{"id":50702,"date":"2020-05-05T00:00:00","date_gmt":"2020-05-05T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/geospatial-analysis-of-nyc-crime-data-with-griddb\/"},"modified":"2025-11-14T07:54:04","modified_gmt":"2025-11-14T15:54:04","slug":"geospatial-analysis-of-nyc-crime-data-with-griddb","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/geospatial-analysis-of-nyc-crime-data-with-griddb\/","title":{"rendered":"GridDB\u3092\u4f7f\u7528\u3057\u305f\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u306e\u72af\u7f6a\u30c7\u30fc\u30bf\u306e\u5730\u7406\u7a7a\u9593\u5206\u6790"},"content":{"rendered":"

GridDB Community Edition 4.1\u4ee5\u964d\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306f\u3001\u958b\u767a\u8005\u304c\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3067\u6642\u7cfb\u5217\u5206\u6790\u3068\u5730\u7406\u7a7a\u9593\u5206\u6790\u306e\u4e21\u65b9\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u308bGeometry\u30c7\u30fc\u30bf\u30bf\u30a4\u30d7\u3092\u5099\u3048\u3066\u3044\u307e\u3059\u3002 \u4ee5\u524d\u306b GridDB\u3067\u306e\u30b8\u30aa\u30e1\u30c8\u30ea\u5024\u306e\u4f7f\u7528<\/a>\u3068\u3001\u30b8\u30aa\u30e1\u30c8\u30ea\u30c7\u30fc\u30bf\u3092\u6d3b\u7528\u3057\u305f\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3<\/a>\u306e\u30d6\u30ed\u30b0\u3067\u3001GridDB\u306e\u30b8\u30aa\u30e1\u30c8\u30ea\u6a5f\u80fd\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u3053\u306e\u30d6\u30ed\u30b0\u3067\u306f\u3001\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u5e02\u306e\u904e\u53bb\u306e\u72af\u7f6a\u306e\u901a\u5831\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u3001Geometry\u30c7\u30fc\u30bf\u30bf\u30a4\u30d7\u306e\u5b9f\u7528\u7684\u306a\u4f7f\u3044\u65b9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u3053\u306e\u30c7\u30fc\u30bf\u3067\u63d0\u4f9b\u3055\u308c\u308b\u72af\u7f6a\u30ec\u30dd\u30fc\u30c8\u306e\u7def\u5ea6\u3068\u7d4c\u5ea6\u3092\u4f7f\u3063\u3066\u3001GridDB\u3067\u901a\u5831\u304c\u767a\u751f\u3057\u305f\u30a8\u30ea\u30a2\u3092\u7279\u5b9a\u3057\u307e\u3059\u3002\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u5e02\u306e\u30aa\u30fc\u30d7\u30f3\u30c7\u30fc\u30bf\u304b\u3089\u72af\u7f6a\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u8fbc\u3080\u65b9\u6cd5\u3092\u793a\u3057\u305f\u5f8c\u3001\u30bb\u30f3\u30c8\u30e9\u30eb\u30d1\u30fc\u30af\u306b\u304a\u3051\u308b\u72af\u7f6a\u4ef6\u6570\u304c\u6708\u3054\u3068\u306b\u3069\u306e\u3088\u3046\u306b\u5909\u308f\u308b\u304b\u3092\u8abf\u3079\u307e\u3059\u3002\u307e\u305f\u3001\u500b\u3005\u306e\u5730\u533a\u306e\u901a\u5831\u4ef6\u6570\u3092\u78ba\u8a8d\u3057\u3066\u3001\u5916\u90e8\u30dd\u30ea\u30b4\u30f3\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002<\/p>\n

