{"id":50798,"date":"2022-05-25T00:00:00","date_gmt":"2022-05-25T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/"},"modified":"2025-11-14T07:55:23","modified_gmt":"2025-11-14T15:55:23","slug":"using-data-science-to-catch-criminals","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/","title":{"rendered":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b"},"content":{"rendered":"

\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084\u58f2\u4e0a\u306e\u6700\u5927\u5316\u306a\u3069\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u30aa\u30fc\u30d7\u30f3\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u30b3\u30f3\u30bb\u30d7\u30c8\u306b\u3088\u308a\u3001\u7d44\u7e54\u306f\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u793e\u4f1a\u554f\u984c\u3092\u51e6\u7406\u3059\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u4eba\u9593\u306e\u96a0\u308c\u305f\u884c\u52d5\u3084\u6587\u5316\u7684\u30d1\u30bf\u30fc\u30f3\u306b\u5bfe\u3057\u3066\u3001\u7d71\u8a08\u7684\u304b\u3064\u30c7\u30fc\u30bf\u4e3b\u5c0e\u306e\u89e3\u6c7a\u7b56\u3092\u63d0\u4f9b\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u308b\u306e\u3067\u3059\u3002<\/p>\n

\u8a18\u4e8b\u306e\u5168\u30b3\u30fc\u30c9\u306f\u3053\u3061\u3089<\/a><\/p>\n

\u6211\u3005\u306f\u3001\u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u306e\u72af\u7f6a\u90e8\u9580\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u5e02\u6c11\u304c\u5831\u544a\u3059\u308b\u72af\u7f6a\u4e8b\u4ef6\u3068\u3001\u8b66\u5bdf\u304c\u5831\u544a\u3059\u308b\u72af\u7f6a\u4e8b\u4ef6\u306e\u95a2\u4fc2\u3092\u7406\u89e3\u3057\u307e\u3059\u3002\u305d\u3057\u3066\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3057\u3001\u5bb9\u6613\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3088\u3046\u306b\u3059\u308b\u305f\u3081\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3068\u3057\u3066GridDB\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n

GridDB\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3068\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n

GridDB\u306f\u62e1\u5f35\u6027\u306e\u9ad8\u3044\u30a4\u30f3\u30e1\u30e2\u30ea\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3042\u308b\u305f\u3081\u3001\u4e26\u5217\u51e6\u7406\u306b\u3088\u308a\u9ad8\u3044\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3068\u52b9\u7387\u6027\u3092\u5b9f\u73fe\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002GridDB\u306epython\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u3067\u3001GridDB\u3068python\u3092\u7c21\u5358\u306b\u63a5\u7d9a\u3057\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u306b\u30c7\u30fc\u30bf\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u3084\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u306b\u5229\u7528\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002<\/p>\n

\u30e9\u30a4\u30d6\u30e9\u30ea<\/h3>\n

\u3044\u304f\u3064\u304b\u306epython\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\u3068\u8996\u899a\u7684\u306a\u5206\u6790\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n

    \n
  1. Pandas: \u7279\u306b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u6271\u3046\u969b\u306b\u5e83\u304f\u5229\u7528\u3055\u308c\u3066\u3044\u308bPython\u30e9\u30a4\u30d6\u30e9\u30ea<\/li>\n
  2. Matplotlib: \u57fa\u672c\u7684\u306a\u30d7\u30ed\u30c3\u30c8\u3092\u7528\u3044\u3066\u30c7\u30fc\u30bf\u3092\u8996\u899a\u7684\u306b\u8868\u73fe\u3059\u308b\u30e9\u30a4\u30d6\u30e9\u30ea<\/li>\n
  3. Numpy: \u6570\u5b66\u7684\u8a08\u7b97\u3092\u4f34\u3046\u30c7\u30fc\u30bf\u3092\u6271\u3046\u305f\u3081\u306ePython\u30e9\u30a4\u30d6\u30e9\u30ea<\/li>\n<\/ol>\n

    \u524d\u51e6\u7406<\/h3>\n

    \u72af\u7f6a\u30c7\u30fc\u30bf\u306b\u306f 2 \u3064\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\u308a\u307e\u3059\u30021 \u3064\u306f\u8b66\u5bdf\u304c\u5831\u544a\u3057\u305f\u72af\u7f6a\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u3059\u3002\u3082\u3046 1 \u3064\u306f\u8b66\u5bdf\u7f72\u306b\u5bc4\u305b\u3089\u308c\u305f\u72af\u7f6a\u306e\u901a\u5831\u306b\u95a2\u3059\u308b\u60c5\u5831\u3067\u3059\u3002<\/p>\n

