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45
scripts/overlay/cities-geojson-generate.py
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45
scripts/overlay/cities-geojson-generate.py
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#!/usr/bin/env python3
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"""
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convert_cities.py
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BoopLabs Pupmap Europe/Nordics Cities GeoJSON
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Version: 2025-10-03
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"""
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import geopandas as gpd
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# ---------- User paths ----------
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cities_shp = "ne_10m_populated_places.shp"
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output_dir = "/mnt/storage/docker-data/pupmap/www/data/"
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# ---------- Europe + Nordics ISO3 ----------
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europe_nordics = [
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'ALB','AND','AUT','BEL','BIH','BGR','HRV','CYP','CZE','DNK','EST','FIN','FRA','DEU',
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'GRC','HUN','ISL','IRL','ITA','LVA','LTU','LUX','MLT','MDA','MCO','MNE','NLD','NOR',
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'POL','PRT','ROU','RUS','SMR','SRB','SVK','SVN','ESP','SWE','CHE','UKR','GBR','VAT'
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]
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# ---------- Load cities shapefile ----------
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print("Processing cities...")
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cities = gpd.read_file(cities_shp)
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# Filter Europe/Nordics
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if 'ADM0_A3' in cities.columns:
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cities_filtered = cities[cities['ADM0_A3'].isin(europe_nordics)].copy()
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else:
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cities_filtered = cities.copy() # fallback: include all
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# Rename columns to match map expectations
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cities_filtered = cities_filtered.rename(columns={
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'NAME': 'name',
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'ADM0NAME': 'country',
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'POP_MAX': 'population'
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})
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# Keep only required columns
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cities_filtered = cities_filtered[['name','country','population','geometry']]
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# Save to GeoJSON
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cities_geojson_path = output_dir + "world_cities_europe.geojson"
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cities_filtered.to_file(cities_geojson_path, driver='GeoJSON')
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print(f"Saved {cities_geojson_path} ({len(cities_filtered)} features)")
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BIN
scripts/overlay/cities1000.zip
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BIN
scripts/overlay/cities1000.zip
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31
scripts/overlay/cities2-geojson-generate.py
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31
scripts/overlay/cities2-geojson-generate.py
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import geopandas as gpd
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# --- Input shapefile ---
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shapefile_path = "ne_10m_populated_places.shp"
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output_geojson = "world_cities_europe_clean.geojson"
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# --- Load cities shapefile ---
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cities = gpd.read_file(shapefile_path)
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# --- Filter Europe + Nordics ---
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europe_iso3 = [
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'AUT','BEL','CHE','DEU','DNK','ESP','FIN','FRA',
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'GBR','ITA','NOR','NLD','SWE','ISL','IRL','LUX'
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]
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cities = cities[cities['ADM0_A3'].isin(europe_iso3)]
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# --- Ensure columns ---
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if 'POP_MAX' not in cities.columns:
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cities['POP_MAX'] = 0
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else:
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cities['POP_MAX'] = cities['POP_MAX'].fillna(0).astype(int)
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cities = cities[['NAME','POP_MAX','geometry']]
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# --- Remove empty geometries ---
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cities = cities[~cities['geometry'].is_empty]
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# --- Save to GeoJSON ---
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cities.to_file(output_geojson, driver="GeoJSON")
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print(f"[+] Saved {len(cities)} cities to {output_geojson}")
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72
scripts/overlay/city-road-geojson-generate.py
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72
scripts/overlay/city-road-geojson-generate.py
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import geopandas as gpd
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import os
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# -----------------------------
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# Settings
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# -----------------------------
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output_dir = "/mnt/storage/docker-data/pupmap/www/data/sources/"
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os.makedirs(output_dir, exist_ok=True)
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# Europe + Nordics country codes (for cities)
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europe_nordics = [
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'AL','AD','AT','BE','BA','BG','HR','CY','CZ','DK','EE','FI','FR','DE',
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'GR','HU','IS','IE','IT','LV','LI','LT','LU','MT','MD','MC','ME','NL',
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'MK','NO','PL','PT','RO','RU','SM','RS','SK','SI','ES','SE','CH','UA','GB'
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]
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# -----------------------------
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# Process Cities
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# -----------------------------
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print("Processing cities...")
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cities = gpd.read_file("ne_10m_populated_places/ne_10m_populated_places.shp")
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cities_filtered = cities[cities['ADM0_A3'].isin(europe_nordics)].copy()
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# Extract coordinates from geometry
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cities_filtered['lon'] = cities_filtered.geometry.x
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cities_filtered['lat'] = cities_filtered.geometry.y
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# Keep needed columns + geometry
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cities_filtered = cities_filtered[['NAME','ADM0NAME','lat','lon','POP_MAX','geometry']]
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cities_filtered = cities_filtered.rename(columns={
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'NAME':'name',
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'ADM0NAME':'country',
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'POP_MAX':'population'
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})
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# Drop any duplicate columns
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cities_filtered = cities_filtered.loc[:, ~cities_filtered.columns.duplicated()]
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cities_geojson_path = os.path.join(output_dir, "world_cities_europe.geojson")
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cities_filtered.to_file(cities_geojson_path, driver='GeoJSON')
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print(f"Saved {cities_geojson_path}")
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# -----------------------------
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# Process Roads
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# -----------------------------
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print("Processing roads...")
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roads = gpd.read_file("ne_10m_roads/ne_10m_roads.shp")
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# Filter by Europe/Nordics bounding box
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minx, miny, maxx, maxy = -25.0, 34.0, 40.0, 72.0
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roads_filtered = roads.cx[minx:maxx, miny:maxy].copy()
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# Remove duplicate columns
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roads_filtered = roads_filtered.loc[:, ~roads_filtered.columns.duplicated()]
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# Pick columns: use FULLNAME/FCLASS if they exist
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if 'FULLNAME' in roads_filtered.columns and 'FCLASS' in roads_filtered.columns:
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roads_filtered = roads_filtered[['FULLNAME','FCLASS','geometry']]
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roads_filtered = roads_filtered.rename(columns={'FULLNAME':'name','FCLASS':'type'})
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else:
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# fallback: take first two non-geometry columns
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geom_col = 'geometry'
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other_cols = [c for c in roads_filtered.columns if c != geom_col]
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roads_filtered = roads_filtered[other_cols[:2] + [geom_col]]
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roads_filtered = roads_filtered.rename(columns={other_cols[0]:'name', other_cols[1]:'type'})
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roads_geojson_path = os.path.join(output_dir, "roads_europe.geojson")
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roads_filtered.to_file(roads_geojson_path, driver='GeoJSON')
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print(f"Saved {roads_geojson_path}")
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print("✅ All GeoJSON files are ready for MapLibre!")
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71
scripts/overlay/city-road2-geojson-generate.py
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71
scripts/overlay/city-road2-geojson-generate.py
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#!/usr/bin/env python3
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"""
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geopandasconvert_fixed.py
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BoopLabs Pupmap Europe/Nordics GeoJSON generator
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Version: 2025-10-03
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"""
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import geopandas as gpd
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# ---------- User paths ----------
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cities_shp = "ne_10m_populated_places.shp"
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roads_shp = "ne_10m_roads.shp"
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output_dir = "/mnt/storage/docker-data/pupmap/www/data/sources/"
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# ---------- Europe + Nordics filter ----------
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europe_nordics = [
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'ALB','AND','AUT','BEL','BIH','BGR','HRV','CYP','CZE','DNK','EST','FIN','FRA','DEU',
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'GRC','HUN','ISL','IRL','ITA','LVA','LTU','LUX','MLT','MDA','MCO','MNE','NLD','NOR',
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'POL','PRT','ROU','RUS','SMR','SRB','SVK','SVN','ESP','SWE','CHE','UKR','GBR','VAT'
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]
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# ---------- Convert cities ----------
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print("Processing cities...")
