The Cloud GIS market is booming, projected to reach $1513.8 million by 2025 with a 17.2% CAGR. Discover key drivers, trends, and leading companies shaping this rapidly evolving sector. Explore market forecasts, regional insights, and the future of cloud-based geographic information systems.

Table contains count and percentage of households in the county that have at least a smartphone but have no other type of computing device at home. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table S2801; data accessed on August 23, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographytotal_HH (Numeric): Total householdssmartphone_no_oth_compt (Numeric): Number of households with smartphone with no other type of computing devicepct_smartphone_no_oth_compt (Numeric): Percent of households with smartphone with no other type of computing device

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 Computer Vision in Geospatial Imagery market is experiencing robust growth, driven by increasing demand for accurate and efficient geospatial data analysis across various sectors. Advancements in artificial intelligence (AI), deep learning, and high-resolution imaging technologies are fueling this expansion. The market's ability to extract valuable insights from aerial and satellite imagery is transforming industries such as agriculture, urban planning, environmental monitoring, and defense. Applications range from precision agriculture using drone imagery for crop health monitoring to autonomous vehicle navigation and infrastructure inspection using high-resolution satellite data. The integration of computer vision with cloud computing platforms facilitates large-scale data processing and analysis, further accelerating market growth. We estimate the 2025 market size to be approximately $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is expected to continue, driven by increasing adoption of advanced analytics and the need for real-time geospatial intelligence. Several factors contribute to this positive outlook. The decreasing cost of high-resolution sensors and cloud computing resources is making computer vision solutions more accessible. Furthermore, the growing availability of large datasets for training sophisticated AI models is enhancing the accuracy and performance of computer vision algorithms in analyzing geospatial data. However, challenges remain, including data privacy concerns, the need for robust data security measures, and the complexity of integrating diverse data sources. Nevertheless, the overall market trend remains strongly upward, with significant opportunities for technology providers and users alike. The key players listed—Alteryx, Google, Keyence, and others—are actively shaping this landscape through innovative product development and strategic partnerships.

The Geographic Information System (GIS) software market is projected to expand significantly, with a market size of XXX million in 2025 and a CAGR of XX% during the forecast period of 2025-2033. The growing adoption of GIS technology across various industries, including urban planning, environmental management, and transportation, is driving market growth. Additionally, the increasing availability of spatial data and the advancements in cloud computing and mobile GIS are further fueling market expansion. Key trends in the GIS software market include the rise of web-based GIS platforms, the integration of artificial intelligence (AI) and machine learning (ML) capabilities, and the growing popularity of open-source GIS solutions. North America and Europe are the major markets for GIS software, while the Asia Pacific region is expected to witness significant growth in the coming years. Major players in the GIS software market include Esri, Hexagon, Pitney Bowes, SuperMap, Bentley Systems, GE, GeoStar, and Zondy Cyber Group. These companies offer a wide range of GIS software products and services to meet the varying needs of different industries and organizations.

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

Cloud GIS Market size was valued at USD 890.81 Million in 2024 and is projected to reach USD 2298.38 Million by 2032, growing at a CAGR of 14.5% from 2026 to 2032.

Key Market Drivers

• Increased Adoption of Cloud Computing: Cloud computing provides scalable resources that can be adjusted based on demand, making it easier for organizations to manage and process large GIS datasets. The pay-as-you-go pricing models of cloud services reduce the need for significant upfront investments in hardware and software, making GIS more accessible to small and medium-sized enterprises.

• Growing Need for Spatial Data Integration: The ability to integrate and analyze large volumes of spatial and non-spatial data helps organizations make more informed decisions. The proliferation of Internet of Things (IoT) devices generates massive amounts of spatial data that can be processed and analyzed using Cloud GIS.

Discover the booming Cloud Native GIS Platform market! This in-depth analysis reveals a $5 billion market in 2025, projected to reach $15 billion by 2033 at a 15% CAGR. Explore key drivers, trends, regional breakdowns, and leading companies shaping this transformative technology.

The GIS Software market is booming, projected to reach $45 billion by 2033 with a CAGR of 12%! Discover key trends, drivers, and restraints shaping this dynamic sector, including the impact of cloud computing, AI, and IoT. Leading companies and regional insights are analyzed in this comprehensive market report.

This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the paper gives some examples from simple and advanced GIS and spatial data analysis highlighting both that big data systems have been around long before the current hype of big data and that they follow some design principles which are inevitable for spatial data including distributed data structures and messaging, which are, however, incompatible with the popular MapReduce paradigm. Throughout this discussion, the need for a replacement or extension of the MapReduce paradigm for spatial data is derived. This paradigm should be able to deal with the imperfect data locality inherent to spatial data hindering full independence of non-trivial computational tasks. We conclude that more research is needed and that spatial big data systems should pick up more concepts like graphs, shortest paths, raster data, events, and streams at the same time instead of solving exactly the set of spatially separable problems such as line simplifications or range queries in manydifferent ways.

Introduction

The Cloud GIS market is on a trajectory of robust growth, projected to reach a value of USD 3,303.1 Million by 2033, from USD 891 Million in 2023, with a compound annual growth rate (CAGR) of 14% during the forecast period spanning from 2024 to 2033. Cloud GIS, a technology leveraging cloud computing to manage geographic information system (GIS) data, is witnessing this expansion due to various factors, including the rising demand for real-time data access, the scalability of cloud services, and ongoing digital transformation efforts across industries.

The Cloud Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing adoption of cloud technologies across various sectors. This growth can be attributed to several factors, including the scalability, flexibility, and cost-effectiveness of cloud-based solutions. These systems enable users to store, manage, and analyze geographical data without substantial investment in hardware infrastructure, making GIS tools accessible to a broader range of industries and organizations.

However, the market faces challenges, notably concerns regarding data security and privacy. As geographic data often includes sensitive information, the potential for data breaches makes some organizations hesitant to adopt cloud-based GIS solutions. Moreover, the reliance on continuous internet connectivity can pose operational challenges in regions with unstable internet services.

Despite these challenges, the Cloud GIS market presents substantial opportunities for new entrants. The ongoing digital transformation and the expanding need for location-based data across sectors like urban planning, environmental monitoring, and transportation logistics create a fertile ground for innovative solutions. New players can differentiate themselves by offering enhanced security features, customized solutions, and robust offline capabilities to address existing market gaps.

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to, or the know how to, take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the storage and sharing of a broad class of hydrologic data including time series, geographic features and rasters, multidimensional space-time data and structured collections of data representing river geometry. Web service tools and a python client library provide researchers with access to high performance computing resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This talk will illustrate web and client based use of data services that support the delineation of watersheds to define a modeling domain, then extract terrain and land use information to automatically configure the inputs required for hydrologic models. These services support the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation and generation of hydrology-based terrain information such as wetness index and stream networks. These services also support the derivation of inputs for the Utah Energy Balance snowmelt model used to address questions such as how climate, land cover and land use change may affect snowmelt inputs to runoff generation. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Presentation at Kansas University GIS Days November 18, 2015

The global Geographic Information System (GIS) solutions market size was valued at USD XX million in 2025 and is projected to expand at a CAGR of XX % over the forecast period, reaching USD XXX million by 2033. The growing adoption of GIS solutions across various industries, such as agriculture, oil & gas, architecture, engineering and construction, transportation, mining, government, healthcare, and others, is driving market growth. The increasing need for accurate and timely geospatial data for decision-making, along with the advancements in cloud computing, artificial intelligence (AI), and machine learning (ML), are key trends contributing to market expansion. However, data security concerns and the high cost of implementation and maintenance may restrain market growth to some extent. Key players in the GIS solutions market include ESRI, Hexagon, Pitney Bowes, SuperMap, Bentley System, GE, GeoStar, Zondy Cyber Group, Caliper, Hitachi Solutions, and KCI. North America holds a significant share of the market due to the early adoption of GIS solutions and the presence of major players. Asia Pacific is expected to witness substantial growth over the forecast period owing to the increasing infrastructure development and urbanization in the region.

Explore the dynamic Cloud Native GIS Platform market, driven by advanced land surveying, environmental monitoring, and smart city initiatives. Discover market size, CAGR, key drivers, and forecast to 2033.

This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

The Geographic Information Systems (GIS) Platform market is booming, projected to reach $2908.4 million by 2025, with a CAGR of 4.9%. Discover key trends, drivers, and regional insights shaping this dynamic market, including the impact of cloud computing, AI, and IoT. Explore market segmentation and leading companies.

Discover the booming spatial analysis software market! Our in-depth analysis reveals a $5 billion market in 2025, projected to reach $15 billion by 2033, driven by AI, cloud computing, and rising demand for location intelligence. Explore market trends, segmentation, key players, and regional growth.

This paper underlines the task characteristics of the emergency management life cycle. Moreover, the characteristics of three ubiquitous technologies including RFID, handheld computers and GIS are discussed and further used as a criterion to evaluate their potential for emergency management tasks. Built on a rather loose interpretation of Task-technology Fit model, a conceptual model presented in this paper advocates that a technology that offers better features for task characteristics is more likely to be adopted in emergency management. Empirical findings presented in this paper reveal the significance of task characteristics and their role in evaluating the suitability of three ubiquitous technologies before their actual adoption in emergency management.

Mobile GIS Data Collection Software Market Outlook

According to our latest research, the global Mobile GIS Data Collection Software market size reached USD 2.14 billion in 2024, and is anticipated to grow at a robust CAGR of 13.7% during the forecast period, reaching approximately USD 6.42 billion by 2033. This strong growth trajectory is primarily driven by the increasing demand for real-time geospatial data across multiple industries, the proliferation of mobile devices, and the integration of advanced technologies such as IoT and AI into GIS solutions. As organizations globally seek to enhance operational efficiency and decision-making capabilities, the adoption of mobile GIS data collection software continues to accelerate, reshaping the landscape of field data management and spatial analytics.

One of the pivotal growth factors for the Mobile GIS Data Collection Software market is the rapid digital transformation across industries such as utilities, transportation, agriculture, and government. Organizations are increasingly leveraging geospatial data to streamline field operations, optimize resource allocation, and improve asset management. The shift towards digitized workflows has created a surge in demand for mobile GIS solutions that enable real-time data capture, analysis, and sharing from remote locations. Furthermore, the growing emphasis on smart infrastructure and sustainable urban planning has amplified the need for accurate, up-to-date geographic information, positioning mobile GIS software as a critical tool in supporting these initiatives. The convergence of cloud computing, 5G connectivity, and mobile technologies is further enhancing the capabilities and accessibility of GIS platforms, making them indispensable for modern enterprises.

Another significant driver is the increasing adoption of IoT and sensor technologies, which are generating vast volumes of spatial data that require efficient collection, processing, and analysis. Mobile GIS data collection software enables seamless integration with IoT devices, allowing for automated data acquisition and real-time monitoring of assets, environmental conditions, and infrastructure. This capability is particularly valuable in sectors like environmental monitoring, utilities management, and agriculture, where timely and accurate geospatial data is essential for informed decision-making. Additionally, advancements in artificial intelligence and machine learning are empowering GIS software to deliver predictive analytics, anomaly detection, and advanced visualization, further expanding the application scope and value proposition of mobile GIS solutions.

The market is also benefiting from the increasing focus on regulatory compliance and safety standards, particularly in industries such as oil and gas, construction, and transportation. Mobile GIS data collection software facilitates compliance by providing accurate and auditable records of field activities, asset inspections, and environmental assessments. Moreover, the growing need for disaster management, emergency response, and public health surveillance is driving government agencies to invest in robust GIS platforms that support rapid data collection and situational awareness. As a result, vendors are continuously innovating to offer user-friendly, scalable, and secure solutions that cater to the evolving needs of diverse end-users, further fueling market expansion.

The integration of Mobile Mapping System technology into mobile GIS solutions is revolutionizing the way geospatial data is collected and analyzed. By utilizing vehicles equipped with advanced sensors and cameras, Mobile Mapping Systems enable the rapid and accurate capture of geospatial data across large areas. This technology is particularly beneficial for urban planning, infrastructure management, and environmental monitoring, where timely and precise data is crucial. As industries strive to enhance their operational capabilities, the adoption of Mobile Mapping Systems is becoming increasingly prevalent, providing a competitive edge through improved data accuracy and efficiency.

Regionally, North America currently dominates the Mobile GIS Data Collection Software market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology providers, high adoption rates of digital soluti