eXtension Foundation, the University of New Hampshire, and Virginia Tech have developed a mapping and data exploration tool to assist Cooperative Extension staff and administrators in making strategic planning and programming decisions. The tool, called the National Extension Web-mapping Tool (or NEWT), is the key in efforts to make spatial data available within cooperative extension system. NEWT requires no GIS experience to use. NEWT provides access for CES staff and administrators to relevant spatial data at a variety of scales (national, state, county) in useful formats (maps, tables, graphs), all without the need for any experience or technical skills in Geographic Information System (GIS) software. By providing consistent access to relevant spatial data throughout the country in a format useful to CES staff and administrators, NEWT represents a significant advancement for the use of spatial technology in CES. Users of the site will be able to discover the data layers which are of most interest to them by making simple, guided choices about topics related to their work. Once the relevant data layers have been chosen, a mapping interface will allow the exploration of spatial relationships and the creation and export of maps. Extension areas to filter searches include 4-H Youth & Family, Agriculture, Business, Community, Food & Health, and Natural Resources. Users will also be able to explore data by viewing data tables and graphs. This Beta release is open for public use and feedback. Resources in this dataset:Resource Title: Website Pointer to NEWT National Extension Web-mapping Tool Beta. File Name: Web Page, url: https://www.mapasyst.org/newt/ The site leads the user through the process of selecting the data in which they would be most interested, then provides a variety of ways for the user to explore the data (maps, graphs, tables).

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of location-based services (LBS) across diverse sectors like automotive, logistics, and smart city initiatives is a primary catalyst. Furthermore, advancements in technologies such as AI, machine learning, and high-resolution satellite imagery are enabling the creation of more accurate, detailed, and feature-rich digital maps. The shift towards cloud-based deployment models offers scalability and cost-effectiveness, further accelerating market growth. While data privacy concerns and the high initial investment costs for sophisticated mapping technologies present some challenges, the overall market outlook remains overwhelmingly positive. The competitive landscape is dynamic, with established players like Google, TomTom, and ESRI vying for market share alongside innovative startups offering specialized solutions. The segmentation of the market by solution (software and services), deployment (on-premise and cloud), and industry reveals significant opportunities for growth in sectors like automotive navigation, autonomous vehicle development, and precision agriculture, where real-time, accurate mapping data is crucial. The Asia-Pacific region, driven by rapid urbanization and technological advancements in countries like China and India, is expected to witness particularly strong growth. The market's future hinges on continuous innovation. We anticipate a rise in the demand for 3D maps, real-time updates, and integration with other technologies like the Internet of Things (IoT) and augmented reality (AR). Companies are focusing on enhancing the accuracy and detail of their maps, incorporating real-time traffic data, and developing tailored solutions for specific industry needs. The increasing adoption of 5G technology promises to further boost the market by enabling faster data transmission and real-time updates crucial for applications like autonomous driving and drone delivery. The development of high-precision mapping solutions catering to specialized sectors like infrastructure management and disaster response will also fuel future growth. Ultimately, the digital map market is poised for continued expansion, driven by technological advancements and increased reliance on location-based services across a wide spectrum of industries. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Complexity in Integration of Traditional Maps with Modern GIS System. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

👨‍💻 Author: Azhar Saleem

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Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

