The geospatial analytics market size is predicted to rise from $93.49 billion in 2024 to $362.45 billion by 2035, growing at a CAGR of 13.1% from 2024 to 2035.

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2031, growing at a CAGR of 12.10% during the forecast period 2024-2031.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth’s surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today’s interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

According to Cognitive Market Research, the global geospatial analytics artificial intelligence market size is USD 100.5 million in 2024 and will expand at a compound annual growth rate (CAGR) of 28.60% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 40.20 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.8% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 30.15 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 23.12 million in 2024 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 5.03 million in 2024 and will grow at a compound annual growth rate (CAGR) of 28.0% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 2.01 million in 2024 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2024 to 2031.
The remote sensing held the highest geospatial analytics artificial intelligence market revenue share in 2024.

Market Dynamics of Geospatial analytics artificial intelligence Market

Key Drivers for Geospatial analytics artificial intelligence Market

Advancements in AI and Machine Learning to Increase the Demand Globally

The global demand for geospatial analytics is significantly driven by advancements in AI and machine learning, technologies that are revolutionizing how spatial data is analyzed and interpreted. As AI models become more sophisticated, they enhance the capability to automate complex geospatial data processing tasks, leading to more accurate and insightful analyses. Machine learning, particularly, enables systems to improve their accuracy over time by learning from vast datasets of geospatial information, including satellite imagery and sensor data. This leads to more precise predictions and better decision-making across multiple sectors such as environmental management, urban planning, and disaster response. The integration of AI with geospatial technologies not only improves efficiency but also opens up new possibilities for innovation, making it a critical driver for increased global demand in the geospatial analytics market.

Government Initiatives and Support for Smart Cities to Propel Market Growth

Government initiatives supporting the development of smart cities are propelling the growth of the geospatial analytics market. As urban areas around the world transform into smart cities, there is a significant increase in demand for advanced technologies that can analyze and interpret geospatial data to enhance urban planning, infrastructure management, and public safety. Geospatial analytics, powered by AI, plays a crucial role in these projects by enabling real-time data processing and insights for traffic control, utility management, and emergency services coordination. These technologies ensure more efficient resource allocation and improved quality of urban life. Government funding and policy support not only validate the importance of geospatial analytics but also stimulate innovation, attract investments, and foster public-private partnerships, thus driving the market forward and enhancing the capabilities of smart city initiatives globally.

Restraint Factor for the Geospatial analytics artificial intelligence Market

Complexity of Data Integration to Limit the Sales

The complexity of data integration poses a significant barrier to the adoption and effectiveness of geospatial analytics AI systems, potentially limiting sales in this market. Geospatial data, inherently diverse and sourced from various collection methods like satellites, UAVs, and ground sensors, comes in multiple formats and resolutions. Integrating such disparate data into a cohesive, usable format for AI analysis is a challenging process that requires advanced data processing tools and expertise. This complexity not only increases the time and costs associated with project implementation but also raises the risk of errors and inefficiencies in data analysis. Furthermore, the difficulty in achieving seamless integration can deter organizations, particularly those with limited IT capabilities, from investing in geospatial analytics solutions. Overcoming these integration challenges is crucial for enabl...

Market Overview:

The Vietnam geospatial analytics market size is projected to exhibit a growth rate (CAGR) of 8.90% during 2024-2032. The increasing product utilization by government authorities in various sectors, various technological advancements in satellite technology, remote sensing, and data collection methods, and the rising development of smart cities represent some of the key factors driving the market.

Geospatial analytics is a field of data analysis that focuses on the interpretation and analysis of geographic and spatial data to gain valuable insights and make informed decisions. It combines geographical information systems (GIS), advanced data analysis techniques, and visualization tools to analyze and interpret data with a spatial or geographic component. It also enables the collection, storage, analysis, and visualization of geospatial data. It provides tools and software for managing and manipulating spatial data, allowing users to create maps, perform spatial queries, and conduct spatial analysis. In addition, geospatial analytics often involves integrating geospatial data with other types of data, such as demographic data, environmental data, or economic data. This integration helps in gaining a more comprehensive understanding of complex phenomena. Moreover, geospatial analytics has a wide range of applications. For example, it can be used in urban planning to optimize transportation routes, in agriculture to manage crop yield and soil quality, in disaster management to assess and respond to natural disasters, in wildlife conservation to track animal migrations, and in business for location-based marketing and site selection.

