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.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

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

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

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.

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.

GapMaps GIS Data by Azira uses location data on mobile phones sourced by Azira which is collected from smartphone apps when the users have given their permission to track their location. It can shed light on consumer visitation patterns (“where from” and “where to”), frequency of visits, profiles of consumers and much more.

Businesses can utilise GIS data to answer key questions including: - What is the demographic profile of customers visiting my locations? - What is my primary catchment? And where within that catchment do most of my customers travel from to reach my locations? - What points of interest drive customers to my locations (ie. work, shopping, recreation, hotel or education facilities that are in the area) ? - How far do customers travel to visit my locations? - Where are the potential gaps in my store network for new developments?
- What is the sales impact on an existing store if a new store is opened nearby? - Is my marketing strategy targeted to the right audience? - Where are my competitor's customers coming from?

Mobile Location data provides a range of benefits that make it a valuable GIS Data source for location intelligence services including: - Real-time - Low-cost at high scale - Accurate - Flexible - Non-proprietary - Empirical

Azira have created robust screening methods to evaluate the quality of Mobile location data collected from multiple sources to ensure that their data lake contains only the highest-quality mobile location data.

This includes partnering with trusted location SDK providers that get proper end user consent to track their location when they download an application, can detect device movement/visits and use GPS to determine location co-ordinates.

Data received from partners is put through Azira's data quality algorithm discarding data points that receive a low quality score.

Use cases in Europe will be considered on a case to case basis.

Abstract

This mapping project aimed to create a database of all financial, health, education, and agricultural service access points in Zambia.

Geographic coverage

National coverage except Ikelenge district

Analysis unit

Health care facilities, financial institutions, educational institutions, agricultural service providers

Universe

The project aimed to capture all open and operational touch-points at the time of fieldwork. Active points were considered to have done a transaction in the last 90 days. Not all points are captured due to several factors including:

i) non-location of the points ii) security areas iii) resistance or lack of cooperation iv) dormancy v) safety of fieldwork staff

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires used in the MSMP are specific to each sector service intermediary. This means that there are 14 different questionnaires, each with its own set of specific questions.

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because

The Spatial Analysis Software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions, the expanding use of drones and other data acquisition technologies for precise geographic data collection, and the rising demand for advanced analytics across diverse sectors. The market's expansion is fueled by the need for efficient geospatial data processing and interpretation in applications such as urban planning, infrastructure development, environmental monitoring, and precision agriculture. Key trends include the integration of Artificial Intelligence (AI) and Machine Learning (ML) for automating analysis and improving accuracy, the proliferation of readily available satellite imagery and sensor data, and the growing adoption of 3D modeling and visualization techniques. While data security concerns and the high initial investment costs for advanced software solutions pose some restraints, the overall market outlook remains positive, with a projected compound annual growth rate (CAGR) exceeding 10% (a reasonable estimate based on the rapid technological advancements and market penetration observed in related sectors). This growth is expected to be particularly strong in the North American and Asia-Pacific regions, driven by substantial government investments in infrastructure projects and burgeoning private sector adoption. The segmentation by application (architecture, engineering, and other sectors) reflects the versatility of spatial analysis software, enabling its use across various industries. Similarly, the choice between cloud-based and locally deployed solutions caters to specific organizational needs and technical capabilities. The competitive landscape is characterized by both established players and emerging technology companies, showcasing the dynamic nature of the market. Major players like Autodesk, Bentley Systems, and Trimble are leveraging their existing portfolios to integrate advanced spatial analysis capabilities, while smaller companies are focusing on niche applications and innovative analytical techniques. The ongoing advancements in both hardware and software, coupled with increasing data availability and affordability, are set to further fuel the market's growth in the coming years. The historical period (2019-2024) likely witnessed moderate growth as the market matured, laying the foundation for the accelerated expansion expected during the forecast period (2025-2033). Continued innovation and industry convergence will be key drivers shaping the future trajectory of the Spatial Analysis Software market.

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

Raw DEM and Soils data

Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)

DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.

DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.

Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)

Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).

Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).

ArcGIS Map Packages

Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).

Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.

Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).

Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019 Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA Department of Anthropology, Washington State University andrew.brown1234@gmail.com – Email andrewgillreathbrown.wordpress.com – Web

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 ...

