In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

Our dataset delivers unprecedented scale and diversity for geospatial AI training:

🌍 Massive scale: 165,000 unique 3D map sequences and locations, 82,500,000 images, 0.73 PB of Data, orders of magnitude larger than datasets currently used for SOTA Vision/Spatial Models.

⏱️ Constantly growing dataset: 12k new 3D Map sequences and locations monthly.

📷 Full-frame, high-res captures: OVER retains full-resolution, dynamic aspect-ratio images with complete Exif metadata (GPS, timestamp, device orientation), multiple resolutions 1920x1080 - 3840x2880, pre-computed COLMAP poses.

🧭 Global diversity: Environments span urban, suburban, rural, and natural settings across 120+ countries, capturing architectural, infrastructural, and environmental variety.

📐 Rich metadata: Per-image geolocation (±3 m accuracy), timestamps, device pose, COLMAP pose; per-map calibration data (camera intrinsics/extrinsics).

🧠 Applications: Spatial Models Training, Multi-view stereo & NeRF/3DGS training, semantic segmentation, novel view synthesis, 3D object detection, geolocation, urban planning, AR/VR, autonomous navigation.

Here we produced the first 10 m resolution urban green space (UGS) map for the main urban clusters across 371 major Latin American cities as of 2017. Our approach applied a supervised classification of Sentinel-2 satellite imagery and UGS samples derived from OpenStreetMap (OSM). The overall accuracy of this UGS map in 11 randomly selected cities was 0.87, evaluated by independently collected validation samples (‘ground truth’). We further improved mapping quality through a visual inspection and additional sample collection. The resulting UGS map enables studies to measure area, spatial configuration, and human exposures to UGS, facilitating studies about the relationship between UGS and human exposures to environmental hazards, public health outcomes, and environmental justice issues in Latin American cities.UGS in this map series includes grass, shrub, forest, and farmland, and non-UGS included buildings, pavement, roads, barren land, and dry vegetation.The UGS map series includes three sets of files:(1) binary UGS maps at 10 m spatial resolution in GEOTIFF format (UGS.zip), with each of the 371 cities being an individual map. Mapped value of 1 indicates UGS, 0 indicates non-UGS, and no data (with value of -32768) indicates areas outside the mapped boundary or water bodies;(2) a shapefile of mapped boundaries (Boundaries.zip). The boundary file contains city name, country name and its ISO-2 country code, and an ID field linking each city's boundary to the corresponding UGS map.(3) .prj files containing projection information for the binary UGS maps and boundary shapefile. The binary UGS maps are projected with World Geodetic System (WGS) 84 / Pseudo-Mercator projected coordinate system (EPSG: 3857), and the boundary shapefile is projected with WGS 1984 geographic coordinate system (EPSG: 4326)Reference: A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap, published by Scientific Data [link].Citation: Ju, Y., Dronova, I., & Delclòs-Alió, X. (2022). A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap. Scientific Data, 9, Article 1. https://doi.org/10.1038/s41597-022-01701-y

👨‍💻 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...

This data repository hosts datasets that are used for students to practice spatial operations introduced in R-as-GIS lectures and workshops.

Comprehensive global 3D Maps dataset with 82 Mln smartphone-captured images including depth, poses, and Exif metadata, across 165K diverse locations. Ideal for Geospatial and Vision AI Models Training.

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).

Maps and spatial apps from agencies around Colorado

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Introduction and Rationale:Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.Contents:Spatial dataNational rasters of land cover in the conterminous United States: 2012-2021Rasters of pixels mismatched between CDL and NVC: 2012-2021Resources in this dataset:Resource Title: SPAN land cover in the conterminous United States: 2012-2021 - SCINet File Name: KammererNationalRasters.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). Spatial Products for Agriculture and Nature ('SPAN') land cover in the conterminous United States from 2012-2021. This raster dataset is available in GeoTIFF format and was created by joining agricultural classes from the USDA-NASS Cropland Data Layer (CDL) to national vegetation from the LANDFIRE National Vegetation Classification v2.0 ('Remap'). Pixels of national vegetation are the same in all rasters provided here and represent land cover in 2016. Agricultural pixels were taken from the CDL in the specified year, so depict agricultural land from 2012-2021. Resource Title: Rasters of pixels mismatched between CDL and NVC: 2012-2021 - SCINet File Name: MismatchedNational.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). This dataset includes pixels that were classified as agriculture in the NVC but, in the CDL, were not agriculture (or were a conflicting agricultural class). For more details, we refer users to the linked publication describing our geospatial processing and validation workflow.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

A Comprehensive Map of the World. A large print static map the the world.