\u7a7a\u9593\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3067GridDB\u306eGeometry\u30c7\u30fc\u30bf\u30bf\u30a4\u30d7\u3092\u4f7f\u7528\u3059\u308b\u4e3b\u306a\u76ee\u7684\u306f\u3001\u30dd\u30a4\u30f3\u30c8\u3001\u30dd\u30ea\u30e9\u30a4\u30f3\uff08\u30d1\u30b9\uff09\u3001\u307e\u305f\u306f\u30dd\u30ea\u30b4\u30f3\uff08\u30a8\u30ea\u30a2\uff09\u304c\u4ea4\u5dee\u3059\u308b\u5834\u6240\u3092\u691c\u7d22\u3067\u304d\u308b\u3088\u3046\u306b\u3059\u308b\u3053\u3068\u3067\u3059\u3002 \u30dd\u30a4\u30f3\u30c8\u3001\u30dd\u30ea\u30e9\u30a4\u30f3\u3001\u304a\u3088\u3073\u30dd\u30ea\u30b4\u30f3\u306f\u3001\u5730\u56f3\u4e0a\u306e\u30d9\u30af\u30c8\u30eb\u30b8\u30aa\u30e1\u30c8\u30ea\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u5b9a\u7fa9\u3059\u308b\u30de\u30fc\u30af\u30a2\u30c3\u30d7\u8a00\u8a9e\u3067\u3042\u308bWell-known-text (WKT)<\/a>\u3092\u4f7f\u7528\u3057\u3066\u5b9a\u7fa9\u3055\u308c\u307e\u3059\u3002<\/p>\n

\u30c7\u30fc\u30bf\u53d6\u308a\u8fbc\u307f<\/h2>\n

\u904e\u53bb\u306e\u72af\u7f6a\u30c7\u30fc\u30bf\u306f\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u5e02\u30aa\u30fc\u30d7\u30f3\u30c7\u30fc\u30bf<\/a>\u304b\u3089\u53d6\u5f97\u3057\u307e\u3057\u305f\u3002 \u6b21\u306e\u8868\u306f\u3001CSV\u30c7\u30fc\u30bf\u30d5\u30a3\u30fc\u30eb\u30c9\u3068GridDB\u30b9\u30ad\u30fc\u30de\u306e\u4e21\u65b9\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n\n
CSV Values<\/th>\nGridDB Schema<\/th>\n<\/tr>\n
\n
    \n
  • CMPLNT_NUM (Unique ID)<\/li>\n
  • CMPLNT_FR_DT (Complaint Date)<\/li>\n
  • CMPLNT_FR_TM (Complaint Time)<\/li>\n
  • CMPLNT_TO_DT<\/li>\n
  • CMPLNT_TO_TM<\/li>\n
  • ADDR_PCT_CD<\/li>\n
  • RPT_DT<\/li>\n
  • KY_CD<\/li>\n
  • OFNS_DESC<\/li>\n
  • PD_CD<\/li>\n
  • PD_DESC<\/li>\n
  • CRM_ATPT_CPTD_CD<\/li>\n
  • LAW_CAT_CD<\/li>\n
  • BORO_NM<\/li>\n
  • LOC_OF_OCCUR_DESC<\/li>\n
  • PREM_TYP_DESC<\/li>\n
  • JURIS_DESC<\/li>\n
  • JURISDICTION_CODE<\/li>\n
  • PARKS_NM<\/li>\n
  • HADEVELOPT<\/li>\n
  • HOUSING_PSA<\/li>\n
  • X_COORD_CD<\/li>\n
  • Y_COORD_CD<\/li>\n
  • SUSP_AGE_GROUP<\/li>\n
  • SUSP_RACE<\/li>\n
  • SUSP_SEX<\/li>\n
  • TRANSIT_DISTRICT<\/li>\n
  • Latitude (Floating Point Latitude)<\/li>\n
  • Longitude (Floating Point Longitude)<\/li>\n
  • Lat_Lon (Lat Lon WKT)<\/li>\n
  • PATROL_BORO<\/li>\n
  • STATION_NAME<\/li>\n
  • VIC_AGE_GROUP<\/li>\n
  • VIC_RACE<\/li>\n
  • VIC_SEX<\/li>\n<\/ul>\n<\/td>\n
\n
public class Complaint {\n    int CMPLNT_NUM;\n    Date CMPLNT_FR_DT;\n    Date CMPLNT_TO_DT;\n    int ADDR_PCT_CD;\n    Date RPT_DT;\n    int KY_CD;\n    String OFNS_DESC;\n    int PD_CD;\n    String PD_DESC;\n    String CRM_ATPT_CPTD_CD;\n    String LAW_CAT_CD;\n    String BORO_NM;\n    String LOC_OF_OCCUR_DESC;\n    String PREM_TYP_DESC;\n    String JURIS_DESC;\n    int JURISDICTION_CODE;\n    String PARKS_NM;\n    String HADEVELOPT;\n    String HOUSING_PSA;\n    int X_COORD_CD;\n    int Y_COORD_CD;\n    String SUSP_AGE_GROUP;\n    String SUSP_RACE;\n    String SUSP_SEX;\n    int TRANSIT_DISTRICT;\n    float Latitude;\n    