Software tools for spatial analysis.

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 ...

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

In this seminar, you will learn about the spatial analysis tools built directly into the ArcGIS.com map viewer. You will learn of the spatial analysis capabilities in ArcGIS Online for Organizations, whether for analyzing your own data, data that's publicly available on ArcGIS Online, or a combination of both. You will learn the overall features and benefits of ArcGIS Online Analysis, how to get started, and how to choose the right approach in order to solve a specific spatial problem.

Discover the booming Spatial Analysis Software market! Our in-depth analysis reveals a $5 billion market projected to reach $12.4 billion by 2033, driven by AI, cloud computing, and rising geospatial data. Learn about key trends, regional insights, and leading companies shaping this dynamic sector.

IntroductionGeographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.MethodsA systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.Results and discussionThe search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.ConclusionsGIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base.

The size of the Spatial Analysis Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

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.

Spatial Crime Pattern Analysis Tools Market Outlook

According to our latest research, the Global Spatial Crime Pattern Analysis Tools market size was valued at $1.47 billion in 2024 and is projected to reach $4.23 billion by 2033, expanding at a robust CAGR of 12.8% during the forecast period of 2024–2033. One of the primary factors fueling this remarkable growth is the increasing reliance on geospatial intelligence and advanced analytics by law enforcement and urban planning agencies to enhance public safety, optimize resource allocation, and proactively address crime hotspots. As digital transformation accelerates across public and private sectors, spatial crime pattern analysis tools are becoming indispensable in modern security and planning frameworks, making them a critical investment for governments, private security firms, and research institutions worldwide.

Regional Outlook

North America currently commands the largest share of the global Spatial Crime Pattern Analysis Tools market, accounting for approximately 37% of the total market value in 2024. This dominance is attributed to the region’s mature technology infrastructure, high adoption of advanced analytics, and robust investments in public safety initiatives. The United States, in particular, has witnessed significant deployments of spatial crime analysis software across federal, state, and local law enforcement agencies. Supportive government policies, the presence of leading technology vendors, and a culture of innovation have further propelled market growth in North America. Additionally, collaborative efforts between public agencies and private tech firms have led to the development of cutting-edge solutions tailored specifically for crime prevention and urban management, further solidifying the region’s leadership in this sector.

The Asia Pacific region is anticipated to be the fastest-growing market for spatial crime pattern analysis tools, with a projected CAGR exceeding 15.2% from 2024 to 2033. Rapid urbanization, increasing investments in smart city initiatives, and growing concerns over public safety are major drivers in this region. Countries such as China, India, and Japan are witnessing unprecedented investments in digital infrastructure and surveillance technologies. Government-led projects aimed at integrating GIS and AI-driven crime analytics into urban management systems are gaining traction. Moreover, the region’s burgeoning population and complex urban landscapes necessitate innovative approaches to crime prevention, making spatial analysis tools a strategic priority for both policymakers and private sector stakeholders.

Emerging economies in Latin America, the Middle East, and Africa are also witnessing gradual adoption of spatial crime pattern analysis tools, albeit at a slower pace compared to developed regions. Challenges such as limited digital infrastructure, budget constraints, and varying regulatory frameworks have somewhat hindered widespread deployment. However, localized demand is growing, especially in major urban centers grappling with rising crime rates and the need for more efficient resource allocation. International aid programs, public-private partnerships, and capacity-building initiatives are helping to bridge the technology gap. As governments in these regions increasingly recognize the value of data-driven crime prevention, adoption rates are expected to climb, unlocking new growth opportunities for solution providers.

Report Scope

The health and societal impacts of COVID-19 have created tremendous interest in the scientific community, resulting in interdisciplinary research teams that combine their expertise to provide new insights into the epidemic. However, spatial computation, exploratory data analysis, and spatial data exploration tools have yet to be integrated into these dashboards. We present a Spatial Online Analytical Platform that integrates spatial analysis tools that enable users to explore and learn more about spatial patterns of COVID-19. We present three interaction classes to support users needs. Our first class allows users to apply user-defined data classifications and custom map color choices. The second class applies a risk index across the time series, informing users of the recent temporal trends. The third class allows users to hypothesize about the presence of spatial clusters and receive results on demand. Our SOLAP platform supports the data analysis and exploration needs of big spatial-temporal data.

