Xverum’s Global GIS & Geospatial Data is a high-precision dataset featuring 230M+ verified points of interest across 249 countries. With rich metadata, structured geographic attributes, and continuous updates, our dataset empowers businesses, researchers, and governments to extract location intelligence and conduct advanced geospatial analysis.

Perfectly suited for GIS systems, mapping tools, and location intelligence platforms, this dataset covers everything from businesses and landmarks to public infrastructure, all classified into over 5000 categories. Whether you're planning urban developments, analyzing territories, or building location-based products, our data delivers unmatched coverage and accuracy.

Key Features: ✅ 230M+ Global POIs Includes commercial, governmental, industrial, and service locations - updated regularly for accurate relevance.

✅ Comprehensive Geographic Coverage Worldwide dataset covering 249 countries, with attributes including latitude, longitude, city, country code, postal code, etc.

✅ Detailed Mapping Metadata Get structured address data, place names, categories, and location, which are ideal for map visualization and geospatial modeling.

✅ Bulk Delivery for GIS Platforms Available in .json - delivered via S3 Bucket or cloud storage for easy integration into ArcGIS, QGIS, Mapbox, and similar systems.

✅ Continuous Discovery & Refresh New POIs added and existing ones refreshed on a regular refresh cycle, ensuring reliable, up-to-date insights.

✅ Compliance & Scalability 100% compliant with global data regulations and scalable for enterprise use across mapping, urban planning, and retail analytics.

Use Cases: 📍 Location Intelligence & Market Analysis Identify high-density commercial zones, assess regional activity, and understand spatial relationships between locations.

🏙️ Urban Planning & Smart City Development Design infrastructure, zoning plans, and accessibility strategies using accurate location-based data.

🗺️ Mapping & Navigation Enrich digital maps with verified business listings, categories, and address-level geographic attributes.

📊 Retail Site Selection & Expansion Analyze proximity to key POIs for smarter retail or franchise placement.

📌 Risk & Catchment Area Assessment Evaluate location clusters for insurance, logistics, or regional outreach strategies.

Why Xverum? ✅ Global Coverage: One of the largest POI geospatial databases on the market ✅ Location Intelligence Ready: Built for GIS platforms and spatial analysis use ✅ Continuously Updated: New POIs discovered and refreshed regularly ✅ Enterprise-Friendly: Scalable, compliant, and customizable ✅ Flexible Delivery: Structured format for smooth data onboarding

Request a free sample and discover how Xverum’s geospatial data can power your mapping, planning, and spatial analysis projects.

At CompanyData.com (BoldData), we specialize in delivering verified global company information tailored for high-impact business decisions. Our Worldwide B2B Location Data service provides accurate latitude and longitude coordinates for over 270 million businesses in 150+ countries. All records are sourced from official trade registers and enriched with precise GEO-coordinates—allowing you to map, segment, and analyze businesses geographically like never before.

Each company profile is more than just a pin on the map. You receive firmographic insights such as company hierarchies, industry codes, employee counts, financials, contact data including emails and mobile numbers, and of course, full location details tied to geospatial coordinates. This level of accuracy makes our database an essential asset for KYC procedures, sales territory planning, logistics optimization, CRM enrichment, and location-based marketing strategies.

Whether you're launching hyper-local campaigns, optimizing supply chain coverage, or building location-aware applications, our geolocation data supports both operational and strategic goals. Developers and analysts can integrate this dataset directly into GIS systems, AI models, or mobile platforms to unlock new levels of spatial intelligence and precision targeting.

CompanyData.com offers flexible access to this data through customized bulk exports, a user-friendly self-service platform, and a robust real-time API. Backed by our full database of 380M+ verified global businesses, this geolocation dataset empowers organizations to understand exactly where their opportunities lie—and how to reach them with confidence and clarity.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

In the absence of spatial data, you can use location description. This course focuses on coordinate, or x,y data, one common and useful type of location description that can be applied to workflows that seek to answer geospatial questions.Exercises can be completed with either ArcGIS Pro or ArcGIS Online.GoalsTransform coordinates into locations on a map.

Geotagged Twitter posts dataset

Dataset used for the research presented in the following paper: Takayuki Hiraoka, Takashi Kirimura, Naoya Fujiwara (2024) "Geospatial analysis of toponyms in geo-tagged social media posts".

We collected georeferenced Twitter posts tagged to coordinates inside the bounding box of Japan between 2012-2018. The present dataset represents the spatial distributions of all geotagged posts as well as posts containing in the text each of 24 domestic toponyms, 12 common nouns, and 6 foreign toponyms. The code used to analyze the data is available on GitHub.