float Longitude;\n    Geometry Lat_Lon;\n    String PATROL_BORO;\n    String STATION_NAME;\n    String VIC_AGE_GROUP;\n    String VIC_RACE;\n    String VIC_SEX;\n}<\/pre>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u7c21\u5358\u306b\u3059\u308b\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u3092\u8907\u6570\u306e\u30b3\u30f3\u30c6\u30ca\u30fc\u306b\u5206\u5272\u3059\u308b\u306e\u3067\u306f\u306a\u304f\u30011\u3064\u306e\u30b3\u30f3\u30c6\u30ca\u30fc\u306e\u307f\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
CSVParser\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u3066CSV\u3092\u7c21\u5358\u306b\u89e3\u6790\u3057\u307e\u3059\u3002<\/p>\n
   Iterable records = CSVFormat.RFC4180.withFirstRecordAsHeader().parse(in);\n   for (CSVRecord record : records) {\n        Complaint c = parseCsvRecord(record);\n        if(c != null)\n            col.put(c);\n   }\n   col.commit();\n<\/pre>\n
parseCsvRecord\u95a2\u6570\u5185\u3067\u3044\u304f\u3064\u304b\u30c7\u30fc\u30bf\u306e\u5909\u66f4\u3092\u3057\u307e\u3059\u3002 \u307e\u305a\u3001\u901a\u5831\u306e\u6642\u9593\u306e\u5f62\u5f0f\u306f\u6a19\u6e96\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u305b\u3093\u304c\u3001\u7c21\u5358\u306b\u89e3\u6790\u3067\u304d\u308b\u3088\u3046MM\/DD\/YYYY\u304a\u3088\u3073HH:MM:SS\u306e\u5f62\u5f0f\u306b\u5909\u66f4\u3057\u307e\u3059\u3002<\/p>\n
String dt[] = r.get(\"CMPLNT_FR_DT\").split(\"\/\");\nString tm[] = r.get(\"CMPLNT_FR_TM\").split(\":\");\nc.CMPLNT_FR_DT = new Date(Integer.parseInt(dt[2])-1900, Integer.parseInt(dt[0])-1, Integer.parseInt(dt[1]), Integer.parseInt(tm[0]), Integer.parseInt(tm[1]), Integer.parseInt(tm[2]));\n<\/pre>\n
\u672a\u52a0\u5de5\u306eCSV\u306b\u306f\u3001\u300c\u7def\u5ea6\u7d4c\u5ea6\u300d\u3067\u72af\u7f6a\u304c\u767a\u751f\u3057\u305f\u30dd\u30a4\u30f3\u30c8\u306eWKT\u30c6\u30ad\u30b9\u30c8\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u304c\u3001\u53d7\u3051\u5165\u308c\u3089\u308c\u308bWKT\u5f62\u5f0f\u306fPOINT\uff08x y\uff09\u3067\u3042\u308a\u3001\u7def\u5ea6\u306fY\u8ef8\u3092\u793a\u3057\u3001\u7d4c\u5ea6\u306fX\u8ef8\u306a\u306e\u3067\u53cd\u8ee2\u3057\u307e\u3059\u3002<\/p>\n
c.Lat_Lon =   Geometry.valueOf(\"POINT(\"+c.Longitude+\" \"+c.Latitude+\")\");\n<\/pre>\n
\u5404\u7ba1\u533a\u5185\u306e\u72af\u7f6a\u4ef6\u6570<\/h2>\n
\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u5e02\u306e\u30aa\u30fc\u30d7\u30f3\u30c7\u30fc\u30bf\u306f\u3001\u3053\u3061\u3089<\/a>\u304b\u3089\u5165\u624b\u53ef\u80fd\u306a\u500b\u3005\u306e\u8b66\u5bdf\u7ba1\u533a\u306eWKT\u30dd\u30ea\u30b4\u30f3\u3082\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059\u3002 \u901a\u5831\u30c7\u30fc\u30bf\u3068\u540c\u69d8\u306b\u3001CSVParser\u3092\u4f7f\u7528\u3057\u3066\u7c21\u5358\u306b\u30ed\u30fc\u30c9\u3067\u304d\u307e\u3059\u304c\u3001\u5404\u5730\u533a\u306f\u8907\u6570\u306e\u30dd\u30ea\u30b4\u30f3\u3067\u69cb\u6210\u3055\u308c\u3001WKT MULTIPOLYGON\u30bf\u30a4\u30d7\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u305f\u3081\u3001MULTIPOLYGON\u3092\u5358\u7d14\u306aPOLYGON\u306b\u5206\u5272\u3059\u308b\u306b\u306f\u3055\u3089\u306b\u51e6\u7406\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n
