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do-me
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Geonames

Modalities:
Tabular
Text
Geospatial
Formats:
parquet
Size:
10M - 100M
Tags:
geospatial
Libraries:
Datasets
Dask
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Dataset card Data Studio Files
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- ---
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- license: cc-by-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-4.0
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+ size_categories:
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+ - 10M<n<100M
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+ ---
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+
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+ # Geonames
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+
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+ A simple parquet conversion of the tab-separated, zipped textfile from https://download.geonames.org/export/dump/ allCountries.zip.
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+
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+ ## Columns
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+
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+ allCountries.zip
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+
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+ ```
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+ The main 'geoname' table has the following fields :
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+ ---------------------------------------------------
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+ geonameid : integer id of record in geonames database
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+ name : name of geographical point (utf8) varchar(200)
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+ asciiname : name of geographical point in plain ascii characters, varchar(200)
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+ alternatenames : alternatenames, comma separated, ascii names automatically transliterated, convenience attribute from alternatename table, varchar(10000)
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+ latitude : latitude in decimal degrees (wgs84)
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+ longitude : longitude in decimal degrees (wgs84)
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+ feature class : see http://www.geonames.org/export/codes.html, char(1)
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+ feature code : see http://www.geonames.org/export/codes.html, varchar(10)
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+ country code : ISO-3166 2-letter country code, 2 characters
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+ cc2 : alternate country codes, comma separated, ISO-3166 2-letter country code, 200 characters
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+ admin1 code : fipscode (subject to change to iso code), see exceptions below, see file admin1Codes.txt for display names of this code; varchar(20)
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+ admin2 code : code for the second administrative division, a county in the US, see file admin2Codes.txt; varchar(80)
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+ admin3 code : code for third level administrative division, varchar(20)
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+ admin4 code : code for fourth level administrative division, varchar(20)
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+ population : bigint (8 byte int)
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+ elevation : in meters, integer
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+ dem : digital elevation model, srtm3 or gtopo30, average elevation of 3''x3'' (ca 90mx90m) or 30''x30'' (ca 900mx900m) area in meters, integer. srtm processed by cgiar/ciat.
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+ timezone : the iana timezone id (see file timeZone.txt) varchar(40)
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+ modification date : date of last modification in yyyy-MM-dd format
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+ ```
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+
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+ ## Conversion
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+
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+ ```python
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+ import pandas as pd
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+ df = pd.read_csv('allCountries.txt', sep='\t', header=None, low_memory=False)
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+ df.to_parquet('geonames_23_03_2025.parquet')
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+ ```
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+
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+ ## Quality
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+
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+ Be warned, the quality - especially for other languages than English - might sometimes be low. Sometimes there are duplicates and very confusing entries.
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+
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+ ## Query with DuckDB
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+
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+ ### Example query for `München`
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+
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+ ```python
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+ import duckdb
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+ import geopandas
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+ df = duckdb.sql(f"SELECT * FROM 'geonames_23_03_2025.parquet' WHERE \"1\" = 'München' ").df() # you can add the country code to the query with AND \"8\" = 'GB'
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+ gdf = geopandas.GeoDataFrame( df, geometry=geopandas.points_from_xy(x=df["5"], y=df["4"]))
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+ gdf
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+ ```
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+
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+ | ID | Name | Alternate Name | Additional Info | Latitude | Longitude | Feature Class | Feature Code | Country Code | Admin Code | Admin1 | Admin2 | Admin3 | Admin4 | Population | Elevation | Time Zone | Last Update | Geometry |
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+ |---------|---------|---------------|----------------|-----------|-----------|---------------|--------------|--------------|------------|--------|--------|---------|----------|------------|-----------|---------------|--------------|------------------------|
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+ | 2867711 | München | Muenchen | None | 51.60698 | 13.31243 | P | PPL | DE | None | 11 | 00 | 12062 | 12062500 | 0 | NaN | Europe/Berlin | 2015-09-04 | POINT (13.312 51.607) |
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+ | 2867713 | München | Munchen | None | 48.69668 | 13.46314 | P | PPL | DE | None | 02 | 092 | 09275 | 09275128 | 0 | NaN | Europe/Berlin | 2013-02-19 | POINT (13.463 48.697) |
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+
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+ Note that using the German spelling the query yields nonsense. Instead, query in English:
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+
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+ ```python
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+ import duckdb
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+ import geopandas
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+ df = duckdb.sql(f"SELECT * FROM 'geonames_23_03_2025.parquet' WHERE \"1\" = 'Munich' AND \"8\" = 'DE' ").df() # you can add the country code to the query with AND \"8\" = 'GB'
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+ gdf = geopandas.GeoDataFrame( df, geometry=geopandas.points_from_xy(x=df["5"], y=df["4"]))
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+ gdf
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+ ```
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+
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+ | ID | Name | Official Name | Alternate Names | Latitude | Longitude | Feature Class | Feature Code | Country Code | Admin Code | Admin1 | Admin2 | Admin3 | Admin4 | Population | Elevation | Time Zone | Last Update | Geometry |
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+ |---------|--------|--------------|-----------------|-----------|-----------|---------------|--------------|--------------|------------|--------|--------|---------|----------|------------|-----------|--------------|--------------|------------------------|
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+ | 2867714 | Munich | Munich | Lungsod ng Muenchen, Lungsod ng München, MUC, Min... | 48.13743 | 11.57549 | P | PPLA | DE | None | 02 | 091 | 09162 | 09162000 | 1260391 | NaN | 524 | Europe/Berlin | 2023-10-12 | POINT (11.575 48.137) |
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+
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+ This query returns only one entry with a city centroid, just as expected.
