All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Dataset

This dataset was created by Agha Abdul Rauf

Released under CC0: Public Domain

Contents

1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate. How to use from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5)) Files: towns.csv - city information regions.csv - list of Russian Federation regions alt_city_names.json - alternative city names Сolumns (towns.csv): Basic info: city - city name (several cities have alternative names marked in alt_city_names.json) population - city population, thousand people, Rosstat estimate as of 1.1.2020 lat,lon - city geographic coordinates Region: region_name - subnational region (oblast, republic, krai or AO) region_iso_code - ISO 3166 code, eg RU-VLD federal_district, eg Центральный City codes: okato oktmo fias_id kladr_id Data sources City list and city population collected from Rosstat publication Регионы России. Основные социально-экономические показатели городов and parsed from publication Microsoft Word files. City list corresponds to this Wikipedia article. Alternative dataset is wiki-based Dadata city dataset (no population data). Comments City groups Ханты-Мансийский and Ямало-Ненецкий autonomous regions excluded to avoid duplication as parts of Тюменская область. Several notable towns are classified as administrative part of larger cities (Сестрорецк is a municpality at Saint-Petersburg, Щербинка part of Moscow). They are not and not reported in this dataset. By individual city Белоозерский not found in Rosstat publication, but should be considered a city as of 1.1.2020 Alternative city names We suppressed letter "ё" city columns in towns.csv - we have Орел, but not Орёл. This affected: Белоозёрский Королёв Ликино-Дулёво Озёры Щёлково Орёл Дмитриев and Дмитриев-Льговский are the same city. assets/alt_city_names.json contains these names. Tests poetry install poetry run python -m pytest How to replicate dataset 1. Base dataset Run: download data stro rar/get.sh convert Саратовская область.doc to docx run make.py Creates: _towns.csv assets/regions.csv 2. API calls Note: do not attempt if you do not have to - this runs a while and loads third-party API access. You have the resulting files in repo, so probably does not need to these scripts. Run: cd geocoding run coord_dadata.py (needs token) run coord_osm.py Creates: coord_dadata.csv coord_osm.csv 3. Merge data Run: run merge.py Creates: assets/towns.csv See code at Github: https://github.com/epogrebnyak/ru-cities

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

Context

Simple Pakistani Cities Data, with latitude, longitude, population, Province. Below is a list of 146 prominent cities in Pakistan. Each row includes a city's latitude, longitude, province and other variables of interest. This is a subset of all 140,909 places in Pakistan (and only some of the fields) that you'll find in our World Cities Database.

Source

https://simplemaps.com/data/pk-cities

Content

The data set contains geolocations of all the cities in India with a population of more than 1000.

There are total 10 columns in the dataset. Geoname - Unique Geo-ID for the city Name - Name of the city ACSII Name - ASCII name of the city for interpretability Alternate Names - Alternate names for the city Latitude - Latitude of the city Longitude - Longitude of the city Population - Population of the city Digital Elevation Model - Digital elevation of the city Country - Country of the city Coordinates - Coordinates of the city

Acknowledgements

The data set is contributed by opendatasoft Data Network

This data set contains points for 1600 populated places, cities and towns, in New Mexico. The points were generated from latitude and longitude coordinates contained in the GNIS file, and therefore, do not have a known scale.

Context

This dataset is related to CDP Analytics challenge: https://www.kaggle.com/c/cdp-unlocking-climate-solutions.

The cities disclosure data provided in this challenge contains geographical cordinates of disclosing cities. However, these coordinates are not completelt reliable. In order to be able to provide geo-visualization of indicators, we need to clean the geographical coordinates and provide a reliable dataset.

This dataset is generated using this notebook: https://www.kaggle.com/shabou/cdp-cities-location-exploratory-analysis

Content

Acknowledgements

Inspiration

Make maps of cities' environmental actions to combat climate change.

This dataset contains information about the 1000 largest US cities by population: population, population growth, geographic coordinates, population rank.

Latitude and longitude coordinates, population size, and mean baseline pneumonia and influenza death rates for 66 large US reporting cities (1910–1920) with 100, 000 or more inhabitants [10].

This dataset contains geographical information including location names and their corresponding latitude and longitude coordinates.

Dataset Card for Dataset Name

This dataset comprises abstracts from Wikivoyage for 160 European cities along with their corresponding country names, coordinates, and populations. The embeddings are derived from the GTE-Large model, incorporating data from the city, country, population, and abstract columns.

