HELP 2021-2022 Catalog [ARCHIVED CATALOG] Print this Page Facebook this Page (opens a new window) Tweet this Page (opens a new window) Add to Portfolio (opens a new window)

DAT 241 - Geospatial Data Analytics

 
Description
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.

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.

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

• Information Literacy
• Technological Competence

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