    \n
    incidents = pd.read_csv(\"datasets\/downsample_police-department-incidents.csv\")\ncalls = pd.read_csv(\"datasets\/downsample_police-department-calls-for-service.csv\")<\/code><\/pre>\n<\/div>\n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u5f62\u3067\u5909\u6570 “incidents” \u3068 “calls” \u306b\u4fdd\u5b58\u3055\u308c\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u5206\u6790\u4e0a\u91cd\u8981\u3067\u306a\u3044\u30ab\u30e9\u30e0\u304c\u3044\u304f\u3064\u304b\u542b\u307e\u308c\u3066\u3044\u308b\u306e\u3067\u3001\u30e1\u30e2\u30ea\u6d88\u8cbb\u3092\u6700\u5c0f\u9650\u306b\u6291\u3048\u3001\u5206\u6790\u306e\u6642\u9593\u52b9\u7387\u3092\u6700\u5927\u306b\u3059\u308b\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304b\u3089\u305d\u308c\u3089\u3092\u524a\u9664\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002<\/p>\n
    \n
    incidents = incidents.drop(['IncidntNum', 'Time', 'X', 'Y', 'Location', 'PdId'], axis = 1)\ncalls = calls.drop(['Crime Id', 'Report Date', 'Offense Date', 'Call Time', 'Call Date Time', \n                        'Disposition', 'Address', 'City', 'State', 'Agency Id','Address Type', \n                        'Common Location'], axis = 1)<\/code><\/pre>\n<\/div>\n
    \u5206\u6790\u306b\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u6b8b\u3063\u3066\u3044\u308b\u30ab\u30e9\u30e0\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n
    Incidents \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\uff1a<\/p>\n
      \n
    1. Category: \u72af\u7f6a\u306e\u7a2e\u985e<\/li>\n
    2. Descript: \u767a\u751f\u3057\u305f\u72af\u7f6a\u4e8b\u4ef6\u306e\u5185\u5bb9<\/li>\n
    3. DayOfWeek: \u72af\u884c\u304c\u884c\u308f\u308c\u305f\u65e5<\/li>\n
    4. Date: \u72af\u7f6a\u304c\u767a\u751f\u3057\u305f\u65e5\u4ed8<\/li>\n
    5. PdDistrict: \u72af\u7f6a\u304c\u884c\u308f\u308c\u305f\u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u306e\u5730\u533a<\/li>\n
    6. Resolution: \u72af\u7f6a\u306b\u5bfe\u3057\u3066\u3068\u3063\u305f\u884c\u52d5<\/li>\n
    7. Address: \u72af\u7f6a\u304c\u884c\u308f\u308c\u305f\u6b63\u78ba\u306a\u5834\u6240<\/li>\n<\/ol>\n
      Calls \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\uff1a<\/p>\n
        \n
      1. Descript: \u5831\u544a\u8005\u304c\u8a18\u8ff0\u3057\u305f\u72af\u7f6a\u306e\u5185\u5bb9<\/li>\n
      2. Date: \u901a\u5831\u96fb\u8a71\u3092\u53d7\u3051\u305f\u65e5\u3002<\/li>\n<\/ol>\n
        \u307e\u305f\u3001\u305d\u308c\u305e\u308c\u306e\u884c\u3092\u500b\u5225\u306b\u8ffd\u8de1\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u4e21\u65b9\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u4e3b\u30ad\u30fc\u30ab\u30e9\u30e0\u3092\u5c0e\u5165\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u30ea\u30bb\u30c3\u30c8\u3057\u3066\u3001\u540d\u524d\u3092 ‘ID’ \u306b\u5909\u66f4\u3057\u307e\u3059\u3002<\/p>\n
        \n
        incidents.reset_index(drop=True, inplace=True)\nincidents.index.name = 'ID'\nincidents.dropna(inplace = True)\n\ncalls.reset_index(drop=True, inplace=True)\ncalls.index.name = 'ID'\ncalls.dropna(inplace = True)<\/code><\/pre>\n<\/div>\n
        \u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u304c\u5b8c\u4e86\u3057\u305f\u3089\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092CSV\u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u30ed\u30fc\u30ab\u30eb\u30c9\u30e9\u30a4\u30d6\u306b\u4fdd\u5b58\u3057\u3001GridDB\u306b\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/p>\n
        \n
        #save it into csv\nincidents.to_csv(\"preprocessed_incidents.csv\")\ncalls.to_csv(\"preprocessed_calls.csv\")<\/code><\/pre>\n<\/div>\n
        \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092GridDB\u306b\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3059\u308b<\/h3>\n
