Geospatial analytics is a type of data analytics that is extracted from location data (geospatial data). Location data describes the relationships between objects relative to the earth’s surface. With improvement in technology and internet collection of geospatial data has become easy such as satellite imagery, cell phone data and social media user data. Apart from core analytical purpose of the geospatial data, the location-based data is used to enrich other datasets for advanced analytics. In this post we will introduce geospatial analysis and look at its importance and use-cases.

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Image from https://geospatial.umd.edu/

Introduction to Geospatial Analytics

Wikipedia defines spatial analysis as “any of the formal techniques which studies entities using their topological, geometric, or geographic properties”. Geospatial analysis involves the analysis and visualization of location datasets to identify hidden insights and trends. Geospatial data such as weather forecasting data can help in prediction of natural disasters. Location based data for bank transactions can help discover the source fraudulent activities.

Benefits of Geospatial Analytics

  1. Better decision making based on insights form geodata.
  2. Provides engaging insights and trends relative to the location.
  3. Helps in understanding the customers with respect to locations. This enables in creating custom solutions/products targeted at specific customers and regions.

Geospatial Analytics Use-Cases

  1. Understanding of Customers. This is helpful in planning for customers demand and supply for the cases of retail.
  2. Determining the most efficient routes for transportation.
  3. Provides information useful for government planning on providing adequate resources such as water, electricity, security etc. to its citizens.
  4. Exploration of natural resources.
  5. Prediction of extreme weather disasters.
  6. ….. and many other domain specific use-cases.

Geospatial Analytics Challenges

  1. Large volume of geospatial data. Location based data is mostly generated in large volumes (big data). This requires more resources for storage and computing resulting to high cost.
  2. Geospatial data comes in different formats. Unifying these different sources without loss of valuable information is a huge task.

Conclusion

Geospatial analysis has become important to any business and organizations as it provides deeper insights and hidden trends from large pools of location data. There are tons of geospatial analysis use-cases such as prediction of natural disasters, targeting customers by location by retail businesses, urban planning and development by government, military planning and execution among others. In this post we have introduced geospatial analysis and looked at its benefits, use-cases and challenges. In the next post we will look creating geospatial analyses in Python using Folium library. To learn how to create geospatial visualizations with plotly check our previous post here.

Introduction to Geospatial Analytics

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