How GIS was needed in Data Analytics

How GIS was needed in Data Analytics

September 15, 20232 min read

GIS has always held a significant future in various industries and domains. With the increasing availability of spatial data, growth in sensor networks, and Internet of Things (IoT) devices, an opportunity to synergize GIS and data analytics was always present.

GIS involves the gathering, storing, and manipulation of geographical data, while Data Analytics focuses on the extraction of insights — gained from the analysis of past data trends and making decisions for the future.

In this post, I explore some of the prominent tasks GIS and Data Analytics have and are accomplishing together.

Tracking and Predicting Events through Location Intelligence: One of the greatest benefits we can realize from this combination is the ability to track, isolate, and predict events to improve the overall functioning of business. For instance, by incorporating location-based data, organizations can have greater accessibility to predict consumer behavior and buying patterns of their customers, which can further help in analyzing optimal locations for new store openings.

Enrichment in Visualization: GIS tools enrich the data by allowing it to be visualized on maps to show spatial patterns and trends. Adding spatial attributes can give a more comprehensive and interpretable analysis of complex data. This can be useful in population density, administration boundaries, elevation, land use, climate change, crime activity, and many more.

Pattern recognition in Healthcare: Since its inception, GIS has been an integral tool for tracking disease outbreaks (including historical), understanding disease spread, and planning healthcare services accordingly. However, if coupled with the field of Data Analytics, it can provide effective and timely insights into health patterns and healthcare resource allocation, which might not be apparent through traditional data analysis.

Decision support system for Environment and Natural Resources: In order to make informed decisions to create sustainable strategies to manage natural resources and protect the environment, GIS and Data Analytics are a dynamic synergy. GIS is used to examine environmental data such as the distribution of natural resources, air quality, land use patterns, and many more. Whereas, data analytics is helpful in leading a better understanding of environmental trends over time and improving assessments of potential impacts on the environment.

Urban Planning and Infrastructure Development: For many years, GIS has been an invaluable tool for civil engineers and urban planners for effective urban planning. It gives the ability to understand a city’s existing requirements and further enables it to develop those requirements effectively. However, together with data analytics, urban planners can acquire an ocean of data to effectively plan for resource allocation, plan public transportation, manage utilities, optimize traffic flow, and many more.

GIS offers various spatial analysis techniques that can enhance data analytics. These techniques can help uncover spatial relationships and trends that can inform business strategies and operational decisions.

Data VisualizationGISPUBLIC Health

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