Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

Geographic & Location Intelligence Data: ✅ Latitude & longitude coordinates for mapping and navigation applications. ✅ Geographic classification, including country, state, city, and postal code. ✅ Business status tracking – Open, temporarily closed, or permanently closed.

Continuous Discovery & Regular Updates: ✅ New POIs continuously added through discovery processes. ✅ Regular updates ensure data accuracy, reflecting new openings and closures.

Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

Ideal for Mapping & Location Analytics: ✅ Supports geospatial analysis & GIS applications. ✅ Enhances mapping & navigation solutions with structured POI data. ✅ Provides location intelligence for site selection & business expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured format (.json) for seamless integration.

🏆Primary Use Cases:

Mapping & Geographic Analysis: 🔹 Power GIS platforms & navigation systems with precise POI data. 🔹 Enhance digital maps with accurate business locations & categories.

Retail Expansion & Market Research: 🔹 Identify key business locations & competitors for market analysis. 🔹 Assess brand presence across different industries & geographies.

Business Intelligence & Competitive Analysis: 🔹 Benchmark competitor locations & regional business density. 🔹 Analyze market trends through POI growth & closure tracking.

Smart City & Urban Planning: 🔹 Support public infrastructure projects with accurate POI data. 🔹 Improve accessibility & zoning decisions for government & businesses.

💡 Why Choose Xverum’s POI Data?

• 230M+ Verified POI Records – One of the largest & most detailed location datasets available.
• Global Coverage – POI data from 249+ countries, covering all major business sectors.
• Regular Updates – Ensuring accurate tracking of business openings & closures.
• Comprehensive Geographic & Business Data – Coordinates, addresses, categories, and more.
• Bulk Dataset Delivery – S3 Bucket & cloud storage delivery for full dataset access.
• 100% Compliant – Ethically sourced, privacy-compliant data.

Access Xverum’s 230M+ POI dataset for mapping, geographic analysis, and location intelligence. Request a free sample or contact us to customize your dataset today!

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on known details at the time of acquisition. These data provide locational and attribute information for places on the Commonwealth Heritage List as determined by the Australian Government Department of the Environment, Heritage and Wildlife Division. Data consist of Commonwealth Heritage List polygons with attribute information describing the place name, class (indigenous, natural, historic), and status. Places subject to confidentiality agreements are not included in these data. Spatial data for places (mostly historic) in cities, is currently being derived using urban cadastral data. These sites are being incorporated into each new release, as they become available. ## Purpose Source dataset for Assets and Receptors databases under the Bioregional Assessment Programme. ## Dataset History The original spatial data were captured and copied from the Register of the National Estate, which were digitised by the Australian Surveying and Land Information Group (AUSLIG) from stable-base overlays produced by the Australian Heritage Commission since 1986. Since 1999, data entry and attribution has been undertaken by Australian Government Department of the Environment staff. Data are captured using topographic and cadastral data at map scales of up to 1:250,000, depending on the size and detail of the property. The majority of the source datasets are maintained and processed as ArcView shapefiles, in geographic projection using datum AGD66. The final dataset described by this metadata has been transformed to the Geocentric Datum of Australia (GDA94) Data quality report - Absolute external positional accuracy: Most features have a positional accuracy of, at most, +/- 100 metres Data quality report - Attribute accuracy: Attribute Information is verified by the Heritage Division. Data quality report - Conceptual consistency: The conversion of the data from the original shapefiles follow existing protocols currently used by the Register of the National Estate. The attribution is assumed to be logically consistent as provided by the Heritage Division. Data quality report - Completeness: The database is under construction. There are current assessment and nomination process being undertaken. ## Dataset Citation Department of the Environment (2014) Commonwealth Heritage List Spatial Database (CHL). Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/57720684-4948-45db-a2c8-37259d531d87.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

These data provide locational and attribute information for places nominated to and included in the National Heritage List as determined by the Australian Government managed by the Department of Sustainability, Environment, Water, Population and Communities, Heritage and Wildlife Division. National Heritage List polygons with attribute information describing the place name, class (indigenous, natural, historic), and status. Places subject to confidentiality agreements are included in these data but the location is generalised to the bounding 250k mapsheet.

The location data for place nominations that have been rejected, are ineligible, removed or destroyed are not included in the publicly downloadable spatial dataset. Places having current assessment and nomination processes involving boundary revisions being undertaken are not available to the public. Spatial data for listed places are available to the public.

