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This python library takes in geo coordinates and creates a voronoi lattice.

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Octocat-spinner-32 README
Octocat-spinner-32 globalmaptiles.py
Octocat-spinner-32 main_example.py
Octocat-spinner-32 sample_city_data.csv
Octocat-spinner-32 voronoi.py
Octocat-spinner-32 voronoi_poly.py
README
infochimps Voronoi

Created by Hohyon Ryu, Aug/22/2011

* Dependencies

 - shapely : sudo apt-get install python-shapely
 - matplotlib (pylab) : sudo apt-get install python-matplotlib
 - Built on Steve Fortune's Python Voronoi Code
   Steve Fortune's homepage: http://netlib.bell-labs.com/cm/cs/who/sjf/index.html

* Files

  README
  globalmaptiles.py - Google Geo-coordinate Library
  voronoi.py        - Voronoi lattice library (line and vertices based)
  voronoi_poly.py   - Voronoi lattice library (polygon based)

  sample_city_data.csv - sample data

  main_example.py   - code example


* Inputs:
  1. Dictionary of the points: (You can use simple sequential number for the dictionary keys or some text to associate with.)
    PointsMap={}
    PointsMap["stationA"]=(-143.22, 38.22)
    PointsMap["stationB"]=(-122.22, 56.22)
    PointsMap[1]=(-122.22, 56.22)

  2. The bounding box (left_top_x, left_top_y, bottom_right_x, bottom_right,y) to generate a voronoi lattice.
    Bounding Box Options: You can either use the name or the coordinates
    "AUSTIN" [30.8, -98.5, 29.535, -97.031]
    "TX" [36.5, -106, 25, -93]
    "US" [55, -130, 23, -60]
    "GUS" [60, -140, 22, -50]
    "KR" [45, 120, 32, 135] (Korea)
    "W" [90, -180, -90, 180] (World, Default)

  3. PlotMap: shows the voronoi lattice on a map. This may be extremely slow if you have more than 1M points. (Default is False, Not available for VoronoiLineEdges)


* Output options:
  1. Stations, Lines and edges
    Example:
    import voronoi_poly
    vl=voronoi_poly.VoronoiLineEdges(PointsMap)

    Output:
      vertices, lines, edges, station_to_edge
          (1) a list of 2-tuples, which are the x,y coordinates of the 
              Voronoi diagram vertices
          (2) a list of 3-tuples (a,b,c) which are the equations of the
              lines in the Voronoi diagram: a*x + b*y = c
          (3) a list of 3-tuples, (l, v1, v2) representing edges of the 
              Voronoi diagram.  l is the index of the line, v1 and v2 are
              the indices of the vetices at the end of the edge.  If 
              v1 or v2 is -1, the line extends to infinity.    
          (4) a dictionary, where keys are the station numbers(vertices), and the 
              values are the number of edges surrounding the vertice.

  2. Polygons
    import voronoi_poly
    vl=voronoi_poly.VoronoiPolygons(PointsMap, BoundingBox="AUSTIN", PlotMap=False)
    Output:
      185: {'coordinate': (3.04, 36.77), 'info': 'Algiers/Algeria', 'obj_polygon': <shapely.geometry.polygon.Polygon object at 0x3152190>}
      
      - You can access the polygon coordinates by the following example:

        from shapely.geometry import Polygon      
        print list(polygon_data.exterior.coords)

  3. Quadkey-based Grids on Polygons
    import voronoi_poly

    vl=voronoi_poly.VoronoiPolygons(PointsMap, BoundingBox="W", PlotMap=False)
    voronoi_poly.GridVoronoi(vl, zl=7, PlotMap=True)  
    Output: Quadkey, Station_name, Longitude, Latitude

  4. GeoJson Polygons
    import voronoi_poly
    voronoi_poly.VoronoiGeoJson_Polygons(PointsMap, BoundingBox="US")
    Output:
      Geojson Polygons

  5. GeoJson MultiPolygons
    import voronoi_poly
    voronoi_poly.VoronoiGeoJson_MultiPolygons(PointsMap, BoundingBox="KR")
    Output:
      Geojson MiltiPolygons


    
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