geostructures 0.9.1

A lightweight implementation of shapes drawn across a geo-temporal plane.

Geostructures

A lightweight implementation of shapes drawn across a geo-temporal plane. Geostructures enables you to quickly draw shapes, convert to/from a variety of other geometric formats, and draw results.

Installation

Geostructures is available on PYPI

$ pip install geostructures

Optional Dependencies

Geostructures does not require any of the below dependencies to function, however maintains optional features that use:

• geopandas
• H3
• MGRS
• scipy
• shapely

Overview

Geostructures provides a python interface for functionally defining various shapes drawn on a map. Unlike other libraries such as Shapely, these shapes retain their mathematical definitions rather than being simplified into N-sided polygons.

The shapes currently supported are:

• Boxes
• Circles
• Ellipses
• LineStrings
• Points
• Polygons
• Rings/Wedges

All shapes may optionally be temporally-bound using a specific datetime or a datetime interval.

Additionally, geostructures provides convenience objects for representing chronologically-ordered (Track) and unordered (FeatureCollection) collections of the above shapes.

Quick Start

For an interactive introduction, please review our collection of Jupyter notebooks.

Creating GeoShapes

from geostructures import *

box = GeoBox(
    Coordinate(-0.154092, 51.539865),   # Northwest corner
    Coordinate(-0.140592, 51.505665),   # Southeast corner
    properties={"name": "box"}
)

circle = GeoCircle(
    Coordinate(-0.131092, 51.509865),   # centerpoint
    radius=500, 
    properties={"name": "circle"}
)

ellipse = GeoEllipse(
    Coordinate(-0.093092, 51.529865),   # centerpoint
    major_axis=1_000,                   # The distance between the centerpoint and the furthest point along the circumference
    minor_axis=250,                     # The distance between the centerpoint and the closest point along the circumference
    rotation=45,                        # The angle of rotation (between 0 and 360)
    properties={"name": "ellipse"}
)

ring = GeoRing(
    Coordinate(-0.116092, 51.519865),   # centerpoint
    inner_radius=800,
    outer_radius=1000,
    properties={"name": "ring"}
)

# Same as a ring, but with a min/max angle
wedge = GeoRing(
    Coordinate(-0.101092, 51.514865),   # centerpoint
    inner_radius=300,
    outer_radius=500,
    angle_min=60,                       # The minimum angle of the wedge
    angle_max=190,                      # The maximum angle of the wedge
    properties={"name": "wedge"}
)

linestring = GeoLineString(
    [
        Coordinate(-0.123092, 51.515865), Coordinate(-0.118092, 51.514665), Coordinate(-0.116092, 51.514865),
        Coordinate(-0.116092, 51.518865), Coordinate(-0.108092, 51.512865)
    ],
    properties={"name": "linestring"}
)

point = GeoPoint(
    Coordinate(-0.116092, 51.519865), 
    properties={"name": "point"}
)

polygon = GeoPolygon(
    [
        Coordinate(-0.116092, 51.509865), Coordinate(-0.111092, 51.509865), 
        Coordinate(-0.113092, 51.506865), Coordinate(-0.116092, 51.509865)  # Note that the last coordinate is the same as the first
    ],
    properties={"name": "polygon"}
)

Converting Between Formats

from geostructures import *
from geostructures.collections import FeatureCollection

polygon = GeoPolygon(
    [
        Coordinate(-0.116092, 51.509865), Coordinate(-0.111092, 51.509865), 
        Coordinate(-0.113092, 51.506865), Coordinate(-0.116092, 51.509865)
    ]
)

# Convert to and from a variety of formats
polygon.to_geojson()
polygon.from_geojson( { a geojson object } )

polygon.to_wkt()
polygon.from_wkt( '<a wkt polygon string>' )

polygon.to_shapely()
polygon.from_shapely( a shapely polygon )

# Collections of shapes have additional supported formats
collection = FeatureCollection([polygon])

# Creates a geopandas DataFrame
collection.to_geopandas()
collection.from_geopandas( a geopandas DataFrame )

# Creates a GeoJSON FeatureCollection
collection.to_geojson()
collection.from_geojson( { a geojson object } )

# Write a FeatureCollection to an ESRI Shapefile
from zipfile import ZipFile
with ZipFile('shapefile_name.zip', 'w') as zfile:
    collection.to_shapefile(zfile)

collection.from_shapefile('shapefile_name.zip')

Plotting Shapes

from geostructures import *
from geostructures.collections import FeatureCollection
from geostructures.visualization.plotly import draw_collection

box = GeoBox(Coordinate(-0.154092, 51.539865), Coordinate(-0.140592, 51.505665))
circle = GeoCircle(Coordinate(-0.131092, 51.509865), radius=500)
collection = FeatureCollection([box, circle])

# Display the results
fig = draw_collection(collection)
fig

Bounding Shapes by Time

from datetime import datetime
from geostructures import *
from geostructures.collections import Track
from geostructures.time import TimeInterval

track = Track([
    # Bound shapes to a specific point in time
    GeoPoint(Coordinate(-0.154092, 51.539865), dt=datetime(2020, 5, 1, 12)),
    GeoPoint(Coordinate(-0.155092, 51.540865), dt=datetime(2020, 5, 1, 13)),
    
    # Or bound them to a span of time
    GeoPoint(
        Coordinate(-0.156092, 51.541865), 
        dt=TimeInterval(
            datetime(2020, 5, 1, 14),
            datetime(2020, 5, 1, 15)
        )
    ),
])

# Slice by datetime
subset = track[datetime(2020, 5, 1, 12):datetime(2020, 5, 1, 13)]

# Get efficient metrics between shapes
track.centroid_distances    # meters
track.speed_diffs           # meters per second
track.time_start_diffs      # timedeltas

Geohashing

Geostructures supports geohashing using both Uber's H3 and the original Niemeyer geohashing algorithm.

from geostructures import *
from geostructures.collections import FeatureCollection
from geostructures.geohash import H3Hasher, NiemeyerHasher
from geostructures.visualization.plotly import h3_choropleth

box = GeoBox(Coordinate(-0.154092, 51.539865), Coordinate(-0.140592, 51.505665))
circle = GeoCircle(Coordinate(-0.131092, 51.509865), radius=500)
collection = FeatureCollection([box, circle])

# Create a hasher
hasher = H3Hasher(resolution=10)
hashmap = hasher.hash_collection(collection)

# Display the results
h3_choropleth(hashmap)

# Alternatively, hash using the Niemeyer algorithm
hasher = NiemeyerHasher(length=8, base=16)
hashmap = hasher.hash_collection(collection)

Projections

This library assumes that all geospatial terms and structures conform to the WGS84 standard (CRS 4326).

Reporting Issues / Requesting Features

The Geostructures team uses Github issues to track development goals. Please include as much detail as possible so we can effectively triage your request.

Contributing

We welcome all contributors! Please review CONTRIBUTING.md for more information.

Developers

Carl Best (Sr. Data Scientist/Project Owner)
https://github.com/ccbest/

Eli Talbert (Sr. Data Scientist/PhD)
https://github.com/etalbert102

Jessica Moore (Sr. Data Scientist)
https://github.com/jessica-writes-code

Richard Marshall (Data Scientist/SME)
https://github.com/RichardMarshall13