This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

Author: A Lisson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): gis, geographic thinkingRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Explain the difference between two types of geospatial technologies - GPS and GIS.
2. Develop basic skills to effectively manipulate and use GPS receivers and ArcGIS software.
3. Explain uses of GPS and GIS.Summary: Students use GPS coordinates to discover geocaches at a local park, and they use ArcGIS to layer maps about the park. Frontenac State park is the example, but any park or area (including school grounds) could be used. Students also investigate careers that use GIS.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Hi, I'm Kiaran Ratcliffe a GIS Consultant within the Education Team at Esri UK. Esri is a company that creates and distributes GIS software, and my focus is on helping schools and universities access and use this software effectively. That means helping educators bring GIS into the classroom in ways that are engaging, inclusive, and relevant. We want students to leave school or university not just knowing how to use GIS, but understanding how to apply it to make a difference—socially, environmentally, and across all kinds of industries.It’s a really rewarding role. We get to support both students and teachers, and help them use modern spatial tools to explore the world, solve problems, and tell powerful stories with data.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

ABSTRACT Watershed delineation, drainage network generation and determination of river hydraulic characteristics are important issues in hydrological sciences. In general, this information can be obtained from Digital Elevation Models (DEM) processing within GIS commercial softwares, such as ArcGIS and IDRISI. On the other hand, the use of open source GIS tools has increased significantly, and their advantages include free distribution, continuous development by user communities and full customization for specific requirements. Herein, we present the IPH-Hydro Tools, an open source tool coupled to MapWindow GIS software designed for watershed topology acquisition, including preprocessing steps in hydrological models such as MGB-IPH. In addition, several tests were carried out assessing the performance and applicability of the developed tool, given by a comparison with available GIS packages (ArcGIS, IDRISI, WhiteBox) for similar purposes. The IPH-Hydro Tools provided satisfactory results on tested applications, allowing for better drainage network and less processing time for catchment delineation. Regarding its limitations, the developed tool was incompatible with huge terrain data and showed some difficulties to represent drainage networks in extensive flat areas, which can occur in reservoirs and large rivers.

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

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.

The global Business Mapping Software market is poised for significant expansion, projected to reach an estimated USD 5,000 million by 2025 and growing at a compound annual growth rate (CAGR) of XX% through 2033. This robust growth is underpinned by the increasing demand for advanced data visualization and spatial analytics across diverse industries. Key drivers include the burgeoning need for optimized logistics and supply chain management, enhanced customer relationship management (CRM) through location intelligence, and improved operational efficiency via dynamic route planning and site selection. The manufacturing sector, in particular, leverages business mapping software for factory layout optimization, resource allocation, and risk assessment in global operations. Similarly, the automotive industry is integrating these solutions for advanced navigation systems, fleet management, and the development of autonomous driving technologies, which heavily rely on precise geospatial data. The financial services sector is also a significant adopter, utilizing mapping software for fraud detection, risk analysis, and identifying optimal branch locations. The market's trajectory is further bolstered by emerging trends such as the widespread adoption of cloud-based solutions, offering greater scalability, accessibility, and cost-effectiveness compared to traditional on-premise installations. This shift democratizes access to sophisticated mapping tools for small and medium-sized enterprises. The integration of AI and machine learning with business mapping platforms is another transformative trend, enabling predictive analytics, pattern recognition, and more intelligent decision-making. However, the market faces certain restraints, including the initial high cost of implementation for some advanced features, the need for specialized skills to leverage the full potential of these tools, and concerns around data privacy and security, especially when dealing with sensitive customer or operational information. Despite these challenges, the continuous innovation and increasing integration of geospatial capabilities into core business processes are expected to drive sustained market growth and adoption across a wide spectrum of industries. Here's a comprehensive report description for Business Mapping Software, incorporating all your specified elements:

