Python for use in ArcGIS Pro 2.3.

The data provides location information of types of pavement maintained by Highways Department. The multiple file formats are available for dataset download in API.

RS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters.The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value.RS-FRIS 5.3 was constructed using remote-sensing data collected in 2021 and 2022. Version 5.3 incorporates depletions for selected completed harvest types through 2025-03-31.Last edit date: 2025-02-12 NameDescriptionUnitsRIU_IDUnique identifier for each inventory unit.n/aLAND_COV_CDLand cover code.n/aLAND_COV_NMLand cover name.n/aAGENumber of years since the stand was initiated; a composite of known dates (where recorded in inventory data) and predicted dates (where not recorded in historical inventory data). Calculated as CURRENT YEAR - ORIGIN_YEAR.yearsORIGIN_YEARYear at which a stand was re-initiated, a composite of known dates (where recorded in inventory data) and predicted dates (where not recorded in historical inventory data). Based on the median of raster cell values.yearBAPredicted basal area.square feet / acreBA_4Predicted basal area of trees > 4" DBH.square feet / acreBA_4_CONIFERPredicted basal area of trees > 4" DBH which are of a conifer species.square feet / acreBA_4_HWDPredicted basal area of trees > 4" DBH which are of a hardwood species.square feet / acreBA_6Predicted basal area of trees > 6" DBH.square feet / acreBA_T100Predicted basal area of the 100 largest trees per acre.square feet / acreBAP_HWDPredicted percent of trees which are of a hardwood species.percent (0-100)BFVOL_GROSSPredicted gross board-foot volume. Values do not account for defect deductions.board feet / acreBFVOL_NETPredicted net board-foot volume.board feet / acreBIOMASS_ALLPredicted above-ground biomass (live and dead).metric tonnes / acBIOMASS_LIVEPredicted above-ground biomass (live).metric tonnes / acCANOPY_LAYERSPredicted count of distinct canopy layers. Units are continuous despite measurements being ordinal.countCARBON_ALLPredicted above-ground carbon (live and dead).metric tonnes / acCARBON_LIVEPredicted above-ground carbon (live).metric tonnes / acCFVOL_DDWMPredicted cubic foot volume of down and dead woody materials.cubic feet / acreCFVOL_TOTALPredicted total cubic-foot volume. This value does not account for merchantability or defect.cubic feet / acreCLOSUREPredicted canopy closure.percent (0-100)COVERPredicted canopy cover.percent (0-100)HT_LOREYPredicted Lorey height. Lorey height is basal-area weighted mean height.feetHT_T40Predicted height of the 40 largest trees per acre.feetHT_T100Predicted mean height of the 100 largest trees per acre.feetHTMAXPredicted maximum tree height.feetQMDPredicted quadratic mean diameter.inchesQMD_6Predicted quadratic mean diameter for trees > 6" DBH.inchesQMD_T100Predicted quadratic mean diameter for top 100 trees per acre.inchesRDPredicted Curtis relative density (RD)unitlessRD_6Predicted Curtis relative density (RD) for trees > 6" DBHunitlessRD_SUMPredicted Curtis relative density (RD), summation methodunitlessSDI_SUMPredicted Reineke's Stand Density Index (SDI), summation methodtrees / acreSDI_SUM_4Predicted Reineke's Stand Density Index (SDI), summation method, for trees > 4" DBH.trees / acreSDI_DF_EModeled maximum stand density index, Douglas-fir, eastern WA. 10" qmd.trees / acreSDI_GF_EModeled maximum stand density index, Grand-fir, eastern WA. 10" qmd.trees / acreSDI_LP_EModeled maximum stand density index, Lodgepole pine, eastern WA. 10" qmd.trees / acreSDI_PP_EModeled maximum stand density index, Ponderosa pine, eastern WA. 10" qmd.trees / acreSDI_WL_EModeled maximum stand density index,Western larch, eastern WA. 10" qmd.trees / acreSDI_DF_WModeled maximum stand density index, Douglas-fir, western WA. 10" qmd.trees / acreSDI_WH_WModeled maximum stand density index, Western hemlock, western WA. 10" qmd.trees / acreSNAG_ACRE_15Predicted number of snags per acre > 15" DBH.count / acreSNAG_ACRE_20Predicted number of snags per acre > 20" DBH.count / acreSNAG_ACRE_21Predicted number of snags per acre > 21" DBH.count / acreSNAG_ACRE_30Predicted number of snags per acre > 30" DBH.count / acreSPECIES1Primary speciesn/aSPECIES2Secondary speciesn/aTREE_ACREPredicted number of trees per acre.count / acreTREE_ACRE_4Predicted number of trees per acre > 4" DBH.count / acreTREE_ACRE_4_CONIFERPredicted number of trees per acre > 4" DBH which are conifer.count / acreTREE_ACRE_6Predicted number of trees per acre > 6" DBH.count / acreTREE_ACRE_8Predicted number of trees per acre > 8" DBH.count / acreTREE_ACRE_11Predicted number of trees per acre > 11" DBH.count / acreTREE_ACRE_20Predicted number of trees per acre > 20" DBH.count / acreTREE_ACRE_21Predicted number of trees per acre > 21" DBH.count / acreTREE_ACRE_30Predicted number of trees per acre > 30" DBH.count / acreTREE_ACRE_31Predicted number of trees per acre > 31" DBH.count / acreRS_COVEREDDescription of the extent of RS-FRIS raster coverage within inventory unit (NONE, PARTIAL, or FULL).n/aRS_COVERED_PCTPercent (0 to 100) of the inventory unit with RS-FRIS raster coverage.percent (0-100)RS_FRIS_POLY_ACRESAcres of RS-FRIS polygon.acres

