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The GUO offers datasets on urbanization, urban population growth, city demographics, slum populations, urban infrastructure and sustainability indicators worldwide.
GUO data is available as Excel/CSV downloads from the UN World Urbanization Prospects portal. Engineers use `pandas.read_excel()` to load city population projections, then visualize urban growth trajectories with matplotlib or plotly for urban policy analysis.
Urban population data from GUO trains AI models for city growth prediction and urban planning optimization. Build RAG systems indexed on UN urbanization reports so LLMs can answer 'Which cities are projected to become megacities by 2050?' with official UN urban forecasts.
# pip install pandas openpyxl
import pandas as pd
# World Urbanization Prospects data from UN
# https://population.un.org/wup/Download/
df = pd.read_excel("WUP2018-F22-Cities_Over_300K_Annual.xls",
sheet_name="DATA", skiprows=16, engine="xlrd")
# 10 most populous cities projected for 2030
df_2030 = df[["Urban Agglomeration", "2030"]].dropna()
df_2030["2030"] = pd.to_numeric(df_2030["2030"], errors="coerce")
print(df_2030.nlargest(10, "2030"))Official dataset source
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