The Southwestern Region is 20.6 million acres. There are six national forests in Arizona, five national forests and a national grassland in New Mexico, and one national grassland each in Oklahoma and the Texas panhandle.The region ranges in elevation from 1,600 feet above sea level and an annual rain fall of 8 inches in Arizona's lower Sonoran Desert to 13,171-foot high Wheeler Peak and over 35 inches of precipitation a year in northern New Mexico. Geographic Information Systems or GIS are computer systems, software and data used to analyze and display spatial or locational data about surface features. One of the strengths of GIS is the capability to overlay or compare multiple feature layers. A user can then analyze the relationship between the layers. Data, reports and maps produced through GIS are used by managers and resource specialists to make decisions about land management activities on National Forests. The National Forests of the Southwestern Region maintain and utilize GIS data for various features on the ground. Some of these datasets are made available for download through this page. Resources in this dataset:Resource Title: GIS Datasets. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=STELPRDB5202474 Selected GIS datasets for the Southwestern Region are available for download from this page.Resource Software Recommended: ArcExplorer,url: http://www.esri.com/software/arcexplorer/index.html

The global vegetation software market is experiencing robust growth, driven by increasing demand for efficient and data-driven solutions across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $8 billion by 2033. Key drivers include the rising adoption of precision agriculture techniques, the increasing need for effective forest management and conservation efforts, and the growing utilization of remote sensing technologies like drones and satellite imagery for data collection and analysis. The surge in demand for accurate and timely vegetation data across public utilities (for managing infrastructure and assessing environmental impact), commercial applications (e.g., landscaping, urban planning), and research initiatives further fuels market expansion. The cloud-based software segment is anticipated to dominate the market due to its scalability, accessibility, and cost-effectiveness compared to on-premises solutions. North America and Europe currently hold significant market shares, fueled by advanced technological infrastructure and substantial investments in agricultural and environmental research. However, Asia-Pacific is poised for significant growth in the coming years, driven by rapid economic development and increasing awareness of sustainable agriculture practices. While the market faces challenges like the high initial investment costs of software and the need for specialized expertise, ongoing technological advancements and increasing government initiatives promoting sustainable resource management are expected to mitigate these restraints. The competitive landscape is characterized by a mix of established players and emerging innovative companies. Accenture, Cyient, and other established IT services companies are leveraging their expertise to develop and implement sophisticated vegetation management solutions. Simultaneously, specialized firms like Agronomix and PlantFactory are offering niche solutions targeting specific segments, fostering innovation and competition. The market's future trajectory will likely be shaped by the continuous integration of artificial intelligence (AI) and machine learning (ML) to enhance the accuracy and efficiency of vegetation monitoring and analysis, alongside the expanding accessibility of high-resolution imagery from various sources. Furthermore, the development of user-friendly interfaces and the integration of vegetation data with other relevant datasets will play a vital role in driving wider adoption across various user groups.

The PALEOMAP project produces paleogreographic maps illustrating the Earth's plate tectonic, paleogeographic, climatic, oceanographic and biogeographic development from the Precambrian to the Modern World and beyond.

A series of digital data sets has been produced consisting of plate tectonic data, climatically sensitive lithofacies, and biogeographic data. Software has been devloped to plot maps using the PALEOMAP plate tectonic model and digital geographic data sets: PGIS/Mac, Plate Tracker for Windows 95, Paleocontinental Mapper and Editor (PCME), Earth System History GIS (ESH-GIS), PaleoGIS(uses ArcView), and PALEOMAPPER.

Teaching materials for educators including atlases, slide sets, VHS animations, JPEG images and CD-ROM digital images.

Some PALEOMAP products include: Plate Tectonic Computer Animation (VHS) illustrating motions of the continents during the last 850 million years.

Paleogeographic Atlas consisting of 20 full color paleogeographic maps. (Scotese, 1997).

Paleogeographic Atlas Slide Set (35mm)

Paleogeographic Digital Images (JPEG, PC/Mac diskettes)

Paleogeographic Digital Image Archive (EPS, PC/Mac Zip disk) consists of the complete digital archive of original digital graphic files used to produce plate tectonic and paleographic maps for the Paleographic Atlas.

GIS software such as PaleoGIS and ESH-GIS.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains diffuse horizontal irradiation (DIF) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

GIS for Wildfire Analytics Market Outlook

According to our latest research, the Global GIS for Wildfire Analytics market size was valued at $1.2 billion in 2024 and is projected to reach $3.9 billion by 2033, expanding at a CAGR of 14.2% during 2024–2033. The primary driver for this robust growth is the escalating frequency and severity of wildfires globally, which has compelled governments, environmental agencies, and private organizations to invest heavily in advanced Geographic Information System (GIS) solutions for wildfire detection, risk assessment, and resource management. The increasing integration of remote sensing, artificial intelligence, and real-time data analytics into GIS platforms is further accelerating adoption, offering unparalleled situational awareness and decision-making capabilities to stakeholders involved in wildfire management and mitigation.

Regional Outlook

North America currently holds the largest share of the GIS for Wildfire Analytics market, accounting for over 42% of global revenue in 2024. This dominance is attributed to the region’s mature technology ecosystem, high incidence of wildfires in the US and Canada, and proactive government policies that mandate the use of advanced analytics and GIS for disaster management. The United States, in particular, has made significant investments in wildfire prevention and response infrastructure, leveraging GIS technologies for real-time fire detection, predictive modeling, and post-event analysis. The presence of leading GIS software vendors, such as Esri and Trimble, further strengthens the regional market, enabling rapid deployment of cutting-edge solutions and fostering continuous innovation through public-private partnerships.

The Asia Pacific region is poised to be the fastest-growing market, projected to register a CAGR of 17.8% through 2033. Rapid urbanization, increasing forest cover, and heightened vulnerability to climate-induced wildfires are driving substantial investments in GIS-enabled wildfire analytics across countries like Australia, China, and India. Governments and forestry departments are actively collaborating with international technology providers to implement large-scale wildfire monitoring systems, supported by significant funding and policy reforms. Australia, in particular, has emerged as a leader in deploying satellite-based GIS platforms for early fire detection and real-time response coordination, following devastating wildfire seasons. The region’s burgeoning tech sector and growing awareness of environmental risks are expected to sustain high growth rates over the forecast period.

In emerging economies such as those in Latin America, the Middle East, and Africa, adoption of GIS for wildfire analytics is gaining momentum but remains constrained by limited infrastructure, funding challenges, and varying levels of regulatory support. While countries like Brazil and South Africa have begun integrating GIS technologies into their national disaster management frameworks, widespread deployment is hindered by budgetary constraints and a shortage of skilled personnel. Nevertheless, international aid programs and partnerships with global technology providers are helping to bridge these gaps, enabling localized pilot projects and capacity-building initiatives. As awareness of the economic and environmental impacts of wildfires grows, these regions are expected to gradually increase their adoption of advanced GIS solutions, albeit at a slower pace compared to developed markets.

Report Scope

Enterprise GIS Market Outlook

According to our latest research, the global enterprise GIS market size reached USD 7.9 billion in 2024, and is poised to grow at a robust CAGR of 11.2% during the forecast period, reaching an estimated USD 21.1 billion by 2033. The primary growth factor driving this expansion is the increasing integration of geospatial data analytics across diverse industries, enabling organizations to derive actionable insights, optimize operations, and support strategic decision-making.

One of the fundamental growth drivers for the enterprise GIS market is the exponential rise in the adoption of spatial data analytics and location intelligence across both public and private sectors. Organizations are leveraging GIS platforms to enhance their operational efficiency, improve resource allocation, and gain a competitive edge by making informed decisions based on geospatial data. The proliferation of Internet of Things (IoT) devices and sensors has further augmented the volume and variety of spatial data, necessitating advanced GIS solutions capable of processing, analyzing, and visualizing complex datasets in real time. Additionally, the growing trend of smart cities and urban digitalization initiatives worldwide has significantly boosted demand for enterprise GIS platforms, as municipalities and urban planners rely on geospatial technologies to manage infrastructure, monitor urban growth, and optimize public services.

Another key growth factor is the rapid evolution of cloud-based GIS solutions, which have democratized access to powerful geospatial tools for organizations of all sizes. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling even small and medium enterprises (SMEs) to harness the capabilities of enterprise GIS without significant upfront investments in hardware and IT infrastructure. The integration of artificial intelligence (AI) and machine learning (ML) algorithms into GIS platforms has further enhanced their analytical capabilities, allowing organizations to automate spatial analysis, predict trends, and uncover hidden patterns within geospatial data. This technological convergence is fostering innovation and expanding the range of applications for enterprise GIS across industries such as utilities, transportation, telecommunications, and oil & gas.

The increasing emphasis on regulatory compliance and environmental sustainability is also fueling the growth of the enterprise GIS market. Governments and regulatory bodies worldwide are mandating the use of geospatial data for land use planning, environmental monitoring, disaster management, and infrastructure development. Enterprise GIS platforms facilitate compliance by providing accurate mapping, real-time monitoring, and robust reporting functionalities. Moreover, the heightened awareness of climate change and the need for sustainable resource management have prompted organizations to adopt GIS solutions for monitoring natural resources, assessing environmental impacts, and developing mitigation strategies. These factors collectively underscore the critical role of enterprise GIS in supporting sustainable development and regulatory adherence.

From a regional perspective, North America continues to dominate the enterprise GIS market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s advanced IT infrastructure, high adoption rates of emerging technologies, and the presence of major GIS vendors. Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, government-led digital transformation initiatives, and substantial investments in smart city projects. Europe also represents a significant market, characterized by stringent environmental regulations and a strong focus on sustainable urban development. In contrast, Latin America and the Middle East & Africa are emerging markets with growing adoption of GIS solutions in sectors such as utilities, transportation, and oil & gas, albeit at a relatively moderate pace compared to the other regions.

Component Analysis

The enterprise GIS market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment constitutes the largest share due to the increasing demand for advanced GIS applications that offer powerful data visualization, spatial analysis, and mapping cap

The BIM Software Market is booming, projected to reach [estimated 2033 value in billions] by 2033, growing at a CAGR of 13.90%. Discover key trends, drivers, and leading companies shaping this dynamic sector. Learn more about market segmentation, regional analysis, and future projections for BIM software adoption. Recent developments include: July 2024 - Esri and Autodesk have deepened their partnership to enhance data interoperability between Geographic Information Systems (GIS) and Building Information Modeling (BIM), with ArcGIS Pro now offering direct-read support for BIM and CAD elements from Autodesk's tools. This collaboration aims to integrate GIS and BIM workflows more seamlessly, potentially transforming how architects, engineers, and construction professionals work with geospatial and design data in the AEC industry., June 2024 - Hexagon, the Swedish technology giant, has acquired Voyansi, a Cordoba-based company specializing in Building Information Modelling (BIM), to enhance its portfolio of BIM solutions. This acquisition not only strengthens Hexagon's position in the global BIM market but also recognizes the talent in Argentina's tech sector, particularly in Córdoba, where Voyansi has been developing design, architecture, and engineering services for global construction markets for the past 15 years., April 2024 - Hyundai Engineering has partnered with Trimble Solution Korea to co-develop a Building Information Modeling (BIM) process management program, aiming to enhance construction site productivity through advanced 3D modeling technology. This collaboration highlights the growing importance of BIM in the construction industry, with the potential to optimize steel structure and precast concrete construction management, shorten project timelines, and reduce costs compared to traditional construction methods.. Key drivers for this market are: Governmental Mandates and International Standards Encouraging BIM Adoption, Boosting Project Performance and Productivity. Potential restraints include: Governmental Mandates and International Standards Encouraging BIM Adoption, Boosting Project Performance and Productivity. Notable trends are: Government Mandates Fueling BIM Growth.

This study is vital for helping farmers to produce appropriate crops based on physical land suitability and for assisting land use planners in decision-making. Large-scale crop production is essential to supply raw materials for industries, focusing on areas with high production potential to boost yields and meet growing demand. However, physical land suitability analysis for major cereal crops is lacking in Mansa Watershed of Southwest Ethiopia. Therefore, this research aimed to assess the physical land suitability for key crops such as wheat, maize, and teff. Utilizing the FAO land evaluation framework, the study employed various data sets, including Sentinel-2A satellite images, soil data, climate information, and elevation models, to determine suitability factors. The Analytical Hierarchy Process (AHP) was used for pairwise comparison of parameters, while Geographic Information System (GIS) software’s weighted overlay tool was applied to evaluate suitability for the specified crops. A vector overlay was utilized for land allocation for each crop. The analysis considered ten criteria: soil pH, depth, texture, drainage, organic matter, slope, altitude, rainfall, temperature, and land use change. Results indicated that approximately 29.6%, 61%, and 50% of the study area were moderately suitable for maize, teff, and wheat production, respectively. Additionally, 52.8%, 38.8%, and 13.9% of the area were marginally suitable for these crops, while 17.6% and 36% of the area were unsuitable for maize and wheat, respectively. Overall, 44% of the land was moderately suitable, and 10% was marginally suitable for the selected crops. Notably, there were no areas classified as highly suitable; most lands were identified as moderately or marginally suitable. Moving forward, sustainable land management practices are necessary to enhance land suitability and improve soil health. Further analyses should also consider irrigation facilities, market access, and processing industries to provide more options for stakeholders and policymakers.