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cities = gpd.read_file(cities_shp)
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# Keep only Europe/Nordics
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cities_filtered = cities[cities['ADM0_A3'].isin(europe_nordics)].copy()
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# Ensure expected properties exist
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cities_filtered = cities_filtered.rename(columns={
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'NAME': 'name',
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'ADM0NAME': 'country',
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'POP_MAX': 'population'
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})
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# Keep only required columns
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cities_filtered = cities_filtered[['name','country','population','geometry']]
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# Save to GeoJSON
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cities_geojson_path = output_dir + "world_cities_europe.geojson"
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cities_filtered.to_file(cities_geojson_path, driver='GeoJSON')
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print(f"Saved {cities_geojson_path} ({len(cities_filtered)} features)")
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# ---------- Convert roads ----------
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print("Processing roads...")
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roads = gpd.read_file(roads_shp)
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# Keep only Europe/Nordics
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roads_filtered = roads[roads['ADM0_A3'].isin(europe_nordics)].copy()
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# Rename the main type column to 'type' (if needed)
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if 'TYPE' in roads_filtered.columns:
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roads_filtered = roads_filtered.rename(columns={'TYPE': 'type'})
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elif 'fclass' in roads_filtered.columns:
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roads_filtered = roads_filtered.rename(columns={'fclass': 'type'})
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else:
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roads_filtered['type'] = 'road' # fallback
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# Keep only necessary columns
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roads_filtered = roads_filtered[['type','geometry']]
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# Remove duplicate column names (sometimes shapefiles have duplicates)
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roads_filtered = roads_filtered.loc[:, ~roads_filtered.columns.duplicated()]
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# Save to GeoJSON
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roads_geojson_path = output_dir + "roads_europe.geojson"
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roads_filtered.to_file(roads_geojson_path, driver='GeoJSON')
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print(f"Saved {roads_geojson_path} ({len(roads_filtered)} features)")
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47
scripts/overlay/clean_map_data.py
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47
scripts/overlay/clean_map_data.py
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import geopandas as gpd
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# --- 1. Clean Cities ---
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cities_path = "world_cities_europe.geojson"
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cities_clean_path = "world_cities_europe_clean.geojson"
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cities = gpd.read_file(cities_path)
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# Ensure POP_MAX exists and numeric
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if 'POP_MAX' not in cities.columns:
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cities['POP_MAX'] = 0
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else:
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cities['POP_MAX'] = cities['POP_MAX'].fillna(0)
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cities['POP_MAX'] = cities['POP_MAX'].astype(int)
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# Remove empty geometries
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cities = cities[~cities['geometry'].is_empty]
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cities.to_file(cities_clean_path, driver="GeoJSON")
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print(f"[+] Cleaned cities saved to {cities_clean_path}")
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# --- 2. Clean Roads ---
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roads_path = "roads_europe.geojson"