Data description

• selected_geotagged_tweet_data/: Number of geotagged twitter posts in each grid cell. Each csv file under this directory associates each grid cell (spanning 30 seconds of latitude and 45 secoonds of longitude, which is approximately a 1km x 1km square, specified by an 8 digit code m3code) with the number of geotagged tweets tagged to the coordinates inside that cell (tweetcount). file_names.json relates each of the toponyms studied in this work to the corresponding datafile (all denotes the full data).
• population/population_center_2020.xlsx: Center of population of each municipality based on the 2020 census. Derived from data published by the Statistics Bureau of Japan on their website (Japanese)
• population/census2015mesh3_totalpop_setai.csv: Resident population in each grid cell based on the 2015 census. Derived from data published by the Statistics Bureau of Japan on e-stat (Japanese)
• population/economiccensus2016mesh3_jigyosyo_jugyosya.csv: Employed population in each grid cell based on the 2016 Economic Census. Derived from data published by the Statistics Bureau of Japan on e-stat (Japanese)
• japan_MetropolitanEmploymentArea2015map/: Shape file for the boundaries of Metropolitan Employment Areas (MEA) in Japan. See this website for details of MEA.
• ward_shapefiles/: Shape files for the boundaries of wards in large cities, published by the Statistics Bureau of Japan on e-stat

The distribution map of Festuca dolichophylla relies on diverse data sources. Geographical coordinates (latitude and longitude) and country initials (countryCode) were extracted from Tropicos, the Gbif repository (up to May 2019), and the iDigBio database (up to July 2021). Additionally, data from other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999), were integrated. The Gbif data points are associated with gbifID numbers for reference. Please note that this compilation provides essential information for understanding the distribution of F. dolichophylla across various regions.

Software

Organized data by geographic coordinates was uploaded to ArcGIS Pro v. 3.2.0 for map production. Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.

Methods

The dataset for the distribution map of Festuca dolichophylla was meticulously collected from various sources.

Data Collection:

Tropicos: Data were extracted from Tropicos until December 2023.

Gbif Repository: Data was sourced from the Gbif repository until May 2019.

iDigBio Database: Additional data points were retrieved from the iDigBio database up to July 2021.

Other Sources: We also incorporated data from various other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999).

Data Organization and Processing:

All collected data points were meticulously organized by coordinates.

We ensured consistency by cross-referencing and validating the data.

The dataset was then uploaded to ArcGIS Pro v. 3.2.0 for map production.

Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.

Funding

Neotropical Grassland Conservancy, Award: Memorial grant 2020

To improve public health and the environment, the United States Environmental Protection Agency (EPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. Through the Geospatial Data Download Service, the public is now able to download the EPA Geodata Shapefile, Feature Class or extensible markup language (XML) file containing facility and site information from EPA's national program systems. The files are Internet accessible from the Envirofacts Web site (https://www3.epa.gov/enviro/). The data may be used with geospatial mapping applications. (Note: The files omit facilities without latitude/longitude coordinates.) The EPA Geospatial Data contains the name, location (latitude/longitude), and EPA program information about specific facilities and sites. In addition, the files contain a Uniform Resource Locator (URL), which allows mapping applications to present an option to users to access additional EPA data resources on a specific facility or site.

This dataset is thought as a utility dataset to find geo locations (longitude and latitude) when you only have the names or codes for the countries. Besides ISO-Alpha-3 and ISO-Alpha-2 codes I added IOC and FIFA codes.

Sometimes there is more than one row for a country. This is when there are different (official) names or historical predecessors of a country. If it is the official name as defined by ISO 3166 Maintenance Agency it is marked with the value 1 in the column "ISO-Name".

The locations given here are not intended to be correct geographical centers of a country or region. These locations should be useful for positioning graphical elements (e.g. pie charts) on a map. Since version 4 of this data I added the coordinate locations given in the matching WikiData entities because they seem to be more centered in the countries shape (unfortunately not for all countries).

Author: A Lisson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): gis, geographic thinkingRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Explain the difference between two types of geospatial technologies - GPS and GIS.
2. Develop basic skills to effectively manipulate and use GPS receivers and ArcGIS software.
3. Explain uses of GPS and GIS.Summary: Students use GPS coordinates to discover geocaches at a local park, and they use ArcGIS to layer maps about the park. Frontenac State park is the example, but any park or area (including school grounds) could be used. Students also investigate careers that use GIS.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. In order to avoid two repetitive ground field efforts, the sampling plan was devised from a combination of both vegetation maps. Using OR logic, overlays were created using both maps as input for each class, and random samples were developed for each class in excess of 30 polygons. Where there were less than 30 polygons sample sites were selected non-randomly from each polygon (i.e. a 100% sample). A total of 512 ground sampling sites were developed from a total of 21 vegetation and land cover classes which are represented on both vegetation maps. Using GIS tools, an ASCII file was generated with ground coordinates representing each of these sites. The 512 sets of coordinates were appropriately re-formatted and directly downloaded as waypoints in three North American Rockwell PLGR GPS receivers. During the week of August 4, 1997 three field crews of two persons each worked together at the monument in a coordinated effort to identify vegetation/cover types at each of the sites. The field crews had a paper map showing the location of the plots and the polygon boundaries (but not attributes) overlaid on topographic data. One team member operated the GPS receiver to navigate to the site, and the other identified the vegetation/cover type and provided a general physical description of the site environs. Sites were considered to be circular with a radius of 50 m. from the coordinate point. Where 2 or more vegetation/cover types occurred, or there was a mosaic of types, all were described within the 50 m. radius of the site coordinate.

