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

City population size is a crucial measure when trying to understand urban life. Many socio-economic indicators scale superlinearly with city size, whilst some infrastructure indicators scale sublinearly with city size. However, the impact of size also extends beyond the city’s limits. Here, we analyse the scaling behaviour of cities beyond their boundaries by considering the emergence and growth of nearby cities. Based on an urban network from African continental cities, we construct an algorithm to create the region of influence of cities. The number of cities and the population within a region of influence are then analysed in the context of urban scaling. Our results are compared against a random permutation of the network, showing that the observed scaling power of cities to enhance the emergence and growth of cities is not the result of randomness. By altering the radius of influence of cities, we observe three regimes. Large cities tend to be surrounded by many small towns for small distances. For medium distances (above 114 km), large cities are surrounded by many other cities containing large populations. Large cities boost urban emergence and growth (even more than 190 km away), but their scaling power decays with distance.

The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987.

WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields:

AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection

ACPD comes as an Arc/Info EXPORT file originally called "ACPD.E00" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command.

References to the WCPD data set can be found in:

• SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK.
• D. Rhind. "Cartographically-related research at Birkbeck College 1987-91" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66.

The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK.

Gaining insight into the food sourcing practices of cities is important to understand their resilience to climate change, economic crisis, as well as pandemics affecting food supply and security. To fill existing knowledge gaps in this area food flow data were collected in West Africa for four cities - Bamako (Mali), Bamenda (Cameroon), Ouagadougou (Burkina Faso), and Tamale (Ghana). The data covers, depending on the city, road, rail, boat, and air traffic. Surveys were conducted for one week on average during the peak harvest, lean, and rainy seasons, resulting in a dataset of over 100,000 entries for 46 unprocessed food commodities. The data collected includes information on the key types of transportation used, and quantity, source, and destination of the food flows. The data were used to delineate urban foodsheds and to identify city-specific factors constraining rural-urban linkages. They can be used to inform academic and policy discussions on urban food system sustainability, to validate other datasets, and to plan humanitarian aid and food security interventions.

Workflow and supplementary information associated with this dataset are found on GitHub (https://zenodo.org/record/7813686#.ZDQmKvbP23A). The data paper provides information on how data were collected and processed as well as how to use the data (https://www.nature.com/articles/s41597-023-02163-6): Karg, H., Akoto-Danso, E.K., Amprako, L. et al. A spatio-temporal dataset on food flows for four West African cities. Sci Data 10, 263 (2023). https://doi.org/10.1038/s41597-023-02163-6

The percentage of persons, out of the total number of persons living in an area, self-identifying as racially Black or African American (and ethnically non-Hispanic). “Black or African American” refers to a person having origins in any of the Black racial groups of Africa. This indicator includes people who identified their race as “Black”. Source: U.S. Census Bureau, American Community Survey Years Available: 2010, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2020, 2017-2021, 2018-2022, 2019-2023

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL

Features may have these attributes:

• name
• amenity
• man_made
• shop
• tourism
• opening_hours
• beds
• rooms
• addr:full
• addr:housenumber
• addr:street
• addr:city

This dataset is one of many "/dataset?tags=openstreetmap">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Abstract

Until 2006 metropolitan and district councils in South Africa, which were previously called Regional Services Councils (RSC), were permitted to raise revenue by taxing firms that operated within the council area. The City of Cape Town taxed firms based on their turnover and wage bill but also used the administration of the RSC levy, as the tax was called, to create an administrative dataset of firms. The city used this data to calculate Gross Geographic product and produce a number of reports on the local economy (cf City of Cape Town 2001). The City undertook a survey of all firms on the RSC tax database in 2000 and linked the firms to location based GIS data. The RSC data is thus a mixture of administrative and survey data. The dataset covers the period 2000 to 2006. In theory any enterprise employing at least 1 worker or with a revenue of R10 000 a year was supposed to pay the RSC levy and thus be included in the database. Thus this dataset should be a census of all formal firms operating with the city of Cape Town during the period covered, except the very smallest self-employed operators. In practice, however those familiar with the RSC have said that there was evasion of the tax, with a possible 30% of firms evading.

Around two thirds of the active firms in 2000 responded to this survey. Some of this survey data (particularly employment) was supposed to be updated every year but in practice we only have useable survey data for the first year of the panel. Much of the employment data was imputed in subsequent years and cannot be used. New entrants were captured in the database but some of the survey information is not available for these firms.

The project to create this research dataset was made possible by an exploratory grant obtained by Andrew Kerr and Martin Wittenberg of DataFirst from the Private Enterprise Development in Low-Income Countries (PEDL) research initiative. PEDL is a joint research initiative of the Centre for Economic Policy Research (CEPR) and the UK Department For International Development (DFID). It aims to develop a research programme focusing on private-sector development in low-income countries.

Geographic coverage

The data covers the Metropolitan area of Cape Town. The data is available with firm-level GPS coordinates

Mode of data collection

Other [oth]

GIS Web Map Application of the 10 City Council Voter Districts

Cities ranking and mega citiesTokyo is the world’s largest city with an agglomeration of 37 million inhabitants, followed by New Delhi with 29 million, Shanghai with 26 million, and Mexico City and São Paulo, each with around 22 million inhabitants. Today, Cairo, Mumbai, Beijing and Dhaka all have close to 20 million inhabitants. By 2020, Tokyo’s population is projected to begin to decline, while Delhi is projected to continue growing and to become the most populous city in the world around 2028.By 2030, the world is projected to have 43 megacities with more than 10 million inhabitants, most of them in developing regions. However, some of the fastest-growing urban agglomerations are cities with fewer than 1 million inhabitants, many of them located in Asia and Africa. While one in eight people live in 33 megacities worldwide, close to half of the world’s urban dwellers reside in much smaller settlements with fewer than 500,000 inhabitants.About the dataThe 2018 Revision of the World Urbanization Prospects is published by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It has been issued regularly since 1988 with revised estimates and projections of the urban and rural populations for all countries of the world, and of their major urban agglomerations. The data set and related materials are available at: https://esa.un.org/unpd/wup/