Students learn about the importance of good data management and begin to explore QGIS and RStudio for spatial analysis purposes. Students will explore National Land Cover Database raster data and made-up vector point data on both platforms.

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

This dataset is about books. It has 1 row and is filtered where the book is Learning GIS using open source software : an applied guide for geo-spatial analysis. It features 7 columns including author, publication date, language, and book publisher.

The Global GIS Mapping Tools Market is poised for significant expansion, projected to reach a substantial market size of $10 billion by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of 12.5% through 2033. This robust growth trajectory is fueled by the increasing demand for advanced spatial analysis and visualization capabilities across a multitude of sectors. Key drivers include the escalating need for accurate geological exploration to identify and manage natural resources, the critical role of GIS in planning and executing complex water conservancy projects for sustainable water management, and the indispensable application of GIS in urban planning for efficient city development and infrastructure management. Furthermore, the burgeoning adoption of cloud-based and web-based GIS solutions is democratizing access to powerful mapping tools, enabling broader use by organizations of all sizes. The market is also benefiting from advancements in data processing, artificial intelligence integration, and the growing availability of open-source GIS platforms. Despite the optimistic outlook, certain restraints could temper the market's full potential. High initial investment costs for sophisticated GIS software and hardware, coupled with a shortage of skilled GIS professionals in certain regions, may pose challenges. However, the overwhelming benefits of enhanced decision-making, improved operational efficiency, and the ability to gain deep insights from spatial data are compelling organizations to overcome these hurdles. The competitive landscape is dynamic, featuring established players like Esri and Autodesk alongside innovative providers such as Mapbox and CARTO, all vying for market share by offering specialized features, user-friendly interfaces, and integrated solutions. The continuous evolution of GIS technology, driven by the integration of remote sensing data, big data analytics, and real-time information, will continue to shape the market's future. Here's a comprehensive report description on GIS Mapping Tools, incorporating your specified requirements:

This in-depth report provides a panoramic view of the global GIS Mapping Tools market, meticulously analyzing its landscape from the Historical Period (2019-2024) through to the Forecast Period (2025-2033), with 2025 serving as both the Base Year and the Estimated Year. The study period encompasses 2019-2033, offering a robust historical context and forward-looking projections. The market is valued in the millions of US dollars, with detailed segment-specific valuations and growth trajectories. The report is structured to deliver actionable intelligence to stakeholders, covering market concentration, key trends, regional dominance, product insights, and critical industry dynamics. It delves into the intricate interplay of companies such as Esri, Hexagon, Autodesk, CARTO, and Mapbox, alongside emerging players like Geoway and Shenzhen Edraw Software, across diverse applications including Geological Exploration, Water Conservancy Projects, and Urban Planning. The analysis also differentiates between Cloud Based and Web Based GIS solutions, providing a granular understanding of market segmentation.

Literature review dataset

This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1^st, 2020 to December 15^th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

The reference to cite the related paper is the following:

Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

The market for Spatial Intelligence Software is projected to grow from XXX in 2022 to XXX by 2033, exhibiting a CAGR of XX% during the forecast period. This growth can be attributed to increasing government initiatives, rising adoption of advanced analytics, and growing awareness of the benefits of spatial intelligence in various industries. Furthermore, rising investment in data analytics and business intelligence tools, coupled with the expanding need for better decision-making capabilities, is anticipated to drive market expansion. The market is segmented based on application and type. On the basis of application, the market is divided into large enterprises and SMEs. Large enterprises are expected to hold a significant market share due to higher spending on technology and increased need for data-driven insights. In terms of type, the market is divided into cloud-based and web-based solutions. Cloud-based solutions are projected to witness substantial growth due to their scalability, cost-effectiveness, and ease of deployment. Leading companies operating in this market include Alteryx, Caliper Corporation, CartoDB, Avuxi, Cubeware, Esri, Fract, Gadberry Group, Galigeo, Board, Geoblink, Qlik, Maptive, Pitney Bowes, and CARTO. North America is expected to dominate the market, followed by Europe and Asia Pacific.