Named Waterbody is a 1:24,000-scale, polygon and line feature-based layer that includes all named waterbodies depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. Named Waterbody features include water, dams, flow connectors, aqueducts, canals, ditches, shorelines, and islands. The layer does not include the marsh areas, tidal flats, rocks, shoals, or channels typically shown on USGS 7.5 minute topographic quadrangle maps. However, the layer includes linear (flow) connector features that fill in gaps between river and stream features where water passes through marshes or underground through pipelines and tunnels. Note that connectors represent general pathways and do not represent the exact location or orientation of actual underground pipelines, tunnels, aqueducts, etc. The Named Waterbody layer is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict related information such as dams and islands. Line features represent single-line rivers and streams, flow connectors, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of shorelines, dams, and closure lines separating adjacent water features. The Named Waterbody layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify waterbody features by type, cartographically represent (symbolize) waterbody features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe waterbody feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The Named Waterbody layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. Derived from the Hydrography layer, the Named Waterbody layer was originally published in 1999. The 2005 edition includes the same water features published in 1999, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors and includes the flow connector features. Connecticut Named Waterbody Polygon includes the polygon features of a layer named Named Waterbody. Named Waterbody is a 1:24,000-scale, polygon and line feature-based layer that includes all named waterbodies depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. Named Waterbody features include water, dams, flow connectors, aqueducts, canals, ditches, shorelines, and islands. The layer does not include the marsh areas, tidal flats, rocks, shoals, or channels typically shown on USGS 7.5 minute topographic quadrangle maps. However, the layer includes linear (flow) connector features that fill in gaps between river and stream features where water passes through marshes or underground through pipelines and tunnels. Note that connectors represent general pathways and do not represent the exact location or orientation of actual underground pipelines, tunnels, aqueducts, etc. The Named Waterbody layer is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict related information such as dams and islands. Line features represent single-line rivers and streams, flow connectors, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of shorelines, dams, and closure lines separating adjacent water features. The Named Waterbody layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify waterbody features by type, cartographically represent (symbolize) waterbody features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe waterbody feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The Named Waterbody layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. Derived from the Hydrography layer, the Named Waterbody layer was originally published in 1999. The 2005 edition includes the same water features published in 1999, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors and includes the flow connector features.

This collection contains supplementary information for the manuscript “Genetic mapping and QTL analysis for peanut smut resistance”, which reports the genetic map and quantitative trait loci associated with resistance to peanut smut, a disease caused by the fungus Thecaphora frezii. The information includes genotyping data of a 103 recombinant inbred line (RIL) population {susceptible Arachis hypogaea subsp.hypogaea × resistant synthetic amphidiploid [(A. correntina × A. cardenasii) × A. batizocoi]⁴ˣ} and parental lines, generated with the Axiom_Arachis2 SNP array. For more information about this dataset contact: Renee Arias: Renee.Arias@usda.gov or Alicia Massa: Alicia.Massa@usda.gov Resources in this dataset:Resource Title: RILs of the mapping population. File Name: RIL_population.JPGResource Title: Data Dictionary. File Name: readme.txtResource Title: Supplementary Data 1: SNP genotypes as called by the Axiom Analysis Suite File name: SD01_RILs_SNPs_whole_Axiom_Arachis2.txt . File Name: SD01_RILs_SNPs_whole_Axiom_Arachis2.txt.zipResource Description: Supplementary Data 1: SNP genotypes as called by the Axiom Analysis Suite File name: SD01_RILs_SNPs_whole_Axiom_Arachis2.txt Single nucleotide polymorphism genotyping of a 103 RIL population and parental lines generated with the Arachis_Axiom2 SNP array. Resource Software Recommended: Axiom_Arachis2,url: https://www.thermofisher.com/us/en/home/life-science/microarray-analysis/microarray-analysis-instruments-software-services/microarray-analysis-software/axiom-analysis-suite.html Resource Title: Supplementary Data 2: Genotyping calls in VCF format File name: SD02_RILs_SNPs_whole_Axiom_Arachis2.vcf. File Name: SD02_core_RILs_SNPs_AxiomArachis2.vcf.zipResource Description: Supplementary Data 2: Genotyping calls in VCF format File name: SD02_RILs_SNPs_whole_Axiom_Arachis2.vcf Core SNP set used to characterize the RIL population and progenitors.

The place names, understood as the proper name of a place or object present in the geographical space, represent an essential territorial reading key, useful both to orient themselves in a “mote” geographical space (e.g. orthophotos, vector data “undressed”) and to locate themselves directly near the toponym itself (positioning on raster images or on the mute geographical space, or around an object of the DBT qualified by the chosen name). Place names are classified according to the type of object whose name they specify, but not all types of place names have a corresponding class defined in the Topographic Base Data, e.g. the name of parts of the mountainous territory; conversely, in other cases there are objects in the Data Base (e.g. natural watercourses, canals, roads, etc.) that include among their attributes the name indicated on the cartography as a placename. The position of the place names is actually functional to the reading of an elaborate cartographic and therefore dependent on the scale of representation.