String polys[] = record.get(\"the_geom\").split(\"\\),\");\nint count=0;\nfor(int i=0; i < polys.length; i++) {\n    String subpoly = polys[i].replace(\"MULTIPOLYGON (\", \"\").replace(\")))\", \")\");\n    query = col.query(\"select * where ST_MBRIntersects(Lat_Lon, ST_GeomFromText('POLYGON\"+subpoly+\")') )\");\n    rs = query.fetch(false);\n    count =+ rs.size();\n}\n<\/pre>\n
\u7d50\u679c\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n
\u7b2c1\u5730\u533a : 243         \u7b2c52\u5730\u533a : 888\n\u7b2c5\u5730\u533a : 177         \u7b2c60\u5730\u533a : 185\n\u7b2c6\u5730\u533a : 216         \u7b2c61\u5730\u533a : n\/a\n\u7b2c71\u5730\u533a : 227        \u7b2c62\u5730\u533a : 210\n\u7b2c72\u5730\u533a : 262        \u7b2c63\u5730\u533a : 324\n\u7b2c7\u5730\u533a : 132         \u7b2c66\u5730\u533a : 261\n\u7b2c9\u5730\u533a : 233         \u7b2c68\u5730\u533a : 233\n\u7b2c22\u5730\u533a : 345        \u7b2c69\u5730\u533a : 220\n\u7b2c10\u5730\u533a : 203        \u7b2c70\u5730\u533a : 369\n\u7b2c13\u5730\u533a : 400        \u7b2c76\u5730\u533a : 135\n\u7b2c14\u5730\u533a : 428        \u7b2c77\u5730\u533a : 334\n\u7b2c17\u5730\u533a : 174        \u7b2c78\u5730\u533a : 211\n\u7b2c20\u5730\u533a : 132        \u7b2c81\u5730\u533a : 12\n\u7b2c18\u5730\u533a : 379        \u7b2c83\u5730\u533a : 498\n\u7b2c19\u5730\u533a : 225        \u7b2c84\u5730\u533a : 175\n\u7b2c23\u5730\u533a : 225        \u7b2c88\u5730\u533a : 174\n\u7b2c24\u5730\u533a : 147        \u7b2c90\u5730\u533a : 290\n\u7b2c25\u5730\u533a : 336        \u7b2c94\u5730\u533a : 102\n\u7b2c79\u5730\u533a : 266        \u7b2c100\u5730\u533a : 8\n\u7b2c26\u5730\u533a : 217        \u7b2c101\u5730\u533a : 0\n\u7b2c28\u5730\u533a : 213        \u7b2c102\u5730\u533a : 283\n\u7b2c30\u5730\u533a : 206        \u7b2c103\u5730\u533a : 367\n\u7b2c32\u5730\u533a : 361        \u7b2c104\u5730\u533a : 511\n\u7b2c73\u5730\u533a : 410        \u7b2c105\u5730\u533a : 481\n\u7b2c33\u5730\u533a : 152        \u7b2c106\u5730\u533a : 227\n\u7b2c34\u5730\u533a : 224        \u7b2c107\u5730\u533a : 294\n\u7b2c75\u5730\u533a : 529        \u7b2c108\u5730\u533a : 262\n\u7b2c40\u5730\u533a : 444        \u7b2c109\u5730\u533a : 299\n\u7b2c41\u5730\u533a : 304        \u7b2c110\u5730\u533a : 431\n\u7b2c42\u5730\u533a : 487        \u7b2c111\u5730\u533a : 138\n\u7b2c43\u5730\u533a : 408        \u7b2c112\u5730\u533a : 178\n\u7b2c48\u5730\u533a : 495        \u7b2c113\u5730\u533a : n\/a\n\u7b2c44\u5730\u533a : 559        \u7b2c114\u5730\u533a : 28\n\u7b2c45\u5730\u533a : 323        \u7b2c115\u5730\u533a : 246\n\u7b2c46\u5730\u533a : 400        \u7b2c120\u5730\u533a : 228\n\u7b2c47\u5730\u533a : 441        \u7b2c121\u5730\u533a : 194\n\u7b2c49\u5730\u533a : 267        \u7b2c122\u5730\u533a : 217\n\u7b2c50\u5730\u533a : 219        \u7b2c123\u5730\u533a : 83\n\u7b2c67\u5730\u533a : 504\n<\/pre>\n