        \u6b21\u306b\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u306b\u3001\u524d\u51e6\u7406\u3055\u308c\u305fCSV\u30d5\u30a1\u30a4\u30eb\u3092pandas\u3092\u4f7f\u3063\u3066\u8aad\u307f\u8fbc\u307f\u3001\u500b\u5225\u306edataframe\u306b\u4fdd\u5b58\u3057\u307e\u3059\u3002<\/p>\n
        \n
        #read the cleaned data from csv\nincident_processed = pd.read_csv(\"preprocessed_incidents.csv\")\ncalls_processed = pd.read_csv(\"preprocessed_calls.csv\")<\/code><\/pre>\n<\/div>\n
        \u884c\u60c5\u5831\u3092\u633f\u5165\u3059\u308b\u524d\u306b\u30012\u3064\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30c7\u30b6\u30a4\u30f3\u3092\u751f\u6210\u3067\u304d\u308b\u3088\u3046\u306b\u3001GridDB\u306b\u30ab\u30e9\u30e0\u60c5\u5831\u3092\u6e21\u3059\u305f\u3081\u306e2\u3064\u306e\u7570\u306a\u308b\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
        \n
        #Create container \nincident_container = \"incident_container\"\n\n# Create containerInfo\nincident_containerInfo = griddb.ContainerInfo(incident_container,\n                    [[\"ID\", griddb.Type.INTEGER],\n                    [\"Category\", griddb.Type.STRING],\n                    [\"Descript\", griddb.Type.STRING],\n                    [\"DayOfWeek\", griddb.Type.STRING],\n                    [\"Date\", griddb.Type.TIMESTAMP],\n                    [\"PdDistrict\", griddb.Type.STRING],\n                    [\"Resolution\", griddb.Type.STRING]],\n                    griddb.ContainerType.COLLECTION, True)\n    \nincident_columns = gridstore.put_container(incident_containerInfo)<\/code><\/pre>\n<\/div>\n
        \n
        #Create container \ncalls_container = \"calls_container\"\n\n# Create containerInfo\ncalls_containerInfo = griddb.ContainerInfo(calls_container,\n                    [[\"ID\", griddb.Type.INTEGER],\n                    [\"Descript\", griddb.Type.STRING],\n                    [\"Date\", griddb.Type.TIMESTAMP]],\n                    griddb.ContainerType.COLLECTION, True)\n    \ncalls_columns = gridstore.put_container(calls_containerInfo)<\/code><\/pre>\n<\/div>\n
        \u6700\u5f8c\u306b\u3001\u4f5c\u6210\u3057\u305f\u30b9\u30ad\u30fc\u30de\u306b\u884c\u3092\u633f\u5165\u3057\u307e\u3059\u3002<\/p>\n
        \n
        # Put rows\nincident_columns.put_rows(incident_processed)\n    \nprint(\"Data Inserted using the DataFrame\")<\/code><\/pre>\n<\/div>\n
        \n
        # Put rows\ncalls_columns.put_rows(calls_processed)\n    \nprint(\"Data Inserted using the DataFrame\")<\/code><\/pre>\n<\/div>\n
        GridDB\u304b\u3089\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/h3>\n
        GridDB\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b\u305f\u3081\u306b\u3001SQL\u30b3\u30de\u30f3\u30c9\u306b\u4f3c\u305fTQL\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b\u524d\u306b\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u4fdd\u5b58\u3059\u308b\u524d\u306b\u3001\u30c7\u30fc\u30bf\u306e\u884c\u3092\u62bd\u51fa\u3059\u308b\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \n
        # Define the container names\n    incident_container = \"incident_container\"\n\n    # Get the containers\n    obtained_data = gridstore.get_container(incident_container)\n    \n    # Fetch all rows - language_tag_container\n    query = obtained_data.query(\"select *\")\n    \n    rs = query.fetch(False)\n    print(f\"{incident_container} Data\")<\/code><\/pre>\n<\/div>\n
        \n
        # Define the container names\n    call_container = \"call_container\"\n\n    # Get the containers\n    obtained_data = gridstore.get_container(call_container)\n    \n    # Fetch all rows - language_tag_container\n    query = obtained_data.query(\"select *\")\n    \n    rs = query.fetch(False)\n    print(f\"{call_container} Data\")<\/code><\/pre>\n<\/div>\n
        \u30c7\u30fc\u30bf\u62bd\u51fa\u306e\u6700\u5f8c\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001\u30ab\u30e9\u30e0\u60c5\u5831\u306e\u9806\u306b\u884c\u3092\u30af\u30a8\u30ea\u3057\u3001\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u3068\u5206\u6790\u306b\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u4fdd\u5b58\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/p>\n
