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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6c983d84-2706-4eca-a485-abb0486d632b",
+   "metadata": {},
+   "source": [
+    "# World Settlement Footprint -  Evolution\n",
+    "* **Products used:** \n",
+    "[wsf_evolution](https://explorer.digitalearth.africa/products/wsf_evolution)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "d95378bc-1750-48b6-a186-8c9ca84677d4",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext",
+    "tags": []
+   },
+   "source": [
+    "**Keywords**: :index:`data used; World Settlement Footprint`,:index:`data used; World Settlement Footprint - Evolution`, :index:`datasets; wsf_evolution` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3a2ea02-23ef-495e-af24-7c692d024ea0",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Background\n",
+    "\n",
+    "According to the UN Department of Economics and Social Affairs, 9.7 billion people will inhabit the planet by the year 2050. 55% of the world's population presently resides in urban areas, and by 2050, that number is projected to increase to 68%. Rapid and haphazard urbanization, when paired with the problems posed by climate change, can increase air pollution, make people more susceptible to catastrophes, and cause problems with the management of resources like water, raw materials, and energy (Mapping Our Human Footprint From Space, 2023).\n",
+    "\n",
+    "To improve the understanding of current trends in global urbanisation, ESA and the German Aerospace Center (DLR), in collaboration with the Google Earth Engine team, are jointly developing the World Settlement Footprint – the world’s most comprehensive dataset on human settlement.\n",
+    "\n",
+    "The **World Settlement Footprint Evolution** was produced by processing seven million images from the US Landsat satellite collected between 1985 and 2015 and shows the annual growth of human settlements globally (Mapping Our Human Footprint From Space, 2023). It is worth noting that **past Landsat-5/7 availability** considerably varies across the world and over time. Independently from the implemented approach, this might then result in a lower quality of the final product where few/no scenes have been collected. To provide users with a suitable and intuitive measure that accounts for the availabilty of Landsat imagery, the **Input Data Consistency (IDC) score** was implemented. It ranges from 6 to 1 with: 6) very good; 5) good; 4) fair; 3) moderate; 2) low; 1) very low. The IDC score supports a proper interpretation of the WSF evolution product (Marconcini et al., 2021).\n",
+    "\n",
+    "The World Settlement Footprint evolution data are now indexed in the DE Africa platform."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d770a690-e411-4dd7-97ac-91611bd07bd6",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Description\n",
+    "\n",
+    "This notebook is a walkthrough on how to use the World Settlement Footprint Evolution data in the data cube. The worked example takes users through the code required to:\n",
+    " \n",
+    "   1. Inspecting the WSF Evolution data in the datacube\n",
+    "   2. Use `dc.load()` function to load in WSF data\n",
+    "   3. Plotting of results for WSF Evolution data\n",
+    "\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bd6a534-e175-4cf4-9dca-3a80d34b0fd7",
+   "metadata": {},
+   "source": [
+    "## Getting started\n",
+    "\n",
+    "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd091564-21a8-4e07-bb8b-a87bda69a2ae",
+   "metadata": {},
+   "source": [
+    "### Load packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8141d3b1-a1f2-431c-85a5-99798c4091c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import datacube\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as mticker\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from matplotlib.patches import Patch\n",
+    "import matplotlib.animation as animation\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "from IPython.display import Image\n",
+    "from datacube.utils.geometry import Geometry\n",
+    "\n",
+    "from deafrica_tools.plotting import display_map\n",
+    "from deafrica_tools.areaofinterest import define_area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "349bdf45-5adf-4719-8a15-80b1151680d0",
+   "metadata": {},
+   "source": [