⚠️Due to the City's transition to a new Computer-Aided Dispatch (CAD) system, the Fire Calls for Service data, dashboards and maps on this site currently include records only through 8/11/25. Regular updates are paused to align and unify old and new data models, but we aim to restore fully automated daily updates by the end of 2025.This transition does not impact Public Safety systems or operations. Data for the pause period (from 8/11/25 until integration completion) is still available upon request via the City’s public records portal. Thank you for your patience!Data from City of Cleveland Public Safety's Computer-aided Dispatch (CAD) system on calls for Cleveland Division of Fire service. Includes information on call time, priority, type, and location.Data starts with calls from 2021 onwards. The data provided is the latest available information and is updated regularly as statistics change.Disposition Type refers to the first disposition code, which is the code determined by Division of Fire after arrival on scene.Map points show approximate locations of Fire calls for service, including 911 calls. Locations are adjusted to the nearest road, and full addresses are not provided to protect individual privacy. Update FrequencyDaily around 8:30AM Eastern Related data item(s):Fire Calls for Service DatasetFire Calls for Service Dashboard ContactsCity of Cleveland, Division of Fire InstructionsZoom into an area of the city you are interested in.Search for an address to zoom to that location.Filter by call date or call disposition type.Use the map layers tool to add wards or neighborhoods to the map.Use the advanced query tool to view calls within a certain distance of a point or area.

This dataset contains files created, digitized, or georeferenced by Chris DeRolph for mapping the pre-urban renewal community within the boundaries of the Riverfront-Willow St. and Mountain View urban renewal projects in Knoxville TN. Detailed occupant information for properties within boundaries of these two urban renewal projects was extracted from the 1953 Knoxville City Directory. The year 1953 was chosen as a representative snapshot of the Black community before urban renewal projects were implemented. The first urban renewal project to be approved was the Riverfront-Willow Street project, which was approved in 1954 according to the University of Richmond Renewing Inequality project titled ‘Family Displacements through Urban Renewal, 1950-1966’ (link below in the 'Other shapefiles' section). For ArcGIS Online users, the shapefile and tiff layers are available in AGOL and can be found by clicking the ellipsis next to the layer name and selecting 'Show item details' for the layers in this webmap https://knoxatlas.maps.arcgis.com/apps/webappviewer/index.html?id=43a66c3cfcde4f5f8e7ab13af9bbcebecityDirectory1953 is a folder that contains:JPG images of 1953 City Directory for street segments within the urban renewal project boundaries; images collected at the McClung Historical CollectionTXT files of extracted text from each image that was used to join occupant information from directory to GIS address datashp is a folder that contains the following shapefiles:Residential:Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and property ownersBlack_rented_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and non-owners of the propertyNon_Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as property owners that were not listed as BlackNon_Black_rented_residential_1953.shp: residential entries in the 1953 City Directory not listed as Black or property ownersResidential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposeslastName: occupant's last namelabelShort: combines the Number and lastName fields for map labeling purposesNon-residential:Black_nonResidential_1953.shp: non-residential entries in the 1953 City Directory listed as Black-occupiedNonBlack_nonResidential_1953.shp: non-residential entries in the 1953 City Directory not listed as Black-occupiedNon-residential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationNAICS6: 2022 North American Industry Classification System (NAICS) six-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS6title: NAICS6 title/short descriptionNAICS3: 2022 North American Industry Classification System (NAICS) three-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS3title: NAICS3 title/short descriptionflag: flags whether the occupant is part of the public sector or an NGO; a flag of '0' indicates the occupant is assumed to be a privately-owned businessrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposesOther shapefiles:razedArea_1972.shp: approximate area that appears to have been razed during urban renewal based on visual overlay of usgsImage_grayscale_1956.tif and usgsImage_colorinfrared_1972.tif; digitized by Chris DeRolphroadNetwork_preUrbanRenewal.shp: road network present in urban renewal area before razing occurred; removed attribute indicates whether road was removed or remains today; historically removed roads were digitized by Chris DeRolph; remaining roads sourced from TDOT GIS roads dataTheBottom.shp: the approximate extent of the razed neighborhood known as The Bottom; digitized by Chris DeRolphUrbanRenewalProjects.shp: boundaries of the East Knoxville urban renewal projects, as mapped by the University of Richmond's Digital Scholarship Lab https://dsl.richmond.edu/panorama/renewal/#view=0/0/1&viz=cartogram&city=knoxvilleTN&loc=15/35.9700/-83.9080tiff is a folder that contains the following images:streetMap_1952.tif: relevant section of 1952 map 'Knoxville Tennessee and Surrounding Area'; copyright by J.U.G. Rich and East Tenn Auto Club; drawn by R.G. Austin; full map accessed at McClung Historical Collection, 601 S Gay St, Knoxville, TN 37902; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphnewsSentinelRdMap_1958.tif: urban renewal area map from 1958 Knox News Sentinel article; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphusgsImage_grayscale_1956.tif: May 18, 1956 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/ARA550590030582/usgsImage_colorinfrared_1972.tif: April 18, 1972 color infrared USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR6197002600096/usgsImage_grayscale_1976.tif: November 8, 1976 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR1VDUT00390010/