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roads_clean_path = "roads_europe_clean.geojson"
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roads = gpd.read_file(roads_path)
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# Remove empty geometries
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roads = roads[~roads['geometry'].is_empty]
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# Add 'type' property based on 'fclass'
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def classify_road(fclass):
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if str(fclass).lower() in ['motorway', 'primary']:
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return 'major'
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else:
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return 'minor'
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if 'fclass' in roads.columns:
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roads['type'] = roads['fclass'].apply(classify_road)
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else:
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# If no fclass, mark everything as minor
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roads['type'] = 'minor'
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roads.to_file(roads_clean_path, driver="GeoJSON")
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print(f"[+] Cleaned roads saved to {roads_clean_path}")
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BIN
scripts/overlay/ne_10m_populated_places.zip
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scripts/overlay/ne_10m_populated_places.zip
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<title>Natural Earth » Blog Archive » Populated Places - Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales </title>
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<h2>Populated Places</h2>
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<div class="entry">
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<div class="downloadPromoBlock" style="float: left;">
|
||||
<div style="float: left; width: 170px;"><img loading="lazy" class="alignnone size-full wp-image-1918" title="pop_thumb" src="https://www.naturalearthdata.com/wp-content/uploads/2009/09/pop_thumb.png" alt="pop_thumb" width="150" height="97" /></div>
|
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<div style="float: left; width: 410px;"><em>City and town points, from Tokyo to Wasilla, Cairo to Kandahar</em><br />
|
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<div class="download-link-div">
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||||
<a class="download-link" rel="nofollow" title="Downloaded 285695 times (Shapefile, geoDB, or TIFF format)" onclick="if (window.urchinTracker) urchinTracker ('https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip');" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip">Download populated places</a> <span class="download-link-span">(2.68 MB) version 5.1.2</span>
|
||||
</div> <div class="download-link-div">
|
||||
<a class="download-link" rel="nofollow" title="Downloaded 88271 times (Shapefile, geoDB, or TIFF format)" onclick="if (window.urchinTracker) urchinTracker ('https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places_simple.zip');" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places_simple.zip">Download simple (less columns)</a> <span class="download-link-span">(636.84 KB) version 5.1.2</span>
|
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</div><br />
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<span id="more-472"></span></div>
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||||
</div>
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||||
<div class="downloadMainBlock" style="float: left;">
|
||||
<p><strong>About</strong></p>
|
||||
<p>Point symbols with name attributes. Includes all admin-0 and many admin-1 capitals, major cities and towns, plus a sampling of smaller towns in sparsely inhabited regions. We favor regional significance over population census in determining our selection of places. Use the scale rankings to filter the number of towns that appear on your map.</p>
|
||||
<p><img loading="lazy" class="alignnone size-full wp-image-1920" title="pop_banner" src="https://www.naturalearthdata.com/wp-content/uploads/2009/09/pop_banner.png" alt="pop_banner" width="580" height="150" srcset="https://www.naturalearthdata.com/wp-content/uploads/2009/09/pop_banner.png 580w, https://www.naturalearthdata.com/wp-content/uploads/2009/09/pop_banner-300x77.png 300w" sizes="(max-width: 580px) 100vw, 580px" /></p>
|
||||
<p><a href="http://www.ornl.gov/sci/landscan/">LandScan</a> derived population estimates are provided for 90% of our cities. Those lacking population estimates are often in sparsely inhabited areas. We provide a range of population values that account for the total “metropolitan” population rather than it’s administrative boundary population. Use the PopMax column to size your town labels. Starting in version 1.1, popMax has been throttled down to the UN estimated metro population for the ~500 largest urban areas in the world. This affects towns in China, India, and parts of Africa where our Landscan counting method usually over estimated.</p>