Vietnam Geospatial Analytics Market Trends:

The Vietnamese government has recognized the importance of geospatial analytics in various sectors, including urban planning, agriculture, disaster management, and environmental monitoring. Initiatives to develop and utilize geospatial data for public projects and policy-making have spurred demand for geospatial analytics solutions. In addition, Vietnam is experiencing rapid urbanization and infrastructure development. Geospatial analytics is critical for effective urban planning, transportation management, and infrastructure optimization. This trend is driving the adoption of geospatial solutions in cities and regions across the country. Besides, Vietnam's agriculture sector is a significant driver of its economy. Geospatial analytics helps farmers and agricultural businesses optimize crop management, soil health, and resource allocation. Consequently, precision farming techniques, enabled by geospatial data, are becoming increasingly popular, which is also propelling the market. Moreover, the development of smart cities in Vietnam relies on geospatial analytics for various applications, such as traffic management, public safety, and energy efficiency. Geospatial data is central to building the infrastructure needed for smart city initiatives. Furthermore, advances in satellite technology, remote sensing, and data collection methods have made geospatial data more accessible and affordable. This has lowered barriers to entry and encouraged the use of geospatial analytics in various sectors. Additionally, the telecommunications sector in Vietnam is expanding, and location-based services, such as navigation and advertising, rely on geospatial analytics. This creates opportunities for geospatial data providers and analytics solutions in the telecommunications industry.

Vietnam Geospatial Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Component Insights:

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• Solution
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solution and services.

Type Insights:

• Surface and Field Analytics
• Network and Location Analytics
• Geovisualization
• Others
  

A detailed breakup and analysis of the market based on the type have also been provided in the report. This includes surface and field analytics, network and location analytics, geovisualization, and others.

Technology Insights:

• Remote Sensing
• GIS
• GPS
• Others
  

The report has provided a detailed breakup and analysis of the market based on the technology. This includes remote sensing, GIS, GPS, and others.

Enterprise Size Insights:

• Large Enterprises
• Small and Medium-sized Enterprises
  

A detailed breakup and analysis of the market based on the enterprise size have also been provided in the report. This includes large enterprises and small and medium-sized enterprises.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

The report has provided a detailed breakup and analysis of the market based on the deployment mode. This includes on-premises and cloud-based.

Vertical Insights:

• Automotive
• Energy and Utilities
• Government
• Defense and Intelligence
• Smart Cities
• Insurance
• Natural Resources
• Others
  

A detailed breakup and analysis of the market based on the vertical have also been provided in the report. This includes automotive, energy and utilities, government, defense and intelligence, smart cities, insurance, natural resources, and others.

Regional Insights:

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• Northern Vietnam
• Central Vietnam
• Southern Vietnam
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northern Vietnam, Central Vietnam, and Southern Vietnam.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Vietnam Geospatial Analytics Market Report Coverage:

The geospatial services market is experiencing robust growth, driven by increasing demand for location intelligence across diverse sectors. Our analysis projects a market size of $150 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. The agricultural sector leverages geospatial data for precision farming, optimizing resource allocation and maximizing yields. Similarly, research institutions and government bodies increasingly utilize geospatial analytics for environmental monitoring, urban planning, and disaster response. The integration of advanced technologies like AI and machine learning further enhances the capabilities of geospatial services, leading to more accurate and insightful analyses. Furthermore, the rising adoption of cloud-based platforms is simplifying data access and processing, making geospatial technologies more accessible to a wider range of users. Market segmentation reveals significant opportunities within specific application areas. Data collection services, encompassing remote sensing and GPS technologies, constitute a substantial segment, while data analysis services, leveraging sophisticated algorithms and modelling techniques, are experiencing rapid growth. Geographically, North America and Europe currently hold the largest market shares, although the Asia-Pacific region is projected to witness the fastest growth due to increasing infrastructure development and technological advancements. However, challenges remain, including data security concerns, the need for skilled professionals, and the high initial investment costs associated with implementing sophisticated geospatial systems. Despite these constraints, the overall market trajectory indicates a promising future for geospatial services, with continued growth driven by technological innovation and the ever-increasing reliance on location-based information across various industries.

The digital filing is created from urban planning announcement data provided by the urban development bureau. The fields include number, administrative district, use zone, zone abbreviation, urban planning name, establishment date, area, building coverage ratio, floor area ratio, maximum volume ratio, urban planning area, detailed planning area, remarks, drawing revision date, publication document number, and project name.

Urban spatial metrics for U.S. Metropolitan Statistical Areas (MSAs), from 1910 to 2010 in 5-year intervals, describing size, density, shape and structure of urban spaces within 2010 MSA boundaries.This dataset represents supplementary information to the publicationUhl, J.H., Connor, D.S., Leyk, S. et al. A century of decoupling size and structure of urban spaces in the United States. Commun Earth Environ 2, 20 (2021). https://doi.org/10.1038/s43247-020-00082-7Source data: Historical Settlement Data Compilation for the United States (HISDAC-US), see https://dataverse.harvard.edu/dataverse/hisdacus.

Geospatial Analytics System Market Outlook

The global geospatial analytics system market size was valued at approximately USD 67 billion in 2023 and is projected to reach around USD 158 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.1% during the forecast period. The growth of this market is being driven by significant advancements in geospatial technologies coupled with an increasing demand for spatial data across various industries.

One of the primary growth factors for the geospatial analytics system market is the rapid adoption of Internet of Things (IoT) devices, which generate vast amounts of location-based data. This data is crucial for analytics systems that rely on geospatial information to provide insights. The proliferation of smartphones and connected devices has further accelerated the demand for geospatial analytics, as these devices generate continuous streams of geolocation data that can be analyzed for various applications such as urban planning, transportation, and disaster management.

Another key driver for the market is the increasing government initiatives aimed at improving national infrastructure and public safety. Governments worldwide are investing heavily in geospatial analytics to enhance urban planning, monitor environmental changes, and manage natural disasters effectively. The integration of geospatial analytics with other emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is also contributing to market growth by enabling more accurate predictions and real-time decision-making capabilities.

The growing awareness about climate change and its adverse effects is also playing a crucial role in the expansion of the geospatial analytics market. As climate change continues to pose significant risks, there is an increasing need for advanced systems that can monitor environmental changes and help in climate change adaptation strategies. Geospatial analytics systems provide crucial insights that aid in understanding and mitigating the impacts of climate change, thus driving their adoption across sectors like agriculture, forestry, and coastal management.

Regionally, North America holds a significant share of the geospatial analytics system market due to the high adoption rate of advanced technologies and substantial investments in infrastructure development. The presence of major market players and extensive research and development activities also contribute to the region's market dominance. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increased government spending on smart city projects, and rising awareness about the benefits of geospatial analytics.

The role of Geographic Information System (GIS) Tools in the geospatial analytics market cannot be overstated. These tools are essential for capturing, storing, analyzing, and managing spatial and geographic data. They enable organizations to visualize complex data sets in a manner that is both accessible and actionable. By integrating GIS tools into their operations, businesses and governments can enhance decision-making processes, optimize resource allocation, and improve overall efficiency. The increasing sophistication of GIS tools, coupled with their ability to integrate with other technologies such as AI and IoT, is driving their adoption across various sectors. This integration facilitates the development of comprehensive solutions that address specific industry needs, from urban planning to environmental monitoring.

Component Analysis

The geospatial analytics system market can be segmented by component into software, hardware, and services. The software segment holds the largest market share, driven by the increasing need for advanced analytical tools and platforms that can process and visualize geospatial data effectively. The software solutions include Geographic Information Systems (GIS), remote sensing software, and spatial analytics tools that enable users to analyze and interpret spatial data for various applications.