It provides a function to search for the new address road name of the road name address system, the new address building location and building number of the new address building, and the shape and attribute information of the new address building. The OGC (Open Geospatial Consortium) standard API is an international standard developed for the sharing and interoperability of spatial data, and enables efficient provision and use of various geographic information such as maps, features, and rasters on the web. The latest OGC API adopts a RESTful structure to enhance development convenience and expandability, and inherits existing standards such as WMS and WFS in a modern way.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

⚠️Due to the City's transition to a new Computer-Aided Dispatch (CAD) system, the Police Calls for Service data, dashboards and maps on this site currently include records only through 11/14/25. Regular updates are paused to align and unify old and new data models, but we aim to restore fully automated daily updates by the end of 2025.This transition does not impact Public Safety systems or operations. Data for the pause period (from 11/14/25 until integration completion) is still available upon request via the City’s public records portal. Thank you for your patience!Data from City of Cleveland Public Safety's Computer-aided Dispatch (CAD) system on calls for Cleveland Division of Police service. Includes information on call time, priority, type, and location.Data starts with calls from 2021. The data provided is the latest available information and is updated regularly as statistics change.Map points show approximate locations of police calls for service, including 911 calls. Locations are adjusted to the nearest road, and full addresses are not provided to protect individual privacy. The City strives to provide the highest-quality information on this platform. The content on this website is provided as a public service, on an ‘as is’ basis. The City makes no warranty, representation, or guarantee of any type as to the content, accuracy, timeliness, completeness, or fitness for any particular purpose or use of any public data provided on this portal; nor shall any such warranty be implied, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose. The City assumes no liability by making data available to the public or other departments. Update FrequencyDaily around 8:30AM Eastern Related data item(s):Police Calls for Service Dataset Police Calls for Service Dashboard Contacts City of Cleveland, Division of Police InstructionsZoom into an area of the city you are interested in.Search for an address to zoom to that location.Filter by call date or call type.Use the map layers tool to add police districts, wards, or neighborhoods to the map.Use the advanced query tool to view calls within a certain distance of a point or area.

The City of Fort Collins GIS Online Mapping tool (FCMaps) provide current, timely and local geographic information in an easy to use viewer. FCMaps is mobile friendly and will work well on tablets and smartphones as well as a desktop browser.

Here you will find locations and names of neighborhoods in Fort Collins.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

Explore dispatched calls for service by count, type, location, and more.

Various historical maps from Vantaa. More detailed information about the different maps can be found in the Layers descriptions section.

The material can be viewed in the City of Vantaa map service:

• The levels can be found in the map service by searching for 'Other historical maps'.