🇦🇺 Australia

This map visualisation service allows access to the set of information layers published in the Spatial Data Infrastructure of Navarra and that correspond to the public data of the SITNA. The Web Map Service (WMS) defined by the OGC (Open Geospatial Consortium) produces spatially referenced data maps, dynamically based on geographic information.

The information provided includes: file identification code, Chinese file name, interpretation data creation time, data creation time, summary, purpose, data creation status, spatial display type, theme keywords, location keywords, ground resolution, data creation scale, map joining method, map entity name, geometric object type, format name, format version, westernmost longitude, easternmost longitude, southernmost latitude, northernmost latitude, maintenance update frequency, reference system identification code, and distribution map URL, etc.

The dataset contains daily grass-reference evapotranspiration (ETo) maps stored as ASCII files. ETo at a 2 km spatial resolution are calculated statewide using the American Society of Civil Engineers version of the Penman-Monteith equation (ASCE-PM). Required input parameters for the ASCE-PM ETo equation are solar radiation, air temperature, relative humidity, and wind speed at two meters height. These parameters are estimated for each 2 km pixel using various methods.

Daily solar radiation is generated from the visible band of the National Oceanic and Atmospheric Administration's (NOAA) Geostationary Operational Environmental Satellite (GOES) using the Heliosat-II model. This model is designed to convert images acquired by the Meteosat satellite into maps of global (direct plus diffused) irradiation received at ground level.

The ORNL DAAC Spatial Data Access Tool (SDAT) is a suite of Web-based applications that enable users to visualize and download spatial data in user-selected spatial/temporal extents, file formats, and projections. SDAT incorporates Open Geospatial Consortium (OGC) standard Web services, including Web Coverage Service (WCS), Web Map Service (WMS), and Web Feature Service (WFS). The SDAT provides ORNL DAAC-archived data sets and additional relevant data products including agriculture, atmosphere, biosphere, climate indicators, human dimensions, land surface, oceans, terrestrial hydrosphere data types, and related model output data sets.

See full Data Guide here. This layer includes polygon features that depict protected open space for towns of the Protected Open Space Mapping (POSM) project, which is administered by the Connecticut Department of Energy and Environmental Protection, Land Acquisition and Management. Only parcels that meet the criteria of protected open space as defined in the POSM project are in this layer. Protected open space is defined as: (1) Land or interest in land acquired for the permanent protection of natural features of the state's landscape or essential habitat for endangered or threatened species; or (2) Land or an interest in land acquired to permanently support and sustain non-facility-based outdoor recreation, forestry and fishery activities, or other wildlife or natural resource conservation or preservation activities. Includes protected open space data for the towns of Andover, Ansonia, Ashford, Avon, Beacon Falls, Canaan, Clinton, Berlin, Bethany, Bethel, Bethlehem, Bloomfield, Bridgewater, Bolton, Brookfield, Brooklyn, Canterbury, Canton, Chaplin, Cheshire, Colchester, Colebrook, Columbia, Cornwall, Coventry, Cromwell, Danbury, Derby, East Granby, East Haddam, East Hampton, East Hartford, East Windsor, Eastford, Ellington, Enfield, Essex, Farmington, Franklin, Glastonbury, Goshen, Granby, Griswold, Groton, Guilford, Haddam, Hampton, Hartford, Hebron, Kent, Killingworth, Lebanon, Ledyard, Lisbon, Litchfield, Madison, Manchester, Mansfield, Marlborough, Meriden, Middlebury, Middlefield, Middletown, Monroe, Montville, Morris, New Britain, New Canaan, New Fairfield, New Milford, New Hartford, Newington, Newtown, Norfolk, North, Norwich, Preston, Ridgefield, Shelton, Stonington, Oxford, Plainfield, Plainville, Pomfret, Portland, Prospect, Putnam, Redding, Rocky Hill, Roxbury, Salem, Salisbury, Scotland, Seymour, Sharon, Sherman, Simsbury, Somers, South Windsor, Southbury, Southington, Sprague, Sterling, Suffield, Thomaston, Thompson, Tolland, Torrington, Union, Vernon, Wallingford, Windham, Warren, Washington, Waterbury, Watertown, West Hartford, Westbrook, Weston, Wethersfield, Willington, Wilton, Windsor, Windsor Locks, Wolcott, Woodbridge, Woodbury, and Woodstock. Additional towns are added to this list as they are completed. The layer is based on information from various sources collected and compiled during the period from March 2005 through the present. These sources include but are not limited to municipal Assessor's records (the Assessor's database, hard copy maps and deeds) and existing digital parcel data. The layer represents conditions as of the date of research at each city or town hall. The Protected Open Space layer includes the parcel shape (geometry), a project-specific parcel ID based on the Town and Town Assessor's lot numbering system, and system-defined (automatically generated) fields. The Protected Open Space layer has an accompanying table containing more detailed information about each feature (parcel). This table is called Protected Open Space Dat, and can be joined to Protected Open Space in ArcMap using the parcel ID (PAR_ID) field. Detailed information in the Protected Open Space Data attribute table includes the Assessor's Map, Block and Lot numbers (the Assessor's parcel identification numbering system), the official name of the parcel (such as the park or forest name if it has one), address and owner information, the deed volume and page numbers, survey information, open space type, the unique parcel ID number (Par_ID), comments collected by researchers during city/town hall visits, and acreage. This layer does not include parcels that do not meet the definition of open space as defined above. Features are stored as polygons that represent the best available locational information, and are "best fit" to the land base available for each.

