spatial_aggregations_tutorial

Walk through on all-things spatial aggregations. The main focus is aggregating climate/exposure variables over (raster) grids into (GIS vector) polygons.

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This tutorial focuses on:

1. Frequently asked questions

• What is the difference between gis vector data and rasters?
• What is the difference between overlay vs rasterize?
• What are multiple rasterization strategies?
• What is the difference between downscaling and upsampling?
• What are computationally efficient options for spatial aggregations?
• How can I perform discrete variable aggregations?

2. Example scripts and end-to-end pipelines for spatial aggregations

• Python example script for spatial aggregations using rasterization (rasterstats package)
• Python example script for discrete variable aggregations: climate types on the USA
• Blazing fast end-to-end pipelines for spatial aggregations

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Main Applications of Spatial Aggregations in Environmental Health Data Science

1. Environmental Monitoring and Modeling:
   • Spatial aggregations enables the integration of satellite imagery with vector data, such as habitat boundaries, to monitor ecosystem changes, deforestation, and wildfire spread¹².
   • It supports modeling continuous environmental phenomena like air pollution, temperature, and precipitation across landscapes¹³.
2. Exposure Assessment, Urban Planning and Public Health
   • Spatial aggregations of climate and pollution data are used to estimate individual or population-level exposure to environmental hazards (e.g., heat waves or air pollutants) by overlaying grids with demographic polygons⁴⁵.
3. Disaster Preparedness and Response:
   • Spatial aggregations are crucial for tracking natural disasters like floods or hurricanes and assessing their health impacts by combining raster hazard maps with population density polygons⁶².
4. Agricultural and Food Security Studies:
   • Spatial aggregations of soil temperature, crop health, and irrigation data help assess food security risks and optimize agricultural practices to mitigate environmental health impacts like malnutrition⁶.

Resources:

• vector vs raster
  • https://community.alteryx.com/t5/Data-Science/Vector-and-Raster-A-Tale-of-Two-Spatial-Data-Types/ba-p/336141
  • https://www.e-education.psu.edu/geog160/node/1935
  • https://r.geocompx.org/raster-vector
• overlay vs rasterize
  • https://en.wikipedia.org/wiki/Rasterisation
  • https://en.wikipedia.org/wiki/Vector_overlay
  • https://pygis.io/docs/e_raster_rasterize.html
  • https://pygis.io/docs/e_vector_overlay.html
• rasterization
  • https://docs.qgis.org/3.34/en/docs/user_manual/processing_algs/gdal/vectorconversion.html#rasterize-vector-to-raster
  • https://www.ecologi.st/spatial-r/old-raster-gis-operations-in-r-with-raster.html#rasterizing-1
  • vector aggregation vs raster aggregation
  • https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/how-aggregate-works.htm
• vector overlay vs raster overlay
  • https://saylordotorg.github.io/text_essentials-of-geographic-information-systems/s12-01-basic-geoprocessing-with-raste.html
  • http://www.gitta.info/Suitability/en/html/unit_BoolOverlay.html
• downscaling
  • https://www.researchgate.net/publication/335651129_An_Overview_of_Theoretical_and_Practical_Issues_in_Spatial_Downscaling_of_Coarse_Resolution_Satellite-derived_Products#pf2

Additional GIS resources: https://mapping.share.library.harvard.edu/

Footnotes

1. Raster vs Vector: Which is Best for Your GIS Needs? - RisingWave ↩ ↩²

2. Raster vs. Vector Data in GIS | Ankur S. | LinkedIn ↩ ↩²

3. Introduction to image and raster data—ArcGIS Pro ↩

4. Data Science in Environmental Health Research - PubMed Central ↩

5. Data Science in Environmental Health Research - PubMed ↩

6. Uncovering the Value of Raster Data Applications - Foursquare ↩ ↩²