Coordination system(s):

• ETRS-GK25 (EPSG:3879)

Addresses for cross-border services:

• WMS: https://gis.vantaa.fi/geoserver/wms?request=GetCapabilities
• Description of the interface service: https://gis.vantaa.fi/interfaces/

Published levels:

• history:land species map_mv
• history:vantaantilat_1983
• history:Tikkurila_1950_construction plan
• history:longitudinal map_1933
• history:longitudinal map_1749
• history:Senate map_1872
• History: King's Map

Level descriptions

Name: Land species map (black and white)

In the soil species map, the soil is mapped to a depth of about one metre. For soil layers deeper than this, the soil type map does not provide information. The mapping of soil types has been carried out on a scale of 1:2,000 or 1:10,000, so the smallest soil types have not been presented. The soil species do not change unambiguously at the boundary line shown on the maps, but rather the boundary line represents the zone of change of soil species.

Timeliness of data: The soil species map was made mainly in the 1980s and represents the conditions of that time. The map has not been updated.

Explanation of abbreviations in the country map:

• Ka or dark raster = rock covered by an open or thin layer of loose soil
• Mr = moraine soil species
• Sr = gravel
• Hk = sand
• Si = silt (previously silt/sand)
• Sa = clay
• Lj = mud
• Tv = peat
• T = area where excavation or filling has taken place

Double marking means that the topsoil species changes to another at a depth of less than one metre from the ground surface. For example, Hk/Sa means that the surface layer is sand to a depth of no more than 0.9 m and the soil is clay to a depth of 1 m.

Name: Facilities in Vantaa

The dataset includes Vantaa's premises according to the time of 1983. In areas where there is no local detailed plan, construction is regulated by the local master plan. The provisions of the master plan tie the number of dwellings to the surface area of the premises at the time of the adoption of the 1983 master plan (6.6.1983). The regulations apply to the detached house areas A4, village areas AT, agricultural areas MT and areas M, which are dominated by agriculture and forestry.

Name: Construction plan for Tikkurila 1950

Map of the construction plan area of Tikkurila in the villages of Tikkurila, Suutarinkylä and Hakkila in the city of Helsinki and in the rural municipality of Uusimaa.

Construction plan surveyor Niilo Tarkka, surveyor in 1937-47. The survey was completed in 1947-50 by surveyor J. Rauniomäki.

Name: Keeper's Map 1933

The National Land Survey of Finland prepared the parish map 1:20 000 between 1825 and 1950. The production took place by parish in 1825–1915 and by map sheet in 1916–1950. From 1927 onwards, the parish maps were published as 10 km x 10 km magazines in the so-called general magazine division.

Name: Keeper map 1749

Friedrich Johan Fonseen's map of the parish of Helsinki from 1749.

Source: Krigsarkivet (Sverige) - War Archive (Sweden). The city's spatial data team has put it into the current coordinate system.

Name: Senate map 1872

The map is based on surveys made by the topographical department of the Russian Ministry of War in 1870-1907 at a scale of 1:21,000 about the southern part of the Pori-Käkisalmi line.

Name: King's Map

The King's Atlas 1776-1805

In 1776-1805, an extensive military survey, the so-called recognition survey, was carried out in Finland. According to the work instructions, the maps had to be drawn up so accurately that no militarily significant terrain would be overlooked. With the help of maps, the warlord had to be able to plan both offensive and defensive actions without knowing the terrain. As a result, the maps depict e.g. roads (including winter roads), water routes, rustolles, crofts and vicarages.

The maps are based on older geometric maps, in which the mappers both supplemented and corrected the data while working in the field. In addition, the mappers have in some cases used the help of local residents to find out the roads, terrain and the name of the locality. The language used in the map is Swedish. The original hand-drawn maps are stored at the Swedish State Archives in Stockholm.

Source: Krigsarkivet, Finska rekognosceringsverket.