The GOES longitudinal location information consists of the west geographic longitude in degrees. The data are separated into spacecraft (GOES 2-12), year, and month. The location is given for each day of the month is in units of degrees and the geographic latitude is assumed to be 0¯.

This is an experimental dataset for the bicycle trips recorded using and geo-game called "Cyclist Geo-C". It contains the geometry of the trips recorded by 60 participants from three European Cities: Münster, Germany; Castelló, Spain; Valletta, Malta. This dataset was collected and analysed for the PhD Thesis "Mobile Services for Green Living" part of the European Joint Doctorate in Geoinformatics and the Geo-C Project.

The dataset is composed of three subsets.

1. There is a point dataset called "trips_od.geojson" which contained the point geometries where each trip started and ended with attributes for latitude, longitude, altitude, and precision coordinates. Each point also had the timestamp which indicates the time when the user started or ended the trip.
2. There is a line dataset called "segments.geojson" which contained the geometries of the straight lines connecting two locations of the participant. Each segment started from an initial point "p_i" recorded at a "t_i” and ended at the next point recorded by the user "p_f” at time “t_f”. The time difference between "t_i” and “t_f” was at most five minutes while the length of the segment was at most one kilometre. Each segment also had the participant and trip identifier, and the segment's sequence number within the trip For each of the trip segments, we calculated the distance and speed using the recorded coordinates and timestamps from "p_i" and "p_f" points. \(trip\_segment = f(p_i,p_f)\) and \(segment\_speed = \frac{distance(p_i,p_f)}{\Delta time(p_i,p_f)}\). Then we classified the segments according to the calculated distance as: “walking segment” when the calculated speed was less than 5 km/h; “cycling segment” when the calculated speed was between 5 and 50 km/h; or “non-cycling segment” when the calculated speed was more than 50 Km/h.
3. There was another line dataset called “trips_tags.geojson ” which contained the geometries of each of the trip paths. A trip was a line (also called polyline by GIS users) defined by the ordered sequence of trip segments. It started from origin point "p_i" of the trip’s first segment and ended at the destination point "p_f" of the trip's last segment. Each trip also had the participant's identification, trip's identification, the number of segments, start and end times.

In addition to the experimental dataset recorded by participants, our analysis used a secondary dataset to define a comparable framework for the three cities. The secondary dataset consisted of the existing bicycle paths in the cities of Münster and Castelló as well as the planned bicycle paths around Valletta. For the city of Münster, the source of the bicycle paths was the OpenStreetMap (we downloaded the line elements with the tags “bicycle=yes” and "cycleway=yes”). For the city of Castelló, we obtained the bicycle paths from the city transport authority, including the city of Valletta, we created a digital version of the national bicycle network plan.

We estimated the number of trips "bikepaths_trips.geojson" and the number of segments "bikepaths_segments.geojson" at each bike path. Also, we provide the areas where participants faced frictions during the experiment which corresponded to low cycling speeds "frictions.geojson".

Finally, we provide a visual reference of the dataset in "frictions_cities.pdf".

Due to the rapid growth of urbanization, species biodiversity is threatened and the innate relationship between humans and nature begins to fade gradually. Urban green spaces play a vital role in reconnecting human and urbanized landscape with its unique characteristics. Meanwhile, virtual gaming technology with applied geographic information has made a spectacular process to promote interactions between humans and their surroundings. Five types of green space were identified in the University of British Columbia Vancouver campus: lawn, planting bed, planting bed on structure, athletic field, and urban forest. A novel approach of combining Light Detection and Ranging (LiDAR) data, ground-based inventory data, geographic information system (GIS) data, and geocoordinates derived from reality game Pokémon GO was applied to explore geospatial gaming technology’s application in mapping cultural use and biodiversity hotspots at a university campus. LiDAR-derived individual tree crown polygons contributed to estimate canopy cover. Manually delineated tree crown from the study area's orthophoto was used to compare the crown area accuracy with LiDAR technology. The point density heat map illustrated the study area's cultural interests, which were generated by Pokestops' geospatial coordinates. A dataset containing two green space assessments was conducted with various factors: native species ratio, species richness, canopy cover, and cultural interest. Both assessments highlighted the importance of urban forest. This green space type achieved 0.396 in the first assessment and 0.501 for the second assessment of cultural and biodiversity values.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Following the vegetation data analysis, the vegetation cover-type map was edited and refined to develop a preliminary association-level vegetation map. Using ArcView 3.2, polygon boundaries were revised onscreen based on the plot data, field observations, classification analyses, aerial photography signatures, and topographic maps. Each polygon was assigned the name of a preliminary vegetation association based on the five information sources listed above. A mirror stereoscope type F-71 and a Bausch and Lomb zoom stereoscope were used to interpret the aerial photography signatures. The field-collected “true” or “reference” GPS coordinates for the remaining 41 points were compared to the coordinates obtained from the mosaic viewed in ArcMap.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