The hardware segment, which includes GPS devices, sensors, and other geospatial data collection tools, is also experiencing significant growth. The demand for advanced hardware components is increasing as more sophisticated and accurate data collection methods are required for various applications such as disaster management, urban plann

The Geographic Information System (GIS) market is experiencing robust growth, projected to reach $5.15 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 20.55% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing urbanization and the need for efficient urban planning are creating significant demand for GIS solutions. Furthermore, advancements in technology, particularly in cloud computing and artificial intelligence (AI), are enhancing GIS capabilities, leading to wider adoption across various sectors. The integration of GIS with other technologies like IoT (Internet of Things) and big data analytics is enabling more sophisticated spatial analysis and decision-making. Industries like transportation, utilities, and agriculture are leveraging GIS for improved asset management, infrastructure planning, and precision farming. The market is segmented by component (software, data, services) and deployment (on-premise, cloud), with the cloud-based deployment model experiencing faster growth due to its scalability and cost-effectiveness. The competitive landscape is characterized by a mix of established players like Esri, Autodesk, and Trimble, and emerging technology providers, creating a dynamic market with significant innovation. However, factors like high initial investment costs and the need for skilled professionals to implement and manage GIS systems pose challenges to market growth. Despite these restraints, the long-term outlook for the GIS market remains positive. The increasing availability of geospatial data, coupled with declining hardware costs and improvements in user interfaces, is making GIS technology more accessible to a wider range of users. The integration of GIS into mobile applications and the rise of location-based services further broaden the market's potential. Government initiatives promoting smart cities and digital infrastructure development are also contributing to market growth. The North American region, particularly the United States, currently holds a significant market share due to early adoption and a robust technology ecosystem. However, other regions, especially in Asia-Pacific and Europe, are experiencing rapid growth, driven by increasing infrastructure investments and the adoption of advanced technologies. Future growth will be influenced by continued technological innovation, the availability of skilled workforce, and government regulations related to geospatial data management.

Aurora:GeoStudio® is a leading geospatial analysis platform that excels in supporting Geographic Data, offering detailed and accurate information associated with specific locations on the Earth’s surface. Geographic data includes various attributes like coordinates, addresses, and place names, which can be visualized and analyzed through maps, charts, or databases. This data is crucial for understanding spatial patterns, relationships, and trends, enabling informed decision-making across various fields.

Core Features:

  1.    Comprehensive Mapping:
  •    Aurora:GeoStudio® provides access to a wide range of customizable maps, including variants from Google, Esri, Open, and Stamen. These maps offer diverse perspectives and detailed geographic information, supporting various analytical needs.
  2.    Satellite Imagery:
  •    The platform integrates high-resolution satellite imagery, allowing users to analyze land use, environmental changes, and urban development. Satellite data provides a bird’s-eye view of specific areas, enhancing the understanding of geographical contexts.
  3.    GPS Coordinates:
  •    Aurora:GeoStudio® supports precise GPS coordinates, enabling accurate location tracking and geospatial analysis. This feature is essential for applications requiring exact positioning, such as navigation, transportation planning, and asset management.
  4.    Geospatial Datasets:
  •    The platform offers extensive geospatial datasets that include information on demographics, land use, environmental conditions, and more. These datasets can be overlaid on maps to provide a richer context for analysis and decision-making.
  5.    Area Usage and Visit Analytics:
  •    Aurora:GeoStudio® provides detailed Area Usage and Visit analytics, helping users understand how different areas are utilized and frequented. This data is vital for urban planning, infrastructure management, and optimizing public spaces.
  6.    Home or Bed-Down Location Data:
  •    The platform can identify home or bed-down locations based on activity patterns, aiding in demographic analysis and urban planning.
  7.    Historic Playback:
  •    Aurora:GeoStudio® offers Historic Playback of geographic data, allowing users to review changes and trends over time. This feature is crucial for assessing the impact of past events and planning future developments.

Applications:

  1.    Urban Planning:
  •    Geographic data supports urban planners in designing more efficient and sustainable cities. By analyzing spatial patterns and relationships, planners can optimize land use, transportation networks, and public services.
  2.    Environmental Management:
  •    Geographic data is essential for monitoring and managing environmental conditions. It helps in tracking changes in land use, assessing the impact of natural disasters, and planning conservation efforts.
  3.    Transportation:
  •    Transportation planners use geographic data to optimize routes, improve infrastructure, and enhance public transit systems. Accurate geographic data ensures efficient and effective transportation planning.
  4.    Disaster Management:
  •    Geographic data plays a critical role in disaster management by providing information on vulnerable areas, assessing damage, and coordinating response efforts. It helps in planning evacuation routes and resource allocation.
  5.    Navigation:
  •    Geographic data is fundamental for navigation systems, providing accurate maps, routes, and location-based services. It enhances the user experience by offering precise and reliable navigation information.

