GeoJSON AOI Parser

Parse and normalize a GeoJSON area of interest, using using PostGIS.

📖 Documentation: https://hotosm.github.io/geojson-aoi-parser/

🖥️ Source Code: https://github.com/hotosm/geojson-aoi-parser

Why do we need this?

• We generally need an Area of Interest (AOI) specified for software to run on a geospatial area.
• GeoJSON is a simple exchange format to communicate this AOI.
• We only care about Polygon data types, but GeoJSON data can be quite variable, with many options for presenting data.
• The goal of this package is to receive GeoJSON data in various forms, then produce a normalised output that can be used for further processing.

Priorities

• Flexible data input: file bytes, dict, string JSON.
• Flexible geometry input:
  • Polygon
  • MultiPolygons
  • GeometryCollection
  • Feature
  • FeatureCollection
• Splits multipolygons into featcol of individual polygons.
• Handle geometries nested inside GeometryCollection.
• Remove any z-dimension coordinates.
• Warn user if CRS is provided, in a coordinate system other than EPSG:4326.
• Normalised output: FeatureCollection containing Polygon geoms.

Capturing The Warnings

If the GeoJSON has an invalid CRS, or coordinates seem off, a warning will be raised.

To install:

pip install geojson-aoi-parser

Basic example:

from geojson_aoi import parse_aoi

polygon_geojson = {
        "type": "Polygon",
        "coordinates": [[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]],
    }

# Where 'db' is some upstream database connection.
feat_col = parse_aoi(db, polygon_geojson)

print(feat_col)
# {
#   'type': 'FeatureCollection',
#   'features': [
#       {
#           'type': 'Feature', 
#           'geometry': 
#               {
#                   'type': 'Polygon', 
#                   'coordinates': [[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]]
#                }
#        }
#     ]
# }

Basic async example:

from geojson_aoi import parse_aoi_async

polygon_geojson = {
        "type": "Polygon",
        "coordinates": [[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]],
    }

# Where 'db' is some upstream database connection.
feat_col = parse_aoi_async(db, polygon_geojson)

print(feat_col)
# {
#   'type': 'FeatureCollection',
#   'features': [
#       {
#           'type': 'Feature', 
#           'geometry': 
#               {
#                   'type': 'Polygon', 
#                   'coordinates': [[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]]
#                }
#        }
#     ]
# }

To halt execution when a warning is raised and act on it:

try:
    featcol = parse_aoi(db, raw_geojson)
except UserWarning as warning:
    log.error(warning.message)
    msg = "Using a valid CRS is mandatory!"
    log.error(msg)
    raise HTTPException(HTTPStatus.BAD_REQUEST, detail=msg)

To record warnings, but allow execution to continue:

import warnings

with warnings.catch_warnings(record=True) as recorded_warnings:
    featcol = parse_aoi(db, raw_geojson)

if recorded_warnings:
    for warning in recorded_warnings:
        if isinstance(warning.message, UserWarning)
            # do stuff with warning
            logger.warning(f"A warning was encountered: {warning.message}")

Create a New DB Connection

• If your app upstream already has a psycopg connection, this can be passed through.
• If you require a new database connection, the connection parameters can be defined as DbConfig object variables:

from geojson_aoi import parse_aoi, DbConfig

db = DbConfig(
    dbname="db1",
    user="user1",
    password="pass1",
    host="localhost",
    port="5432",
)

featcol = parse_aoi(db, raw_geojson)

• Or alternatively as variables from your system environment:

GEOJSON_AOI_DB_NAME=aoi
GEOJSON_AOI_DB_USER=aoi
GEOJSON_AOI_DB_PASSWORD=pass
GEOJSON_AOI_DB_HOST=localhost
GEOJSON_AOI_DB_PORT=5432

then

from geojson_aoi import parse_aoi

featcol = parse_aoi(db, raw_geojson)

((All credit for DbConfig goes to https://github.com/hotosm/pg-nearest-city/))

History

• Initially I tried to write a pure-Python implementation of this, no dependencies.
• I underestimated the amount of work that is! It could be possible to reverse engineer C++ Geos or georust/geos, but it's more hassle than it's worth.
• As all of the target install candidates for this package use a db driver anyway, I thought it wisest (and most time efficient) to use the PostGIS Geos implementation (specifically for the unary_union and convex_hull algorithms).
• An additional advantage is the potential to port this to PGLite when the PostGIS extension is available, meaning AOI processing easily in the browser.