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.

Maps and spatial apps from agencies around Colorado

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

🌍 Massive scale: 165,000 unique 3D map sequences and locations, 82,000,000 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.

🤗 1k Scenes Sample: You can access our 1,000-scene sample under the CC-BY-NC license at this link: https://huggingface.co/datasets/OverTheReality/OverMaps_1k

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).

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.

👨‍💻 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 is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.ArcGIS INSPIRE Open Data is a lightweight solution for European public sector organizations implementing the INSPIRE and PSI-2/Open Data Directives. See the Getting to know ArcGIS INSPIRE Open Data story map to learn more.Geodatabase (GDB) templates are available on the ArcGIS INSPIRE Open Data demonstration Hub. INSPIRE Alternative Encoding documentation on GitHub is publicly available per the Implementing Rules on interoperability of spatial data sets and services (Commission Regulation (EU) No 1089/2010). These resources are provided as-is and are freely available.

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.

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

A global analysis of accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time.

To model the time required for individuals to reach their most accessible city, we first quantified the speed at which humans move through the landscape. The principle underlying this work was that all areas on Earth, represented as pixels within a 2D grid, had a cost (that is, time) associated with moving through them that we quantified as a movement speed within a cost or ‘friction’ surface. We then applied a least-cost-path algorithm to the friction surface in relation to a set of high-density urban points. The algorithm calculated pixel-level travel times for the optimal path between each pixel and its nearest city (that is, with the shortest journey time). From this work we ultimately produced two products: (a) an accessibility map showing travel time to urban centres, as cities are proxies for access to many goods and services that affect human wellbeing; and (b) a friction surface that underpins the accessibility map and enables the creation of custom accessibility maps from other point datasets of interest. The map products are in GeoTIFF format in EPSG:4326 (WGS84) project with a spatial resolution of 30 arcsecs. The accessibility map pixel values represent travel time in minutes. The friction surface map pixels represent the time, in minutes required to travel one metre. This DANS data record contains these two map products.

About this itemDescription: "A Road Map to Minnesota Treasure" is a static map created by Hannah White (Master of Geographic Information Sciences). It was awarded a U-Spatial Mapping Prize, namely an honorable mention in Graduate Level Cartography.Author/ContributorHannah White, Master of Geographic Information SciencesOrganizationUniversity of MinnesotaOrg Websiterc.umn.edu/uspatial

Land-use control and planning instruments face new challenges amid growing pressure for urbanization and annexation of valuable agricultural land and natural areas. This paper presents the land-use change propensity map, which shows the local potential for specific land-use changes. Propensity is derived empirically on the basis of historical land-use changes, with an explicit evaluation of characteristics that contributed to land-use change. Each step in creating a propensity map is described: selecting data that best represents land-use changes, identifying potential drivers of land-use change and the statistical inference of their impact on land-use change on the basis of observed historical land-use changes. The resulting propensity for land-use change is represented in the form of a binary logit model that evaluates the probability of specific land-use changes. A series of propensity maps for the territory of the Tábor microregion in the Czech Republic was created to demonstrate the method. The scale of the propensity maps is 1:310,000, and they cover an area of 1002 km2. Each propensity map represents the specific propensity for conversion from non-urban uses to family, multi-family and individual recreation houses. The evaluated propensity can be further compared to existing or proposed land-use regulations.

Dataset

This dataset was created by Ahsan Bin Hasan Pasha

Contents

About this itemStory Maps are a powerful platform that integrate spatial thinking with storytelling to present information in a compelling, interactive and easy to understand format. The University of Minnesota StoryMaps team provides support and resources for faculty looking to incorporate spatial tools such as StoryMaps, Survey 123 and other web-based GIS applications into their classrooms. The UMN StoryMaps site has examples of student projects, samples of project ideas/assignments/rubrics and user guides for students. This team’s work has received national recognition for promoting the role of spatial thinking and StoryMaps in higher education, K12 and informal learning spaces.Author/ContributorU-SpatialOrganizationUniversity of MinnesotaOrg Websitesystem.umn.edu

Spatial Mapping Software Market Outlook

As per our latest research, the global spatial mapping software market size in 2024 stands at USD 7.2 billion, with a robust compound annual growth rate (CAGR) of 13.7% projected through 2033. By the end of 2033, the market is forecasted to reach a valuation of USD 22.1 billion. This impressive growth trajectory is primarily driven by the increasing adoption of location-based services, the proliferation of smart city initiatives, and the rising demand for geospatial analytics across various industries. The market is experiencing significant momentum as organizations seek advanced solutions for spatial data visualization, real-time mapping, and efficient resource management, thereby fueling the expansion of spatial mapping software globally.

The rapid digital transformation across industries is a major growth factor for the spatial mapping software market. As businesses and governments increasingly rely on data-driven decision-making, the ability to visualize, analyze, and interpret spatial data has become essential. Urbanization and the expansion of smart cities are creating a surge in demand for mapping solutions that enable planners and administrators to optimize infrastructure, manage assets, and monitor environmental impact. Furthermore, the integration of spatial mapping software with emerging technologies such as artificial intelligence, Internet of Things (IoT), and 5G networks is enhancing the precision and real-time capabilities of these platforms. This convergence is paving the way for innovative applications in areas such as autonomous vehicles, disaster response, and precision agriculture, further propelling market growth.

Another significant driver for the spatial mapping software market is the growing need for efficient asset management and risk mitigation. Organizations across sectors such as utilities, transportation, and defense are leveraging spatial mapping software to monitor and manage critical assets, detect anomalies, and ensure operational continuity. The ability to overlay real-time data on geographic maps provides unparalleled situational awareness, enabling quick and informed decision-making. Additionally, advancements in cloud computing have democratized access to sophisticated mapping tools, allowing even small and medium enterprises to benefit from spatial analytics without substantial infrastructure investments. The trend towards remote work and distributed operations post-pandemic has also accelerated the adoption of cloud-based mapping solutions, making spatial mapping an integral part of modern enterprise workflows.

Environmental monitoring and disaster management represent pivotal growth avenues for the spatial mapping software market. Climate change, urban sprawl, and natural disasters necessitate advanced solutions for tracking environmental changes, predicting hazards, and coordinating emergency responses. Spatial mapping software is being utilized to model flood zones, monitor deforestation, and track pollution, providing governments and organizations with actionable insights for sustainable development and disaster resilience. The increasing frequency and intensity of natural disasters globally have heightened the importance of real-time geospatial intelligence, driving investments in mapping technologies. As environmental regulations become stricter and public awareness grows, the demand for spatial mapping solutions in environmental monitoring is expected to remain strong throughout the forecast period.

The integration of Spatial Mapping Processor technology is revolutionizing the capabilities of spatial mapping software. This advanced processor enhances the speed and accuracy of data processing, allowing for more detailed and real-time analysis of spatial data. By leveraging the power of spatial mapping processors, organizations can achieve higher precision in mapping applications, which is crucial for sectors such as autonomous vehicles and smart city planning. The processor's ability to handle complex algorithms efficiently is enabling new levels of innovation in geospatial analytics, providing users with deeper insights and improved decision-making capabilities. As the demand for high-performance mapping solutions grows, the role of spatial mapping processors in driving technological advancements cannot be overstated.

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Golden eagle (Aquila chrysaetos) nest site model spatial data and maps as described in Dunk JR, Woodbridge B, Lickfett TM, Bedrosian G, Noon BR, LaPlante DW, et al. (2019) Modeling spatial variation in density of golden eagle nest sites in the western United States. PLoS ONE 14(9): e0223143. https://doi.org/10.1371/journal.pone.0223143

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

ABSTRACT This paper presents a GIS methodological approach for mapping forest landscape multifunctionality. The aims of the present study were: (1) to integrate and prioritize production and protection functions by multicriteria spatial analysis using the Analytic Hierarchy Process (AHP); and (2) to produce a multifunctionality map (e.g., production, protection, conservation and recreation) for a forest management unit. For this, a study area in inner Portugal occupied by forest and with an important protection area was selected. Based on maps for functions identified in the study area, it was possible to improve the scenic value and the biodiversity of the landscape to mitigate fire hazard and to diversify goods and services. The developed methodology is a key tool for producing maps for decision making support in integrated landscape planning and forest management.

Here you will find a selection of maps that have been produced by various King County departments and divisions and are hosted on the KCGIS Center website.