DATA QUALITY REPORT - COMPLETENESS

The database is live and ongoing. There are current assessment and nomination process being undertaken.

DATA QUALITY REPORT - CONCEPTUAL CONSISTENCY

The conversion of the data from the original shapefiles follow existing protocols currently used by the Register of the National Estate. The attribution is assumed to be logically consistent as provided by the Heritage and Wildlife Division of the Australian Government Department of Sustainability, Environment, Water, Population and Communities.

DATA QUALITY REPORT - POSITIONAL ACCURACY

Most features have a positional accuracy of, at most, +/- 100 metres

DATA QUALITY REPORT - ATTRIBUTE ACCURACY

Attribute Information is verified by the Heritage Division.

Dataset History

The original spatial data for some places were captured and copied from the Register of the National Estate, which were digitised by the Australian Surveying and Land Information Group (AUSLIG) from stable-base overlays produced by the Australian Heritage Commission since 1986. Since 1999, data entry and attribution has been undertaken by the Australian Government Department of Sustainability, Environment, Water, Population and Communities, Heritage Division staff. Data are captured using topographic and cadastral data at map scales of up to 1:250,000, depending on the size and detail of the property. The majority of the source datasets are maintained and processed as ESRI shapefiles, in geographic projection using datum GDA94 The final dataset described by this metadata has been transformed to the Geocentric Datum of Australia (GDA94).

This dataset was exported from SDE by ERIN on 17/09/2013 for use in compiling preliminary bioregional assets lists for the Office of Water Science Bioregional Assessment Program. Field "ElemetID" was added and a unique identifier created for each spatial feature for use in the BA Programme.

Dataset Citation

Department of the Environment (2014) National Heritage List Spatial Database (NHL) (v2.1). Bioregional Assessment Source Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/26daa8d7-a90e-47f3-982b-0df362414e65.

This dataset provides an initial version of the locations of golf courses in New Mexico, in point form, with limited attributes, compiled using available data from a variety of sources. The locations have been digitized from location descriptions, supplemented by digital orthophotography cross-checking. The dataset may be refined in the future to include new records and more attributes, and provide better positional accuracy.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

** Lineage: **Geoscience Australia (GA) received ESRI shapefiles for power datasets. The shapefiles were in CKIG92. GA projected these shapefiles into UTM (WGS84) zone 47S using ArcView. Positional Accuracy: Some positional errors persist in the data. ** Attribute Accuracy: **These details are believed to be true and accurate.

Lineage: Contours were generated from the LiDAR digital elevation model (DEM) using the freeware GIS package Quantum GIS. When compared to a sample area generated from the same data but using ArcMap, the contours were of equal or better quality. Positional Accuracy: The contours generated from the DEM will replicate any inaccuracies of the DEM. As a guide, the DEM data is vertically accurate to 15cm and horizontally accurate to 30cm. Attribute Accuracy: Not Relevant Disclaimer

Lineage: Cocos road locations and road names were provided by Main Roads WA to Geoscience Australia in April 2013. This data was integrated with the existing 1:30,000 road dataset and the results manually edited and checked against the 2011 orthophotography. Positional Accuracy: Accurate in relation to the 2011 orthophotography. Attribute Accuracy: Limited field checking has been undertaken on this data and therefore some information may not be accurate. Disclaimer