The 1966 polygons included in this data release represent the main body portion of the 37 named glaciers of Glacier National Park (GNP) and 2 named glaciers on the U.S. Forest Service’s Flathead National Forest land. This is a subset of the original mapping effort derived from 1:24000 scale mapping of named glaciers and permanent snowfields within Glacier National Park, Montana which were digitized by Richard Menicke (Glacier National Park) and Carl Key (U.S. Geological Survey) in 1993. These data are based on USGS 7.5 minute quadrangle mapping published from 1966 through 1968 which were the result of the earliest park-wide aerial surveys of snow and ice features in GNP. Examination of the aerial photographs shows that seasonal snow was present at some of the glaciers, limiting the ability of the 1966-1968 cartographers to see and map the glacier ice margins. This resulted in some smoothed and generalized outlines of the glaciers where the cartographers were likely guessing where the ice margins were under the snow. In addition, some photographs show exposed glacier margin ice with irregular patterns that are not represented by the mapped ice margin. It appeared that the original cartographers used a more generalized outline for the glaciers and were not concerned with small scale ice features even when they were evident in the photographs. Despite the generalized nature of the glacier outlines, which were also limited by mapping technology and standards of the time, the dataset represents the baseline for the glacier margins derived from aerial photography. In several cases, because of the generalized nature of the 1966-1968 mapping, a glacier perimeter did not seem as if it reflected likely location in the basin topography. In these cases the original USGS aerial imagery was referred to for verification and revision if the error seemed significant. Specifics of margin revision are detailed in attribute files for those glaciers that warranted change as part of the time series analysis conducted by Dan Fagre and Lisa McKeon (USGS) in February - August, 2016. For each glacier, determination of what constituted the "main body" was made in accordance with USGS criteria outlined in Supplemental Information section of the xml file and some disconnected patches were eliminated in the interest of keeping this analysis strictly to glacier main bodies.

This dataset is about books. It has 1 row and is filtered where the book is Collecting antique maps : an introduction to the history of cartography. It features 7 columns including author, publication date, language, and book publisher.

Neighborhood map atlas district areas are derived from the Seattle City Clerk's Office Geographic Indexing Atlas. These are the largest neighborhood areas and have been supplemented with alternate names from other sources in 2020. They are subdivided further into the neighborhood map atlas sub-areas called neighborhoods. The sub-neighborhoods field contains a comma delimited list of all the sub-areas and their alternate names.