Amazon Geoglyphs Spatial Analysis Dataset 2

DATA & Tools

Data Overview and Sources

This dataset was constructed for Phase 2 research analyzing spatial relationships between Amazon geoglyphs and environmental conditions. The analysis includes NDVI and NDMI calculations and grid-based anomaly detection.

Data sources: - Sentinel-2 Composites: forobs.jrc.ec.europa.eu/sentinel/sentinel2_composite - Pan-tropical cloud-free annual composites (2020) - jqjacobs.net: Archaeogeodesy Placemarks (Amazon geoglyph category extracted from Google Earth KML)

File Structure

amazon_geoglyphs_analysis/
├── data/
│  ├── sites_geoglyphs.gpkg      # Site locations (extracted geoglyph coordinates)
│  ├── focus_rgb_swir1_nir_red.tif  # Sentinel-2 composite (RGB: SWIR1, NIR, RED channels)
│  ├── focus_ndvi.tif         # NDVI index (vegetation greenness)
│  ├── focus_ndmi.tif         # NDMI index (vegetation moisture)
│  ├── focus_area.gpkg        # Analysis boundary (study area extent)
│  ├── amazon_grid_anomaly.gpkg    # Grid-based anomaly analysis
│  └── amazon_basin.gpkg       # Amazon basin boundaries
└── analysis_project.qgz       # QGIS project (integrated analysis workflow)

QGIS Processing Workflow

1. Satellite Data Processing (focus_rgb_swir1_nir_red.tif)

• (1) Data Source: Downloaded Sentinel-2 tiles S15_W075 and S15_W065 (2020, false color composite)
• (2) Raster → Miscellaneous → Build Virtual Raster: Merge two tiles into single virtual raster
• (3) Vector → Geoprocessing Tools → Clip Raster by Mask Layer: Clip merged raster to focus_area boundary

2. NDVI Calculation (focus_ndvi.tif)

• (1) Raster → Raster Calculator: Calculate Normalized Difference Vegetation Index Expression: ("focus_rgb_swir1_nir_red@2" - "focus_rgb_swir1_nir_red@3") / ("focus_rgb_swir1_nir_red@2" + "focus_rgb_swir1_nir_red@3")
• (2) Formula: NDVI = (NIR - RED) / (NIR + RED)
• (3) Layer Properties → Symbology: Apply RdYlGr color ramp for vegetation visualization

3. NDMI Calculation (focus_ndmi.tif)

• (1) Raster → Raster Calculator: Calculate Normalized Difference Moisture Index Expression: ("focus_rgb_swir1_nir_red@2" - "focus_rgb_swir1_nir_red@1") / ("focus_rgb_swir1_nir_red@2" + "focus_rgb_swir1_nir_red@1")
• (2) Formula: NDMI = (NIR - SWIR1) / (NIR + SWIR1)
• (3) Purpose: Monitor vegetation water content and drought conditions

4. Grid-based Anomaly Analysis (amazon_grid_anomaly.gpkg)

Layer 1: g_005_ndmi_ndvi (Fine-scale Grid Statistics)

• (1) Vector → Research Tools → Create Grid: Create 0.005° interval grid (~550m resolution)
• (2) Vector → Analysis Tools → Zonal Statistics: Calculate zonal statistics for NDVI and NDMI by grid cell
  • Statistics: Mean
  • Target rasters: focus_ndvi.tif, focus_ndmi.tif

Layer 2: g_050_anomaly_count (Anomaly Frequency Analysis)

• (1) Vector → Research Tools → Select by Expression: Identify anomalous grid cells Expression: "ndvi_mean" <= "ndvi_p10" AND "ndmi_mean" <= "ndmi_p10"
• (2) Vector → Research Tools → Create Grid: Create 0.05° interval grid (~5.5km resolution)
• (3) Vector → Analysis Tools → Join Attributes by Location (Summary): Count anomalous fine-scale grids within coarse-scale grids
• (4) Purpose: Identify areas with consistently low vegetation greenness and moisture (potential archaeological signatures)

