overture2hdx 0.1.1

Lib to convert Overture data in GIS formats and upload it to HDX

Overture Map Data 2 HDX

This project is designed to export geographic data from Overture Maps and upload it to the Humanitarian Data Exchange (HDX). The data is processed using DuckDB and can be exported in various formats such as GeoJSON, GPKG, and ESRI Shapefile.

Features

• Export geographic data from Overture Maps.
• Upload data to HDX.
• Support for multiple output formats.
• Configurable via YAML and environment variables.
• Logging setup using environment variables or parameters.

Installation

pip install overture2hdx

Configuration

The application is configured using a YAML file and environment variables.

YAML Configuration

Example config.yaml:

iso3: npl
geom: '{"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {}, "geometry": {"coordinates": [[]], "type": "Polygon"}}]}'
key: osgeonepal_pkr
subnational: true
frequency: yearly
categories:
- Roads:
    select:
        - id
        - names.primary as name
        - class as class
        - subclass as subclass
        - UNNEST(JSON_EXTRACT(road_surface, '$[*].value')) as road_surface
        - UNNEST(JSON_EXTRACT(sources, '$[*].dataset')) AS source
    hdx:
        title: Roads of Pokhara
        notes: Overturemaps Export for Pokhara. Data might have errors but has gone through validation checks.
        tags:
        - geodata
        - transportation
        - roads
    theme:
        - transportation
    feature_type:
        - segment
    formats:
        - gpkg
        - shp

Code Overview

Config: Class to handle configuration. OvertureMapExporter: Class to handle the export process. setup_logging: Function to set up logging.

Default export config

Following is the default yaml used

Category	Description	License	Overture Theme Docs	Attributes	File Formats
Hospitals	Health POIs such as hospitals and clinics, sourced from Meta and Microsoft open data	CDLA Permissive 2.0	Places	id, name, name_en, category, address, phone, email, website, confidence, source	gpkg, shp
Schools	Educational POIs including schools, colleges, and universities from Meta and Microsoft	CDLA Permissive 2.0	Places	id, name, name_en, category, address, phone, website, confidence, source	gpkg, shp
Rivers	Rivers, lakes, and other water features from OpenStreetMap via the Base theme	ODbL 1.0	Base	id, name, name_en, subtype, class, is_salt, wikidata, source	gpkg, shp
Land Use	Land use areas like farmland and forests from OpenStreetMap landuse tags	ODbL 1.0	Base	id, name, name_en, subtype, class, surface, wikidata, source	gpkg, shp
Transportation Hubs	POIs like airports, train stations, and terminals from Meta and Microsoft open data	CDLA Permissive 2.0	Places	id, name, name_en, category, address, phone, website, confidence, source	gpkg, shp
Settlements	Cities, towns, villages, and hamlets from OSM, geoBoundaries, and Esri	ODbL 1.0	Divisions	id, name, name_en, population, region, wikidata, source	gpkg, shp
Roads	Roads including highways and local roads from OpenStreetMap and TomTom	ODbL 1.0	Transportation	id, name, name_en, class, subclass, subtype, road_surface, source	gpkg, shp
Buildings	Building footprints from OSM, Microsoft, Google, and Esri	ODbL 1.0	Buildings	id, name, name_en, class, subtype, height, level, num_floors, num_floors_underground, is_underground, has_parts, roof_material, roof_shape, roof_color, source	gpkg, shp

Default python example

import json

geom = json.dumps(
    {
        "type": "FeatureCollection",
        "features": [
            {
                "type": "Feature",
                "properties": {},
                "geometry": {
                    "coordinates": [
                        [
                            [83.98047393581618, 28.255338988044088],
                            [83.973540694181, 28.230486421513703],
                            [83.91927014759125, 28.214265947308945],
                            [83.97832224013575, 28.195093119231174],
                            [83.96971545741735, 28.158212628626416],
                            [84.00175181531534, 28.19361814379657],
                            [84.03187555483152, 28.168540447741847],
                            [84.01059767533235, 28.208788347541898],
                            [84.0342663278089, 28.255549578267903],
                            [83.99960011963498, 28.228801292171724],
                            [83.98047393581618, 28.255338988044088],
                        ]
                    ],
                    "type": "Polygon",
                },
            }
        ],
    }
)
iso3 = "NPL"
dataset_name = "Pokhara, Nepal"
key = "osegonepal_pkr"
subnational = True
frequency = "yearly"

from overture2hdx import DEFAULT_CONFIG_YAML

config_yaml = DEFAULT_CONFIG_YAML.format(
    iso3=iso3,
    geom=geom,
    key=key,
    subnational=subnational,
    frequency=frequency,
    dataset_name=dataset_name,
)


from overture2hdx import Config, Exporter

config = Config(config_yaml=config_yaml)
exporter = Exporter(config)
results = exporter.export()
print(results)

How it works?

Author and License

Kshitij Raj Sharma , License : GNU GENERAL PUBLIC LICENSE V3