Python Shapely – How to Convert Polygon to List of Coordinates Using Shapely

coordinatesgeopandaspolygonpythonshapely

I received a code, that expects a list of coordinates such as this as input

area_polygon = [{'lat': 46.037286, 'lng': 14.471329},
                {'lat': 46.036733, 'lng': 14.467378},
                {'lat': 46.034822, 'lng': 14.468441}]

However, I have input data in form of shapely.Polygon and it looks like this:

POLYGON ((14.471329 46.037286, 14.467378 46.036733, 14.468441 46.034822))

I work with shapely and GeoPandas libraries and I know how to switch lats and longs. But I don't know how to do everything else to transform a Polygon to the form presented.

Best Answer

Here is another solution using the mapping():

returns a new, independent geometry with coordinates copied from the context.

import json
from shapely.geometry import Polygon, mapping

poly = Polygon([[14.471329,46.037286],[14.467378,46.036733],[14.468441,46.034822]])

poly_mapped = mapping(poly)

poly_coordinates = poly_mapped['coordinates'][0]

poly_ = [{'lat': coords[1],'lon': coords[0]} for coords in poly_coordinates]

print(json.dumps(poly_))

This results in:

[{"lat": 46.037286, "lon": 14.471329}, {"lat": 46.036733, "lon": 14.467378}, {"lat": 46.034822, "lon": 14.468441}, {"lat": 46.037286, "lon": 14.471329}]

Mind that the above output will contain one point two times, the first and last points, to avoid that use the following piece of code:

poly_ = poly_[:-1]

print(json.dumps(poly_))

This results in:

[{"lat": 46.037286, "lon": 14.471329}, {"lat": 46.036733, "lon": 14.467378}, {"lat": 46.034822, "lon": 14.468441}]

References:

Extract points/coordinates from a polygon in Shapely

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[GIS] Converting Shapely MultiPolygon to Polygon: Technique doesn’t always work

You need to understand the Shapely binary predicates:

1) If the two polygons intersects the result of union or unary_union (in red) is a Polygon therefore you can computes the exterior

2) If the two polygons are disconnected, the result is necessary a MultiPolygon (in red with two polygons)

And if you work with Shapefiles, without topology, this may occur

A solution is to compute the Concave Hull but it is not really an union.

[GIS] Assign a point to polygon using pandas and shapely

If you're working with spatial data and Pandas you should take a look at GeoPandas.

The example below demonstrates how to perform a spatial join in GeoPandas (which uses Shapely). A GeoDataFrame object is created from a list of cities and their coordinates and is joined to an ESRI Shapefile containing countries.

import pandas
import geopandas
import geopandas.tools
from shapely.geometry import Point

# Create a DataFrame with some cities, including their location
raw_data = [
    ("London", 51.5, -0.1),
    ("Paris", 48.9, 2.4),
    ("San Francisco", 37.8, -122.4),
]
places = pandas.DataFrame(raw_data, columns=["name", "latitude", "longitude"])

# Create the geometry column from the coordinates
# Remember that longitude is east-west (i.e. X) and latitude is north-south (i.e. Y)
places["geometry"] = places.apply(lambda row: Point(row["longitude"], row["latitude"]), axis=1)
del(places["latitude"], places["longitude"])

# Convert to a GeoDataFrame
places = geopandas.GeoDataFrame(places, geometry="geometry")

# Declare the coordinate system for the places GeoDataFrame
# GeoPandas doesn't do any transformations automatically when performing
# the spatial join. The layers are already in the same CRS (WGS84) so no
# transformation is needed.
places.crs = {"init": "epsg:4326"}

# Load the countries polygons
countries = geopandas.GeoDataFrame.from_file("ne_110m_admin_0_countries.shp")
# Drop all columns except the name and polygon geometry
countries = countries[["name", "geometry"]]

# Perform the spatial join
result = geopandas.tools.sjoin(places, countries, how="left")

# Print the results...
print(result.head())

Note that the spatial join feature is still fairly new and hasn't made it into the stable branch yet - you'll need to download and install the development version.

https://github.com/geopandas/geopandas

The result looks like this:

       name_left             geometry  index_right      name_right
0         London    POINT (-0.1 51.5)           57  United Kingdom
1          Paris     POINT (2.4 48.9)           55          France
2  San Francisco  POINT (-122.4 37.8)          168   United States

You can also use GeoPandas to plot the data with matplotlib:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(1)
countries.plot(ax=ax, color="#cccccc")
places.plot(ax=ax, markersize=5, color="#cc0000")
plt.show()

Best Answer

Related Solutions

[GIS] Converting Shapely MultiPolygon to Polygon: Technique doesn’t always work

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