Land Management Software Market Outlook

The global land management software market size is projected to grow significantly from USD 1.5 billion in 2023 to USD 3.8 billion by 2032, reflecting an impressive compound annual growth rate (CAGR) of 10.5% during this period. This robust growth is driven by multiple factors including advancements in geospatial technologies, the increasing need for efficient land utilization, and heightened regulatory requirements for land management practices.

One of the primary growth factors of the land management software market is the rapid technological advancements in geospatial and remote sensing technologies. These innovations are making it easier to manage land resources more efficiently and accurately. The integration of Geographic Information System (GIS) technologies and remote sensing allows for real-time data collection and analysis, which significantly enhances decision-making processes. Furthermore, the advent of Artificial Intelligence (AI) and Machine Learning (ML) in land management software is expected to optimize land use and improve predictive capabilities, driving the market’s growth.

Another significant growth factor is the increasing global emphasis on sustainable land management practices. As governments and private enterprises become more aware of the environmental impact of land use, there is a growing demand for software solutions that can help monitor, manage, and mitigate these impacts. Policies and regulations aimed at promoting sustainable land use are being enacted globally, compelling landowners and managers to adopt advanced land management software. These regulatory pressures are expected to drive significant adoption of advanced land management solutions over the forecast period.

The rising need for efficient land utilization, particularly in urban areas, is also a crucial growth driver. With global urbanization rates climbing, the need to manage land resources in urban settings has never been more critical. Land management software helps in the optimal allocation and use of land resources, facilitating better urban planning and development. This is particularly vital in densely populated regions where space is at a premium and efficient land use can significantly impact economic and social outcomes.

Regionally, North America is anticipated to dominate the land management software market, attributed to the region's advanced technological infrastructure and high adoption rates of innovative land management solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing investments in smart city projects, and the rising need for efficient land management practices in agriculture and forestry sectors.

Component Analysis

The land management software market is segmented by component into software and services. The software segment is expected to account for the largest market share during the forecast period, driven by continuous advancements in software capabilities and increasing demand for integrated land management solutions. These software solutions offer comprehensive functionalities, including land use planning, property management, and environmental monitoring, which are crucial for efficient land resource management.

Software solutions in land management are increasingly incorporating advanced technologies such as GIS, AI, and ML to provide enhanced functionalities and greater accuracy. These technologies enable real-time data analysis and predictive modeling, which are essential for making informed decisions about land use. The growing adoption of cloud-based land management software is also contributing to the segment’s growth, as it offers greater flexibility, scalability, and cost-effectiveness compared to traditional on-premises solutions.

On the services front, there is a rising demand for consulting, implementation, and maintenance services. As organizations and governments adopt more sophisticated land management software, they require expert guidance to ensure successful deployment and integration with existing systems. Professional services help in customizing the software solutions to meet specific needs, training users, and providing ongoing support, thereby enhancing the overall efficiency and effectiveness of land management practices.

Furthermore, the increasing complexity of land management projects, particularly in urban and environmentally sensitive areas, is driving the demand for comprehensiv

This 90 minute session will cover data discovery and extraction via the CHASS Census Analyzer and basic GIS visualization. We will highlight the added value features of using CHASS compared to Statistics Canada Census Profiles. We will provide an overview of the steps involved in visualizing Census data in ArcGIS, including data elements and major processes. This session will also feature a critical discussion on visualizing Census data in GIS software, focusing on the technical expertise required to produce usable visualizations as well as the responsibility (and credit) for producing visualizations.

These data are digital elevation models (DEMs) of difference (DoD). They are a geospatial dataset created in raster (.tif) format and quantify vertical (z) topographic change between two dates. The data were created to support analysis of landscape change following the 7th February 2021 avalanche-debris flow in Chamoli District, Uttarakhand, India. The data also supported numerical modelling using CAESAR-Lisflood (see related data https://catalogue.ceh.ac.uk/documents/7023cb77-c797-475e-872c-6f1e2b63dcc1). They are most commonly imported into GIS software, where they can be analysed or support other forms of geospatial analysis.