The global Urban Planning Software market is experiencing robust growth, with a market size of $8.87 billion in 2025 and a projected Compound Annual Growth Rate (CAGR) of 7.81% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization globally necessitates efficient and sustainable urban planning, fueling demand for sophisticated software solutions. Government initiatives promoting smart city development and infrastructure modernization are further boosting market adoption. The integration of advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), and Geographic Information Systems (GIS) within urban planning software enhances its capabilities, leading to improved decision-making and resource allocation. Furthermore, the growing adoption of cloud-based solutions offers scalability and accessibility, contributing to market growth. While the market faces challenges such as high initial investment costs and the need for skilled professionals to operate these complex systems, the long-term benefits of improved urban planning and resource management outweigh these limitations. The market is segmented by deployment (cloud-based and web-based), end-user (government, real estate, and infrastructure companies), and geography, with North America currently holding a significant market share due to early adoption and technological advancements. However, regions like APAC are witnessing rapid growth, driven by substantial infrastructure development projects and increasing government investments. The competitive landscape is characterized by a mix of established players and innovative startups, fostering innovation and competition. The continued growth of the Urban Planning Software market is expected to be fueled by several factors. The rising adoption of Building Information Modeling (BIM) for improved collaboration and design efficiency within urban projects will be a major driver. Furthermore, the growing need for data-driven insights for better urban planning and sustainable development strategies will further bolster the market. Increased focus on environmental sustainability and climate change mitigation will also drive demand for software capable of integrating environmental impact assessments into urban planning. The market's expansion will also be influenced by the increasing adoption of mobile-based solutions, providing greater accessibility and flexibility for urban planners. Competition among vendors will intensify, pushing innovation and driving the development of more sophisticated and user-friendly software solutions, ensuring continuous growth in the coming years. Specific regional growth patterns are expected to be influenced by factors such as economic conditions, government policies, and technological maturity levels in different areas.

The data is digitized from the urban planning announcement provided by the Urban Development Bureau. The fields include number, administrative district, use zone, zone abbreviation, urban plan name, establishment date, area, building coverage ratio, volume ratio, maximum volume, urban planning area, detailed planning area, remarks, revision date, publication number, and project name.

The National Urban Change Indicator (NUCI) is a change indicator dataset covering the lower 48 United States that uses Maxar’s PCM®, imagery-derived change detection, to map persistent changes to the landscape resulting from urban development. The input data for the PCM process are a multi-temporal stack of precision, co-registered Landsat multispectral scenes. This NUCI 2016 layer provides a history of change areas on an annual basis from 1987 through 2016Co-Registered Geospatial DataIn addition to capturing the PCM-determined date of change, the NUCI 2016 dataset is attributed with data elements extracted from the following co-registered geospatial data sets:2011 National Land Cover Data (NLCD 2011) Land Cover: Each change polygon is attributed with NLCD 2011 land cover name and class number of the area covered by the polygon. If more than one land cover category is present, attributes are also provided for the secondary (by percentage pixel count) and tertiary classes. The percentage of polygon area for each class is also captured and provided.Urban Gravity: Each change polygon is attributed with an “Urban Gravity” value. The Urban Gravity is calculated by treating the Impervious Surface (percent impervious by pixel) data of the NLCD 2011 dataset as units of mass and then calculating a “gravitational pull” as the inverse square distance measure at the center of the change polygon. The higher the Urban Gravity value, the closer the polygon center is to existing concentrations of NLCD 2011 mapped impervious surface areas.Distance to Water: Distance, in meters, to the nearest water body as defined by the NLCD land cover dataset. Values greater than 2,000 meters are shown as “999999”.Shuttle Radar Topography Mission (SRTM)Height Variance: Each change polygon is attributed with a measure of the average elevation variance, in meters, across the polygon. The measure is calculated from the 3 arc-second SRTM digital elevation data using a standard variance filter over a 7x7 kernel.Additional NotesThis tile layer is intended for visualization purposes. The NUCI feature layer can be used as input to spatial analysis tools and applications.A NUCI 2016 change is defined as one which meets the “3-observation change (3oc)” criteria where the detected state of change has persisted for three independent date observations.This version of NUCI 2016 was filtered to focus on changes related to human activity in order to mute spurious changes and false positives.

This COVADIS data standard concerns local urban planning documents (PLUs) and land use plans (POS that are equivalent to PLU). This data standard provides a technical framework describing in detail how to dematerialise these urban planning documents into a geographical database that is exploitable by a GIS and interoperable tool. This COVADIS data standard was developed on the basis of the specifications for the dematerialisation of urban planning documents updated in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The dematerialisation of the graphic documents of a PLU, POS generates a spatial data set composed of several catalogs of objects: The ZONE_URBA class containing the urban areas corresponding to the PLU zoning plan (R.123-5 to 8): urban areas (U), urban areas (AU), agricultural areas (A) and natural and forest areas (N). A settlement is attached to each zone. The regulation may lay down different rules, depending on whether the purpose of the constructions will concern housing, hotel accommodation, offices, commerce, crafts, industry, farming or forestry or the function of warehouse. The PRESCRIPTION class containing all surface, linear and point requirements for PLU or POS (R123-11). They are superimposed on an area of the urban planning document and generally exert an additional constraint on the settlement of the area. A regulation is attached to each prescription.

This dataset is about books and is filtered where the book is GIS in Italian urban planning, featuring 5 columns: author, BNB id, book, book publisher, and publication date. The preview is ordered by publication date (descending).

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2031, growing at a CAGR of 12.10% during the forecast period 2024-2031.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth’s surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today’s interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

Weekly snapshot of Cleveland City Planning Commission datasets that are featured on the City Planning Zoning Viewer. For the official, most current record of zoning info, use the CPC Zoning Viewer.This file is an open-source geospatial (GIS) format called GeoPackage, which can contain multiple layers. It is similar to Esri's file geodatabase format. Free and open-source GIS software like QGIS, or software like ArcGIS, can read the information to view the tables and map the information.It includes the following mapping layers officially maintained by Cleveland City Planning Commission:Planner Assignment AreasPlanned Unit Development OverlayResidential FacilitiesResidential Facilities 1000 ft. BufferPolice DistrictsLandmarks / Historic LayersLocal Landmark PointsLocal Landmark ParcelsLocal Landmark DistrictsNational Historic DistrictsCentral Business DistrictDesign Review RegionsDesign Review DistrictsOverlay Frontage LinesForm & PRO Overlay DistrictsLive-Work Overlay DistrictsSpecific SetbacksStreet CenterlinesZoningUpdate FrequencyWeekly on Mondays at 4:30 AMContactCity Planning Commission, Zoning & Technology

Land Management Software Market size was valued at USD 1.69 Billion in 2024 and is projected to reach USD 2.62 Billion by 2031, growing at a CAGR of 5.65% from 2024 to 2031.

The growth of land management software is primarily driven by the increasing demand for efficient land use, advancements in geospatial technology, regulatory compliance, and the need for data-driven decision-making. As global populations grow and urbanization accelerates, there is a growing need for efficient land resource management. Land management software offers tools to optimize land use, enhance productivity in agriculture, forestry, and urban planning, and ensure sustainable development practices.

Advancements in geospatial technology, such as Geographic Information Systems (GIS), remote sensing, and satellite imagery, have significantly enhanced the capabilities of land management software, enabling more accurate mapping, monitoring, and analysis of land resources. Regulatory compliance and environmental concerns also drive the adoption of land management software among government agencies, landowners, and businesses.

Data-driven decision-making is another driving factor, as land management software provides powerful analytical tools for processing large volumes of spatial data, generating insights, and supporting data-driven decision-making processes. The growing awareness of climate change risks and the need for resilient land management practices drives the adoption of software solutions that enable climate-smart land management.

Precision agriculture practices are increasingly emphasized in the agricultural sector, with land management software playing a critical role in supporting these practices. The emergence of integrated land management platforms that combine GIS, asset management, and workflow automation capabilities is also driving the adoption of comprehensive software solutions.

In conclusion, the growth of land management software is driven by the need for efficient land use, advancements in technology, regulatory requirements, and the recognition of the importance of sustainable land management practices in addressing global challenges such as food security, environmental degradation, and climate change.

The global urban planning software market was valued at USD 1.69 billion in 2025 and is projected to reach USD 3.29 billion by 2033, exhibiting a CAGR of 8.04% during the forecast period. Rising urbanization and increasing population density have led to a growing demand for efficient urban planning to manage land use, infrastructure, and transportation. Additionally, the integration of Geographic Information Systems (GIS) and data analytics into urban planning software is enhancing decision-making capabilities and streamlining planning processes. Key market drivers include increasing government investments in urban infrastructure, rising adoption of cloud-based solutions due to their flexibility and cost-effectiveness, and the growing need for integrated planning systems that can address multiple aspects of urban development. However, factors such as limited awareness of the benefits of urban planning software in emerging markets and data security concerns may restrain market growth. Regional analysis indicates that North America and Europe hold significant market shares due to the presence of established urban planning practices and well-developed infrastructure. Asia Pacific is expected to witness substantial growth potential owing to rapid urbanization and increasing investments in smart city initiatives. The urban planning software market is projected to reach USD 2.29 billion by 2028 from USD 1.69 billion in 2023, at a CAGR of 8.04% during the forecast period. The market is driven by the increasing demand for efficient and sustainable urban planning solutions to address the challenges of urbanization and population growth. Recent developments include: , The Urban Planning Software Market is projected to reach USD 2.29 billion by 2028 from USD 1.69 billion in 2023, at a CAGR of 8.04% during the forecast period. The market is driven by the increasing demand for efficient and sustainable urban planning solutions to address the challenges of urbanization and population growth.Key developments in the market include: In June 2023, Bentley Systems acquired CityEngine, a leading provider of 3D urban modeling software. This acquisition strengthened Bentley's position in the urban planning software market and expanded its portfolio of solutions for city planning and design. In March 2023, Esri, a leading provider of geographic information systems (GIS) software, launched ArcGIS Urban, a comprehensive suite of tools for urban planning and management. This launch enhances Esri's offerings for the urban planning market and provides a comprehensive solution for city planners and urban designers.These developments indicate the growing importance of urban planning software in addressing the challenges of urbanization and creating sustainable and livable cities., Urban Planning Software Market Segmentation Insights. Key drivers for this market are: Smart city initiatives Cloud-based solutions AI and machine learning integration Geospatial data analytics Collaborative planning tools. Potential restraints include: Increasing urbanization Government initiatives Technological advancements Rising demand for sustainable urban planning Growing adoption of cloud-based solutions.

The rapid population growth in British Columbia has led to the necessity of innovative housing solutions. Local municipalities in BC, such as Bowen Island, are exploring the implementation of Density Transfer Modelling (DTM) as a planning tool to address these challenges. The study examines Density Transfer Modelling by Geographic Information System (GIS) application on Bowen Island, managed under the Islands Trust Act, to balance development with ecological preservation. This involves identifying "donor" sites (areas of high ecological value with existing development) to transfer development rights from, and "receiver" sites (areas suitable for increased urban density) using the Normalized Difference Built-up Index and residential density classifications. Two main Comprehensive Development Areas (CDAs) on Bowen Island, Arbutus Ridge and Snug Cove are highlighted. The DTM calculates that this area supports the development of up to 30 additional detached homes in Arbutus Ridge Development Area. The Snug Cove Comprehensive Development Area (Snug Cove CDA) has been identified as a key area for increased residential development with a focus on increasing affordability and creating a pedestrian-friendly environment. According to DTM calculations Snug Cove Residential Area supports the development of 2186 dwelling units. The goal of the Snug Cove Development Area is to build a variety of housing types, including duplexes, triplexes, and multi-unit buildings, clustered near essential services and transportation hub(ferry). Both CDAs exemplify how density transfer modellings can be effectively utilized within designated development areas to support sustainable urban planning goals.

Unlock precise, high-quality GIS data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

This dataset is about book subjects and is filtered where the books is GIS in Italian urban planning, featuring 4 columns: authors, book subject, books, and publication dates. The preview is ordered by number of books (descending).

