How to display a Choropleth map in Jupyter Notebook

Here is the code: %matplotlib inline import geopandas as gpd import matplotlib as mpl # make rcParams available (optional) mpl.rcParams[’figure.dpi’]= 144 # increase dpi (optional)   world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")) world = world[world.name != ‘Antarctica’] # remove Antarctica (optional) world[’gdp_per_person’] = world.gdp_md_est / world.pop_est g = world.plot(column=’gdp_per_person’, cmap=’OrRd’, scheme=’quantiles’) g.set_facecolor(’#A8C5DD’) # make the ocean blue (optional)%matplotlib …

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Create a European city map with population density

Datasets: – Urban morphological zones 2000 (EU): https://www.eea.europa.eu/data-and-maps/data/urban-morphological-zones-2000-2 – Population count (World): http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev10/ – Administrative regions (World): http://gadm.org/ The map is European since the “urban” data from the European Environmental Agency (EEA) only covers Europe. Caveats The UMZ data ended up in PostGIS with srid 900914. You can use prj2epsg.org to convert the contents of …

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How to create a world-wide PostgreSQL database of administrative regions

The GADM database contains geographical data for administrative regions, e.g. countries, regions and municipalities. As always, once you have the data in the right format, it is easy to import it into a database. The data is available from GADM in several formats. All data has the coordinate reference system in longitude/latitude and theWGS84 datum. …

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