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GIS Outline

https://geo-python.github.io/site/

https://automating-gis-processes.github.io/site/

Objectives

  • Learn how to execute spatial analysis in a coding (Python environment)
  • Overview of Coding Python
  • Spatial analysis packages in Python
  • Spatial data types
  • Spatial analyses
  • Visualizing data

Schedule

Week Theme
1 Python crash course
2 Shapely and geometric objects (points, lines and polygons)
3 Managing spatial data with Geopandas (reading and writing data, projections, table joins)
4 OpenStreetMap data (osmnx) and Network analysis (networkx)
5 Visualization: static and interactive maps

1. Python Crash Course

  • Where to write Python?

    • Notebooks: Local (ArcGIS Pro | Anaconda); Cloud (Binder | Jupyter Server)
    • IDE's: Local (Spyder | PyCharm)
    • ACTIVITY: Open Jupyter associated with ArcGIS & Open A Taste of Python

  • A taste of Python

    • Simple Python math
    • Functions
    • Math operations (importing libraries)
    • Combining functions
    • Variables
    • Updating variables
    • Variable values
    • Data types
    • Character input

  • More on Python

    • Conda environments
    • Installing packages

2. Shapely and geometric objects (points, lines and polygons)

  • GIS data models: vector vs raster
  • Geometric objects
    • Question: How would you represent various features by these objects?
  • The Shapely library: how to import
    • GEOS library
  • Exercise
    • Create a point & show
    • Create two more points (what are the numbers?)
    • Print points
    • Type points
    • Tab complete
  • Create a linestring
    • From points
    • From coordinates
    • Introducing Lists
  • Line operations
  • Polygons
  • Geometry collections
  • EXERCISE
    • Import the shapely geometry objects (Point, LineString, Polygon)
    • Create a point feature called "Durham" at coordinate (X=689,420, Y=3,985,329)
    • Create a second point feature called "ChapelHill" at coordinate (X=675,424, Y=3,976,067)
    • Create a second point feature called "Raleigh" at coordinate (X=712,904, Y=3,962,967)
    • Compute the distance between Durham and Chapel Hill
    • Compute the area encompassed by "the Triangle"

3. Managing spatial data with Geopandas

  • Intro to GeoPandas
    • Series, DataFrames → GeoSeries, GeoDataframe
    • Importing shapefiles into GeoPandas
    • GeoDataframe attributes
    • Plotting GeoDataframes
    • Geometries in GeoPandas
    • Subsetting
    • Grouping
    • Writing out to shapefiles
  • Projections
    • GeoDataframe "crs" attribute
    • Projection: Copy then "to_crs"
  • Calculating distances

Getting spatial data

  • Data formats: tabular | vector | raster

  • Vector Data Format Pandas Geopandas/Fiona ArcGIS API
    Text (CSV, JSON, KML)
    Binary (Shapefile/Geodatabase)
    Web services
  • Raster Data Format Numpy ArcGIS API
    Text (ASCII)
    Binary (TIF, Img, Arc GRID, NetCDF)

Wrangling tabular data

  • Calculations
  • Summarize | group | pivot | transform
  • Appends | joins
  • Selections

Wrangling spatial data

  • Coordinate reference systems - getting data aligned
    • Geographic vs Projected coordinate systems
    • Equal area, equal distance, conformal, hybrid
    • Datums, spheroids, ellipses
    • Common crs's
    • Spatial transformations: (Vector | Raster)
  • Spatial analyses
    • Extractions: Attribute, Spatial
    • Overlays: Intersect, Spatial join, Clip, Union

Network analysis

Visualizing data

Exercise ideas

1. Mapping DCFC locations

  • Convert table of existing DCFC locations to shapely points
  • Transform to UTM

2. Calculating distances between DFCF locations

3. Demographics surrounding DCFC locations

  • Read Census block_groups shapefile into shapely objects
  • Select block_groups within 3 miles of each DCFC
  • Summarize block_group demographics