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+
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+ ## Visualize with deck.gl
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+
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+ ```python
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+ import pydeck as pdk
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+ import pandas as pd
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+ import numpy as np
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+
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+ # load some gdf
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+ gdf["coordinates"] = gdf.apply(lambda x: [x.geometry.x, x.geometry.y], axis=1)
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+
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+ # Define a layer to display on a map
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+ layer = pdk.Layer(
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+ "ScatterplotLayer",
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+ # coordinates is an array
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+ gdf[["1","coordinates"]], # super important! only pass what's needed. If geometry column from geopandas is passed, error!
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+ pickable=True,
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+ opacity=0.99,
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+ stroked=True,
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+ filled=True,
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+ radius_scale=6,
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+ radius_min_pixels=1,
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+ radius_max_pixels=100,
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+ line_width_min_pixels=1,
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+ get_position="coordinates",
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+ get_radius="1000",
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+ get_fill_color=[255, 140, 0],
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+ get_line_color=[255, 140, 0],
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+ )
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+
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+ # Set the viewport location
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+ view_state = pdk.ViewState(latitude=np.mean(gdf.geometry.y), longitude=np.mean(gdf.geometry.x), zoom=12, bearing=0, pitch=0)
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+
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+ # Render
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+ r = pdk.Deck(layers=[layer], initial_view_state=view_state,height=2000, tooltip={"text": "{1}"})
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+ r.to_html("scatterplot_layer.html")
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+ ```
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c4da8719565937fb268b32/sxpg_RYZLBmdHN1kQ_9K-.png)
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+
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+ ## Sample
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+
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+ | ID | Name | Official Name | Alternate Names | Latitude | Longitude | Feature Class | Feature Code | Country Code | Admin Code | Admin1 | Admin2 | Admin3 | Admin4 | Population | Elevation | Time Zone | Last Update |
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+ |----------|-------------------------------|------------------------------------|-------------------------------------------------|-----------|-----------|---------------|--------------|--------------|------------|--------|--------|--------|--------|------------|-----------|---------------|--------------|
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+ | 2994701 | Roc Meler | Roc Meler | Roc Mele, Roc Meler, Roc Mélé | 42.58765 | 1.74180 | T | PK | AD | AD,FR | 02 | NaN | NaN | NaN | 0 | 2811 | Europe/Andorra | 2023-10-03 |
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+ | 3017832 | Pic de les Abelletes | Pic de les Abelletes | Pic de la Font-Negre, Pic de la Font-Nègre, Pic ... | 42.52535 | 1.73343 | T | PK | AD | FR | A9 | 66 | 663 | 66146 | 0 | NaN | 2411 | Europe/Andorra | 2014-11-05 |
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+ | 3017833 | Estany de les Abelletes | Estany de les Abelletes | Estany de les Abelletes, Etang de Font-Negre, Ét... | 42.52915 | 1.73362 | H | LK | AD | FR | A9 | NaN | NaN | NaN | 0 | NaN | 2260 | Europe/Andorra | 2014-11-05 |
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+ | 3023203 | Port Vieux de la Coume d’Ose | Port Vieux de la Coume d'Ose | Port Vieux de Coume d'Ose, Port Vieux de Coume ... | 42.62568 | 1.61823 | T | PASS | AD | NaN | 00 | NaN | NaN | NaN | 0 | NaN | 2687 | Europe/Andorra | 2014-11-05 |
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+ | 3029315 | Port de la Cabanette | Port de la Cabanette | Port de la Cabanette, Porteille de la Cabanette | 42.60000 | 1.73333 | T | PASS | AD | AD,FR | B3 | 09 | 091 | 09139 | 0 | NaN | 2379 | Europe/Andorra | 2014-11-05 |
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+ | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
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+ | 13216940 | GLORIA Seamount | GLORIA Seamount | NaN | 45.03000 | -15.53500 | U | SMU | NaN | NaN | 00 | NaN | NaN | NaN | 0 | NaN | -9999 | NaN | 2025-02-19 |
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+ | 13216941 | Yubko Hills | Yubko Hills | NaN | 13.01820 | -134.41130 | U | HLSU | NaN | NaN | 00 | NaN | NaN | NaN | 0 | NaN | -9999 | NaN | 2025-02-19 |
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+ | 13216942 | Maguari Seamount | Maguari Seamount | NaN | 0.68832 | -44.31278 | U | SMU | NaN | NaN | 00 | NaN | NaN | NaN | 0 | NaN | -9999 | NaN | 2025-02-19 |
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+ | 13216943 | Quintana Seamount | Quintana Seamount | NaN | -32.74950 | -38.67696 | U | SMU | NaN | NaN | 00 | NaN | NaN | NaN | 0 | NaN | -9999 | NaN | 2025-02-19 |
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+ | 13216944 | Satander Guyot | Satander Guyot | NaN | -1.92806 | -37.82161 | U | DEPU | NaN | NaN | 00 | NaN | NaN | NaN | 0 | NaN | -9999 | NaN | 2025-02-19 |
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+
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+ 13111559 rows × 19 columns