An Overview of Geospatial Analysis and its Implementation

Geospatial analytical technique entails collecting data about the Earth's surface or built environment and the subsequent use to develop predictions about human behavior or natural events.

FREMONT CA: Geospatial analytics has its origins in the earliest forms of mapping and surveying, if not earlier. However, the term began to be connected with geographic information systems (GIS) used to catalog natural resources in the 1960s. Since then, geospatial data has been used in a broad range of ways and with various types of technology—satellite imaging, GPS, coordinate systems, position sensors, and other location-based data.

Governments use geospatial data for various purposes, ranging from tracking extreme weather events to analyzing and controlling traffic patterns to assessing the impact of population increase on energy, transportation, and housing needs as part of urban planning. Additionally, the military has historically relied on geographical data to determine optimal resource deployment.

However, more recently, the cloud computing revolution and improvements in machine learning have enabled rapid analysis of an ever-growing amount of geographic data, enabling decision-making and the development of entirely new business processes.

Geospatial imaging has emerged as a critical source of truth due to its ability to verify and explain geospatial studies. With improved quality and availability of geospatial imagery, its utility in a new generation of geospatial analytics-based applications has expanded. Numerous solutions contribute to a complete understanding of a critical part of the global economy: property.

Property data has always been challenging to obtain, and the impact of geospatial analytics on businesses in property-related industries such as insurance and real estate is nothing short of transformative. For example, rather than conducting time and labor-intensive onsite inspections, vital information about the external state is becoming increasingly available on-demand.

How geospatial analytics is implemented

Modern geospatial analytics are powered by artificial intelligence (AI) and extract structured data from imagery using high-resolution geospatial imagery and computer vision. When it comes to property, this includes the size of the structure and its condition, the presence of solar panels, the presence of overhanging trees, and the presence of a backyard swimming pool, yard debris, or vegetation encroachment around the property that could pose a fire hazard, among other things.

Today's most advanced solutions can supply this type of data for tens of millions, if not hundreds of millions, of business and residential premises nationwide, with the same speed and coverage as traditional property record data.

Modern analytics enables consumers to examine a single property and compare it to similar properties within the same neighborhood, zip code, city, region, or kind throughout the country by delivering quick, accurate data on property conditions. Additionally, they can examine local, regional, and national trends that may affect the company's value or risk profile over time.

The value of this intelligence is contingent upon the resolution and recency of the picture it analyses and the machine learning (ML) algorithms used to do so.

See Also : 

Real Estate Business Review