\u30bb\u30f3\u30c8\u30e9\u30eb\u30d1\u30fc\u30af<\/h2>\n
\u307e\u305a\u6700\u521d\u306e\u5730\u7406\u7a7a\u9593\u5206\u6790\u3068\u3057\u3066\u3001\u30bb\u30f3\u30c8\u30e9\u30eb\u30d1\u30fc\u30af\u3067\u306e\u72af\u7f6a\u4ef6\u6570\u3092\u6708\u3054\u3068\u306b\u8abf\u3079\u3001\u72af\u7f6a\u306e\u901a\u5831\u4ef6\u6570\u304c\u6c17\u6e29\u306b\u5fdc\u3058\u3066\u5897\u6e1b\u3059\u308b\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002 \u30bb\u30f3\u30c8\u30e9\u30eb\u30d1\u30fc\u30af\u306f\u5357\u5317\u8ef8\u306b\u4f4d\u7f6e\u5408\u308f\u305b\u3055\u308c\u3066\u3044\u306a\u3044\u305f\u3081\u3001\u57fa\u672c\u7684\u306a\u5730\u7406\u7a7a\u9593\u5206\u6790\u30af\u30a8\u30ea\u306e\u3088\u3046\u306b\u5883\u754c\u30dc\u30c3\u30af\u30b9(where lat > min && lat < max && lon > min && lon < max) \u3092\u5358\u7d14\u306b\u4f7f\u7528\u3059\u308b\u3053\u3068\u306f\u3067\u304d\u307e\u305b\u3093\u3002<\/p>\n
\u4ee3\u308f\u308a\u306b\u3001\u30bb\u30f3\u30c8\u30e9\u30eb\u30d1\u30fc\u30af\u306e\u5404\u30b3\u30fc\u30ca\u30fc\u306e\u30dd\u30ea\u30b4\u30f3\u3092\u4f5c\u6210\u3057\u3001\u305d\u306e\u30dd\u30ea\u30b4\u30f3\u3068\u4ea4\u5dee\u3059\u308b\u72af\u7f6a\u306e\u901a\u5831\u4ef6\u6570\u3092\u7167\u4f1a\u3057\u307e\u3059\u3002
\n\u30bb\u30f3\u30c8\u30e9\u30eb\u30d1\u30fc\u30af\u306e\u30dd\u30a4\u30f3\u30c8\u3092\u898b\u3064\u3051\u3066WKT\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u69cb\u7bc9\u3059\u308b\u306b\u306f\u3001Wicket<\/a>\u3092\u4f7f\u3046\u3068\u4fbf\u5229\u3067\u3059\u3002<\/p>\n
\"Using<\/p>\n
\u3053\u308c\u3067\u3001TQL\u30af\u30a8\u30ea\u3092\u4f5c\u6210\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n
String CentralParkWKT = \"POLYGON((-73.97308900174315 40.764422448981996,-73.98192956265623 40.76812781417226,-73.9584064734938 40.80087951931638,-73.94982340464614 40.797240957024385,-73.97308900174315 40.764422448981996))\";\n\nfor(int month=0; month <= 11; month++) {\n    int count=0;\n    for (int year=108; year <= 118; year++) {\n        Date start = new Date(year, month, 1);\n        Date end = new Date(year, month+1, 1);\n\n\tQuery query = col.query(\"select * where ST_MBRIntersects(Lat_Lon, ST_GeomFromText('\"+CentralParkWKT+\"')) and CMPLNT_FR_DT >= TO_TIMESTAMP_MS(\"+start.getTime()+\")  and CMPLNT_FR_DT < TO_TIMESTAMP_MS(\"+ end.getTime()+\") \");\n        RowSet rs = query.fetch(false);\n        count += rs.size(); \n    }\n    System.out.println(month+\": \"+count);\n}\n<\/pre>\n
\u3067\u306f\u3001\u5148\u307b\u3069\u306e\u8cea\u554f\u3001\u6c17\u6e29\u304c\u5909\u5316\u306b\u4f34\u3044\u72af\u7f6a\u306e\u4ef6\u6570\u306f\u5909\u5316\u3059\u308b\u304b\u3001\u306e\u7b54\u3048\u306f\u3069\u3046\u3060\u3063\u305f\u3067\u3057\u3087\u3046\u304b\uff1f<\/p>\n