|
||||
<p>Population estimates were derived from the LANDSCAN dataset maintained and distributed by the Oak Ridge National Laboratory. These data were converted from raster to vector and pixels with fewer than 200 persons per square kilometer were removed from the dataset as they were classified as rural. Once urban pixels were selected, these pixels were aggregated into contiguous units. Concurrently Thiessen polygons were created based on the selected city points. The Thiessen polygons were used to intersect the contiguous city boundaries to produce bounded areas for the cities. As a result, our estimates capture a metropolitan and micropolitan populations per city regardless of administrative units.</p>
|
||||
<p>Once intersected, the contiguous polygons were recalculated, using aerial interpolation assuming uniform population distribution within each pixel, to determine the population total. This process was conducted multiple times, for each scale level, to produce population estimates for each city at nested scales of 1:300 million, 1:110 million, 1:50 million, 1:20 million, and 1:10 million. </p>
|
||||
<div class="download-link-div">
|
||||
<a class="download-link" rel="nofollow" title="Downloaded 6887 times (Shapefile, geoDB, or TIFF format)" onclick="if (window.urchinTracker) urchinTracker ('https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_urban_areas_landscan.zip');" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_urban_areas_landscan.zip">Download landscan urban areas</a> <span class="download-link-span">(13.01 MB) version 4.0.0</span>
|
||||
</div>
|
||||
<p><strong>Population ranks</strong></p>
|
||||
<p>Are calculated as rank_max and rank_min using this general VB formula that can be pasted into ArcMap Field Calculator advanced area (set your output to x):</p>
|
||||
<blockquote><p>
|
||||
a = [pop_max]</p>
|
||||
<p>if( a > 10000000 ) then<br />
|
||||
x = 14<br />
|
||||
elseif( a > 5000000 ) then<br />
|
||||
x = 13<br />
|
||||
elseif( a > 1000000 ) then<br />
|
||||
x = 12<br />
|
||||
elseif( a > 500000 ) then<br />
|
||||
x = 11<br />
|
||||
elseif( a > 200000 ) then<br />
|
||||
x = 10<br />
|
||||
elseif( a > 100000 ) then<br />
|
||||
x = 9<br />
|
||||
elseif( a > 50000 ) then<br />
|
||||
x = 8<br />
|
||||
elseif( a > 20000 ) then<br />
|
||||
x = 7<br />
|
||||
elseif( a > 10000 ) then<br />
|
||||
x = 6<br />
|
||||
elseif( a > 5000 ) then<br />
|
||||
x = 5<br />
|
||||
elseif( a > 2000 ) then<br />
|
||||
x = 4<br />
|
||||
elseif( a > 1000 ) then<br />
|
||||
x = 3<br />
|
||||
elseif( a > 200 ) then<br />
|
||||
x = 2<br />
|
||||
elseif( a > 0 ) then<br />
|
||||
x = 1<br />
|
||||
else<br />
|
||||
x = 0<br />
|
||||
end if</p></blockquote>
|
||||
<p><strong>Issues</strong></p>
|
||||
<p>While we don’t want to show every admin-1 capital, for those countries where we show most admin-1 capitals, we should have a complete set. If you find we are missing one, please log it in the Cx tool at right.</p>
|
||||
<p><strong>Version History</strong></p>
|
||||
<ul>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 5.1.2 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip">5.1.2</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 5.1.0 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip?version=5.1.0">5.1.0</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 5.0.1 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip?version=5.0.1">5.0.1</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 5.0.0 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip?version=5.0.0">5.0.0</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 4.1.0 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip?version=4.1.0">4.1.0</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 4.0.0 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip?version=4.0.0">4.0.0</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 3.3.1 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip?version=3.3.1">3.3.1</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 3.0.0 of ne_10m_populated_places.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip?version=3.0.0">3.0.0</a>
|
||||
</li>
|
||||
<li>
|
||||
2.0.0
|
||||
</li>
|
||||
<li>
|
||||
1.4.0
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<p><a href="https://github.com/nvkelso/natural-earth-vector/blob/master/CHANGELOG">The master changelog is available on Github »</a>
|
||||
</div>
|
||||
|
||||
|
||||
<p class="postmetadata2">
|
||||
<small>
|
||||
This entry was posted
|
||||
on Monday, September 14th, 2009 at 2:00 am and is filed under <a href="https://www.naturalearthdata.com/download/downloads/10m-cultural-vectors/" rel="category tag">10m-cultural-vectors</a>.
|
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You can follow any responses to this entry through the <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/feed/">RSS 2.0</a> feed.
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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
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<div class="post" id="post-2718">
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<h2>Roads</h2>
|
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<div class="entry">
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<div class="downloadPromoBlock" style="float: left;">
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<div style="float: left; width: 170px;"><img loading="lazy" class="downloadPromoImage" title="urban_area_thumb" src="https://www.naturalearthdata.com/wp-content/uploads/2010/05/na_rds_thumb.png" alt="urban_area_thumb" width="150" height="100" align="left" /></div>
|
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<div style="float: left; width: 410px;"><em>Transportation. </em><div class="download-link-div">
|
||||
<a class="download-link" rel="nofollow" title="Downloaded 88783 times (Shapefile, geoDB, or TIFF format)" onclick="if (window.urchinTracker) urchinTracker ('https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip');" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Download roads</a> <span class="download-link-span">(8.65 MB) version 5.0.0</span>
|
||||
</div>
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||||
<div class="download-link-div">
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||||
<a class="download-link" rel="nofollow" title="Downloaded 18254 times (Shapefile, geoDB, or TIFF format)" onclick="if (window.urchinTracker) urchinTracker ('https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads_north_america.zip');" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads_north_america.zip">Download North America supplement</a> <span class="download-link-span">(45.57 MB) version 4.0.0</span>
|
||||
</div>
|
||||
<p><span id="more-2718"></span></p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="downloadMainBlock" style="float: left;">
|
||||
<p><strong>About</strong></p>
|
||||
<p>The “basic” roads at 10m scale are from CEC North America Environmental Atlas with no attributes and only 1 scale rank class. The “basic” is only available in North America due to the source. We’d like to expand this to world wide, do you have data to contribute?</p>
|
||||
<p>The “supplementary” data in North America includes preliminary scale ranking and full list of attributes and is graciously contributed by <a href="http://www.xnrproductions.com/">XNR Productions</a>.</p>
|
||||
<p><em>(below) North America.</em><em> </em></p>
|
||||
<p><img loading="lazy" title="na_rds_banner" src="https://www.naturalearthdata.com/wp-content/uploads/2010/05/na_rds_banner.png" alt="na_rds_banner" width="580" height="150" /></p>
|
||||
<p><strong>Database background notes</strong></p>
|
||||
<p>The database is at 1:1,000,000 scale (but holds up to 1:250,000 scale), covering all of the United States and Canada, Alaska, Hawaii, Puerto Rico, Mexico, and Belize and includes the following attributes:</p>
|
||||
<p>NUMBER:</p>
|
||||
<ul>
|
||||
<li>Route number</li>
|
||||
</ul>
|
||||
<p>CLASS:</p>
|
||||
<ul>
|
||||
<li> Interstate (Interstates and Quebec Autoroutes)</li>
|
||||
<li> Federal (US Highways, Mexican Federal Highways, Trans-Canada Highways)</li>
|
||||
<li> State (US State, Mexican State, and Canadian Provincial Highways)</li>
|
||||
<li> Other (any other road class)</li>
|
||||
<li> Closed (road is closed to public)</li>
|
||||
<li> U/C (road is under construction)</li>
|
||||
</ul>
|
||||
<p>TYPE:</p>
|
||||
<ul>
|
||||
<li> Tollway</li>
|
||||
<li> Freeway</li>
|
||||
<li> Primary</li>
|
||||
<li> Secondary</li>
|
||||
<li> Other Paved</li>
|
||||
<li> Unpaved</li>
|
||||
<li> Winter (ice road, open winter only)</li>
|
||||
<li> Trail</li>
|
||||
<li> Ferry</li>
|
||||
</ul>
|
||||
<p>DIVIDED:</p>
|
||||
<ul>
|
||||
<li>Whether or not the road is divided. This field is only listed for roads of TYPE Tollway, Freeway, Primary, or Secondary.</li>
|
||||
</ul>
|
||||
<p>STATE:</p>
|
||||
<ul>
|
||||
<li>State or province the road is in.</li>
|
||||
</ul>
|
||||
<p><strong>Issues</strong></p>
|
||||
<ul>
|
||||
<li> BC ferries (AK and WA portions clip on BC boundary)</li>
|
||||
<li>normalize eastern Canada minor road coverage (remove some or add to US?)</li>
|
||||
</ul>
|
||||
<p><strong>Related projects</strong></p>
|
||||
<p><a href="http://geoservice.pbl.nl/website/flexviewer/index.html?config=cfg/PBL_GRIP.xml&center=5.2,52.1333&scale=5000000">Global Roads Inventory Project</a> – From the Netherlands Environmental Assessment Agency. The data in the GRIP database is presented as is. GRIP is a combination of some commercial and many publicly collected datasets and classified along the UNSDI-T datamodel. GRIP data scales vary between 1:100K to 1:1M. The GRIP density rasters and publicly collected features of GRIP are available under a BY-SA Creative Commons Licence. </p>
|
||||
<p><strong>Version History</strong></p>
|
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<ul>
|
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<li>
|
||||
<a rel="nofollow" title="Download version 5.0.0 of ne_10m_roads.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">5.0.0</a>
|
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</li>
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||||
<li>
|
||||
<a rel="nofollow" title="Download version 4.0.0 of ne_10m_roads.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip?version=4.0.0">4.0.0</a>
|
||||
</li>
|
||||
<li>
|
||||
<a rel="nofollow" title="Download version 3.0.0 of ne_10m_roads.zip" href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip?version=3.0.0">3.0.0</a>
|
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</li>
|
||||
<li>
|
||||
2.0.0
|
||||
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|
||||
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|
||||
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|
||||
</li>
|
||||
<li>
|
||||
1.2.0
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<p><a href="https://github.com/nvkelso/natural-earth-vector/blob/master/CHANGELOG">The master changelog is available on Github »</a></p>
|
||||
</div>
|
||||
|
||||
|
||||
<p class="postmetadata2">
|
||||
<small>
|
||||
This entry was posted
|
||||
on Monday, September 14th, 2009 at 1:30 am and is filed under <a href="https://www.naturalearthdata.com/download/downloads/10m-cultural-vectors/" rel="category tag">10m-cultural-vectors</a>, <a href="https://www.naturalearthdata.com/download/downloads/featured-on-homepage/" rel="category tag">Featured on Homepage</a>.
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You can follow any responses to this entry through the <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/roads/feed/">RSS 2.0</a> feed.
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1
scripts/overlay/ne_10m_roads/ne_10m_roads.VERSION.txt
Normal file
1
scripts/overlay/ne_10m_roads/ne_10m_roads.VERSION.txt
Normal file
@@ -0,0 +1 @@
|
||||
5.0.0
|
||||
1
scripts/overlay/ne_10m_roads/ne_10m_roads.cpg
Normal file
1
scripts/overlay/ne_10m_roads/ne_10m_roads.cpg
Normal file
@@ -0,0 +1 @@
|
||||
UTF-8
|
||||
BIN
scripts/overlay/ne_10m_roads/ne_10m_roads.dbf
Normal file
BIN
scripts/overlay/ne_10m_roads/ne_10m_roads.dbf
Normal file
Binary file not shown.
1
scripts/overlay/ne_10m_roads/ne_10m_roads.prj
Normal file
1
scripts/overlay/ne_10m_roads/ne_10m_roads.prj
Normal file
@@ -0,0 +1 @@
|
||||
GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
|
||||
BIN
scripts/overlay/ne_10m_roads/ne_10m_roads.shp
Normal file
BIN
scripts/overlay/ne_10m_roads/ne_10m_roads.shp
Normal file
Binary file not shown.
BIN
scripts/overlay/ne_10m_roads/ne_10m_roads.shx
Normal file
BIN
scripts/overlay/ne_10m_roads/ne_10m_roads.shx
Normal file
Binary file not shown.
