Geospatial analytics applies spatial or geographical data to analyze and understand relationships, trends, and patterns within a geographic region. Methodologies from computer science, geography, statistics, and mathematics are combined in geospatial analysis to comprehend spatial relationships and patterns.
Geospatial analytics comprises the analysis of location-based, remote sensing, spatial, and Geographic Information System (GIS) data, among others. Numerous industries, including urban planning, environmental monitoring, transportation, natural resource management, and disaster response, find applications for geospatial analytics.
Increasing Urbanization Drives the Global Market
The rapid and unplanned urbanization process poses many challenges to municipalities' ecological and social sustainability. The utilization of geospatial analytics is critical in facilitating infrastructure construction, enhancing urban planning, and optimizing resource allocation. It enables the surveillance of urban growth, the determination of optimal land areas, the streamlining of transportation infrastructure, the control of traffic congestion, the reduction of greenhouse gas emissions, and the improvement of urban inhabitants' quality of life.
As per the United Nations' projections, the proportion of the world's population residing in urban areas will increase from 56% in 2021 to 68% by 2050. This results in a significant demand for geospatial analytics to support urban development initiatives and smart city initiatives. For instance, the Smart Dubai initiative aims to transform Dubai into an environmentally sustainable and intelligent metropolis using geospatial analytics by 2030. This will be accomplished by applying data and technology to improve its inhabitants' efficiency, safety, and welfare. These factors are expected to stimulate the expansion of the global market.
Integration of AI and Machine Learning Creates Tremendous Opportunities
The integration of artificial intelligence (AI) and machine learning (ML) presents the geospatial analytics market with a tremendously lucrative opportunity. Advanced analytics algorithms enhance the ability to derive actionable insights from geospatial data. Large quantities of data can be analyzed autonomously by machine learning algorithms, which can identify intricate patterns and trends that would be challenging to discern using traditional approaches. Thus, major actors are incorporating AL and ML solutions into geo-analytics tools at an increasing rate.
For instance, SoilMate, a European provider of geo-analysis tools, unveiled SoilMate.ai, a web-based service centered on remote earth analytics, in October 2023. SoilMate presents an innovative methodology for extracting valuable insights from geospatial data, catering to a wide audience, including seasoned professionals, researchers, and organizations in the agriculture and forestry industries, all interested in sustainable supply chains. This development is expected to create opportunities for the expansion of the market.
North America is the most significant global geospatial analytics market shareholder and is expected to expand substantially during the forecast period. The primary driver behind the growth of the geospatial analytics market in North America is the increasing demand for geospatial solutions in various sectors, such as transportation, defense, government, and healthcare. The region is distinguished by a notable concentration of renowned corporations, including Esri, Trimble, Hexagon, and IBM, that offer sophisticated and customized geospatial solutions to fulfill the diverse demands of their customers. These major competitors are introducing fresh products to increase their market share.
ESRI, a United States-based geospatial image analytics solutions provider, introduced the ArcGIS platform in January 2021. The ArcGIS Platform developed by ESRI functions according to a cloud consumption model. App developers frequently utilize this technology to incorporate location capabilities into their products, company operations, and applications. It promotes the ease of access to geospatial technology.