metadata

license: cc-by-4.0
size_categories:
  - 10M<n<100M
tags:
  - geospatial

Geonames

A simple parquet conversion of Geonames place database and ZIP codes.

Source

The tab-separated, zipped textfiles allCountries.zip from:

Columns

allCountries.zip

The main 'geoname' table has the following fields :
---------------------------------------------------
geonameid         : integer id of record in geonames database
name              : name of geographical point (utf8) varchar(200)
asciiname         : name of geographical point in plain ascii characters, varchar(200)
alternatenames    : alternatenames, comma separated, ascii names automatically transliterated, convenience attribute from alternatename table, varchar(10000)
latitude          : latitude in decimal degrees (wgs84)
longitude         : longitude in decimal degrees (wgs84)
feature class     : see http://www.geonames.org/export/codes.html, char(1)
feature code      : see http://www.geonames.org/export/codes.html, varchar(10)
country code      : ISO-3166 2-letter country code, 2 characters
cc2               : alternate country codes, comma separated, ISO-3166 2-letter country code, 200 characters
admin1 code       : fipscode (subject to change to iso code), see exceptions below, see file admin1Codes.txt for display names of this code; varchar(20)
admin2 code       : code for the second administrative division, a county in the US, see file admin2Codes.txt; varchar(80) 
admin3 code       : code for third level administrative division, varchar(20)
admin4 code       : code for fourth level administrative division, varchar(20)
population        : bigint (8 byte int) 
elevation         : in meters, integer
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.
timezone          : the iana timezone id (see file timeZone.txt) varchar(40)
modification date : date of last modification in yyyy-MM-dd format

Conversion

import pandas as pd
df = pd.read_csv('allCountries.txt', sep='\t', header=None, low_memory=False)
df.to_parquet('geonames_23_03_2025.parquet')

Quality

Be warned, the quality - especially for other languages than English - might sometimes be low. Sometimes there are duplicates and very confusing entries.

Query with DuckDB

Example query for `München`

import duckdb
import geopandas
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'
gdf = geopandas.GeoDataFrame(    df,    geometry=geopandas.points_from_xy(x=df["5"], y=df["4"]))
gdf

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
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)
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)

Note that using the German spelling the query yields nonsense. Instead, query in English:

import duckdb
import geopandas
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'
gdf = geopandas.GeoDataFrame(    df,    geometry=geopandas.points_from_xy(x=df["5"], y=df["4"]))
gdf

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
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

This query returns only one entry with a city centroid, just as expected.

Visualize with deck.gl

import pydeck as pdk
import pandas as pd
import numpy as np 

# load some gdf
gdf["coordinates"] = gdf.apply(lambda x: [x.geometry.x, x.geometry.y], axis=1)

# Define a layer to display on a map
layer = pdk.Layer(
    "ScatterplotLayer",
                # coordinates is an array 
    gdf[["1","coordinates"]], # super important! only pass what's needed. If geometry column from geopandas is passed, error!
    pickable=True,
    opacity=0.99,
    stroked=True,
    filled=True,
    radius_scale=6,
    radius_min_pixels=1,
    radius_max_pixels=100,
    line_width_min_pixels=1,
    get_position="coordinates",
    get_radius="1000",
    get_fill_color=[255, 140, 0],
    get_line_color=[255, 140, 0],
)

# Set the viewport location
view_state = pdk.ViewState(latitude=np.mean(gdf.geometry.y), longitude=np.mean(gdf.geometry.x), zoom=12, bearing=0, pitch=0)

# Render
r = pdk.Deck(layers=[layer], initial_view_state=view_state,height=2000, tooltip={"text": "{1}"})
r.to_html("scatterplot_layer.html")

Sample

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
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
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
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
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
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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13216940	GLORIA Seamount	GLORIA Seamount	NaN	45.03000	-15.53500	U	SMU	NaN	NaN	00	NaN	NaN	NaN	0	NaN	-9999	NaN
13216941	Yubko Hills	Yubko Hills	NaN	13.01820	-134.41130	U	HLSU	NaN	NaN	00	NaN	NaN	NaN	0	NaN	-9999	NaN
13216942	Maguari Seamount	Maguari Seamount	NaN	0.68832	-44.31278	U	SMU	NaN	NaN	00	NaN	NaN	NaN	0	NaN	-9999	NaN
13216943	Quintana Seamount	Quintana Seamount	NaN	-32.74950	-38.67696	U	SMU	NaN	NaN	00	NaN	NaN	NaN	0	NaN	-9999	NaN
13216944	Satander Guyot	Satander Guyot	NaN	-1.92806	-37.82161	U	DEPU	NaN	NaN	00	NaN	NaN	NaN	0	NaN	-9999	NaN

13111559 rows × 19 columns