+    "### Connect to the datacube"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3bc08905-6282-4692-870b-c08915ff4175",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc = datacube.Datacube(app=\"wsf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da5c3566-0165-40e7-bf19-5148d7ca9224",
+   "metadata": {},
+   "source": [
+    "### List measurements\n",
+    "\n",
+    "We can inspect the data available for WSF using datacube's `list_measurements` functionality. The table below lists the products and measurements available for the three WSF datasets indexed within DE Africa's datacube. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e37e4fc7-fa25-4248-a040-84f0b5618e00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>dtype</th>\n",
+       "      <th>units</th>\n",
+       "      <th>nodata</th>\n",
+       "      <th>aliases</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>product</th>\n",
+       "      <th>measurement</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">wsf_evolution</th>\n",
+       "      <th>wsfevolution</th>\n",
+       "      <td>wsfevolution</td>\n",
+       "      <td>int32</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[wsfevolution]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>idc_score</th>\n",
+       "      <td>idc_score</td>\n",
+       "      <td>uint8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[idc, idcscore, idcs]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    name  dtype units  nodata  \\\n",
+       "product       measurement                                       \n",
+       "wsf_evolution wsfevolution  wsfevolution  int32     1     0.0   \n",
+       "              idc_score        idc_score  uint8     1     0.0   \n",
+       "\n",
+       "                                          aliases  \n",
+       "product       measurement                          \n",
+       "wsf_evolution wsfevolution         [wsfevolution]  \n",
+       "              idc_score     [idc, idcscore, idcs]  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product_name = ['wsf_evolution'] \n",
+    "\n",
+    "dc_measurements = dc.list_measurements()\n",
+    "dc_measurements.loc[product_name].drop('flags_definition', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6b73623-effd-415e-858d-1cc4311746f2",
+   "metadata": {},
+   "source": [
+    "### Analysis parameters\n",
+    "\n",
+    "To define the area of interest, there are two methods available:\n",
+    "\n",
+    "1. By specifying the latitude, longitude, and buffer. This method requires you to input the central latitude, central longitude, and the buffer value in square degrees around the center point you want to analyze. For example, `lat = 10.338`, `lon = -1.055`, and `buffer = 0.1` will select an area with a radius of 0.1 square degrees around the point with coordinates (10.338, -1.055).\n",
+    "\n",
+    "2. By uploading a polygon as a `GeoJSON or Esri Shapefile`. If you choose this option, you will need to upload the geojson or ESRI shapefile into the Sandbox using Upload Files button <img align=\"top\" src=\"../Supplementary_data/upload_files_icon.png\"> in the top left corner of the Jupyter Notebook interface. ESRI shapefiles must be uploaded with all the related files `(.cpg, .dbf, .shp, .shx)`. Once uploaded, you can use the shapefile or geojson to define the area of interest. Remember to update the code to call the file you have uploaded.\n",
+    "\n",
+    "To use one of these methods, you can uncomment the relevant line of code and comment out the other one. To comment out a line, add the `\"#\"` symbol before the code you want to comment out. By default, the first option which defines the location using latitude, longitude, and buffer is being used.\n",
+    "\n",
+    "The default location is Mansoura, Egypt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f3de7a78-0385-4238-9fdf-846f7ad4d173",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 1: Specify the latitude, longitude, and buffer\n",
+    "aoi = define_area(lat= 31.0412, lon=31.3845, buffer=0.2)\n",
+    "\n",
+    "# Method 2: Use a polygon as a GeoJSON or Esri Shapefile. \n",
+    "#aoi = define_area(vector_path='aoi.shp')\n",
+    "\n",
+    "#Create a geopolygon and geodataframe of the area of interest\n",
+    "geopolygon = Geometry(aoi[\"features\"][0][\"geometry\"], crs=\"epsg:4326\")\n",
+    "geopolygon_gdf = gpd.GeoDataFrame(geometry=[geopolygon], crs=geopolygon.crs)\n",
+    "\n",
+    "# Get the latitude and longitude range of the geopolygon\n",