Geographic Information System (GIS) Tools Market Outlook

The global Geographic Information System (GIS) Tools market is poised for significant expansion, with a projected market size of approximately $15.2 billion in 2023, anticipated to reach $28.6 billion by 2032, reflecting a compound annual growth rate (CAGR) of 7.3%. This growth can be attributed to the increasing integration of advanced GIS technologies across various sectors such as agriculture, transportation, and government services, driven by the need for efficient data management and spatial analysis capabilities. The adoption of GIS tools is further influenced by the growing demand for real-time geographic data, which plays a crucial role in decision-making processes across multiple industries.

One of the primary growth factors for the GIS Tools market is the burgeoning demand for high-precision mapping and spatial data analytics. Industries such as agriculture and construction are increasingly relying on GIS technology to optimize resource management and streamline operations. The ability of GIS tools to provide detailed insights into geographical patterns and trends allows companies to make informed decisions, thereby improving operational efficiency and reducing costs. Additionally, advancements in remote sensing technology and data collection methods have significantly enhanced the accuracy and reliability of GIS data, further fueling its adoption across various sectors.

The increasing deployment of GIS tools in urban planning and smart city projects is another key driver of market growth. Governments worldwide are leveraging GIS technology to enhance infrastructure planning, improve public services, and manage environmental resources more effectively. The integration of GIS in smart city initiatives enables authorities to monitor and manage urban environments in real-time, leading to better resource allocation and improved quality of life for residents. As cities continue to expand and evolve, the demand for advanced GIS solutions is expected to grow exponentially, providing significant opportunities for market players.

Furthermore, the rise of location-based services and telematics has expanded the application of GIS tools in the transportation and logistics sectors. Companies are utilizing GIS technology to optimize route planning, track assets, and enhance supply chain management. The integration of GIS with telematics systems allows for real-time monitoring and analysis of vehicle movements, improving fleet efficiency and reducing operational costs. As the transportation industry continues to embrace digital transformation, the demand for GIS tools is likely to increase, further driving market growth.

In terms of regional outlook, North America currently leads the GIS Tools market, driven by high adoption rates of advanced technologies and significant investments in infrastructure development. The presence of major GIS solution providers and a well-established IT infrastructure further contribute to the region's dominance. However, the Asia Pacific region is expected to witness the highest growth during the forecast period, driven by rapid urbanization, increasing government initiatives for infrastructure development, and the growing adoption of GIS technology in emerging economies such as China and India. Europe and the Middle East & Africa regions are also expected to experience steady growth, supported by advancements in GIS applications and the rising need for efficient spatial data management solutions.

The role of a Gis Data Collector is increasingly becoming pivotal in the GIS Tools market. These professionals are responsible for gathering, verifying, and maintaining the spatial data that forms the backbone of GIS applications. With the growing emphasis on high-precision mapping and real-time data analysis, the demand for skilled Gis Data Collectors is on the rise. They play a crucial role in ensuring the accuracy and reliability of geospatial information, which is essential for effective decision-making across various sectors. As industries continue to leverage advanced GIS technologies, the expertise of Gis Data Collectors will be indispensable in facilitating seamless data integration and enhancing the overall quality of GIS solutions.

Component Analysis

The GIS Tools market can be segmented by component into software, hardware, and services, each playing a vital role in the overall market dynamics. The software segment is expected to hold the largest market

Software Geographic Information Systems Market Outlook

As of 2023, the Software Geographic Information Systems (GIS) market size was valued at approximately USD 9.1 billion and is projected to reach around USD 18.6 billion by 2032, reflecting a robust Compound Annual Growth Rate (CAGR) of 8.5%. This remarkable growth is primarily driven by the increasing demand for spatial data across various industries, coupled with the advancement in geospatial technologies. The growing integration of GIS with mainstream business operations for better decision-making and the surge in urbanization and smart city initiatives are significant factors propelling the market forward. The continuous evolution in software capabilities, including enhanced data visualization and integration capabilities, further contributes to the rising adoption of GIS solutions worldwide.

One of the pivotal growth drivers of the Software GIS market is the expanding requirement for spatial data and analytics to enhance operational efficiency across multiple industry verticals. Industries such as urban planning, transportation, agriculture, and natural resources management are increasingly relying on GIS solutions for data-driven decision-making. The ability of GIS to provide real-time, location-based insights is revolutionizing how businesses plan, manage resources, and optimize their operations. Moreover, the rapid digitization and adoption of IoT (Internet of Things) technologies are also bolstering the demand for GIS software, as businesses seek to leverage interconnected devices for better data collection and analysis. The integration of GIS with IoT platforms allows for more comprehensive and precise spatial insights, thus driving market growth.