  Dataset Sources

Wikivoyage data World cities database

The Urban Landsat: Cities from Space data set contains images for 66 urban areas and the raw, underlying data for 28 of these places. Each image shows a Landsat false color composite in UTM projection. The R/G/B layers correspond to TM/ETM+ bands 7/4/2. Each pixel is 30x30 meters in area and most images are 30x30 km in area. A 2% linear stretch has been applied to the images. The Landsat data files contain six reflected bands of calibrated exoatmospheric reflectance stored in ENVI band sequential (BSQ) format. Geographic coordinates are included in the header files. The data files contain 1000x1000x6 4 byte floating point numbers as indicated in the header files.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

This Wyoming Cities coverage contains data for 109 Wyoming towns, cities, and Census Designated Areas. The coverage was created from U.S. Census Bureau Tiger Data. It contains many of the same attributes as the Census Bureau .dbf files, but there are a few modifications. A code has been added to distinguish towns, cities, and CDPs. Also a Countyseat item has been added to provide a way to display the County Seats only. Latitude/Longitude coordinates were also converted from the Census Bureau format to be used in GENERATE.

This is the small dataset that specifies the location and population of cities in Mazovian region (Poland)This dataset is a part of a project titled "Modelling and forecasting business location in context of economies of density. Theoretical, methodological and empirical approach using spatial econometrics and spatial machine learning" financed by National Science Center, Poland (Krakow, Poland) [OPUS 21 call, grant number UMO-2021/41/B/HS4/00285].

This political boundary layer is the most accurate representing the city and town boundaries in the Commonwealth of Massachusetts.

This datalayer has been created from latitude and longitude coordinates found in the 68-volume Harbor and Lands Commission Town Boundary Atlas. This Atlas series, and updates since it was published, describes the legal boundary for each of the 351 municipalities in Massachusetts. These coordinates were recorded from surveys of the location of each boundary marker around the periphery of each community. Each survey was tied into higher order monumented survey control points. The Atlases also include detailed descriptions of each community's boundary and location maps for each of the original boundary marker locations. The original surveys were conducted in the 1890s. The Atlas series was published in the early 1900s and has since been updated by the Survey Section of the Massachusetts Highway Department with changes as they are approved by the legislature. MassGIS staff collaborated closely with staff from the Survey Section during the development of this data layer. MassGIS staff keyed the coordinates into a database; that data entry was double-checked by staff from the Survey Section. Staff from the Survey Section then converted the latitude/longitude coordinates to the NAD83 datum and also created a version of the coordinates in state plane coordinates with units of meters. MassGIS used the state plane coordinates to "generate" points in ArcGIS. Boundary arcs from the existing USGS-derived municipal boundary data layer were then snapped to the survey-derived points. The differences between the municipal boundary arcs digitized from those on the USGS quads and those created by snapping to the survey-derived coordinates are typically plus or minus 12 feet, although these differences are sometimes less and sometimes more. Some municipal boundary arcs (about 15% of the total) follow the edge of a road or rail right-of-way or a stream or river channel. In these cases, the new boundary arcs were "heads up" digitized based on features visible on the statewide 1:5,000 color orthos from imagery flown in 2001.

For communities with a coastal boundary, MassGIS collaborated with the Massachusetts Water Resources Authority and the Department of Environmental Protection to complete a 1:12,000 scale coastline.

City/Town names' labels are included in this service.

(This service was published from a map document using the Web Mercator projection for the data frame.)

For full metadata please see http://www.mass.gov/itd/townsurvey.

1. The coordinate data is a manually referenced position of each relative address location in the computer system of each household registration office, which is not an accurate coordinate position and is for reference only.2. The door number does not serve as the basis for the existence of a building address based on the presence of coordinates. Any ambiguity is subject to review and appraisal by the responsible household registration office.3. Coordinate reference system TWD97.4. The encoding of the CSV file is utf8.

Context

This is a list of 10,000 fast-food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Inspiration

You can use this data to rank cities with the most and least fast-food restaurants across the U.S. E.g.:

1. Cities with the most and least McDonald's per capita
2. Fast food restaurants per capita for all states
3. Fast food restaurants with the most locations nationally
4. Major cities with the most and least fast food restaurants per capita
5. Small cities with the most fast-food restaurants per capita
6. States with the most and least fast food restaurants per capita
7. The number of fast-food restaurants per capita

If you like the dataset, do upvote!

Unit, telephone, fax, address, latitude coordinates, longitude coordinates