recsite_poly:

This data set shows the polygons for recreation site areas under BLM management in Oregon and Washington representing the physical extent of the managed site. Users should be aware that recsite point and polygon data are not stand alone datasets and must be viewed together to gain an accurate and complete understanding of recsites on the ground.

Xtract.io’s Park & Ride Polygon Data delivers a detailed geospatial mapping of commuter parking facilities and transit hubs across the US and Canada. This transit polygon dataset, part of our GIS data offerings, includes precise geocoded geofences, making it essential for urban planners, transportation authorities, and mobility researchers.

Use Cases & Benefits: - Support transit planning and urban mobility modeling - Inform transportation infrastructure mapping - Analyze commuter flow patterns and hub accessibility - Enable site selection for new transit facilities - Empower mobility intelligence with spatial clarity

Polygon Creation Methodology - Manual crafting via GIS tools (QGIS, ArcGIS) using aerial imagery and street-level views for high-precision boundaries. - Integration of official transit layout and elevation plans to enrich polygon accuracy and context. - Multi-stage quality validation to ensure relevance, completeness, and accuracy for mobility datasets.

Customization & Delivery - Custom polygon creation for new commuter hubs, transit sites, or infrastructure projects. - Enhanced feature capture, including entry/exit points, parking layouts, and adjacent pathways. - Flexible formats (WKT, GeoJSON, Shapefile, GDB) for smooth GIS and transportation system integration. - Regular update cycles (30/60/90 days) to reflect evolving transit needs.

Unlock the Power of Transit Geospatial Data

With our Park & Ride polygon and POI data, you can: - Conduct advanced transportation analyses. - Identify optimal zones for infrastructure expansion. - Leverage geospatial insights for strategic transit decision-making.

Why LocationsXYZ? Cities and transit authorities trust LocationsXYZ for its accurate, handcrafted polygon data. Transform how you plan and execute urban mobility initiatives with precision and confidence.

Geophysics that are shown as polygons. Sometimes the real position of geophysical lines cannot be shown because of confidentiality reasons and in this case a polygon that shows the approximate location is used instead. In other cases the geophysics is best represented by a polygon – for example for 3D seismic surveys.

To create and display land information in Land Polygon featureclass in the Santa Clara County Region as of FY 2021. THE GIS DATA IS PROVIDED "AS IS". THE COUNTY MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OR MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE ACCURACY, COMPLETENESS, VALUE, QUALITY, VALIDITY, MERCHANTABILITY, SUITABILITY, AND CONDITION, OF THE GIS DATA. USER'S OF COUNTY'S GIS DATA ARE HEREBY NOTIFIED THAT CURRENT PUBLIC PRIMARY INFORMATION SOURCES SHOULD BE CONSULTED FOR VERIFICATION OF THE DATA AND INFORMATION CONTAINED HEREIN. SINCE THE GIS DATA IS DYNAMIC, IT WILL BY ITS NATURE BE INCONSISTENT WITH THE OFFICIAL COUNTY DATA. ANY USE OF COUNTY'S GIS DATA WITHOUT CONSULTING OFFICIAL PUBLIC RECORDS FOR VERIFICATION IS DONE EXCLUSIVELY AT THE RISK OF THE PARTY MAKING SUCH USE.

Comprehensive Park & Ride polygon dataset across the US and Canada. Features commuter parking geofences, transit hub boundaries, and public parking polygons, optimized for transit planning, urban mobility analysis, and transportation infrastructure mapping.