Another significant factor contributing to the growth of the Software GIS market is the advancement in cloud computing technologies. The shift from traditional on-premises deployment to cloud-based GIS solutions is gaining traction due to the numerous advantages offered by the cloud. Cloud-based GIS provides enhanced scalability, flexibility, and cost-effectiveness, making it an attractive option for businesses of all sizes. Additionally, cloud solutions facilitate easier collaboration and data sharing among different stakeholders, fostering a more integrated approach to spatial data management. The growing investment in cloud infrastructure by major players in the technology sector further supports the widespread adoption of cloud-based GIS solutions, enabling businesses to harness the power of spatial data in a more efficient and streamlined manner.

Furthermore, the increasing emphasis on environmental conservation and sustainable development is driving the demand for GIS applications in environmental monitoring and management. GIS software is extensively used for mapping and analyzing environmental data, helping organizations to monitor changes in land use, assess natural resource availability, and evaluate the impact of human activities on the environment. As governments and organizations worldwide strive to meet sustainability goals and address climate change challenges, GIS solutions are becoming indispensable tools for informed decision-making and strategic planning. The integration of GIS with emerging technologies such as AI and machine learning is also enhancing the capabilities of these systems, enabling more sophisticated analysis and predictive modeling.

The application of GIS in Transportation is becoming increasingly significant as the demand for efficient and sustainable transport systems grows. GIS technology enables transportation planners and operators to analyze spatial data in real-time, optimizing route planning and improving logistics operations. By integrating GIS with technologies like GPS and telematics, transportation systems can provide more accurate and timely information, enhancing decision-making processes. This integration is crucial for managing transportation networks effectively, reducing costs, and improving service delivery. As urban areas continue to expand and the need for smart transportation solutions rises, GIS in Transportation is expected to play a pivotal role in shaping the future of mobility.

Component Analysis

The Software segment of the GIS market is experiencing significant growth, driven by the continuous innovation and development of advanced GIS software solutions. Software providers are focusing on enhancing the functionality and usability of their products, incorporating features such as 3D visualization, real-time data process

ABSTRACT The upper Teles Pires River basin is a key hydrological resource for the state of Mato Grosso, but has suffered rapid land use and cover change. The basin includes areas of Cerrado biome, as well as transitional areas between the Amazon and Cerrado vegetation types, with intensive large-scale agriculture widely-spread throughout the region. The objective of this study was to explore the spatial and temporal dynamics of land use and cover change from 1986 to 2014 in the upper Teles Pires basin using remote sensing and GIS techniques. TM (Thematic Mapper) and TIRS (Thermal Infrared Sensor) sensor images aboard the Landsat 5 and Landsat 8, respectively, were employed for supervised classification using the “Classification Workflow” in ENVI 5.0. To evaluate classification accuracy, an error matrix was generated, and the Kappa, overall accuracy, errors of omission and commission, user accuracy and producer accuracy indexes calculated. The classes showing greatest variation across the study period were “Agriculture” and “Rainforest”. Results indicated that deforested areas are often replaced by pasture and then by agriculture, while direct conversion of forest to agriculture occured less frequently. The indices with satisfactory accuracy levels included the Kappa and Global indices, which showed accuracy levels above 80% for all study years. In addition, the producer and user accuracy indices ranged from 59-100% and 68-100%, while the errors of omission and commission ranged from 0-32% and 0-40.6%, respectively.

GIS Collectors Market Outlook

The global GIS collectors market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 3.2 billion by 2032, growing at a CAGR of 8.5% during the forecast period. This growth can be attributed to the rising demand for accurate geographic data collection and analysis across various industries. The drive towards digital transformation and the increasing adoption of advanced technologies in sectors like construction, utilities, and environmental monitoring are significant growth factors for this market.

One of the primary growth factors for the GIS collectors market is the increasing need for precise and reliable geographic data in urban planning and development. As cities expand and infrastructures develop, there is a growing demand for geospatial data to plan and manage urban regions effectively. GIS collectors provide accurate data collection, which facilitates better decision-making processes in urban planning. Moreover, the integration of GIS technology with other advanced technologies like IoT and AI is further enhancing its applicability and adoption in urban development projects.

The agriculture sector is also significantly driving the growth of the GIS collectors market. Precision farming techniques rely heavily on accurate geospatial data to monitor and manage agricultural fields effectively. GIS collectors enable farmers to collect and analyze data on soil health, crop conditions, and water availability, which helps in optimizing resources and improving crop yields. The increasing emphasis on sustainable farming practices and the need to meet the food demands of a growing global population are further boosting the adoption of GIS collectors in agriculture.