1\u6708: 30\n2\u6708: 23\n3\u6708: 36\n4\u6708: 33\n5\u6708: 29\n6\u6708: 19\n7\u6708: 26\n8\u6708: 49\n9\u6708: 25\n10\u6708: 23\n11\u6708: 26\n12\u6708: 18\n<\/pre>\n
12\u6708\u30688\u6708\u306e\u6570\u5b57\u306f\u3053\u306e\u4eee\u8aac\u3092\u652f\u6301\u3057\u307e\u3059\u304c\u30011\u6708\u30686\u6708\u306e\u6570\u5b57\u306f\u652f\u6301\u3057\u306a\u3044\u305f\u3081\u3001\u3053\u306e\u4eee\u8aac\u306f\u4e8b\u5b9f\u3067\u306f\u306a\u3044\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3057\u305f\u3002<\/p>\n
\u30c7\u30fc\u30bf\u3092\u3088\u308a\u8a73\u3057\u304f\u898b\u305f\u308a\u3001\u5b8c\u5168\u306a\u30b3\u30fc\u30c9\u3092\u78ba\u8a8d\u3057\u305f\u308a\u3059\u308b\u5834\u5408\u306f\u3001\u3053\u3061\u3089<\/a>\u304b\u3089\u30c7\u30fc\u30bf\u3092\u5165\u624b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
GridDB Community Edition 4.1\u4ee5\u964d\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306f\u3001\u958b\u767a\u8005\u304c\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3067\u6642\u7cfb\u5217\u5206\u6790\u3068\u5730\u7406\u7a7a\u9593\u5206\u6790\u306e\u4e21\u65b9\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u308bGeometry\u30c7\u30fc\u30bf\u30bf\u30a4\u30d7\u3092\u5099\u3048\u3066\u3044\u307e\u3059\u3002 \u4ee5\u524d\u306b Grid […]<\/p>\n","protected":false},"author":71,"featured_media":49109,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50702","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\nGridDB\u3092\u4f7f\u7528\u3057\u305f\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u306e\u72af\u7f6a\u30c7\u30fc\u30bf\u306e\u5730\u7406\u7a7a\u9593\u5206\u6790 | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"GridDB Community Edition\" \/>\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-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/ja\/\u672a\u5206\u985e\/geospatial-analysis-of-nyc-crime-data-with-griddb\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GridDB\u3092\u4f7f\u7528\u3057\u305f\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u306e\u72af\u7f6a\u30c7\u30fc\u30bf\u306e\u5730\u7406\u7a7a\u9593\u5206\u6790 | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"GridDB Community Edition\" \/>\n<meta property=\"og:url\" content=\"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/ja\/\u672a\u5206\u985e\/geospatial-analysis-of-nyc-crime-data-with-griddb\/\" \/>\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=\"2020-05-05T07:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-14T15:54:04+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.griddb.net\/wp-content\/uploads\/2020\/05\/53248-carriage_2560x1707.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Owen\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" 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