+    "lat_range = (geopolygon_gdf.total_bounds[1], geopolygon_gdf.total_bounds[3])\n",
+    "lon_range = (geopolygon_gdf.total_bounds[0], geopolygon_gdf.total_bounds[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "841ee697-6283-496b-b442-d325e18b54e4",
+   "metadata": {},
+   "source": [
+    "### View the selected location\n",
+    "The next cell will display the selected area on an interactive map.\n",
+    "Feel free to zoom in and out to get a better understanding of the area you'll be analysing.\n",
+    "Clicking on any point of the map will reveal the latitude and longitude coordinates of that point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7bbc31a3-4b3b-4059-b4b2-50f4e9b6ba94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
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+       "    \n",
+       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
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+       "        &lt;script&gt;\n",
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+       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
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+       "    \n",
+       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
+       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
+       "            &lt;style&gt;\n",
+       "                #map_530287a9bb336ddee4df4a344daf7bda {\n",
+       "                    position: relative;\n",
+       "                    width: 100.0%;\n",
+       "                    height: 100.0%;\n",
+       "                    left: 0.0%;\n",
+       "                    top: 0.0%;\n",
+       "                }\n",
+       "            &lt;/style&gt;\n",
+       "        \n",
+       "&lt;/head&gt;\n",
+       "&lt;body&gt;\n",
+       "    \n",
+       "    \n",
+       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_530287a9bb336ddee4df4a344daf7bda&quot; &gt;&lt;/div&gt;\n",
+       "        \n",
+       "&lt;/body&gt;\n",
+       "&lt;script&gt;\n",
+       "    \n",
+       "    \n",
+       "            var map_530287a9bb336ddee4df4a344daf7bda = L.map(\n",
+       "                &quot;map_530287a9bb336ddee4df4a344daf7bda&quot;,\n",
+       "                {\n",
+       "                    center: [31.041199999999996, 31.384499999999996],\n",
+       "                    crs: L.CRS.EPSG3857,\n",
+       "                    zoom: 10,\n",
+       "                    zoomControl: true,\n",
+       "                    preferCanvas: false,\n",
+       "                }\n",
+       "            );\n",
+       "\n",
+       "            \n",
+       "\n",
+       "        \n",
+       "    \n",
+       "            var tile_layer_3332ec6f51602010c4276273ff27d86a = L.tileLayer(\n",
+       "                &quot;http://mt1.google.com/vt/lyrs=y\\u0026z={z}\\u0026x={x}\\u0026y={y}&quot;,\n",
+       "                {&quot;attribution&quot;: &quot;Google&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
+       "            ).addTo(map_530287a9bb336ddee4df4a344daf7bda);\n",
+       "        \n",
+       "    \n",
+       "            var poly_line_38ee61859fc999f9dbe1abf9a616b45e = L.polyline(\n",
+       "                [[30.8412, 31.1845], [30.8412, 31.5845], [31.2412, 31.5845], [31.2412, 31.1845], [30.8412, 31.1845]],\n",
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+       "            ).addTo(map_530287a9bb336ddee4df4a344daf7bda);\n",
+       "        \n",
+       "    \n",
+       "                var lat_lng_popup_99a73b39cdfc87eaac2a9a90481655b4 = L.popup();\n",
+       "                function latLngPop(e) {\n",
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+       "                        .setLatLng(e.latlng)\n",
+       "                        .setContent(&quot;Latitude: &quot; + e.latlng.lat.toFixed(4) +\n",
+       "                                    &quot;&lt;br&gt;Longitude: &quot; + e.latlng.lng.toFixed(4))\n",
+       "                        .openOn(map_530287a9bb336ddee4df4a344daf7bda);\n",
+       "                    }\n",
+       "                map_530287a9bb336ddee4df4a344daf7bda.on(&#x27;click&#x27;, latLngPop);\n",
+       "            \n",
+       "&lt;/script&gt;\n",
+       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7f5d73db8fd0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display_map(x=lon_range, y=lat_range)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "533862aa-1a1f-4407-a648-57f189e39c16",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Loading of the WSF Evolution Dataset\n",