Additionally, environmental monitoring is emerging as a crucial application area, contributing to the market's expansion. With growing environmental concerns and the need for sustainable resource management, there is an increasing demand for technologies that can monitor and analyze environmental conditions efficiently. GIS collectors provide valuable data for tracking changes in land use, vegetation cover, and water resources, which is essential for conservation efforts and policy-making. The adoption of GIS collectors in environmental monitoring is expected to rise as governments and organizations focus more on environmental sustainability.

Regionally, North America is expected to dominate the GIS collectors market during the forecast period, owing to the early adoption of advanced technologies and significant investments in geospatial data infrastructure. The presence of major market players and extensive applications in urban planning, environmental monitoring, and agriculture are driving the market in this region. Furthermore, the Asia Pacific region is anticipated to exhibit the highest growth rate due to rapid urbanization, increasing government initiatives for smart cities, and rising demand for precision agriculture practices.

Product Type Analysis

The GIS collectors market is segmented by product type into handheld GIS collectors, mobile GIS collectors, and desktop GIS collectors. Handheld GIS collectors are portable devices that allow users to collect geospatial data on-site with ease. These devices are typically used in field surveys, environmental monitoring, and utility management. The demand for handheld GIS collectors is driven by their convenience, ease of use, and ability to provide real-time data collection in remote and challenging environments. As industries continue to prioritize field data accuracy and efficiency, the adoption of handheld GIS collectors is expected to grow significantly.

Mobile GIS collectors, often integrated with smartphones and tablets, offer enhanced flexibility and connectivity for geospatial data collection. These devices leverage mobile networks and cloud-based platforms to facilitate seamless data transfer and real-time analysis. The growing adoption of mobile GIS collectors can be attributed to the increasing reliance on mobile technology and the need for real-time data access and sharing. Industries such as transportation, utilities, and urban planning are increasingly deploying mobile GIS collectors to improve operational efficiency and decision-making processes.

Desktop GIS collectors, on the other hand, are primarily used for high-precision geospatial data collection and analysis in office environments. These devices are equipped with advanced software and processing capabilities, making them ideal for complex data analysis and large-scale projects. The deman

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

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/

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

A recent reanalysis of the spatial distribution of artifacts from the Bell site (47WN9) near Oshkosh, Wisconsin, provides an updated interpretation of seventeenth and early eighteenth century activities associated with the Grand Village of the Meskwaki in that locale. Although archaeologists now routinely conduct analyses of artifact distributions at the intrasite level, using Geographic Information Systems (GIS) and other mapping technologies to record and visualize spatial data, sometimes even in real-time during excavations, application of GIS methods to older collections are rarer. This case study (1) documents methods used to map feature-associated artifacts and identify spatial patterning, (2) presents new visualizations of artifact distributions at the Bell site, and (3) revises earlier interpretations of the community plan of the site (Behm 1998, 2008). The results demonstrate the importance of continuing to update curation and research strategies for previously collected artifacts and archaeological data, using a method that is widely applicable to other archaeological sites and that makes effective use of existing cultural resources.

Cloud GIS Market Outlook

The global Cloud GIS market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% over the forecast period. The growth of the Cloud GIS market can be attributed to several factors, including the increasing demand for cloud-based geographic information systems (GIS) across various sectors, advancements in geospatial technologies, and rising investments in smart city projects.

One of the primary growth factors driving the Cloud GIS market is the increasing demand for real-time geospatial data and location-based services. As businesses and governments recognize the value of real-time data for decision-making, there has been a surge in the adoption of Cloud GIS solutions. These solutions offer scalable, flexible, and cost-effective ways to collect, store, analyze, and visualize geographic data, making them indispensable in sectors such as transportation, logistics, and urban planning.

Another significant growth driver is the rapid advancement in geospatial technologies, such as remote sensing, satellite imagery, and geographic data analytics. These technological advancements have expanded the capabilities of GIS systems, enabling more sophisticated data analysis and mapping solutions. The integration of AI and machine learning with GIS is further enhancing the ability to derive actionable insights from complex geospatial data, thus fueling the market growth.

Investments in smart city projects are also contributing to the growth of the Cloud GIS market. Governments and urban planners are increasingly leveraging Cloud GIS to manage and optimize urban infrastructure, transportation systems, and public services. Smart cities use geospatial data to improve resource management, enhance public safety, and provide better services to citizens. This trend is expected to continue, driving further demand for Cloud GIS solutions.