Overview: 142: Areas used for sports, leisure and recreation purposes. Traceability (lineage): This dataset was produced with a machine learning framework with several input datasets, specified in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ) Scientific methodology: The single-class probability layers were generated with a spatiotemporal ensemble machine learning framework detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). The single-class uncertainty layers were calculated by taking the standard deviation of the three single-class probabilities predicted by the three components of the ensemble. The HCL (hard class) layers represents the class with the highest probability as predicted by the ensemble. Usability: The HCL layers have a decreasing average accuracy (weighted F1-score) at each subsequent level in the CLC hierarchy. These metrics are 0.83 at level 1 (5 classes):, 0.63 at level 2 (14 classes), and 0.49 at level 3 (43 classes). This means that the hard-class maps are more reliable when aggregating classes to a higher level in the hierarchy (e.g. 'Discontinuous Urban Fabric' and 'Continuous Urban Fabric' to 'Urban Fabric'). Some single-class probabilities may more closely represent actual patterns for some classes that were overshadowed by unequal sample point distributions. Users are encouraged to set their own thresholds when postprocessing these datasets to optimize the accuracy for their specific use case. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: The LULC classification was validated through spatial 5-fold cross-validation as detailed in the accompanying publication. Completeness: The dataset has chunks of empty predictions in regions with complex coast lines (e.g. the Zeeland province in the Netherlands and the Mar da Palha bay area in Portugal). These are artifacts that will be avoided in subsequent versions of the LULC product. Consistency: The accuracy of the predictions was compared per year and per 30km*30km tile across europe to derive temporal and spatial consistency by calculating the standard deviation. The standard deviation of annual weighted F1-score was 0.135, while the standard deviation of weighted F1-score per tile was 0.150. This means the dataset is more consistent through time than through space: Predictions are notably less accurate along the Mediterrranean coast. The accompanying publication contains additional information and visualisations. Positional accuracy: The raster layers have a resolution of 30m, identical to that of the Landsat data cube used as input features for the machine learning framework that predicted it. Temporal accuracy: The dataset contains predictions and uncertainty layers for each year between 2000 and 2019. Thematic accuracy: The maps reproduce the Corine Land Cover classification system, a hierarchical legend that consists of 5 classes at the highest level, 14 classes at the second level, and 44 classes at the third level. Class 523: Oceans was omitted due to computational constraints.

This dataset provides an initial version of the locations of parks in New Mexico, in point form, with limited attributes, compiled using available data from a variety of sources. The locations have been digitized from location descriptions, supplemented by digital orthophotography cross-checking. The dataset may be refined in the future to include new records and more attributes, and provide better positional accuracy.

Baltimore City Highways. No metadata was provided with this dataset; the UVM Spatial Analysis Lab has attempted to evaluate this dataset and generate metadata. When compared to high-resolution imagery and detailed street data offsets as great as 50m were observed. Due to positional accuracy errors this dataset should be used with caution. There are no attributes associated with this dataset. For the best available transportation data use the Roads_GDT_MSA dataset.

   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.


   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Access APINSW Features of Interest Category - Place Point Please Note WGS 84 = GDA94 service This dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into GDA2020 …Show full description Access APINSW Features of Interest Category - Place Point Please Note WGS 84 = GDA94 service This dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS84 = GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that these original services will adopt the new multiCRS functionally. Place point is a point feature class within the Features of interest Category. There is no overall accuracy reported in the database, however accuracy of the individual feature instances of each feature class can be found in the database tables. The currency of the feature instances in this dataset can be found in “feature reliability date” or “attribute reliability date” attributes. All feature instances in this class are attributed with a planimetric accuracy value. It is expected that the 90% of well-defined points with the same planimetric accuracy are within 0.5mm of that map scale. Depending on the capture source, capture method, digital update and control point upgrade, every feature instance reported has a positional accuracy within the range of 1m - 100m. Place Points included in the layer include: City - A centre of population, commerce and culture with all essential services; a town of significant size and importance, generally accorded the legal right to call itself a city under, either, the Local Government Act, the Crown Lands Act or other instruments. This point feature dataset is part of Spatial Services Defined Administrative Data Sets. City data points are positioned within the cadastral parcel in which they are located. Locality - A bounded area within the landscape that has a rural character. Locality data points are positioned within the cadastral parcel in which they are located. Region - A region is a relatively large tract of land distinguished by certain common characteristics, natural or cultural. Natural unifying features could include same drainage basin, similar landforms, or climatic conditions, a special flora or fauna, or the like. This point feature dataset is part of Spatial Services Defined Administrative Data Sets. Region data points are positioned within the cadastral parcel in which they are located.Rural Place - A place, site or precinct in a rural landscape, generally of small extent, the name of which is in current use. This point feature dataset is part of Spatial Services Defined Administrative Data Sets. Rural place data points are positioned within the cadastral parcel in which they are located. Suburb - A gazetted boundary of a suburb or locality area as defined by the Geographical Names Board of NSW. This point feature dataset is part of Spatial Services Defined Administrative Data Sets. Suburb data points are positioned within the cadastral parcel in which they are located. Town - A commercial nucleus offering a wide range of services and a large number of shops, often several of the same type. Depending on size, the residential area can be relatively compact or (in addition) dispersed in clusters on the periphery. This point feature dataset is part of Spatial Services Defined Administrative Data Sets. Town data points are positioned within the cadastral parcel in which they are located. Urban Place - A place, site or precinct in an urban landscape, the name of which is in current use, but the limits of which have not been defined under the address locality program. This point feature dataset is part of Spatial Services Defined Administrative Data Sets. Urban place data points are positioned within the cadastral parcel in which they are located. Village - A cohesive populated place in a rural landscape, which may provide a limited range of services to the local area. Residential subdivisions are in urban lot sizes. This point feature dataset is part of Spatial Services Defined Administrative Data Sets. Village data points are positioned within the cadastral parcel in which they are located. MetadataType Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets Features of Interest Category of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, a range of themes from the NSW FSDF including, transport, imagery, positioning, water and land cover. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS84 Geographic 2D WGS84 Equivalent To GDA94 Spatial Extent Full state Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

