• What and Why is Spatial Analytics?

    Spatial Analytics is a method used to analyze geographical locations and spatial relationships by utilizing Geographic Information Systems (GIS) and other spatial data technologies. It enables companies to analyze the locations, relationships, attributes, and proximities in geospatial data to extract insights using geographical modeling.

    This methodology has become commonplace nowadays. For instance, Google Maps provides users with the best navigation routes through real-time traffic information and road data. By analyzing real-time traffic flow and historical data, it can predict traffic congestion and provide users with faster and smarter navigation solutions. Additionally, Google Maps allows users to find the nearest store around a specific location, among other features. In terms of web services, some Internet companies improve their services through the collection and analysis of user location data. This includes understanding user behavior, location preferences, geographical distribution, and other information to provide more personalized and localized services.

  • How to Perform Spatial Analysis in Alteryx?

    While many organizations traditionally believe that geospatial analysis is the exclusive domain of GIS experts with access to specialized GIS systems, tools like Alteryx can help organizations make better use of spatial analysis and incorporate it into their descriptive, predictive, and normative workflows.

    Alteryx, a no-code/low-code, self-service platform, requires no specialized skillsets and is designed to put automation in the hands of all data workers. The built-in spatial analytics tools from Alteryx make geospatial analytics easier than ever thought. It performs field service and resource optimization, asset location planning, transportation and logistics management, location-specific service delivery, and emergency response planning.

    Below are some examples:

    Geocoding enriches data insights.


    Use heatmap to find patterns in dense data for visual decision making.


    Find nearest for optimized warehouse distribution priority and routes to reduce cost and decrease pollution.


    Poly-split/Poly-combine to identify coverage area for telecommunications roll out.


    Trade Area drivetime radii for new store locations and marketing opportunities.


    Grid to identify an area of interest within a spatial boundary.




Pujiang Zhang
Author: Pujiang Zhang

A recent graduate with a Master of Information Technology from the Queensland University of Technology with a literature background in digital media. My academic journey has fueled my passion for making informed decisions through data analysis, and I'm fascinated by the intersection of Artificial Intelligence and its societal impacts. Beyond the world of data, I find joy in activities like jogging and swimming. I also have a strong interstate in philosophy and history, dedicating my spare time to exploring the depths of these subjects.