Regionally, North America is expected to hold the largest market share in the Cloud GIS market during the forecast period. The region's dominance can be attributed to the presence of leading technology companies, high adoption rates of advanced technologies, and substantial investments in infrastructure development. Additionally, Asia Pacific is anticipated to witness the highest growth rate due to rapid urbanization, increasing internet penetration, and government initiatives promoting digitalization and smart city projects.

Component Analysis

The Cloud GIS market is segmented by component into software and services. Within the software segment, cloud-based GIS solutions offer various functionalities, including data storage, data analysis, and visualization tools. These solutions are gaining traction due to their scalability, flexibility, and ability to integrate with other enterprise systems. Cloud GIS software allows organizations to access and analyze geographic data in real-time, facilitating better decision-making and strategic planning. As businesses and governments increasingly rely on geographic data, the demand for advanced GIS software solutions is expected to rise significantly.

On the other hand, the services segment encompasses various offerings such as consulting, integration, maintenance, and support services. These services are crucial for the successful implementation and operation of Cloud GIS systems. Consulting services help organizations understand their specific GIS needs and develop tailored solutions, while integration services ensure seamless integration of GIS with existing IT infrastructure. Maintenance and support services provide ongoing assistance to ensure the smooth functioning of GIS systems. The growing complexity of geospatial data and the need for specialized expertise are driving the demand for professional services in the Cloud GIS market.

Moreover, the shift towards cloud-based solutions has led to the emergence of new service models such as GIS-as-a-Service (GaaS). GaaS allows organizations to access GIS capabilities on a subscription basis, eliminating the need for significant upfront investments in hardware and software. This model is particularly beneficial for small and medium-sized enterprises (SMEs) that may not have the resources to invest in traditional GIS systems. As the adoption of GaaS increases, the services segment is expected to experience substantial growth.

In addition to these core services, many Cloud GIS providers offer value-added services such as data analytics, cus

Interagency Wildland Fire Perimeter History (IFPH) Overview This national fire history perimeter data layer of conglomerated agency perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2024 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer, links are provided where possible below. In addition, many agencies are now using WFIGS as their authoritative source, beginning in mid-2020.Alaska fire history (WFIGS pull for updates began 2022)USDA FS Regional Fire History Data (WFIGS pull for updates began 2024)BLM Fire Planning and Fuels (WFIGS pull for updates began 2020)National Park Service - Includes Prescribed Burns (WFIGS pull for updates began 2020)Fish and Wildlife Service (WFIGS pull for updates began 2024)Bureau of Indian Affairs (Incomplete, 2017-2018 from BIA, WFIGS pull for updates began 2020)CalFire FRAS - Includes Prescribed Burns (CALFIRE only source, non-fed fires)WFIGS - updates included since mid-2020, unless otherwise noted Data LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoritative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.Attributes This dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer maintained by IrWIN. (This unique identifier may NOT replace the GeometryID core attribute) FORID - Unique identifier assigned to each incident record in the Fire Occurence Data Records system. (This unique identifier may NOT replace the GeometryID core attribute) INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name. FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT). AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin. SOURCE - System/agency source of record from which the perimeter came. DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy. MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Other GIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9 UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001 LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456. UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMP COMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or Unknown GEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID). Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4,781 Records thru 2021), other federal sources for AK data removed. No RX data included.CA: GEOID = OBJECT ID of downloaded file geodatabase (8,480 Records, federal fires removed, includes RX. Significant cleanup occurred between 2023 and 2024 data pulls resulting in fewer perimeters).FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2,959 Records), includes RX.BIA: GEOID = "FireID" 2017/2018 data (382 records). No RX data included.NPS: GEOID = EVENT ID 15,237 records, includes RX. In 2024/2023 dataset was reduced by combining singlepart to multpart based on valid Irwin, FORID or Unique Fire IDs. RX data included.BLM: GEOID = GUID from BLM FPER (23,730 features). No RX data included.USFS: GEOID=GLOBALID from EDW records (48,569 features), includes RXWFIGS: GEOID=polySourceGlobalID (9724 records added or replaced agency record since mid-2020)Attempts to repair Unique Fire ID not made. Attempts to repair dates not made. Verified all IrWIN IDs and FODRIDs present via joins and cross checks to the respective dataset. Stripped leading and trailing spaces, fixed empty values to