Lineage: Digitisation was done from scratch off the 2011 orthophotography within Quantum GIS. Using the ArcMap 'zonal statistics' tool the minimum, mean and maximum heights were found for each building polygon from the 2011 digital elevation model and the 2011 digital surface model (DSM). This information was then joined to the building polygon attribute table. To find the building height from ground to roof, the difference between the Mean DSM and mean DEM was calculated and added as a field to the attribute table. To find the maximum height of each building the difference between the Maximum DSM and Mean DEM was calculated. Polygon area, perimeter, and x and y coordinates of each building were also attached as attributes. Positional Accuracy: Accuracy is high as the layer was based on the 2011 orthophotography. Error may have been introduced through the digitisation process. Building lean in the orthophotography may also contribute to polygons which are slightly inaccurately placed. Attribute Accuracy: Height attribute accuracy is inaccurate for building polygons which have tree cover above them, as the tree elevation would influence the digital surface model. Particularly the Max_height field may include tree heights rather than building heights in some cases. Attribute accuracy could be improved by using the raw 2011 lidar data (.las files) which are classified at 'buildings' to attach heights. This method was tested and was found to be extremely time consuming. When the methods were compared, the majority of height data only varied around 20-30cm and only the height max field was significantly improved. Image building lean may also have impacted the accuracy of the building polygons and therefore the building heights. Disclaimer

This dataset provides an initial version of the locations of convention centers in New Mexico, in point form, with limited attributes, compiled using available data from a variety of sources. The locations have been digitized from location descriptions, supplemented by digital orthophotography cross-checking. The dataset may be refined in the future to include new records and more attributes, and provide better positional accuracy.

With recent technological advancements, quantitative analysis has become an increasingly important area within professional sports. However, the manual process of collecting data on relevant match events like passes, goals and tacklings comes with considerable costs and limited consistency across providers, affecting both research and practice. In football, while automatic detection of events from positional data of the players and the ball could alleviate these issues, it is not entirely clear what accuracy current state-of-the-art methods realistically achieve because there is a lack of high-quality validations on realistic and diverse data sets. This paper adds context to existing research by validating a two-step rule-based pass and shot detection algorithm on four different data sets using a comprehensive validation routine that accounts for the temporal, hierarchical and imbalanced nature of the task. Our evaluation shows that pass and shot detection performance is highly dependent on the specifics of the data set. In accordance with previous studies, we achieve F-scores of up to 0.92 for passes, but only when there is an inherent dependency between event and positional data. We find a significantly lower accuracy with F-scores of 0.71 for passes and 0.65 for shots if event and positional data are independent. This result, together with a critical evaluation of existing methodologies, suggests that the accuracy of current football event detection algorithms operating on positional data is currently overestimated. Further analysis reveals that the temporal extraction of passes and shots from positional data poses the main challenge for rule-based approaches. Our results further indicate that the classification of plays into shots and passes is a relatively straightforward task, achieving F-scores between 0.83 to 0.91 ro rule-based classifiers and up to 0.95 for machine learning classifiers. We show that there exist simple classifiers that accurately differentiate shots from passes in different data sets using a low number of human-understandable rules. Operating on basic spatial features, our classifiers provide a simple, objective event definition that can be used as a foundation for more reliable event-based match analysis.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Contains Petroleum Wells. Populated from original well completion reports. This is a different dataset from GEDIS boreholes (purpose = petroleum). The wells in both datasets overlap in most cases. This dataset is actively maintained. ## Dataset History Data Set Source: From Minerals and Petroleum's DbMap Oracle Database Collection Method: Processing Steps: andr import_petrol_wells which reads dbmap - and updates GEDIS where necessary and then creates this dataset Positional Accuracy: Varies from 10 metres to 1km - accuracy is specified for each location Attribute Accuracy: Very Good Logical Consistency: No Specified ## Dataset Citation "Victorian Department of State Development, Business and Innovation" (2014) Victorian Petroleum Wells 2014. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/bb3f4d30-3572-4ac4-ac77-ee281846f071.

