Explore the booming Indoor GIS Software market, driven by warehouse logistics, asset management, and military applications. Discover market size, CAGR, drivers, trends, and regional growth from 2025-2033.

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

The Asia-Pacific Geographic Information System (GIS) market is expanding rapidly, due to the widespread application of geospatial technology in industries like as agriculture, construction, mining, and manufacturing. Government initiatives fostering infrastructure development and smart city projects amplify this growth. The incorporation of new technologies like cloud computing, artificial intelligence, and big data analytics into GIS platforms improves their capabilities, resulting in more effective decision-making and resource management. This is likely to enable the market size to surpass USD 16.55 Billion valued in 2024 to reach a valuation of around USD 34.95 Billion by 2032.Several important companies help shape the competitive landscape of the Asia-Pacific GIS Market. SuperMap Software Co., Ltd., located in Beijing, China, has established itself as a prominent GIS platform maker, providing a wide range of GIS software products and services to a variety of industries. MapmyIndia, headquartered in New Delhi, India, specializes in digital map data and location-based services, serving sectors such as automotive, logistics, and consumer technology. Esri Australia provides location intelligence solutions, catering to industries like defense, emergency management, and environmental monitoring. These companies, among others, play pivotal roles in advancing GIS technologies and meeting the diverse needs of sectors such as urban planning, transportation, and environmental management within the region. The rising demand for Asia Pacific GIS is enabling the market to grow at a CAGR of 9.08% from 2026 to 2032.

Discover the booming Indoor GIS Software market! This comprehensive analysis reveals a $1.5B market in 2025 projected to reach $5B by 2033, driven by smart buildings, warehouse logistics, and expanding applications. Explore key trends, regional insights, and leading companies shaping this dynamic sector.

The global location intelligence software market is projected to be valued at $15 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 7.5%, reaching approximately $30 billion by 2034.

WIGeoGIS, a long-standing TomTom partner, provides global road data enriched with comprehensive routing and navigation attributes. These datasets are ideal for professional users and software developers in logistics, telematics, transport management, and freight forwarding.

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

The Location Analytics Software market is booming, projected to reach $37.83 billion in 2025 with a strong CAGR. This comprehensive analysis explores market drivers, trends, restraints, key players (IBM, Microsoft, Google), and regional growth. Discover the future of location intelligence and its impact on various industries.

Explore the expanding Geographic Information System (GIS) market, projected at USD 10,880 million in 2025 with a 5.8% CAGR. Discover key drivers, industry applications, hardware & software trends, and regional growth opportunities.

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

Discover the booming Real-Time Bus Query Software market, projected to reach $6.14 billion by 2033 with a 15% CAGR. This in-depth analysis explores market drivers, trends, restraints, and key players like Moovit and Transit, providing valuable insights for investors and businesses.

This information is being published so that can be used for research projects / ransparency, and recognised that the data is both detailed, and requires specialist GIS software for analysis Between 23 November 2023 and 4 February 2024, we held a significant public consultation looking at 10 policies to deliver a more sustainable future for York’s transport. There are ten policy areas which we believe will help us deliver our ambitions for transport:

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of location-based services (LBS) and geographic information systems (GIS) across industries like real estate, tourism, logistics, and urban planning is a major catalyst. Businesses are increasingly leveraging interactive maps to enhance customer engagement, improve operational efficiency, and gain valuable insights from geospatial data. Furthermore, advancements in mapping technologies, including the integration of AI and machine learning for improved data analysis and visualization, are contributing to market growth. The accessibility of user-friendly tools, coupled with the decreasing cost of cloud-based solutions, is also making interactive map creation more accessible to a wider range of users, from individuals to large corporations. However, the market also faces certain challenges. Data security and privacy concerns surrounding the use of location data are paramount. The need for specialized skills and expertise to effectively utilize advanced mapping technologies may also hinder broader adoption, particularly among smaller businesses. Competition among established players like Mapbox, ArcGIS StoryMaps, and Google, alongside emerging innovative solutions, necessitates constant innovation and differentiation. Nevertheless, the overall market outlook remains positive, with continued technological advancements and rising demand for data visualization expected to propel growth in the coming years. Specific market segmentation data, while unavailable, can be reasonably inferred from existing market trends, suggesting a strong dominance of enterprise-grade solutions, but with substantial growth expected from simpler, more user-friendly tools designed for individuals and small businesses.

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

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.

ASEAN Geospatial Analytics Market size was valued at USD 0.67 Billion in 2024 and is projected to reach USD 1.69 Billion by 2032, growing at a CAGR of 12.00% from 2026 to 2032.

ASEAN Geospatial Analytics Market Drivers

Increasing Adoption of Location-Based Services (LBS): Industries like transportation, retail, and healthcare are increasingly utilizing LBS, driving the demand for real-time location intelligence and geospatial analytics. Rising Investments in Smart Cities and Urban Planning: Governments across ASEAN are investing in smart city initiatives to optimize infrastructure, resource management, and promote sustainable urban development, creating a strong need for geospatial tools. Growth in Innovative Solutions by Market Vendors: Major players are continuously developing and launching innovative geospatial analytics solutions, expanding the market's capabilities and applications. Increasing 5G Rollout and Integration of Advanced Technologies: The deployment of 5G networks and the integration of AI, ML, VR, AR, and IoT into geospatial analytics solutions are enhancing their power and applicability across various sectors. Evolving Role of National Geospatial Organizations: National geospatial organizations within ASEAN are playing a greater role in driving market growth by promoting the use and development of geospatial technologies.