Getting started with EUBUCCO

import urllib.request
import pandas as pd
import geopandas as gpd
from shapely import wkt
import matplotlib.pyplot as plt

url = 'https://api.eubucco.com/v0.1/files/46c25dfc-aa93-49a5-bab8-df69d2fae81d/download'
path = 'Vichy.gpkg.zip'
urllib.request.urlretrieve(url, path)

df = gpd.read_file(path) # if you have unzipped the file removed .zip
df.head()

df = gpd.read_file(path, rows=1000)
df.tail()

df.plot() # plot all building footprints from the file

df.plot(column='height') # plot building footprints and color-code building heights

# plot individual building footprint
df.iloc[[0]].plot()

# plot distribution of building heights
df['height'].hist(bins=20)

def match_gadm_info(df_temp,df_overview):
    """ function to match country, region and city info from overview table with building level data
        df_temp (dataframe):=   building level dataframe
        df_overview:=           overview table
    """
    # remove numbering at end of id str 
    df_temp['id_temp'] = df_temp['id'].str.rsplit('-',1).apply(lambda x: x[0])
    # merge with overview file
    df_out = df.merge(df_overview, left_on='id_temp',right_on='id')
    # keep only relevant columns
    df_out = df_out[['id_x','id_source','country','region','city','height','age','type','type_source','geometry']]
    # rename back to 'id' and return
    return df_out.rename(columns={'idx_x':'id'})

# define path to overview file
path_overview_file = 'admin-codes-matches-v0.1.zip'

# read in overview file
df_overview = pd.read_csv(path_overview_file)

# check overview file
df_overview.head()

# match gadm info to bldg lvl data and assign to df
df = match_gadm_info(df,df_overview)

# check df: we have some additional columns added now
df.head()