Reducer/zonal_statistics.ipynb

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        "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
        "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/Reducer/zonal_statistics.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Reducer/zonal_statistics.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Reducer/zonal_statistics.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install Earth Engine API and geemap\n",
        "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
        "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Installs geemap package\n",
        "import subprocess\n",
        "\n",
        "try:\n",
        "    import geemap\n",
        "except ImportError:\n",
        "    print('Installing geemap ...')\n",
        "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "import ee\n",
        "import geemap"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an interactive map \n",
        "The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map = geemap.Map(center=[40,-100], zoom=4)\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add Earth Engine Python script "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Add Earth Engine dataset\n",
        "# Load a region representing the United States\n",
        "region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017') \\\n",
        "  .filter(ee.Filter.eq('country_na', 'United States'))\n",
        "\n",
        "# Load MODIS land cover categories in 2001.\n",
        "landcover = ee.Image('MODIS/051/MCD12Q1/2001_01_01') \\\n",
        "  .select('Land_Cover_Type_1')\n",
        "\n",
        "# Load nightlights image inputs.\n",
        "nl2001 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F152001') \\\n",
        "  .select('stable_lights')\n",
        "nl2012 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012') \\\n",
        "  .select('stable_lights')\n",
        "\n",
        "# Compute the nightlights decadal difference, add land cover codes.\n",
        "nlDiff = nl2012.subtract(nl2001).addBands(landcover)\n",
        "\n",
        "# Grouped a mean 'reducer': change of nightlights by land cover category.\n",
        "means = nlDiff.reduceRegion(**{\n",
        "  'reducer': ee.Reducer.mean().group(**{\n",
        "    'groupField': 1,\n",
        "    'groupName': 'code',\n",
        "  }),\n",
        "  'geometry': region.geometry(),\n",
        "  'scale': 1000,\n",
        "  'maxPixels': 1e8\n",
        "})\n",
        "\n",
        "# Print the resultant Dictionary.\n",
        "print(means.getInfo())\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Display Earth Engine data layers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
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