        \n
        # Iterate and create a list\n    retrieved_data= []\n    while rs.has_next():\n        data = rs.next()\n        retrieved_data.append(data)\n\n# Convert the list to a pandas data frame\n    incidents = pd.DataFrame(retrieved_data,\n                        columns=['ID', 'Category', 'Descript', 'DayOfWeek', 'Date', \n                                 'PdDistrict', 'Resolution','Address'])<\/code><\/pre>\n<\/div>\n
        \n
        # Iterate and create a list\n    retrieved_data= []\n    while rs.has_next():\n        data = rs.next()\n        retrieved_data.append(data)\n\n    # Convert the list to a pandas data frame\n    calls = pd.DataFrame(retrieved_data,\n                        columns=['ID', 'Descript', 'Date'])<\/code><\/pre>\n<\/div>\n
        “incidents” \u3068 “calls” \u306e 2 \u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3068\u3057\u3066\u4fdd\u5b58\u3055\u308c\u3001\u30c7\u30fc\u30bf\u5206\u6790\u306e\u305f\u3081\u306b\u30af\u30ea\u30fc\u30f3\u30a2\u30c3\u30d7\u3055\u308c\u305f\u30c7\u30fc\u30bf\u304c\u5229\u7528\u53ef\u80fd\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n
        \u30c7\u30fc\u30bf\u89e3\u6790\u3068\u53ef\u8996\u5316<\/h2>\n
        \u307e\u305a\u3001\u30b0\u30eb\u30fc\u30d7\u5316\u3055\u308c\u305f\u30c7\u30fc\u30bf\u3092\u6570\u3048\u308b\u306e\u306b\u5f79\u7acb\u3064Null\u307e\u305f\u306f\u30bc\u30ed\u5024\u3067\u69cb\u6210\u3055\u308c\u308b\u5217\u3092\u5c0e\u5165\u3057\u3066\u5206\u6790\u3092\u59cb\u3081\u307e\u3059\u3002<\/p>\n
        \n
        incidents['NumOfIncidents'] = np.zeros(len(incidents))\ncalls['NumOfCalls'] = np.zeros(len(calls))<\/code><\/pre>\n<\/div>\n
        \u5831\u544a\u3055\u308c\u305f\u72af\u7f6a\u306f\u3001\u8b66\u5bdf\u81ea\u8eab\u306b\u3088\u3063\u3066\u5831\u544a\u3055\u308c\u306a\u304c\u3089\u3001\u7570\u306a\u308b\u30ab\u30c6\u30b4\u30ea\u30fc\u306b\u5c5e\u3057\u3066\u3044\u307e\u3059\u3002\u3067\u306f\u3001\u305d\u306e\u30c7\u30fc\u30bf\u3092\u3082\u3068\u306b\u3001\u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u3067\u8d77\u3053\u308b\u4e3b\u306a\u72af\u7f6a\u3092\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n
        \n
        incident_categories = incidents.groupby([\"Category\"]).count()\nn_largest = incident_categories['NumOfIncidents'].nlargest(n=10)\nincident_categories.reset_index(inplace = True)\nincident_categories = incident_categories[[\"Category\",\"NumOfIncidents\"]]\nn_largest.plot(kind = 'barh')\nplt.show()<\/code><\/pre>\n<\/div>\n
        \"\"<\/a><\/p>\n
        \u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u3067\u8d77\u3053\u308b\u72af\u7f6a\u306e\u307b\u3068\u3093\u3069\u306f\u3001\u300c\u7a83\u76d7\u300d\u306b\u95a2\u9023\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u306e\u8abf\u67fb\u3092\u3055\u3089\u306b\u9032\u3081\u3066\u3001\u3053\u306e\u72af\u7f6a\u304c\u591a\u304f\u767a\u751f\u3057\u3066\u3044\u308b\u5730\u57df\u3092\u8abf\u3079\u308b\u3053\u3068\u3082\u51fa\u6765\u307e\u3059\u3002<\/p>\n
        \n
        Theft_address = incidents[incidents['Category']==\"LARCENY\/THEFT\"]\nTheft_address = Theft_address.groupby([\"Address\"]).count()\nn_largest = Theft_address['NumOfIncidents'].nlargest(n=10)\nTheft_address.reset_index(inplace = True)\nTheft_address = Theft_address[[\"Address\",\"NumOfIncidents\"]]\nn_largest.plot(kind = 'barh')\nplt.show()<\/code><\/pre>\n<\/div>\n
        \"\"<\/a><\/p>\n
        \u3053\u3053\u3067\u3001\u6bce\u65e5\u306e\u4e8b\u4ef6\u3068\u6bce\u65e5\u306e\u901a\u8a71\u306e\u5024\u304c\u6642\u9593\u3068\u3068\u3082\u306b\u3069\u306e\u3088\u3046\u306b\u5909\u5316\u3057\u3066\u3044\u308b\u304b\u3092\u77e5\u308b\u305f\u3081\u306b\u3001\u8b66\u5bdf\u304b\u3089\u5831\u544a\u3055\u308c\u305f\u6bce\u65e5\u306e\u901a\u8a71\u3068\u6bce\u65e5\u306e\u4e8b\u4ef6\u3092\u500b\u5225\u306b\u8abf\u67fb\u3057\u3066\u307f\u307e\u3059\u3002\u30ad\u30fc\u304b\u3089Date\u5217\u3092\u524a\u9664\u3059\u308b\u3053\u3068\u3067\u3001\u7d50\u679c\u306f\u3001\u4e21\u65b9\u306e\u30b1\u30fc\u30b9\u3067\u5404\u65e5\u4ed8\u306e\u89b3\u5bdf\u306b\u5bfe\u3057\u3066\u8907\u6570\u306e\u884c\u3092\u6301\u3064\u7d30\u9577\u3044\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \n
