Youssef Harby Youssef-Harby

Overture Maps Data Downloader with Optional GeoJSON Clipping

This Python script downloads geospatial data from Overture Maps, based on user-defined themes and data types (e.g., buildings, transportation). The script can download specific data within a given bounding box (bbox) and export it in GeoParquet or GeoPackage format.

Features:

Allows downloading multiple themes/types (e.g., buildings, transportation) using the overturemaps Python library.
Bounding box (bbox) filtering to limit data to a specific geographic extent.
Optionally clips geometries to the exact boundaries of a GeoJSON file, if provided.

Supports output in GeoParquet or GeoPackage formats.

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I hereby claim:

I am youssef-harby on github.
I am yharby (https://keybase.io/yharby) on keybase.
I have a public key ASBmkyd87Ju7hwN_Uej1lBn8r2SbYZsl12bXghbTDcxPNwo

To claim this, I am signing this object:

	__doc__ = """
	# Overview

	This example shows how to open High Resolution Rapid Refresh (HRRR) meteorology
	"surfaces." These surfaces are slices in vertical space to create single level
	maps of variables like temperature, wind speed components, pressure, etc. Each
	surface is opened using the Zarr archive described in Amazon's opendata registry
	described at https://registry.opendata.aws/noaa-hrrr-pds/.

	# Examples

	import pyarrow as pa
	import pyarrow.parquet as pq
	from pathlib import Path
	import json
	import pandas as pd


	def process_parquet_file(parquet_path):
	# Read the Parquet file into a PyArrow Table
	table = pq.read_table(parquet_path)

	import httpx
	import datetime
	import hashlib
	import hmac

	# AWS credentials
	access_key = "secret"
	secret_key = "secret"
	region = "eu-central-1" # e.g. 'us-west-1'
	bucket = "bucket-name"

	# dataset ref : https://www.kaggle.com/datasets/max-mind/world-cities-database/code
	# pip install duckdb

	import duckdb

	con = duckdb.connect()

	data = """
	-- Environment setup
	SET enable_progress_bar = true;

	import duckdb

	con = duckdb.connect('./duckdb.duckdb')
	data = """
	SET memory_limit = '32GB';
	SET threads TO 16;
	SET enable_progress_bar = true;
	SET enable_progress_bar_print = true;
	INSTALL httpfs;
	INSTALL spatial;

	import csv
	import re


	def extract_lat_long(csv_file, url_column):
	pattern = r".+!3d(-?\d+\.\d+)!4d(-?\d+\.\d+).+"
	data = []

	# Read the CSV file and extract latitude and longitude values
	with open(csv_file, 'r') as f: