Metadata record for the manuscript: Projecting future populations of urban agglomerations: around the world and through the 21st century

This metadata record provides details of the data supporting the claims of the related manuscript: “Projecting future populations of urban agglomerations: around the world and through the 21st century ”.

The data consist of HTML files with interactive maps for future populations projections of urban agglomerations, and HTML file displaying figures for postdictions of urban agglomerations, as well as 5 .csv files containing the raw data.

The related study estimated population trends throughout the 21st century for approximately 20,000 urban agglomerations in 151 countries by working within the Shared Socioeconomic Pathways (SSPs) and using a simple urban growth model.

The following resources, which were among the sources of the data analyzed in the related study, are available from the links below.

- Postdiction results for 1794 urban agglomerations http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#postdiction-for-1794-agglomerations-link

- Temporal evolution from 2010 to 2100 of the geographical distribution of urban agglomerations, arranged by population scale, as predicted within the various SSP scenarios

These data are also available in raw .csv form via the 'Raw data' link on the same page, and also in the 5 files included as part of this data record.

- Available urban-population data include the UN’s World Urbanization Prospects 2018 (https://population.un.org/wup/) and Gridded Population of the World, v4 (https://doi.org/10.7927/H4BC3WMT). Available settlement-point data include, in addition to the above urban population sources, World Gazetteer (https://www.arcgis.com/home/item.html?id=346ce13fa2d4468a9049f71bcc250f37) and GeoNames (https://www.geonames.org/). GDP per capita data is available from OECD.stat (https://stats.oecd.org/), Global Metro Monitor (https://www.brookings.edu/research/global-metro-monitor/), and World Development Indicators (http://datatopics.worldbank.org/world-development-indicators/). OpenStreetMap is available at https://www.openstreetmap.org/. Scenario data for SSPs are available at the IIASA-SSP database (https://doi.org/10.1016/j.gloenvcha.2016.05.009).

Code used for the analysis can be downloaded from the author's lab's website: http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#codes. These are written in R. They are provided only for the purpose of tracing the analytical procedure. They are not executable without appropriate datasets.