        daily_incidents = incidents.groupby([\"Date\"]).count()\ndaily_incidents.reset_index(inplace = True)\ndaily_incidents = daily_incidents[[\"Date\",\"NumOfIncidents\"]]\n\n\ndaily_calls = calls.groupby([\"Date\"]).count()\ndaily_calls.reset_index(inplace = True)\ndaily_calls = daily_calls[[\"Date\",\"NumOfCalls\"]]<\/code><\/pre>\n<\/div>\n
        \u6b21\u306b\u3001\u7279\u5b9a\u306e\u65e5\u4ed8\u306b\u304a\u3051\u308b\u901a\u8a71\u3068\u30a4\u30f3\u30b7\u30c7\u30f3\u30c8\u306e\u5024\u30921\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3067\u53d6\u5f97\u3059\u308b\u305f\u3081\u306b\u3001\u65e5\u4ed8\u30ab\u30e9\u30e0\u3067\u4e21\u65b9\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3057\u307e\u3059\u3002<\/p>\n
        \n
        shared_dates = pd.merge(daily_incidents, daily_calls, on='Date', how = 'inner')<\/code><\/pre>\n<\/div>\n
        \u4e21\u5217\u3092\u6563\u5e03\u56f3\u306b\u30d7\u30ed\u30c3\u30c8\u3057\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u9069\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u304c\u793a\u3059\u30d1\u30bf\u30fc\u30f3\u306e\u76f4\u7dda\u3092\u5f97\u307e\u3057\u3087\u3046\u3002\u305d\u306e\u305f\u3081\u306b\u3001\u307e\u305a\u30c7\u30fc\u30bf\u3092\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u9069\u5408\u3055\u305b\u3001\u305d\u306e\u30c7\u30fc\u30bf\u70b9\u3092\u4f7f\u3063\u3066\u6563\u5e03\u56f3\u3092\u63cf\u304d\u3001\u6b21\u306b\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u5f97\u3089\u308c\u305f\u30c7\u30fc\u30bf\u70b9\u3092\u4f7f\u3063\u3066\u76f4\u7dda\u3092\u63cf\u304d\u3001”Treg1″ \u3068\u3057\u3066\u4fdd\u5b58\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002<\/p>\n
        \n
        d1 = np.polyfit(shared_dates.index,shared_dates['NumOfCalls'],1)\nf1 = np.poly1d(d1)\nshared_dates.insert(3,'Treg1',f1(shared_dates.index))\nax = shared_dates.plot.scatter(x = 'Day', y='NumOfCalls')\nshared_dates.plot(y='Treg1',color='Red',ax=ax)<\/code><\/pre>\n<\/div>\n
        \"\"<\/a><\/p>\n
        \u540c\u69d8\u306b\u30012\u3064\u76ee\u306e\u5217\u306b\u3064\u3044\u3066\u3082\u5225\u306e\u7dda\u5f62\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3057\u3001\u30e2\u30c7\u30eb\u306e\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3092 “Treg2” \u30ab\u30e9\u30e0\u306b\u4fdd\u5b58\u3057\u307e\u3059\u3002<\/p>\n
        \n
        d2 = np.polyfit(shared_dates.index,shared_dates['NumOfIncidents'],1)\nf2 = np.poly1d(d2)\nshared_dates.insert(4,'Treg2',f2(shared_dates.index))\nax = shared_dates.plot.scatter(x='Day' ,y='NumOfIncidents')\nshared_dates.plot(y='Treg2',color='Red',ax=ax)<\/code><\/pre>\n<\/div>\n
        \"\"<\/a><\/p>\n
        2 \u3064\u306e\u5909\u6570\u306e\u95a2\u4fc2\u3092\u3055\u3089\u306b\u8abf\u67fb\u3057\u3001\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306b\u3001\u76f8\u95a2\u4fc2\u6570\u306e\u30c6\u30af\u30cb\u30c3\u30af\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u7d71\u8a08\u5b66\u3067\u6700\u3082\u30b7\u30f3\u30d7\u30eb\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u30d4\u30a2\u30bd\u30f3\u76f8\u95a2\u4fc2\u6570\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u306f-1\u304b\u3089+1\u307e\u3067\u5909\u5316\u3057\u3001-1\u306f\u5f37\u3044\u8ca0\u306e\u76f8\u95a2\u3001+1\u306f\u5f37\u3044\u6b63\u306e\u76f8\u95a2\u3092\u793a\u3057\u307e\u3059\u3002Python\u306e’corr’\u95a2\u6570\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n
        \n
        correlation = shared_dates['NumOfIncidents'].corr(shared_dates['NumOfCalls'])<\/code><\/pre>\n<\/div>\n
