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2023-2024 Catalog 
2023-2024 Catalog [ARCHIVED CATALOG]

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DAT 241 - Geospatial Data Analytics

Credits: 3
3 Skills Lab Hours

Prerequisites: DAT 102  

Students assemble, analyze and present map-based data in this first course in geospatial analysis. Since many datasets now include spatial components, students approach the exciting sub-field of spatial data analytics with a focus on improving organizational decision making by creating static and interactive maps. To build a foundation of spatial reasoning, students explore map projections and x-y-z coordinate systems through hands-on exercises. Students then engage software tools to digitally represent spatial data from a variety of domains including municipal administration, ecology, transportation, marketing and epidemiology. Finally, the course prepares students to integrate spatial analysis into data pipelines by connecting mapping software packages with relational databases and web servers.

Learning Outcomes
Upon successful completion of the course, the student will:

  1. Demonstrate the principal components of map projections and coordinate reference systems to compare their strengths and weaknesses for various analytic applications.
  2. Build digital maps that visualize layers of point, line and polygon based data.
  3. Design map symbology systems such as choropleth shading, proportional centroid sizing and feature labeling to appropriately emphasize feature layers to inform decision making.
  4. Apply appropriate spatial analytic algorithms to data layers, visualize their output and interpret the results using domain-specific knowledge.
  5. Present the results of spatial analysis using static layouts and interactive maps in language accessible to an audience of knowledgeable non-experts.
Listed Topics
  1. Map projection systems
  2. Spatial coordinate systems
  3. Point, line and polygon data layers
  4. Choropleth shading
  5. Map symbology
  6. Geospatial analysis software packages
  7. Spatial data digital encoding schemas (e.g. GeoJSON, ESRI Shapefile, KML, etc.)
  8. Kernel density smoothing (i.e. “heat maps”)
  9. K-nearest neighbor analysis
  10. Vector and raster data types
  11. Map layouts
Reference Materials
Appropriate textbooks and resources selected by the instructor
Students who successfully complete this course acquire general knowledge, skills and abilities that align with CCAC’s definition of an educated person. Specifically, this course fulfills these General Education Goals:
  • Information Literacy
  • Technological Competence
Approved By: Dr. Quintin B. Bullock Date Approved: 12/14/2020
Last Reviewed: 12/14/2020

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