The global Geographic Information Systems (GIS) market is projected to reach a value of USD 2890.3 million by 2033, expanding at a CAGR of 5.3% during the forecast period (2025-2033). The growing demand for GIS solutions for urban planning, infrastructure management, environmental monitoring, and disaster response is driving market growth. Additionally, the increasing adoption of cloud-based GIS platforms and the integration of GIS with other technologies such as artificial intelligence (AI) and the Internet of Things (IoT) are contributing to the market's expansion. Key trends shaping the GIS market include the rise of location intelligence, which involves using GIS data to make informed decisions about spatial relationships and patterns. The increasing availability of open-source GIS software and data is also driving market growth, as it enables organizations to access and utilize GIS without significant upfront costs. Furthermore, the adoption of GIS by governments and businesses for decision-making and planning purposes is contributing to the market's expansion. Among the application segments, transport and logistics are expected to witness significant growth as GIS plays a crucial role in optimizing routes, managing fleet operations, and improving supply chain efficiency.

The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole.

Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model".

The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals - Please note that this dataset has been moved to a separate catalog post (https://doi.org/10.5878/t8s9-6y15) and unpublished due to licensing restrictions on its source dataset. • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals

In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city.

The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’.

The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives.

1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press.
2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

12 GIS-layers of plots in Gothenburg, from 1960 to 2015, in 5-year intervals. Only built upon plots (plots with buildings) are included. File format: shapefile (.shp), MapinfoTAB (.TAB). The coordinate system used is SWEREF 99TM, EPSG:3006.

See the attached Technical Documentation for the description and further details on the production of the datasets. See the attached Report for the description of the related research project.

This COVADIS data standard concerns local planning documents (LDPs) and land use plans (POSs that are PLU). This data standard provides a technical framework describing in detail how to dematerialise these planning documents into a spatial database that can be used by a GIS tool and interoperable. This standard of data concerns both the graphic zoning plans, the superimposed requirements and the regulations applying to each type of area.This standard of COVADIS data was developed on the basis of the specifications for the dematerialisation of urban planning documents updated in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard provides definitions and a structure for organising and storing existing PLU/POS spatial data in an infrastructure in digital form, while the CNIG specification serves to frame the digitisation of such data. The ‘Data Structure’ section presented in this COVADIS standard provides additional recommendations for the storage of data files (see Part C). These are choices specific to the MAA and MEDDE data infrastructure that do not apply outside their context. Communal maps are the subject of another COVADIS data standard.

The Project Development Dashboard presents information about projects in the City of Vancouver categorized by various stages of development. Development types captured in this Dashboard include: commercial, industrial, mixed use--residential, multi-family residential, post decision review, residential, residential land division, and TYPE IV Application Review.NOTE:This product and the information shown is provided "AS IS" and exists for informational purposes only. The City of Vancouver (COV) makes no warranties regarding the accuracy of such data. This product and information is not prepared, nor is suitable, for legal, engineering, or surveying purposes. Any sale, reproduction or distribution of this information, or products derived therefrom, in any format is expressly prohibited. Data are provided by multiple sources and subject to change without notice.

This COVADIS data standard concerns local planning documents (LDPs) and land use plans (POSs that are PLU). This data standard provides a technical framework describing in detail how to dematerialise these planning documents into a spatial database that can be used by a GIS tool and interoperable. This standard of data concerns both the graphic zoning plans, the superimposed requirements and the regulations applying to each type of area.This standard of COVADIS data was developed on the basis of the specifications for the dematerialisation of urban planning documents updated in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard provides definitions and a structure for organising and storing existing PLU/POS spatial data in an infrastructure in digital form, while the CNIG specification serves to frame the digitisation of such data. The ‘Data Structure’ section presented in this COVADIS standard provides additional recommendations for the storage of data files (see Part C). These are choices specific to the MAA and MEDDE data infrastructure that do not apply outside their context. Communal maps are the subject of another COVADIS data standard.

[Metadata] 2020 Census Urban Areas for the State of Hawaii. Source: US Census Bureau, 2023. 2020 Census Urban Areas consist of 5,000 or more people or 2,000 or more housing units. For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/uac20.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/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.

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