Spatial Data Science in Higher Education

Resources for teaching and research

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Combining the power of spatial analysis with data science

Incorporating spatial analysis into data science combines the strengths of both disciplines to extract deeper insights from data. By learning both spatial analysis and data science, students gain unique skills and a competitive edge in solving complex problems.

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Building blocks of spatial data science

Spatial data science is comprised of several key components.

  • Data integration and engineering

    Cleaning, enriching, and manipulating data for analysis

  • Visualization and exploration

    Exploring maps to identify trends and patterns in the data

  • Spatial analysis

    Using location analysis to solve problems, sometimes in combination with other models and analysis methods

  • GeoAI

    Advance integration with AI, machine learning, and deep learning

  • Big data analytics

    Utilize tools and methods for processing big data analysis

  • Modeling and scripting

    Use scripts and models on repetitive tasks to streamline processes and minimize errors

  • Sharing and collaboration

    Turn data into information and action by sharing and collaborating

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User story

Why adopting GIS in data science programs works

The University of Wisconsin-Madison’s data science major has a key differentiator: a geospatial data science track that introduces students to GIS.

Read the user story

On-demand webinars

Explore previously recorded webinars on the topic of spatial data science in higher education.

Access webinar recordings
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Resources by topic

Find resources on topics like data engineering in ArcGIS Pro, spatial analysis, GeoAI, python scripting, R-ArcGIS integration, and more.

Data engineering and visualization

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Spatial analysis

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GeoAI

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Python scripting

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R-ArcGIS integration

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Sharing and collaboration

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Get started with support

Request help from Esri’s education team to add spatial data science to your teaching and research.