Aurora:GeoStudio® provides robust support for Geographic Data, making it an indispensable tool for urban planning, environmental management, transportation, and more. By integrating comprehensive mapping, satellite imagery, GPS coordinates, and geospatial datasets, the platform offers valuable insights into spatial patterns, relationships, and trends. Features like Area Usage and Visit analytics, Home or Bed-Down Location data, and Historic Playback further enhance its analytical capabilities. Aurora:GeoStudio® empowers users to make informed decisions, optimize operations, and develop strategic initiatives based on accurate and detailed geographic data. This comprehensive understanding of geographic dynamics leads to better planning, resource management, and overall efficiency in various fields.

The Spanish geospatial analytics market is estimated to be valued at $0.48 million in 2025, and is projected to grow at a CAGR of 9.03% from 2025 to 2033. The growth of the market is primarily driven by the increasing adoption of geospatial solutions for decision-making processes across various industries and government organizations. The growing need for accurate and timely information for effective planning, operations, and resource management is further fueling the demand for advanced geospatial analysis capabilities. Key trends in the Spanish geospatial analytics market include the integration of artificial intelligence (AI) and machine learning (ML) for advanced data analysis, the adoption of cloud-based geospatial platforms for seamless data sharing and access, and the proliferation of open data initiatives. The increasing availability of high-quality data from sources like satellite imagery, aerial surveys, and IoT sensors is providing opportunities for developing more comprehensive and data-driven insights. However, data privacy concerns and the need for skilled professionals with expertise in geospatial technologies represent notable restraints in the market. Recent developments include: November 2023 - Hexagon’s Manufacturing Intelligence branch unveiled Nexus Connected Worker, a collection of manufacturing software solutions that links employees to up-to-the-minute data for informed insights and reporting on operations, maintenance, quality, and audits. The suite offers strong integration with enterprise systems and serves as a hub for digital depictions of assets, processes, and production sites to aid in real-time decision-making.. Key drivers for this market are: Increase in Adoption of Smart City Development and Urban Planning, Introduction of 5G to Boost Market Growth. Potential restraints include: High Costs and Operational Concerns Associated with Geospatial Analytics. Notable trends are: Increase in Adoption of Smart City Development and Urban Planning.

Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.

The triad of host, agent, and environment has become a widely accepted framework for understanding infectious diseases and human health. While modern medicine has traditionally focused on the individual, there is a renewed interest in the role of the environment. Recent studies have shifted from an early-twentieth-century emphasis on individual factors to a broader consideration of contextual factors, including environmental, climatic, and social settings as spatial determinants of health. This shifted focus has been particularly relevant in the context of the COVID-19 pandemic, where the built environment in urban settings is increasingly recognized as a crucial factor influencing disease transmission. However, operationalizing the complexity of associations between the built environment and health for empirical analyses presents significant challenges. This study aims to identify key caveats in the operationalization of spatial determinants of health for empirical analysis and proposes guiding principles for future research. We focus on how the built environment in urban settings was studied in recent literature on COVID-19. Based on a set of criteria, we analyze 23 studies and identify explicit and implicit assumptions regarding the health-related dimensions of the built environment. Our findings highlight the complexities and potential pitfalls, referred to as the ‘spatial trap,' in the current approaches to spatial epidemiology concerning COVID-19. We conclude with recommendations and guiding questions for future studies to avoid falsely attributing a built environment impact on health outcomes and to clarify explicit and implicit assumptions regarding the health-related dimensions.

Download UrbanTreeCanopy_2019.zip. The following information was produced from the 2019 Urban Tree Canopy Assessment for Jefferson County, KY sponsored by Trees Louisville. It is based on 2019 LOJIC Base Map data. It includes shapefiles and rasters. The study was performed by the University of Vermont Spatial Analysis Lab.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.

The size of the India Geospatial Analytics market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 14.82% during the forecast period.Geospatial analytics in the India market uses GIS and other technologies to analyze spatial data and provide valuable insights. Actually, geospatial analytics is a practice, which involves gathering, processing, and interpreting data on locations and their attributes that go with them. This includes geographic coordinates, images, or sensor readings. It helps business and governments make informed decisions regarding resource management, urban planning, transportation, environment monitoring, and disaster response. Increasing government initiatives, growth in private sector adoption, and the advancements of AI and machine learning are making the Indian market more and more driven forward. Recent developments include: January 2023: Eris India, a company providing Geographic Information System (GIS) software and solutions, announced that the company is developing a policy map to offer data to help states and policymakers in decision-making. The Policy Maps have been designed to provide meaningful insights into various government functions., July 2022: Google announced a new partnership in India with local authorities and organizations in order to provide customized features for the diverse needs of the people in the country. Also, Google is building helpful maps that provide more visual and accurate navigation.. Key drivers for this market are: Increasing Demand of Location Based Service, Growing Availability of Spatial Data. Potential restraints include: High Initial Cost in Implementing Geospatial Analytics Solutions. Notable trends are: Increasing Demand of Location Based Service.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

The Geospatial Imagery Analytics Marketsize was valued at USD 11.88 USD Billion in 2023 and is projected to reach USD 83.39 USD Billion by 2032, exhibiting a CAGR of 32.1 % during the forecast period.Geospatial analytics gathers, manipulates, and displays geographic information system (GIS) data and imagery including GPS and satellite photographs. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. geospatial visualization enables businesses to better understand complex information and make informed decisions. They can quickly see patterns and trends and assess the impact of different variables by visualizing data in a spatial context. The field encompasses several techniques and algorithms, such as spatial interpolation, spatial regression, spatial clustering, and spatial autocorrelation analysis, which help extract insights from various geospatial data sources. The growing adoption of location-based services in various industries, including agriculture, defense, and urban planning, is driving the demand for geospatial imagery analytics. Recent developments include: August 2023: onX, a digital navigation company, partnered with Planet Labs PBC, a satellite imagery provider, to introduce a new feature called ‘Recent Imagery’. This feature offers onX app users updated satellite imagery maps every two weeks, enhancing the user experience across onX Hunt, onX Offroad, and onX Backcountry apps. This frequent data update helps outdoor enthusiasts access real-time information for safer and more informed outdoor activities., August 2023: Quant Data & Analytics, a provider of data products and enterprise solutions for real estate and retail, partnered with Satellogic Inc. to utilize Satellogic’s high-resolution satellite imagery to enhance property technology in Saudi Arabia and the Gulf region., April 2023: Astraea, a spatiotemporal data and analytics platform, introduced a new ordering service that grants customers scalable access to top-tier commercial satellite imagery from providers such as Planet Labs PBC and others., May 2022: Satellogic Inc. established a partnership with UP42. This geospatial developer platform enables direct access to Satellogic’s satellite tasking capabilities, including high-resolution multispectral and wide-area hyperspectral imagery, through the UP42 API-based platform., April 2022: TomTom International BV, a geolocation tech company, broadened its partnership with Maxar Technologies, a space solution provider. This expansion involves integrating high-resolution global satellite imagery from Maxar’s Vivid imagery base maps into TomTom’s product lineup, enhancing their visualization solutions for customers.. Key drivers for this market are: Growing Demand for Location-based Insights across Diverse Industries to Fuel Market Growth. Potential restraints include: Complexity and Cost Associated with Data Acquisition and Processing May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

MCGD_Data_V2 contains all the data that we have collected on locations in modern China. Altogether there are 466,162 entries. The data include the name of locations and their variants in Chinese, pinyin, and any recorded transliteration; the name of the province in Chinese and in pinyin; Province ID; the latitude and longitude; the Name ID and Location ID. The Name IDs all start with N followed by seven digits, except for locations in Taiwan that start with "T" (data from Geonames). This is the internal ID system of MCGD. Locations IDs that start with "DH" are data points extracted from China Historical GIS (Harvard University); those that start with "D" are locations extracted from the data points in Geonames; those that have only digits (8 digits) are data points we have added from various map sources.

One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates.