Geospatial analysis

Geospatial analysis involves the collection, manipulation, and exhibition of geographic information system (GIS) data and imagery, including GPS and satellite photographs. Geospatial data analytics uses geographic coordinates and identifiers such as street address and postal code to create geographic models and data visualizations for modeling and trend prediction. Geospatial analytics sources data from various technologies, such as GPS, location sensors, social media, mobile devices, and satellite imagery to construct data visualizations (such as maps, graphs, statistics, and cartograms). This can provide insight into certain phenomena by establishing trends in the connections between people and places.

IBM defined geospatial data as information that describes objects, events, or other features with a location on or near the earth's surface. Geospatial data focuses on the combination of locational information (commonly coordinates on the earth) and attribute information (the characteristics of the examined object, event, or phenomena) with temporal data (the time or life span at which the location and attributes exist). The location provided may be static in the short term (e.g., the location of a piece of equipment, an earthquake event, children living in poverty) or dynamic (e.g., a moving vehicle or pedestrian, the spread of an infectious disease).

According to IBM, as of 2020 the geospatial analytics market had been undergoing considerable and steady growth, with predictions pointing to 12.9% annual sales growth until 2025, achieving the total approximate value of USD 96.3 billion by that year.

According to University of Southern California (USC)'s Geographic Information Science and Technology department, some of the top industries for Geographic Information Science (GIS) include supply chain management, insurance, forestry, urban planning, banking, and healthcare. Various industries use geospatial analytics in the following ways:

• Governments can use geospatial insights about health, disease, and weather to inform the public in the event of a natural disaster or large-scale health emergency.

• Electric utilities providers can use geospatial data to support predictions of potential disruptions in service and to optimize maintenance and crew schedules.

• Insurers can more accurately project risks and warn policy holders about issues they may be facing in the near future.

• Agricultural lenders can improve their methodology of assessing credit risk scores and diminish the risk of unfavorable loan placement.