This dataset provides an initial version of the locations of museums and cultural centers in New Mexico, in point form, with limited attributes, compiled using available data from a variety of sources. The locations have been digitized from location descriptions, supplemented by digital orthophotography cross-checking. The dataset may be refined in the future to include new records and more attributes, and provide better positional accuracy.

Wrecks and Obstructions Dataset

Wrecks and Obstructions Hydrographic Survey Data

By Homeland Infrastructure Foundation [source]

About this dataset

Initially implemented in 1981, the dataset is a result of NOAA's Automated Wreck and Obstruction Information System (AWOIS), established for effective planning of hydrographic survey operations. AWOIS serves as a comprehensive repository for numerous reported wrecks and obstructions that pose navigation hazards within coastal waters surrounding the United States.

The dataset undergoes meticulous review during hydrographic survey planning, with certain records identified for further field investigation by dedicated units. The findings from these investigations are duly incorporated into AWOIS, ensuring that a comprehensive history of each wreck or obstruction is readily accessible. It is important to note that while AWOIS strives to include as many wrecks as possible, it may not account for every known or reported wreck in existence.

For enhanced accuracy, this dataset contains essential attributes such as wreck or obstruction position information from reliable sources denoted by positionSo. The quality of position information is assessed using positionQu, providing an indication of its reliability. Additionally, precise latitude and longitude coordinates aid in pinpointing these locations on a map.

Each record is uniquely identified through an assigned identifier labeled record to facilitate easy referencing within the dataset. Details regarding vessel terminology (vesselTerm) help describe different types of vessels associated with each wreck or obstruction.

The presence of nautical chart numbers (chart) allows users to correlate specific wrecks or obstructions with respective navigational charts. Meanwhile, historical insights about these hazards are provided under the attribute history, offering valuable context about their origins and significance.

Depth measurement data provided under depth enables users to understand at which levels these wrecks or obstructions are located. The type of sounding measurement techniques employed to determine the depths is indicated by soundingTy.

Furthermore, this dataset includes information regarding the year in which vessels sank or obstructions were created, as represented by the attribute yearSunk. This temporal aspect supports historical analysis and facilitates understanding of each wreck or obstruction's timeline.

How to use the dataset

• Understanding the Columns: Familiarize yourself with the available columns in this dataset. Each column represents a specific attribute or characteristic of the wrecks or obstructions. The column names are as follows:

  • X, Y: The X-coordinate and Y-coordinate of the location of the wreck or obstruction.
  • record: A unique identifier for each wreck or obstruction record.
  • vesselTerm: The term used to describe the type of vessel involved in the wreck or obstruction.
  • chart: The nautical chart number associated with the wreck or obstruction.
  • positionQu: The quality of the position information for the wreck or obstruction.
  • positionSo: The source of the position information for the wreck or obstruction.
  • depth: The depth at which the wreck or obstruction is located.
  • soundingTy: The type of sounding measurement used to determine the depth.
  • yearSunk: The year in which the vessel sank or when an obstruction was created.
  • history: A brief history or description of each wreck or obstruction record.

• Exploring Location Information: Location information is vital when studying wrecks and obstructions. Utilize latitude (latitudeDD) and longitude (longitudeD) coordinates provided in decimal degrees to locate specific wrecks within U.S coastal waters.

• Analyzing Position Quality: Consider evaluating position quality (positionQu) before relying on location data for navigation purposes. High-quality positions have more accurate information compared to low-quality positions.

• Understanding Wreck/Obstruction Details: Each record includes descriptive information about the wrecks or obstructions (history). This information can provide valuable insights into the nature, origin, and historical significance of each wreck or obstruction.

• Identifying Type of Vessel Involved: The vesselTerm column provides valuable information about the type of vessel involved in each wreck or obstruction. Understanding vessel types can enhance your understanding and analysis of these records.

• Analyzing Depth Information: The depth at which a wreck or obstruction is located (depth) is crucial for navigati...