5 BUSINESS APPLICATIONS OF GEO-SPATIAL DATA SCIENCE

Spatial data science is an interdisciplinary field at intersection of spatial science and data science. It involves tasks of spatial data handling and management, GIS and spatial data analytics and visualization. Spatial data science relies on both causality and data driven correlations to answer any spatial questions.
(Geo-) Spatial data science applies to a number of real-world problems. Businesses can make right decisions at right time based on analysis and interpretations of geo-spatial data. In the following, we will be looking at 5 interesting business applications or use cases of spatial data science:

1. Investment Portfolio Optimization

To diversify its investment portfolio, an investment group or financial institute is thinking to purchase commercial timberland across United States of America (USA) and Canada. What would be the best states or regions for the investment? What is the risk in terms of growth, wildfire and weather? Spatial data science can answer all those questions based on spatial data collected over last few years.

2. Smart City Planning

A city government can integrate and manage all of its geospatial data set at the central spatial DBMS. Then to its departments it can give the required access and permissions for the spatial database. The resources across departments may have different levels of authority. Ultimately such an infrastructure would help city government to take timely decisions based on analysis and interpretation of city’s spatial data.

3. Disease Monitoring

A public health authority can spatially map disease incidents data, and check if any spatial patterns exist between administrative district and disease prevalence.
If the spatial pattern exists, the authority can focus on how to figure out regional factors that would contribute to higher or lower diseases prevalence.

4. Threat Monitoring

A law enforcement agency can develop spatial data framework, which would present an optimal design of surveillance network against potential infiltration. Such framework would allow them to optimally deploy resources and root out any patterns of crime or violations.

5. Resource & Route Optimization

Imagine, you are running a taxi call service company and you want to provide a new Passenger Finder service to the taxi drivers. You can have an integrated solution based on spatial data science, where not you it would help you to find the spatial location of the passengers but also key performance metrics, predictions and anomaly detection.