Spatial Data Processing
Analysis of large volumes of geodata to answer business questions and understand risk
Spatially-enabled databases and statistical analysis
Related Case Studies
Not every spatial query results in a map
When most people think of GIS and location-based services, they think of data displayed in maps.
However, there are many times when you don't need a visual representation of data and only care about how location impacts some other decision process. For example: cellular phone service patterns in time, length, distance, and location of phone calls can be mined can be used to dynamically allocate bandwidth for better service and price policy planning for maximum profit; patterns in cancer incidence and mortality to identify at risk populations; or what products to stock in a supermarket based on the demographics of the neighborhood and location relative to competitors.
Spatial data processing utilizes the tools and technologies of GIS without the necessary production of a map. Spatial data processing utilizes large volumes of geodata to answer business questions, identify risk and solve critical problems. The end result can be numeric, a code, a list of products or of course, a map.
Spatial data processing can be very resource intensive and complex, since it usually involves the analysis of large amounts of spatial and non-spatial data from various diverse applications, and can additionally incorporate the dimension of time.
GIS and Risk Management
Location-based and geographic data permeate insurance data, (e.g. addresses, zip codes, and weather zones). In fact all of the data that the industry works with has a fundamental geographic component - an address for the customer being insured. Since the process of risk management can involve analyzing huge volumes of spatial and non-spatial data in a fast and efficient manner, use of GIS functionality within the overall framework of a risk management software application provides many benefits
Farallon has implemented advanced spatial data processing in the insurance industry to help better set premium levels based upon risk. Firms can analyze a broad range of location-based information that can be statistically analyzed to determine whether specific spatial relationships between risk factors and the location of policy holders correlate with levels of risk.
GIS in Underwriting
The process of underwriting involves developing a suitable insurance policy and its pricing. GIS can be used as an interface and analysis tool to the required spatial data: the location coordinates, the risk profile, and the guidelines specific to different regions. This information can be used by the underwriters from anywhere within the organization.
The development of a scaleable and robust interface to GIS analyses represents an appropriate step in augmenting risk estimation models, underwriting, catastrophe response and sales and marketing of insurance products.