Snapshot img

Geospatial Analytics Market Size 2025-2029

The geospatial analytics market size is forecast to increase by USD 178.6 billion, at a CAGR of 21.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of geospatial analytics in sectors such as healthcare and insurance. This trend is fueled by the ability of geospatial analytics to provide valuable insights from location-based data, leading to improved operational efficiency and decision-making. Additionally, emerging methods in data collection and generation, including the use of drones and satellite imagery, are expanding the scope and potential of geospatial analytics. However, the market faces challenges, including data privacy and security concerns. With the vast amounts of sensitive location data being collected and analyzed, ensuring its protection is crucial for companies to maintain trust with their customers and avoid regulatory penalties. Navigating these challenges and capitalizing on the opportunities presented by the growing adoption of geospatial analytics requires a strategic approach from industry players. Companies must prioritize data security, invest in advanced analytics technologies, and collaborate with stakeholders to build trust and transparency. By addressing these challenges and leveraging the power of geospatial analytics, businesses can gain a competitive edge and unlock new opportunities in various industries.

What will be the Size of the Geospatial Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for location-specific insights across various sectors. Urban planning relies on geospatial optimization and data enrichment to enhance city designs and improve infrastructure. Cloud-based geospatial solutions facilitate real-time data access, enabling location intelligence for public safety and resource management. Spatial data standards ensure interoperability among different systems, while geospatial software and data visualization tools provide valuable insights from satellite imagery and aerial photography. Geospatial services offer data integration, spatial data accuracy, and advanced analytics capabilities, including 3D visualization, route optimization, and data cleansing. Precision agriculture and environmental monitoring leverage geospatial data to optimize resource usage and monitor ecosystem health. Infrastructure management and real estate industries rely on geospatial data for asset tracking and market analysis. Spatial statistics and disaster management applications help mitigate risks and respond effectively to crises. Geospatial data management and quality remain critical as the volume and complexity of data grow. Geospatial modeling and interoperability enable seamless data sharing and collaboration. Sensor networks and geospatial data acquisition technologies expand the reach of geospatial analytics, while AI-powered geospatial analytics offer new opportunities for predictive analysis and automation. The ongoing development of geospatial technologies and applications underscores the market's continuous dynamism, providing valuable insights and solutions for businesses and organizations worldwide.

How is this Geospatial Analytics Industry segmented?

The geospatial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TechnologyGPSGISRemote sensingOthersEnd-userDefence and securityGovernmentEnvironmental monitoringMining and manufacturingOthersApplicationSurveyingMedicine and public safetyMilitary intelligenceDisaster risk reduction and managementOthersTypeSurface and field analyticsGeovisualizationNetwork and location analyticsOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Technology Insights

The gps segment is estimated to witness significant growth during the forecast period.The market encompasses various applications and technologies, including geospatial optimization, data enrichment, location-based services (LBS), spatial data standards, public safety, geospatial software, resource management, location intelligence, geospatial data visualization, geospatial services, data integration, 3D visualization, satellite imagery, remote sensing, GIS platforms, spatial data infrastructure, aerial photography, route optimization, data cleansing, precision agriculture, spatial interpolation, geospatial databases, transportation planning, spatial data accuracy, spatial analysis, map projections, interactive maps, marketing analytics, data storytelling, geospati

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.

Spatial Data Modeller, SDM, is a collection of tools for use with GIS software for adding categorical maps with interval, ordinal, or ratio scale maps to produce a predictive map of where something of interest is likely to occur. The tools include the data-driven methods of Weights of Evidence, Logistic Regression, and two supervised and one unsupervised neural network methods, and categorical tools for a knowledge-driven method Fuzzy Logic. All of the tools have help files that include references to publications discussing the applications of the methods implemented in the tool. Several of the tools create output rasters, tables, or files that require the user to enter a name. Default values are provided in most cases to serve as suggestions of the style of naming that has been found useful. These names, following ArcGIS conventions, can be changed to meet the user’s needs. To make all of the features of SDM work properly it is required that several Environment parameters are set. See the discussion of Environment Settings below for the details. The Weights of Evidence, WofE, and Logistic Regression, LR, tools addresses area as the count of unit cells. It is assumed in the WofE and LR tools that the data has spatial units of meters. If your data has other spatial units, these WofE and LR tools may not work properly.

Website:

http://www.ige.unicamp.br/sdm/