File:Wiki commons de animation 1990-2022.gif

from SPARQLWrapper import SPARQLWrapper, XML

#https://wcqs-beta.wmflabs.org
def query(x1, y1, x2, y2):
    queryString = """
        SELECT ?fileLabel ?image ?coor ?date
        WHERE
        {
        hint:Query hint:optimizer "None".
        SERVICE wikibase:box {
            ?file wdt:P1259 ?coor.
            bd:serviceParam wikibase:cornerWest "Point(""" + str(y1) + """ """ + str(x1) + """)"^^geo:wktLiteral.
            bd:serviceParam wikibase:cornerEast "Point(""" + str(y2) + """ """ + str(x2) + """)"^^geo:wktLiteral.
        }  
        ?file schema:contentUrl ?url;
                wdt:P571 ?date.
        bind(iri(concat("http://commons.wikimedia.org/wiki/Special:FilePath/", wikibase:decodeUri(substr(str(?url),53)))) AS ?image)  
        SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en" }
        }
    """
    sparql = SPARQLWrapper("https://wcqs-beta.wmflabs.org/sparql")

    sparql.setReturnFormat(XML)
    sparql.setQuery(queryString)

    return str(sparql.query().response.read().decode("utf-8"))
data_str = []

for y in range(47, 55 + 1):
    for x in range(5, 15 + 1):
        data_str.append(query(y, x, y + 1, x + 1))

#-----------------------------------------------------------------------------------------

import pandas as pd

df_concat = None
for dstr in data_str:
    df = pd.read_xml(dstr, xpath="//*[contains(name(),'binding')]", elems_only=True)

    df = df.stack().reset_index()
    df = pd.concat([df[df["level_0"] % 4 == 0][0].reset_index(), df[df["level_0"] % 4 == 1][0].reset_index(), df[df["level_0"] % 4 == 2][0].reset_index(), df[df["level_0"] % 4 == 3][0].reset_index()], axis=1)[0]

    df.columns = ["Coordinate", "Date", "Dataelement", "File"]
    df["Lat"] = df["Coordinate"].str.extract(r"Point\([^ ]* ([^\)]*)\)").astype(float)
    df["Lon"] = df["Coordinate"].str.extract(r"Point\(([^ ]*).*").astype(float)

    if df_concat is None:
        df_concat = df
    else:
        df_concat = pd.concat([df_concat, df]).drop_duplicates().reset_index(drop=True)
df = df_concat

#-----------------------------------------------------------------------------------------

import reverse_geocoder as rg

tmp = tuple(zip(df["Lat"], df["Lon"]))
results = rg.search(tmp)
df["Locationname"] = [o["name"] for o in results]
df["admin1"] = [o["admin1"] for o in results]
df["admin2"] = [o["admin2"] for o in results]
df["cc"] = [o["cc"] for o in results]

#-----------------------------------------------------------------------------------------

df_processed = df

df_processed = df_processed[df_processed["cc"] == "DE"]

df_processed = df_processed[(df_processed['Date'] > '1950-01-01') & (df_processed['Date'] < '2023-01-01')]

#-----------------------------------------------------------------------------------------

from PIL import Image, ImageDraw, ImageOps
from IPython.display import display

im = Image.new("L", (1080, 1920))
pixels = im.load()

for year in range(1990, 2022 + 1):
    df_processed_ = df_processed[df_processed['Date'].astype('datetime64[D]').dt.to_period('Y') == str(year)]

    for y, x, d in df_processed_[["Lat", "Lon", "Date"]].to_numpy():
        x_ = (x-5)*100
        y_ = (y-46)*100
        pixels[x_, y_] = int(pixels[x_, y_] + 10)

    ImageOps.flip(im).save(str(year) + "_img.png", 'PNG')

im = ImageOps.flip(im)
display(im)