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Empowering Spatial Analysis with SpaPy: A Python Library for Geospatial Tasks

A student developed a program to identify potential illegal campsites, which led to the creation of SpaPy - a Python library for diverse spatial data tasks. SpaPy supports shapefiles, raster formats, and GeoJSON, providing geospatial processing, analysis, and visualization capabilities. It utilizes open-source libraries such as numpy, scipy, shapely, pyproj, PIL, and osgeo, making it flexible and accessible for GIS professionals.

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Empowering Spatial Analysis with SpaPy: A Python Library for Geospatial Tasks

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  1. Spatial Programming • A student who worked for the National Wilderness Preservation System (NWPS) wrote a program to find areas with a high potential for illegal campsites: – Near trails, but not too near – With something to block the view from the trails (i.e. trees) – Near water • He was then asked to do this for all 800 wilderness areas in the US!

  2. More Spatial Programming • A student worked for a company that was analyzing the impact of palm oil mills in Indonesia. • Then, he was asked to do to it for the other 608!

  3. And More… • I’m processing over 10,000 LiDAR files to create a 1 meter DEM for the Eel/Wiyot River watershed • I’ve processed Microsoft's building footprints for the US dataset to get all the building footprints for Humboldt County • Etc.

  4. What we need • We need to be able to work with a diverse array of spatial data to complete challenging tasks. • For this, we need a Python library that: – Loads and saves standard file formats (shapefiles, TIFF) – Includes spatial processing and analysis – Includes visualizing of data – Is reliable, expandable, and relatively easy to use for GIS folks.

  5. Situation • Esri provides a Python connector for ArcGIS Pro. It is slow, relatively hard to use, and we cannot expand it. It also requires an Esri license. • QGIS does not provide a Python connector • The open-source world has a variety of libraries available – Some work well – Some are nightmares! – And there are compatibly issues

  6. Solution? • Create our own library • SpaPy was created by students and myself over the last 5 years. • It provides: – Support for shapefiles, all raster formats supported by GDAL, and now GeoJSON – Standard geospatial processing and analysis – Viewing of data and rendering to image files – Pretty well documented and tested – Expandable and allows access to data

  7. Open Source Libraries • numpy – numeric functions • scipy – scientific functions • shapely – data types for working with points, polygons, and polylines (line strings in open source terminology) • pyproj – projecting data in SpaReferencing • PIL (Pillow) – rendering maps to images in SpaView

  8. Open Source Libraries • matplotlib – creating maps in charts in SpaPlot (largely replaced by SpaView) • osgeo – wrapper for the GIS libraries: – gdal – raster file I/O and some other useful functions – osr – source reference definitions – ogr – vector file I/O • shapefile (PyShp) – note that this library is imported as “shapefile” but installed as “pyshp”.

  9. Using SpaPy • The SpaPy.org website contains installation instructions and tutorials • For the next several weeks, we’ll be following those tutorials. • Then, we’ll come back to learn more Python so we can do even more cool stuff.

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