The Connecticut Department of Environmental Protection's (CTDEP) Permanently Protected Open Space Phase Mapping Project Phase 1 (Protected Open Space Phase1) layer includes permanently protected open space parcels in towns in Phase 1 that meet the CTDEP's definition for this project, the Permanently Protected Open Space Mapping (CT POSM) Project. The CTDEP defines permanently protected open space as (1) Land or interest in land acquired for the permanent protection of natural features of the state's landscape or essential habitat for endangered or threatened species; or (2) Land or an interest in land acquired to permanently support and sustain non facility-based outdoor recreations, forestry and fishery activities, or other wildlife or natural resource conservation or preservation activities.

Towns in Phase 1 of the CT POSM project are situated along the CT coast and portions of the Thames River and are the following: Branford, Bridgeport, Chester, Clinton, Darien, Deep River, East Haven, East Lyme, Essex, Fairfield, Greenwich, Groton, Guilford, Hamden, Ledyard, Lyme, Madison, Milford, Montville, New Haven, New London, North Branford, North Haven, Norwalk, Norwich, Old Lyme, Old Saybrook, Orange, Preston, Shelton, Stamford, Stonington, Stratford, Waterford, West Haven, Westbrook, Westport.

For the purposes of the project a number of categories or classifications of open space have also been created. These include: Land Trust, Land Trust with buidlings, Private, Private with buildings, Utility Company, Utility Company with buildings, Federal, State, Municipal, Municipal with buildings, Conservation easement, and non-DEP State land. The layer is based on information from various sources collected and compiled during the period from August 2002 trhough October 2003. These sources include municipal Assessor's records (the Assessor's database, hard copy maps and deeds) and existing digital parcel data. The layer represents conditions on the date of research at each city or town hall.

The Protected Open Space Phase1 layer includes the parcel shape (geometry), a project-specific parcel ID based on the Town and Town's Assessor lot numbering system, and system-defined (automatically generated) fields. In addition, the Protected_Open_Space_Phase1 layer has an accompanying table containing more detailed information about each parcel's collection, standardization and storage. This table is called Protected Open Space Phase1 Data and can be joined to Protected Open Space Phase1 in ArcMap using the parcel ID (PAR_ID) field. Detailed information includes the Assessor's Map, Block and Lot numbers (the Assessor's parcel identification numbering system), the official name of the parcel (such as the park or forest name if it has one), address and owner information, the deed volume and page numbers, survey information, open space type, the project-specific parcel ID number (Par_ID), comments collected by researchers during city/town hall visits, acreage collected during site reconaissance and the data source. This layer does not include parcels that do not meet the definition of open space as defined above. Features are stored as polygon feature type that represent the best available locational information, i.e. "best fit" to the land base available for each.

Phase 1 of the Protected Open Space Mapping (POSM) Project was accomplished by a contractor using only a querying process to identify open space. The contractor obtained assessor's data from the various towns and created programs to cull open space parcels strictly by query processes. We have found many errors and omissions in the data, but at this point in the project we cannot revisit all the coastal towns. Therefore, this data is being sent with a disclaimer for accuracy. You are welcome to use it but not to publish it. Please note that we do not include any water company parcels despite them being listed as part of our criteria because we must first obtain written clarification and clearance from the U.S. Department of Homeland Security.

We have since changed our data collection method for Phase 2 of this project. DEP staff now visit each town hall and thoroughly research the land records. The project is expected to be complete by 2010.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS. This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Taichung City Government 110-year Space Map Query System Monthly Homepage Visits