Data reported in research published in Crop Science, “Mapping the quantitative field resistance to stripe rust in a hard winter wheat population ‘Overley’ × ‘Overland.’” Authors are Wardah Mustahsan, Mary J. Guttieri, Robert L. Bowden, Kimberley Garland-Campbell, Katherine Jordan, Guihua Bai, Guorong Zhang from USDA Agricultural Research Service and Kansas State University. This study was conducted to identify quantitative trait loci (QTL) associated with field resistance to stripe rust, also known as yellow rust (YR), in hard winter wheat. Stripe rust infection type and severity were rated in recombinant inbred lines (RILs, n=204) derived from a cross between hard red winter wheat cultivars ‘Overley’ and ‘Overland’ in replicated field trials in the Great Plains and Pacific Northwest. RILs (n=184) were genotyped with reduced representation sequencing to produce SNP markers from alignment to the ‘Chinese Spring’ reference sequence, IWGSC v2.1, and from alignment to the reference sequence for ‘Jagger’, which is a parent of Overley. Genetic linkage maps were developed independently from each set of SNP markers. QTL analysis identified genomic regions on chromosome arms 2AS, 2BS, 2BL, and 2DL that were associated with stripe rust resistance using multi-environment best linear unbiased predictors for stripe rust infection type and severity. Results for the two linkage maps were very similar. PCR-based SNP marker assays associated with the QTL regions were developed to efficiently identify these genomic regions in breeding populations.Field response to YR was evaluated in seven trials: Rossville, KS (2018 and 2019), Hays, KS (2019), Pullman, WA (2019 and 2020) and Central Ferry, WA (2019 and 2020). An augmented experimental design was used at Rossville, KS with highly replicated checks and two full replications of RILs (n=187 in 2018; n=204 in 2019). The field experiment at Hays was arranged in a partially replicated augmented design with one or two replications of each RIL (n=194). The parental checks (Overley and Overland) were represented in three blocks for each of the two field replications at Hays, and RILs were distributed among blocks; not all RILs were present in each replication. RILs were arranged in an augmented design with two replications at Pullman (n=204 RILs) and Central Ferry (n=155 RILs in 2019; n=204 in 2020). At Pullman and Central Ferry.The trials at Rossville, KS were inoculated using an inoculum consisting of equal parts of four isolates that were all virulent to Yr9. Two isolates were collected in Kansas in 2010 and had virulence to Yr17 but not QYr.tamu-2B. The other two isolates were from Kansas in 2012 and had virulence to QYr.tamu-2B, but not Yr17. Susceptible spreader rows (KS89180B, carrying Yr9) were inoculated several times during the tillering stage in the evenings with an ultra-low volume sprayer using a suspension of 2 mL of fresh urediniospores in 1 L of Soltrol 170 isoparaffin oil. Trials at Pullman, WA and Central Ferry, WA were evaluated under natural inoculum supplemented by a mixture of isolates collected in the previous field season. The trial at Hays, KS was evaluated under natural infection.Data collection at Rossville, KS began once the susceptible check (KS89180B) had an infection severity coverage of ~10% and continued until senescence. In Rossville, disease ratings (IT and SEV) were collected on 16, 22, and 28th of May 2019. Most ratings in Rossville were taken some time after heading from Zadoks stages 55 to 70. In Pullman, disease ratings were collected on July 1 and 12. In Central Ferry, disease ratings were taken on 12th and 18th of June 2019. The second rating date was used for subsequent statistical analysis. In Hays, disease ratings were taken on June 1, 2019, when the plants were in early booting or heading stages (Zadoks 31-41). Stripe rust evaluations were measured using two disease rating scales: IT (0-9; from no infection to highly susceptible, Line and Qayoum, 1992) and SEV based on visual estimation of the percent flag leaf area affected by the pathogen including associated chlorosis and necrosis (0-100%).DNA was extracted from seedlings, and genotyping-by-sequencing was conducted as described previously (Guttieri, 2020) on a subset of 189 lines (187 RILS and 2 parents) of which 23 RILs were F6-derived and 164 RILs were F9-derived. Single nucleotide polymorphisms (SNPs) were identified in parallel using reference-based calling in the TASSEL pipeline (Bradbury et al., 2007) using both the IWGSC v2.1 reference genome (Zhu et al., 2021) and the Jagger reference sequence (Wheat Genomes Project (http://www.10wheatgenomes.com/10-wheat-genomes-project-and-the-wheat-initiative/). The TASSEL pipeline was executed with the following parameters: minimum read count = 1, minimum quality score = 0, minimum locus coverage = 0.19, and minimum minor allele frequency = 0.005, minimum heterozygous proportion = 0, and removal of minor SNP states. The resulting SNP datasets from each reference sequence were filtered in TASSEL by taxa (RILs) and sites (SNPs). The RILs were filtered to include those RILs for which at least 20% sites were present. The sites were filtered to include sites for which > 60% of RILs were called, minor allele frequency (MAF) > 0.25, maximum allele frequency < 0.75, maximum heterozygous proportion = 0.25, and removal of minor SNP states. The ABH plugin in TASSEL was applied to this reduced dataset to identify parental genotypes.Resources in this dataset:Resource Title: Multilocation Stripe Rust Data File Name: MultiLocRawData_Yr.xslxResource Title: OvOv_CS_TasselSNPCalls File Name: KSM17-OvOv-parentsmerge1.hmp.txt Resource Description: Output of TASSEL GBS SNP calling pipeline using Chinese Spring v2 refseq. Starting point for map construction pipeline.Resource Title: OvOv GBS SNP Calls Jagger RefSeq File Name: KSM17-OvOv-Jaggerpmerge1.hmp.txt Resource Description: TASSEL output from reference-based SNP calling using the Jagger reference sequenceResource Title: QTL-Associated KASP Markers with IT and SEV BLUPs File Name: KASP_Data_IT_SEV.xlsx Resource Description: Multilocation best linear unbiased predictors (BLUPs) for stripe rust infection type and severity of recombinant inbred lines. KASP assay results for QTL-associated SNPs, coded Overley = 2, Overland = 0, Het = 1, Missing = "."