The Indoor GIS Software market is experiencing robust growth, driven by the increasing need for precise location-based services within enclosed spaces. The market, valued at approximately $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of smart buildings and IoT devices provides a wealth of data that Indoor GIS software can effectively leverage for enhanced operational efficiency and improved user experiences. Secondly, the burgeoning e-commerce sector and the consequent demand for optimized warehouse logistics and efficient supply chain management are significantly boosting market demand. Thirdly, the expansion of applications into sectors like healthcare, retail, and security is further diversifying market opportunities. Cloud-based solutions are witnessing higher adoption due to their scalability, cost-effectiveness, and ease of deployment compared to on-premise solutions. However, concerns regarding data security and privacy, as well as the relatively high initial investment costs for implementing Indoor GIS systems, pose challenges to market growth. Segmentation reveals strong demand across various applications. Warehouse logistics and asset management currently dominate the market share due to the clear ROI benefits of improved inventory management and asset tracking. The military and security sectors also present lucrative growth opportunities, driven by the need for sophisticated indoor navigation and situational awareness. Geographically, North America and Europe currently hold the largest market shares, attributed to the high concentration of technologically advanced businesses and early adoption of Indoor GIS technologies. However, Asia-Pacific is expected to show significant growth in the coming years, propelled by rapid urbanization and expanding industrial sectors in countries like China and India. Companies like Mapedin, Esri, and others are key players driving innovation and shaping the competitive landscape. The ongoing development of advanced features such as real-time location tracking, augmented reality integration, and improved data analytics capabilities will further fuel market growth in the coming years.

The global GIS Consulting Service market is expected to reach 1637 million by 2023, growing at a CAGR of 15% during the forecast period. Geospatial data analytics, predictive modeling, and situational awareness are key drivers of the market growth. The rising adoption of GIS in various industries, such as transportation, agriculture, energy, and government, is contributing to the market's expansion. The market is segmented based on type, application, and region. By type, the market is divided into custom mapping services, GIS mapping software development, and others. The custom mapping services segment is expected to hold the largest share of the market due to the increasing demand for customized maps for specific purposes. By application, the market is segmented into transportation, agriculture, energy, and others. The transportation segment is expected to witness the highest growth rate due to the growing use of GIS in traffic management, route optimization, and logistics. By region, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America is expected to hold the largest share of the market due to the presence of key players and the early adoption of GIS technology. Asia Pacific is expected to experience the highest growth rate due to the increasing infrastructure development and urbanization in the region.

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 size and share of the market is categorized based on Type (Hardware, Software, Services) and Application (Transport and Logistics, Agriculture, Construction, Mining and Geology, Oil & Gas, Aerospace and Defense, Utilities, Government) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Contains river system overlays that depicts rivers which are heavily utilized and relied upon by Alaskan communities. These overlays are strictly for visualization purposes and are not intended for navigational use. This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section.

PTV Digital Data Streets is an in-depth digitized street network based on HERE or TomTom, and is available for the whole of Europe and many countries around the world. It is offered in the specifications GIS and ROUTE and can be combined with a wide range of demographic data.

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 size and share of the market is categorized based on Application (Urban Planning, Environmental Management, Natural Resource Management, Emergency Response, Transportation & Logistics, Business Intelligence) and Product (GIS Software, GIS Data, GIS Services, GIS Applications, GIS Platforms) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The SDE Transport Database (STRANSPORT) is a subset of the former SDE Best Available Geographic Database (SBAGD). STRANSPORT contains only the transport feature datasets and feature classes such as aviation, marine, road and rail.

SBAGD is a historic database comprising the GEODATA TOPO 250K Series 3 data and any updates that were made from 2008-2013. This vector data represents major topographic features and has been sourced through many programs such as the National Topographic Information Coordination Initiative (NTICI).

The topographic data complies with the Topographic Data and Map Specifications for the National Topographic Database & NTMS Series 1:250 000 & 1:100 000 scale topographic map products version 6.0.

To use this dataset please contact the Spatial Platforms team (eGIS), spatialplatforms@ga.gov.au, and obtain an SDE login for use within your selected GIS software.

Biodiversity objects are to date almost absent from the BIM world. Developing the biodiversity theme in the BIM environment offers unexplored research opportunities with strong impact at the same time for biodiversity and transport infrastructure management. Efficient mainstreaming of biodiversity in transport infrastructure would require the GIS, BIM, Digital Twin dedicated software interoperability.

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Geospatial Analytics Market Size 2024-2028

The geospatial analytics market size is forecast to increase by USD 127.2 billion at a CAGR of 18.68% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of geospatial data analytics in sectors such as healthcare and insurance. This trend is driven by the abundance of data being generated through emerging methods like remote sensing, IoT, and drones. However, data privacy and security concerns remain a challenge, as geospatial data can reveal sensitive information.
Organizations must implement robust security measures to protect this valuable information. In the US and North America, the market is expected to grow steadily, driven by the region's advanced technological infrastructure and increasing focus on data-driven decision-making. Companies in this space should stay abreast of emerging trends and address concerns related to data security to remain competitive.

What will be the Size of the Geospatial Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for location intelligence in various industries, particularly Medium Scale Enterprises (MSEs). This market is driven by the integration of Artificial Intelligence (AI) and machine learning (ML), enabling advanced data analysis and prediction capabilities. The Internet of Things (IoT) is also fueling market growth, as real-time location data is collected and analyzed for various applications, including disaster risk reduction. Hexagon and Luciad are among the key players in this market, offering advanced geospatial analytics solutions. Big data analysis, digital globe imagery, and Pitney Bowes' location intelligence offerings are also contributing to market expansion.
The integration of AI, ML, and 5G technology is expected to further accelerate growth, with applications ranging from supply chain optimization to web-based GIS platforms built using JavaScript and HTML5.

How is this Geospatial Analytics Industry segmented and which is the largest segment?

The geospatial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments.

Technology

  GPS
  GIS
  Remote sensing
  Others


End-user

  BFSI
  Government and utilities
  Telecom
  Manufacturing and automotive
  Others


Component

  Software
  Service


Type

  Surface & Field Analytics
  Network & Location Analytics
  Geovisualization
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Technology Insights

The GPS segment is estimated to witness significant growth during the forecast period. The market is driven by various sectors including Defense & Internal Security, Retail & Logistics, Energy & Utilities, Agriculture, Healthcare & Life Sciences, Infrastructure, and GIS. Among these, GPS, a satellite-based radio navigation system, was the largest segment in 2023. Operated by the US Space Force, GPS enables geolocation and time information transmission to receivers, facilitating georeferencing, positioning, navigation, and time and frequency control. This technology is widely used in industries such as logistics, transportation, and surveying, making it a significant contributor to the market's growth. The Energy & Utilities sector also leverages geospatial analytics for infrastructure planning, asset management, and maintenance, further fueling market expansion.

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The GPS segment was valued at USD 29.90 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American region dominates the market due to the region's early adoption of advanced technologies and the maturity of industries, particularly in healthcare and the industrial sector. The healthcare industry's need for high-level analytics, driven by the COVID-19 pandemic, is a significant factor fueling market growth. In the industrial sector, the abundance of successful technology implementations leads to a faster rate of adoption. Geospatial analytics plays a crucial role in various applications. These applications provide valuable insights for businesses and governments, enabling informed decision-making and improving operational efficien

Descriptive statistics of census tract the geospatial-based macroscale indicators composition, and the walkability index in Belo Horizonte, Brazil. (n = 3,933).