        \u76f8\u95a2\u4fc2\u6570\u306f0.1469688\u3067\u3042\u308a\u30012\u3064\u306e\u5909\u6570\u306e\u9593\u306b\u306f\u975e\u5e38\u306b\u5f31\u3044\u6b63\u306e\u76f8\u95a2\u304c\u3042\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\n
        \u7d50\u8ad6<\/h2>\n
        \u7d71\u8a08\u7684\u306b\u306f\u3001\u8b66\u5bdf\u304c\u901a\u5831\u3059\u308b\u4e8b\u4ef6\u306e\u6570\u306f\u3001\u8b66\u5bdf\u304c\u53d7\u3051\u305f\u901a\u5831\u306e\u6570\u306b\u5de6\u53f3\u3055\u308c\u306a\u3044\u3068\u7d50\u8ad6\u4ed8\u3051\u3089\u308c\u307e\u3059\u3002\u307e\u305f\u3001\u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u306e\u4e2d\u3067\u3082\u7279\u306b\u6cbb\u5b89\u306e\u60aa\u3044\u5730\u57df\u3092\u62bd\u51fa\u3057\u3001\u8b66\u5bdf\u306b\u3088\u308b\u7279\u5225\u306a\u8b66\u5099\u304c\u5fc5\u8981\u3067\u3042\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3057\u305f\u3002\u3059\u3079\u3066\u306e\u5206\u6790\u306f\u3001\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u3067GridDB\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u4f7f\u7528\u3057\u3066\u884c\u308f\u308c\u305f\u305f\u3081\u3001\u30b7\u30fc\u30e0\u30ec\u30b9\u304b\u3064\u52b9\u7387\u7684\u306b\u7d71\u5408\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n
         \u8a18\u4e8b\u306e\u5168\u30b3\u30fc\u30c9\u306f\u3053\u3061\u3089<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
        \u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084\u58f2\u4e0a\u306e\u6700\u5927\u5316\u306a\u3069\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u30aa\u30fc\u30d7\u30f3\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u30b3\u30f3\u30bb […]<\/p>\n","protected":false},"author":41,"featured_media":49396,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50798","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\n\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/using-data-science-to-catch-criminals\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084\" \/>\n<meta property=\"og:url\" content=\"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/using-data-science-to-catch-criminals\/\" \/>\n<meta property=\"og:site_name\" content=\"GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/griddbcommunity\/\" \/>\n<meta property=\"article:published_time\" content=\"2022-05-25T07:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-14T15:55:23+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1440\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"griddb-admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@GridDBCommunity\" \/>\n<meta name=\"twitter:site\" content=\"@GridDBCommunity\" \/>\n<meta name=\"twitter:label1\" content=\"\u57f7\u7b46\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"griddb-admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593\" \/>\n\t<meta name=\"twitter:data2\" content=\"3\u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/\"},\"author\":{\"name\":\"griddb-admin\",\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/person\/4fe914ca9576878e82f5e8dd3ba52233\"},\"headline\":\"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b\",\"datePublished\":\"2022-05-25T07:00:00+00:00\",\"dateModified\":\"2025-11-14T15:55:23+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/\"},\"wordCount\":50,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#organization\"},\"image\":{\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage\"},\"thumbnailUrl\":\"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg\",\"inLanguage\":\"ja\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/\",\"url\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/\",\"name\":\"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b | GridDB: Open Source Time Series Database for