{
  "id": "33f7e31abcb843979875501a28547bc2",
  "item": "Analyze_Urban_Heat_Using_Kriging.ipynb",
  "itemType": "file",
  "owner": "esri_notebook",
  "uploaded": 1556166003000,
  "modified": 1556166003000,
  "guid": null,
  "name": null,
  "title": "EBK Regression: Identify urban heat islands",
  "type": "Notebook",
  "typeKeywords": [
    "Notebook",
    "Python"
  ],
  "description": "The urban heat island effect is the tendency for city centers to have significantly higher temperatures than surrounding rural areas. You will interpolate temperature measurements to identify areas with both high temperatures and a high density of residents over the age of 65, who are at highest risk for heat-related illnesses.",
  "tags": [
    "EBK",
    "Kriging",
    "spatial interpolation",
    "cross validation",
    "model evaluation",
    "prediction"
  ],
  "snippet": "Interpolate temperature measurements to identify city areas with high temperatures and high density of residents.",
  "thumbnail": "thumbnail/urban_heat.png",
  "documentation": null,
  "extent": [],
  "categories": ["Data Science and Analysis"],
  "lastModified": -1,
  "spatialReference": null,
  "accessInformation": null,
  "licenseInfo": " - Uses arcpy",
  "culture": "english (united states)",
  "properties": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced",
    "notebookRuntimeVersion": "10.7.1"
  },
  "url": null,
  "proxyFilter": null,
  "access": "public",
  "size": 3073033,
  "appCategories": [],
  "industries": [],
  "languages": [],
  "largeThumbnail": null,
  "banner": null,
  "screenshots": [],
  "listed": false,
  "commentsEnabled": true,
  "numComments": 0,
  "numRatings": 0,
  "avgRating": 0,
  "numViews": 128,
  "scoreCompleteness": 96,
  "groupDesignations": null
}