53
scripts/overlay/roads-geojson-generate.py
Normal file
53
scripts/overlay/roads-geojson-generate.py
Normal file
@@ -0,0 +1,53 @@
|
||||
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
convert_roads.py
|
||||
BoopLabs Pupmap Europe/Nordics Roads GeoJSON
|
||||
Version: 2025-10-03
|
||||
"""
|
||||
|
||||
import geopandas as gpd
|
||||
|
||||
# ---------- User paths ----------
|
||||
roads_shp = "ne_10m_roads.shp"
|
||||
output_dir = "/mnt/storage/docker-data/pupmap/www/data/"
|
||||
|
||||
# ---------- Europe + Nordics ISO3 ----------
|
||||
europe_nordics = [
|
||||
'ALB','AND','AUT','BEL','BIH','BGR','HRV','CYP','CZE','DNK','EST','FIN','FRA','DEU',
|
||||
'GRC','HUN','ISL','IRL','ITA','LVA','LTU','LUX','MLT','MDA','MCO','MNE','NLD','NOR',
|
||||
'POL','PRT','ROU','RUS','SMR','SRB','SVK','SVN','ESP','SWE','CHE','UKR','GBR','VAT'
|
||||
]
|
||||
|
||||
# ---------- Load roads shapefile ----------
|
||||
print("Processing roads...")
|
||||
roads = gpd.read_file(roads_shp)
|
||||
|
||||
# Filter Europe/Nordics only if ADM0_A3 exists
|
||||
if 'ADM0_A3' in roads.columns:
|
||||
roads_filtered = roads[roads['ADM0_A3'].isin(europe_nordics)].copy()
|
||||
else:
|
||||
roads_filtered = roads.copy() # fallback: include all
|
||||
|
||||
# Determine road type column
|
||||
if 'TYPE' in roads_filtered.columns:
|
||||
roads_filtered = roads_filtered.rename(columns={'TYPE': 'type'})
|
||||
elif 'fclass' in roads_filtered.columns:
|
||||
roads_filtered = roads_filtered.rename(columns={'fclass': 'type'})
|
||||
elif 'ROADCLASS' in roads_filtered.columns:
|
||||
roads_filtered = roads_filtered.rename(columns={'ROADCLASS': 'type'})
|
||||
else:
|
||||
roads_filtered['type'] = 'road' # fallback
|
||||
|
||||
# Keep only necessary columns
|
||||
roads_filtered = roads_filtered[['type','geometry']]
|
||||
|
||||
# Remove duplicates and empty geometries
|
||||
roads_filtered = roads_filtered.loc[:, ~roads_filtered.columns.duplicated()]
|
||||
roads_filtered = roads_filtered[roads_filtered.geometry.notnull()]
|
||||
|
||||
# Save to GeoJSON
|
||||
roads_geojson_path = output_dir + "roads_europe.geojson"
|
||||
roads_filtered.to_file(roads_geojson_path, driver='GeoJSON')
|
||||
print(f"Saved {roads_geojson_path} ({len(roads_filtered)} features)")
|
||||
|
||||
23
scripts/overlay/roads2-geojson-generate.py
Normal file
23
scripts/overlay/roads2-geojson-generate.py
Normal file
@@ -0,0 +1,23 @@
|
||||
import geopandas as gpd
|
||||
|
||||
# Load roads shapefile
|
||||
gdf = gpd.read_file("/mnt/storage/docker-data/pupmap/www/data/sources/ne_10m_roads.shp")
|
||||
|
||||
# Filter by Europe/Nordics bounding box
|
||||
bbox = (-25.0, 34.0, 35.0, 72.0) # lon_min, lat_min, lon_max, lat_max
|
||||
gdf = gdf.cx[bbox[0]:bbox[2], bbox[1]:bbox[3]]
|
||||
|
||||
# Ensure WGS84 CRS
|
||||
gdf = gdf.to_crs(epsg=4326)
|
||||
|
||||
# Add 'type' property if missing
|
||||
if 'type' not in gdf.columns:
|
||||
gdf['type'] = 'road'
|
||||
else:
|
||||
gdf['type'] = gdf['type'].fillna('road')
|
||||
|
||||
# Save to GeoJSON
|
||||
gdf.to_file("/mnt/storage/docker-data/pupmap/www/data/roads_europe.geojson", driver="GeoJSON")
|
||||
|
||||
print("Cleaned roads saved to roads_europe.geojson")
|
||||
|
||||
Reference in New Issue
Block a user