This dataset is the product of a geospatial interpolation using groundwater-level data obtained from a U.S. Geological Survey (USGS) synoptic survey of 129 groundwater wells in Fauquier County, VA from October 29 through November 2, 2018 and selected points from the National Hydrography Dataset (NHD) to represent equal-altitude contour lines of groundwater levels in 50-foot intervals. Methodology is detailed in USGS SIR 2022-5014 "Groundwater-level contour map of Fauquier County, VA, October - November 2018." Attributes include groundwater-level altitude in both decimal feet and meters. Horizontal coordinates are referenced to the geographic coordinate system North American Datum of 1983 (NAD 1983) and groundwater-level altitudes are referenced to the National Geodetic Vertical Datum of 1929 (NGVD 29). The projected coordinate system is Albers Conic Equal Area with a central meridian of -96.0, standard parallels of 29.5 and 45.5, and a latitude of origin of 23.0.

A comprehensive self-hosted geospatial database of street names, coordinates, and address data ranges for Enterprise use. The address data are georeferenced with industry-standard WGS84 coordinates (geocoding).

All geospatial data are provided in the official local languages. Names and other data in non-Roman languages are also made available in English through translations and transliterations.

Use cases for the Global Address Database (Geospatial data)

• Address capture and validation

• Parcel delivery

• Master Data Management

• Logistics and Shipping

• Sales and Marketing

Additional features

• Fully and accurately geocoded

• Multi-language support

• Address ranges for streets covered by several zip codes

• Comprehensive city definitions across countries

• Administrative areas with a level range of 0-4

• International Address Formats

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

• UNLOCODE and IATA codes (geocoded)

• Time zones and Daylight Saving Time (DST)

• Population data: Past and future trends

Data export methodology

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A list of Place Names extracted from the ǂKhomani San | Hugh Brody Collection held by the University of Cape Town (UCT) Library.Effort has been made to geocode as many place names as possible with their geographic coordinates (Latitude & Longitude).The data set is available in three formats:• a comma separated values table (CSV); • a KMZ spatial data layer, compatible with Google Maps, Google Earth and most GIS packages; • a ZIP archive of an ESRI shapefile, compatible with most GIS packagesThis data set is incomplete. Not all resources in the collection have been processed, additional place names may be missing from the list. Geocoding was performed as accurately as our reference resources allowed, but some locations may have been misplaced.We would like to thank African Tongue and the communities of the region for their assistance with the creation of this data set.The ǂKhomani San are the first people of the southern Kalahari. They lived as hunters and gatherers in the immense desert in the northwest corner of South Africa. For them, it is a land rich in wildlife, plants, trees, great sand dunes and dry riverbeds. When the ǂKhomani San share their history, they tell a story of dispossession from their lands, erasure of their way of life, and disappearance of their language. To speak of their past is to search in memory for all that was taken from them in the colonial, apartheid and post-apartheid era. They also tell a story of reclamation and recovery of lands, language, and even of memory itself. Coordinate Reference System: Geographic Coordinate System WGS1984 (GCS WGS84)Fields - Due to software limitations diacritics were not used in field names:Place_Name: Name of placeLatitude: Latitude Ordinate GCS WGS84Longitude: Longitude Ordinate GCS WGS84Notes_Loc: Any extra information about the place name location, either from the collection or discovered by the authors.Source: The source of the geographic coordinatesLocal Name: This is the name as it may have changed locallyEng: English nameAfr: Afrikaans namekqz_Kora: Kora namenaq_Nama: Nama namengh_Nuu: Nuu nametsn_Tswana: Tswana namegla_Scottish_Gaelic: Gaelic namefra_French: French nameNotes_ling: notes of linguistic interest

Contains NY State Plane Coordinate System Zones. For use to see what State Plane Zone in New York of an area you are working in is.Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions