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 ...

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:

Location Analytics Market Outlook

The global location analytics market size was valued at approximately USD 18.2 billion in 2023 and is projected to reach around USD 65.4 billion by 2032, growing at a robust CAGR of 15.2% during the forecast period from 2024 to 2032. This impressive growth can be attributed to the escalating demand for spatial data and analytical solutions across various industries, aiming to enhance decision-making processes and optimize business operations.

One of the primary growth factors driving the location analytics market is the increasing use of smartphones and the proliferation of Internet of Things (IoT) devices. These advancements have led to an explosion of location-based data, offering businesses the capability to analyze real-time information and make informed decisions. Moreover, the integration of location analytics with Geographic Information System (GIS) technologies has further enhanced the scope of applications, making it indispensable for sectors such as retail, transportation, and healthcare.

Another significant growth driver is the rising need for businesses to gain a competitive edge through location-based insights. Companies are leveraging location analytics to understand customer behaviors, optimize supply chains, and enhance marketing strategies. For instance, retail businesses are increasingly using location analytics to determine ideal store locations, manage inventory efficiently, and provide personalized customer experiences. Similarly, in the transportation and logistics sector, location analytics is crucial for route optimization, fleet management, and reducing operational costs.

The emergence of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is also catalyzing the growth of the location analytics market. By incorporating AI and ML algorithms, location analytics solutions can offer predictive insights and trend analyses, enabling businesses to anticipate market changes and act proactively. This technological advancement is particularly beneficial for risk management and emergency response applications, where timely and accurate data is critical.

In this context, Points-of-Interest (POI) Data Solutions have emerged as a crucial component in the location analytics ecosystem. POI data provides detailed information about specific locations, such as businesses, landmarks, and other significant places, which can be leveraged by companies to enhance their spatial analysis capabilities. By integrating POI data, businesses can gain deeper insights into consumer behavior, optimize location-based services, and improve decision-making processes. This data is particularly valuable for sectors like retail and hospitality, where understanding the proximity and accessibility of various points of interest can significantly impact customer engagement and operational efficiency. As the demand for precise and comprehensive location data continues to grow, POI Data Solutions are set to play a pivotal role in advancing the capabilities of location analytics platforms.

Regionally, North America is expected to dominate the location analytics market due to the early adoption of advanced technologies and the presence of major market players in the region. Additionally, the Asia Pacific region is anticipated to witness significant growth, driven by the rapid urbanization, increasing smartphone penetration, and government initiatives promoting smart city projects. Europe is also poised for substantial growth, supported by stringent data regulations and the growing demand for spatial data analytics in various industries.

Component Analysis

The location analytics market can be segmented by component into software and services. The software segment includes tools and platforms that facilitate spatial data analysis, while the services segment encompasses consulting, integration, and maintenance services. The software segment is expected to hold a significant market share due to the increasing adoption of location analytics software solutions by enterprises to gain actionable insights from spatial data. These software solutions are designed to integrate seamlessly with existing business systems, providing users with real-time data analysis and visualization capabilities.

Location analytics software is further categorized into desktop, mobile, and web-based platforms. Desktop solutions are traditionally used for comprehensive geospati

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.

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.

Geospatial Analytics Software Market Outlook

The global geospatial analytics software market size is projected to grow from USD 50.1 billion in 2023 to USD 114.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.5%. This remarkable growth is largely driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, agriculture, transportation, and disaster management. The surge in the utilization of geospatial data for strategic decision-making, coupled with advancements in technology such as artificial intelligence (AI) and big data analytics, plays a pivotal role in propelling market growth.

One of the key growth factors of the geospatial analytics software market is the rapid digital transformation occurring globally. Governments and enterprises are increasingly recognizing the value of geospatial data in enhancing operational efficiency and strategic planning. The rise in smart city initiatives across the world has bolstered the demand for geospatial analytics, as cities leverage these technologies to optimize infrastructure, manage resources, and improve public services. Additionally, the integration of AI and machine learning with geospatial analytics has enhanced the accuracy and predictive capabilities of these systems, further driving their adoption.

Another significant driver is the growing need for disaster management and climate change adaptation. As the frequency and intensity of natural disasters increase due to climate change, there is a heightened demand for geospatial analytics to predict, monitor, and mitigate the impact of such events. Geospatial software aids in mapping hazard zones, planning evacuation routes, and assessing damage post-disaster. This capability is crucial for governments and organizations involved in disaster management and mitigation, thereby boosting the market growth.

The transportation and logistics sector is also a major contributor to the growth of the geospatial analytics software market. The advent of autonomous vehicles and the continuous evolution of logistics and supply chain management have heightened the need for precise geospatial data. Geospatial analytics enables real-time tracking, route optimization, and efficient fleet management, which are critical for the smooth operation of transportation systems. This trend is expected to continue, driving the demand for geospatial analytics solutions in transportation and logistics.

On a regional level, North America is anticipated to dominate the geospatial analytics software market, driven by technological advancements and substantial investments in geospatial technologies. The presence of major market players and the high adoption rate of advanced technologies in sectors such as defense, agriculture, and urban planning contribute to this dominance. However, the Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, government initiatives for smart cities, and increasing investments in infrastructure development.

GIS Software plays a crucial role in the geospatial analytics software market, offering powerful tools for data visualization, spatial analysis, and geographic mapping. As organizations across various sectors increasingly rely on geospatial data for strategic decision-making, GIS Software provides the necessary infrastructure to manage, analyze, and interpret this data effectively. Its integration with other technologies such as AI and machine learning enhances its capabilities, enabling more accurate predictions and insights. This makes GIS Software an indispensable component for industries like urban planning, agriculture, and transportation, where spatial data is pivotal for optimizing operations and improving outcomes. The growing demand for GIS Software is a testament to its importance in driving the geospatial analytics market forward.

Component Analysis

The geospatial analytics software market is segmented into software and services when considering components. The software segment includes comprehensive solutions that integrate various geospatial data types and provide analytical tools for mapping, visualization, and data processing. This segment is expected to hold the largest market share due to the increasing adoption of these solutions in various industries for efficient data management and decision-making. The continuous advancements in software capabilities, such as the inclusion of AI and machine learning algorithms

Discover a simple method and approach that guides everything you do with spatial analysis. Learn best practices, explore case studies, and get workflows to help you more successfully analyze your data.

Mixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Animal mortality on roads is one of the main concerns on wildlife conservation. Due to their habitat requirements, amphibians became one of the most commonly road-killed group and this may affect their population viability. Implementation of mitigation measures may overcome the problem. However, due to the extensive road network, their application is very expensive and required a better understanding in where they should be implemented. Mortality hotspots can be identified as clusters of road-killed records) using GIS (Geographic Information Systems). Although there are several statistical methods available, it is lacking a comparison analysis of them in order to understand their pros and contras. The aim of this study was to analyse possible differences between global, multi-scale and local spatial analysis methods in defining hotspots using amphibian road fatality data collected in northern Portugal country roads. We calculated the Nearest neighbor index, Morans I and Getis-ord General in order to compare the global clustering of points in seven sampled roads, and three were identified as clustered. We used Ripley K-function, Ripley L-function and F function to calculate the best scale for Malo's equation and Kernel density analysis in detecting hotspots and we compared their detection performance with Local Indicators of Association (LISA) (i.e Local Moran's I and Getis-ord Gi). Three different GIS software applications were used: ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). Results showed the importance of using multidistance spatial cluster analysis to define the best scale for hotspot detection with Malo´s equation and Kernel density analysis. Here we also suggest the advantages of Local Indicators of Association (LISA) for detecting clusters with the contribution of each individual observation (Local Morans I and Getis-ord Gi).

This project evaluated ecosystem damage and recovery by developing a time series of habitat maps for the Rookery Bay National Estuarine Research Reserve. Habitat maps were created based on WorldView-2 and Landsat-8 satellite imagery from 2010-2018 using an automated technique and validated with a field campaign. Landsat images were mapped using the Support Vector Machine machine learning method...

The Greater Metro Manila Area is one of the world's megacities and is home to about 12 million people. It is located in a region at risk from earthquakes, volcanic eruptions, tropical cyclones, riverine flooding, landslides and other natural hazards. Major flooding affected the Greater Metro Manila Area in September 2009 following the passage of Typhoon Ketsana (known locally as Typhoon Ondoy). Following this event, the Australian Aid Program supported Geoscience Australia to undertake a capacity building project with its partner agencies in the Government of the Philippines. The output of this project has been a series of risk information products developed by agencies in the Collective Strengthening of Community Awareness for Natural Disasters (CSCAND) group. These products quantify the expected physical damage and economic loss to buildings caused by earthquakes, tropical cyclone severe wind and riverine flooding across the Greater Metro Manila Area.

Spatial data is a key input to the development of hazard models and information on exposure, or the 'elements at risk'. The development of a spatially enabled exposure database was a crucial element in the construction of risk information products for the Greater Metro Manila Area. The database provides one central repository to host consistent information about the location, size, type, age, residential population and structural characteristics of buildings within the area of interest. Unique spatial analysis techniques were employed to quantify and record important aspects of the built environment, for inclusion in the database. The process of exposure data development within the Greater Metro Manila Area, including a new application developed by Geoscience Australia for estimating the geometric characteristics of buildings from high resolution elevation data and multi-spectral imagery, will be presented.

To better understand community-level impacts of the built environmental quality on residents with less economic resources to acquire food, it is fruitful to combine qualitative and quantitative approaches to the investigation. We explored how the level of spatial accessibility in communities change if we incorporate even a few factors of barriers on journey to food voiced in a Photovoice study. The resulting population coverage by food outlets was dramatically reduced in both rural and urban communities, suggesting that the usual proximity-based spatial analysis likely grossly underestimate the population at risk of lacking access to food. Therefore, a ‘real’ spatial accessibility can only be understood by incorporating factors of barriers to get to food outlets, informed by the insights of community members.

Node of the Institute of Statistics and Cartography of Andalusia. Regional Government of Andalusia. WMS Population Mesh Service. Integrated in the Spatial Data Infrastructure of Andalusia following the guidelines of the Statistical and Cartographic System of Andalusia. WMS map service of spatial distribution of the population of Andalusia in cells of 250m x 250m. The information represented in these maps has been georeferenced from the location of the postal address where each of the inhabitants of Andalusia resides. To facilitate the representation of the information and to preserve statistical confidentiality, a regular mesh has been drawn with cells of 250 meters on the side, where all the information that corresponds in each case has been added. Information that could not be georeferenced has been estimated using spatial analysis techniques. On December 23, 2019, the demographic statistical information of the population data, corresponding to January 1, 2018, is presented. The website of the Institute of Statistics and Cartography of Andalusia offers a visualization service: "Spatial distribution of the population of Andalusia" for interactive consultation https://www.juntadeandalucia.es/institutodeestadisticaycartografia/distributionpob/index.htm

Geographic Information System (GIS) Analytics Market Outlook

The global Geographic Information System (GIS) Analytics market size is projected to grow remarkably from $9.1 billion in 2023 to $21.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. This substantial growth can be attributed to several factors such as technological advancements in GIS, increasing adoption in various industry verticals, and the rising importance of spatial data for decision-making processes.

The primary growth driver for the GIS Analytics market is the increasing need for accurate and efficient spatial data analysis to support critical decision-making processes across various industries. Governments and private sectors are investing heavily in GIS technology to enhance urban planning, disaster management, and resource allocation. With the world becoming more data-driven, the reliance on GIS for geospatial data has surged, further propelling its market growth. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with GIS is revolutionizing the analytics capabilities, offering deeper insights and predictive analytics.

Another significant growth factor is the expanding application of GIS analytics in disaster management and emergency response. Natural disasters such as hurricanes, earthquakes, and wildfires have highlighted the importance of GIS in disaster preparedness, response, and recovery. The ability to analyze spatial data in real-time allows for quicker and more efficient allocation of resources, thus minimizing the impact of disasters. Moreover, GIS analytics plays a pivotal role in climate change studies, helping scientists and policymakers understand and mitigate the adverse effects of climate change.

The transportation sector is also a major contributor to the growth of the GIS Analytics market. With the rapid urbanization and increasing traffic congestion in cities, there is a growing demand for effective transport management solutions. GIS analytics helps in route optimization, traffic management, and infrastructure development, thereby enhancing the overall efficiency of transportation systems. The integration of GIS with Internet of Things (IoT) devices and sensors is further enhancing the capabilities of traffic management systems, contributing to the market growth.

Regionally, North America is the largest market for GIS analytics, driven by the high adoption rate of advanced technologies and significant investment in geospatial infrastructure by both public and private sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the rapid urbanization, infrastructural developments, and increasing government initiatives for smart city projects. Europe and Latin America are also contributing significantly to the market growth owing to the increasing use of GIS in urban planning and environmental monitoring.

Component Analysis

The GIS Analytics market can be segmented by component into software, hardware, and services. The software segment holds the largest market share due to the continuous advancements in GIS software solutions that offer enhanced functionalities such as data visualization, spatial analysis, and predictive modeling. The increasing adoption of cloud-based GIS software solutions, which offer scalable and cost-effective options, is further driving the growth of this segment. Additionally, open-source GIS software is gaining popularity, providing more accessible and customizable options for users.

The hardware segment includes GIS data collection devices such as GPS units, remote sensing instruments, and other data acquisition tools. This segment is witnessing steady growth due to the increasing demand for high-precision GIS data collection equipment. Technological advancements in hardware, such as the development of LiDAR and drones for spatial data collection, are significantly enhancing the capabilities of GIS analytics. Additionally, the integration of mobile GIS devices is facilitating real-time data collection, contributing to the growth of the hardware segment.

The services segment encompasses consulting, implementation, training, and maintenance services. This segment is expected to grow at a significant pace due to the increasing demand for professional services to manage and optimize GIS systems. Organizations are seeking expert consultants to help them leverage GIS analytics for strategic decision-making and operational efficiency. Additionally, the growing complexity o

Aim Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions.  Location White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA.  Taxon High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata.  Methods We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional pot...

This project presents a comprehensive spatial analysis and species distribution modeling (SDM) of American beech (Fagus grandifolia), integrating a range of geospatial and statistical methods to assess both current habitat suitability and future distribution projections under various climate scenarios. The workflow began by merging selected ecoregions to delineate the study area and aligning all spatial datasets to a common coordinate reference system. Raster datasets—including presence data and the target group background data—were processed by resampling to a coarser resolution (1km²) for ecological consistency and then masked to the study area, ensuring that only relevant data were analyzed.

Subsequent data processing involved extracting non‐NA pixel values, applying k‑means clustering and Fisher’s natural breaks classification to determine optimal thresholds for categorizing the presence and background raster data. These thresholds served as critical cut-offs for distinguishing between different levels of basal area—a key ecological parameter. The derived threshold values were then used to filter raster data, facilitating the extraction of presence (and background) points which were subsequently converted into spatial vector (sf) objects.

A pivotal aspect of the analysis was the measurement of spatial autocorrelation. This was accomplished through the computation of semivariograms for both the basal area and target group datasets. By fitting a spherical model to the semivariogram, the study was able to quantify the range of spatial autocorrelation, thereby providing insights into the spatial structure inherent in the ecological data. This analysis not only confirmed the degree of spatial clustering but also informed subsequent modeling efforts by highlighting the spatial dependency in the dataset.

Environmental variables were then carefully selected and processed, with multicollinearity among predictors being assessed using the variance inflation factor (VIF) and visualized through correlation matrices. The refined set of environmental predictors was integrated into the BIOMOD2 framework, where the modeling data were formatted to include both species presence–absence data and environmental layers. A diverse array of algorithms—including ANN, CTA, FDA, GAM, GBM, GLM, MARS, MAXENT, MAXNET, RF, SRE, and XGBOOST—was employed to develop individual models using a k‑fold cross-validation approach, ensuring robust model evaluation based on metrics such as TSS, ROC, Kappa, and Boyce.

Ensemble modeling strategies were also implemented. Selected models (GAM, GBM, GLM, MARS, MAXNET, and RF) were combined using both algorithm-specific ensemble approaches and an “all models” ensemble strategy, where predictions were weighted (with options to incorporate basal area weights) and then aggregated to produce ensemble forecasts. Variable importance metrics and response curves were generated to elucidate the influence of each predictor on species distribution.

Looking forward, the project incorporated future climate scenarios (SSP1, SSP2, and SSP3) for multiple time periods (2011–2040, 2041–2070, and 2071–2100). For each scenario, ensemble forecasting was performed to predict potential shifts in habitat suitability. In addition, a Multivariate Environmental Similarity Surface (MESS) analysis was conducted to assess the reliability of model predictions when extrapolating to novel future conditions. Finally, a range size analysis was carried out by comparing current and future model predictions, quantifying changes in the potential distribution area of American beech.

In summary, this study integrates advanced spatial data processing, semivariogram-based spatial autocorrelation analysis, multivariate statistical techniques, and ensemble species distribution modeling to provide a robust evaluation of American beech habitat suitability and projected distribution shifts in response to climate change.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of cloud-based GIS solutions offering enhanced accessibility and scalability, the escalating need for precise spatial data analysis in urban planning and resource management, and the expanding application of GIS in geological exploration for efficient resource discovery and extraction. Furthermore, advancements in location-based services (LBS) and the integration of GIS with other technologies such as IoT and AI are creating new opportunities and driving market expansion. While the market size in 2025 is estimated at $15 billion (a reasonable assumption considering similar market sizes for related technologies), the Compound Annual Growth Rate (CAGR) is projected to remain strong, likely exceeding 8% through 2033. This sustained growth indicates a highly promising market outlook for vendors and investors. However, market growth is not without challenges. High initial investment costs for sophisticated GIS software and the requirement for skilled personnel to operate and maintain these systems can pose barriers to entry, particularly for smaller organizations. Additionally, data security concerns and the need for robust data management strategies are critical factors impacting market adoption. Despite these constraints, the continued integration of GIS tools into various business processes and the growing availability of user-friendly, affordable solutions are expected to mitigate these challenges and propel the market towards sustained and significant growth in the coming years. Segmentation reveals a strong preference for cloud-based solutions due to their flexibility and cost-effectiveness, with the geological exploration and urban planning applications exhibiting the highest growth rates. Key players such as Esri, Autodesk, and Hexagon are strategically positioned to capitalize on these trends.

Spatial interpolation is critical in geographic information systems (GIS) and environmental science, particularly when dealing with high-dimensional and nonlinear data. Classical methods like Kriging and inverse distance weighting (IDW) often struggle with the complexities of irregular terrain and sparse datasets, and are inadequate for capturing the nonlinear characteristics of high-dimensional spatial data. In this paper, we introduce a novel interpolation method based on the Denoising Diffusion Probabilistic Model (DDPM), which incorporates ConvNeXt V2 blocks within a UNet architecture. To validate the performance of our model, we employ the Copernicus Digital Elevation Model (COP-DEM) dataset for simulation experiments. Experimental results demonstrate that the proposed DDPM method significantly outperforms classical interpolation techniques, particularly in scenarios with high-density control points, producing high-quality interpolation results with strong transferability. This approach shows considerable promise for spatial interpolation in high-dimensional, complex terrains, offering a more robust alternative to traditional methods. It not only addresses key challenges in interpolation accuracy but also opens up new possibilities for applying generative models in other spatial data processing domains, including environmental monitoring and geospatial modeling.

This data set archives all inputs, outputs and scripts needed to reproduce the findings of W.H. Farmer and G.F. Koltun in the 2017 Journal of Hydrology Regional Studies article entitled “Geospatial Tools Effectively Estimated Nonexceedance Probabilities of Daily Streamflow at Ungauged and Intermittently Gauged Locations in Ohio”. Input data includes observed streamflow values, in cubic feet per second, for 152 streamgages in and around Ohio from 01 January 2009 through 31 August 2015. Data from the Ohio Environmental Protection Agency on where and when water quality samples were taken are also provided. Geospatial locations are provided for all streamgages and sampling sites considered. ESRI ArcGIS shapefiles are available for all maps produced in the original publication. Comma-separated-value files contain the output data required to reproduce every figure in the report. This archive also includes an R script capable of reading the input files and producing output files and figures. See the README.txt file for a full description of model application.

Our study utilizes two novel techniques, cyclical immunofluorescence imaging and single-cell spatial transcriptomics, to spatially map the tumor microenvironment of a large sample set of pediatric high-grade gliomas (pHGG). Using these methods, we have identified an abundant immunosuppressive myeloid cell population that has not been described in the context of pediatric high-grade gliomas. We validate our findings using an in vitro assay, spatial analysis on additional pHGG biopsies, independent spatial transcriptomic data, bulk RNA sequencing data of an expanded cohort of in-house patients, and publicly available bulk RNA sequencing data of an even larger cohort of pHGG patients.