IoT\",\"isPartOf\":{\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage\"},\"thumbnailUrl\":\"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg\",\"datePublished\":\"2022-05-25T07:00:00+00:00\",\"dateModified\":\"2025-11-14T15:55:23+00:00\",\"description\":\"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084\",\"inLanguage\":\"ja\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"ja\",\"@id\":\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage\",\"url\":\"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg\",\"contentUrl\":\"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg\",\"width\":1920,\"height\":1440},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#website\",\"url\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/\",\"name\":\"GridDB: Open Source Time Series Database for IoT\",\"description\":\"GridDB is an open source time-series database with the performance of NoSQL and convenience of SQL\",\"publisher\":{\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"ja\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#organization\",\"name\":\"Fixstars\",\"url\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"ja\",\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/griddb.net\/wp-content\/uploads\/2019\/04\/fixstars_logo_web_tagline.png\",\"contentUrl\":\"https:\/\/griddb.net\/wp-content\/uploads\/2019\/04\/fixstars_logo_web_tagline.png\",\"width\":200,\"height\":83,\"caption\":\"Fixstars\"},\"image\":{\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/griddbcommunity\/\",\"https:\/\/x.com\/GridDBCommunity\",\"https:\/\/www.linkedin.com\/company\/griddb-by-toshiba\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/person\/4fe914ca9576878e82f5e8dd3ba52233\",\"name\":\"griddb-admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"ja\",\"@id\":\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/5bceca1cafc06886a7ba873e2f0a28011a1176c4dea59709f735b63ae30d0342?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/5bceca1cafc06886a7ba873e2f0a28011a1176c4dea59709f735b63ae30d0342?s=96&d=mm&r=g\",\"caption\":\"griddb-admin\"},\"url\":\"https:\/\/griddb.net\/ja\/author\/griddb-admin\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b | GridDB: Open Source Time Series Database for IoT","description":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/using-data-science-to-catch-criminals\/","og_locale":"ja_JP","og_type":"article","og_title":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b | GridDB: Open Source Time Series Database for IoT","og_description":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084","og_url":"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/using-data-science-to-catch-criminals\/","og_site_name":"GridDB: Open Source Time Series Database for IoT","article_publisher":"https:\/\/www.facebook.com\/griddbcommunity\/","article_published_time":"2022-05-25T07:00:00+00:00","article_modified_time":"2025-11-14T15:55:23+00:00","og_image":[{"width":1920,"height":1440,"url":"https:\/\/griddb.net\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg","type":"image\/jpeg"}],"author":"griddb-admin","twitter_card":"summary_large_image","twitter_creator":"@GridDBCommunity","twitter_site":"@GridDBCommunity","twitter_misc":{"\u57f7\u7b46\u8005":"griddb-admin","\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593":"3\u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#article","isPartOf":{"@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/"},"author":{"name":"griddb-admin","@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/person\/4fe914ca9576878e82f5e8dd3ba52233"},"headline":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b","datePublished":"2022-05-25T07:00:00+00:00","dateModified":"2025-11-14T15:55:23+00:00","mainEntityOfPage":{"@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/"},"wordCount":50,"commentCount":0,"publisher":{"@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#organization"},"image":{"@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage"},"thumbnailUrl":"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg","inLanguage":"ja","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/","url":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/","name":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3067\u72af\u7f6a\u8005\u3092\u6355\u307e\u3048\u308b | GridDB: Open Source Time Series Database for IoT","isPartOf":{"@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#website"},"primaryImageOfPage":{"@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage"},"image":{"@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage"},"thumbnailUrl":"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg","datePublished":"2022-05-25T07:00:00+00:00","dateModified":"2025-11-14T15:55:23+00:00","description":"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306e\u529b\u306f\u3001\u6280\u8853\u3084\u30d3\u30b8\u30cd\u30b9\u306e\u8ab2\u984c\u89e3\u6c7a\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002\u305d\u306e\u4f7f\u3044\u9053\u306f\u3001\u65b0\u3057\u3044\u6280\u8853\u3092\u751f\u307f\u51fa\u3059\u305f\u3081\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3001\u6d88\u8cbb\u8005\u306b\u5411\u3051\u305f\u5e83\u544a\u3001\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3051\u308b\u5229\u76ca\u3084","inLanguage":"ja","potentialAction":[{"@type":"ReadAction","target":["https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/"]}]},{"@type":"ImageObject","inLanguage":"ja","@id":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-data-science-to-catch-criminals\/#primaryimage","url":"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg","contentUrl":"\/wp-content\/uploads\/2022\/04\/san-francisco_1920x1440.jpg","width":1920,"height":1440},{"@type":"WebSite","@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#website","url":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/","name":"GridDB: Open Source Time Series Database for IoT","description":"GridDB is an open source time-series database with the performance of NoSQL and convenience of SQL","publisher":{"@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"ja"},{"@type":"Organization","@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#organization","name":"Fixstars","url":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/","logo":{"@type":"ImageObject","inLanguage":"ja","@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/logo\/image\/","url":"https:\/\/griddb.net\/wp-content\/uploads\/2019\/04\/fixstars_logo_web_tagline.png","contentUrl":"https:\/\/griddb.net\/wp-content\/uploads\/2019\/04\/fixstars_logo_web_tagline.png","width":200,"height":83,"caption":"Fixstars"},"image":{"@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/griddbcommunity\/","https:\/\/x.com\/GridDBCommunity","https:\/\/www.linkedin.com\/company\/griddb-by-toshiba"]},{"@type":"Person","@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/person\/4fe914ca9576878e82f5e8dd3ba52233","name":"griddb-admin","image":{"@type":"ImageObject","inLanguage":"ja","@id":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/5bceca1cafc06886a7ba873e2f0a28011a1176c4dea59709f735b63ae30d0342?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/5bceca1cafc06886a7ba873e2f0a28011a1176c4dea59709f735b63ae30d0342?s=96&d=mm&r=g","caption":"griddb-admin"},"url":"https:\/\/griddb.net\/ja\/author\/griddb-admin\/"}]}},"_links":{"self":[{"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/posts\/50798","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/users\/41"}],"replies":[{"embeddable":true,"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/comments?post=50798"}],"version-history":[{"count":1,"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/posts\/50798\/revisions"}],"predecessor-version":[{"id":51631,"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/posts\/50798\/revisions\/51631"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/media\/49396"}],"wp:attachment":[{"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/media?parent=50798"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/categories?post=50798"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/griddb.net\/ja\/wp-json\/wp\/v2\/tags?post=50798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}