愛媛県の不審者マップ https://imabari.github.io/fushinsha_map/
import pathlib import pandas as pd import requests import folium GEO_URL = "https://raw.githubusercontent.com/geolonia/japanese-addresses/master/data/latest.csv" def fetch_file(url, dir="."): r = requests.get(url) r.raise_for_status() p = pathlib.Path(dir, pathlib.PurePath(url).name) p.parent.mkdir(parents=True, exist_ok=True) with p.open(mode="wb") as fw: fw.write(r.content) return p if __name__ == "__main__": # 数字を漢数字 kanji = str.maketrans("1234567890", "一二三四五六七八九〇") p_geo = fetch_file(GEO_URL, "src") df_geo = pd.read_csv(p_geo) # 愛媛県のみ抽出 df_geo_ehime = df_geo[df_geo["都道府県名"] == "愛媛県"].copy() # 市名と町名を結合 df_geo_ehime["address"] = df_geo_ehime["市区町村名"] + df_geo_ehime["大字町丁目名"] # 愛媛県警の不審者情報をスクレイピング df_tmp = ( pd.read_html( "http://www.police.pref.ehime.jp/fushinsha.htm", match="概 要", header=0 )[0] .fillna("") .astype(str) ) # 内容を正規化 df_tmp["概 要"] = df_tmp["概 要"].str.normalize("NFKC") # 項目ごとに分割 df = df_tmp["概 要"].str.extract("(.+)◆.+:(.+)◆.+:(.+)◆.+:(.+)◆.+:(.+)") # 列名変更 df.rename(columns={0: "管轄署", 1: "種別", 2: "日時", 3: "場所", 4: "状況"}, inplace=True) # 前後の空白文字を削除 df = df.applymap(lambda s: s.strip()) # かっこを削除 df["管轄署"] = df["管轄署"].str.strip("()") # 住所間違い訂正 df["場所"] = df["場所"].str.replace("常磐", "常盤") # 住所を漢数字に変換 df["場所"] = df["場所"].apply(lambda s: s.translate(kanji)) # 町名までに修正 df["住所"] = df["場所"].str.replace( "(路上|施設|店舗|付近|一般住宅|住宅|アパート|マンション|公園|屋外|緑地|駐輪場|駐車場|河川敷|児童).*", "", regex=True ) # 「甲乙丙の」を削除 df["address"] = df["住所"].str.rstrip("甲乙丙の") # 北新田がないので新田に訂正 df["address"] = df["address"].str.replace("西条市新田字北新田", "西条市新田") df["count"] = df.groupby("address").cumcount() # 種別の確認 df["種別"].unique() # アイコンの色を追加 df["color"] = df["種別"].replace( { "のぞき・盗撮": "pink", "身体露出": "orange", "ちかん": "gray", "不審者": "purple", "声かけ": "green", "暴行": "red", "つきまとい": "blue", "写真撮影": "lightred", "建造物侵入": "darkred", "住居侵入": "darkred", "のぞき": "pink", "動画撮影": "lightred", } ) # 上記に該当しない場合は黒 df["color"] = df["color"].fillna("black") # 色の種類 colors = { "lightred", "darkred", "darkblue", "pink", "gray", "green", "orange", "purple", "lightgray", "blue", "beige", "cadetblue", "darkgreen", "darkpurple", "lightblue", "black", "lightgreen", "red", "white", } # 住所から緯度経度をマージ df_ehime = df.merge(df_geo_ehime, how="left", on="address") p_csv = pathlib.Path("map", "ehime.csv") p_csv.parent.mkdir(parents=True, exist_ok=True) df_ehime.to_csv(p_csv, encoding="utf_8_sig") # 欠損を確認 df_nan = df_ehime[df_ehime.isnull().any(axis=1)] p_nan = pathlib.Path("map", "nan.csv") df_nan.to_csv(p_nan, encoding="utf_8_sig") # 欠損を削除 df_ehime.dropna(inplace=True) map = folium.Map(location=[34.06604300, 132.99765800], zoom_start=10) for i, r in df_ehime.iterrows(): folium.Marker( location=[r["緯度"], r["経度"] + r["count"] * 0.0002], popup=folium.Popup( f'<p>{r["管轄署"]}</p><p>{r["種別"]}</p><p>{r["日時"]}</p><p>{r["場所"]}</p><p>{r["状況"]}</p>', max_width=300, min_width=150, ), icon=folium.Icon(color=r["color"]), ).add_to(map) p_map = pathlib.Path("map", "index.html") map.save(str(p_map))