+    "\n",
+    "The WSF Evolution dataset will be loaded using the `dc.load` function. For more discussion on how to load data using the datacube, refer to the [Introduction to loading data](../Beginners_guide/03_Loading_data.ipynb) notebook.\n",
+    "\n",
+    "The cell below loads the WSF Evolution dataset. Notice the `product` and `measurements` values. \n",
+    "These will be updated for the subsequent data when they are being loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64356299-e600-4cef-a633-c6e0f71ad91a",
+   "metadata": {},
+   "outputs": [
+    {
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+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:       (y: 1463, x: 1288)\n",
+       "Coordinates:\n",
+       "    time          datetime64[ns] 2000-07-01T23:59:59.999500\n",
+       "  * y             (y) float64 3.796e+06 3.795e+06 ... 3.752e+06 3.752e+06\n",
+       "  * x             (x) float64 3.009e+06 3.009e+06 ... 3.047e+06 3.047e+06\n",
+       "    spatial_ref   int32 6933\n",
+       "Data variables:\n",
+       "    wsfevolution  (y, x) int32 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0\n",
+       "    idc_score     (y, x) uint8 6 6 6 6 6 6 6 6 6 6 6 6 ... 6 6 6 6 6 6 6 6 6 6 6\n",
+       "Attributes:\n",
+       "    crs:           epsg:6933\n",
+       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8d8882f4-497e-44fe-be5a-f1327046d879' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d8882f4-497e-44fe-be5a-f1327046d879' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1463</li><li><span class='xr-has-index'>x</span>: 1288</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-59c37106-85b7-41d8-b519-4dd9807ee92a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-59c37106-85b7-41d8-b519-4dd9807ee92a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-07-01T23:59:59.999500</div><input id='attrs-efafa1ed-2253-46e2-ab0c-e47a23c00ba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efafa1ed-2253-46e2-ab0c-e47a23c00ba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-885d5c9a-aef6-41d1-9b8b-ce4e8378a4d2' class='xr-var-data-in' type='checkbox'><label for='data-885d5c9a-aef6-41d1-9b8b-ce4e8378a4d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2000-07-01T23:59:59.999500000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.796e+06 3.795e+06 ... 3.752e+06</div><input id='attrs-69297ea1-db4a-428b-bf5a-97a9fc470f18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69297ea1-db4a-428b-bf5a-97a9fc470f18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cef60c5-88fc-46fc-bd87-758f393ed33a' class='xr-var-data-in' type='checkbox'><label for='data-7cef60c5-88fc-46fc-bd87-758f393ed33a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-30.0</dd><dt><span>crs :</span></dt><dd>epsg:6933</dd></dl></div><div class='xr-var-data'><pre>array([3795525., 3795495., 3795465., ..., 3751725., 3751695., 3751665.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.009e+06 3.009e+06 ... 3.047e+06</div><input id='attrs-529cb95d-f55a-4a39-9ce8-cf1cdab8101a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-529cb95d-f55a-4a39-9ce8-cf1cdab8101a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26df5e97-6f62-4c1b-9592-c924908de8e8' class='xr-var-data-in' type='checkbox'><label for='data-26df5e97-6f62-4c1b-9592-c924908de8e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>30.0</dd><dt><span>crs :</span></dt><dd>epsg:6933</dd></dl></div><div class='xr-var-data'><pre>array([3008865., 3008895., 3008925., ..., 3047415., 3047445., 3047475.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>6933</div><input id='attrs-d10a3896-a086-4371-a474-6dff40d5485c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d10a3896-a086-4371-a474-6dff40d5485c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bcc2e6f-3e33-499d-afec-da290d0d4da0' class='xr-var-data-in' type='checkbox'><label for='data-9bcc2e6f-3e33-499d-afec-da290d0d4da0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / NSIDC EASE-Grid 2.0 Global&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Cylindrical_Equal_Area&quot;],PARAMETER[&quot;standard_parallel_1&quot;,30],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;6933&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_cylindrical_equal_area</dd></dl></div><div class='xr-var-data'><pre>array(6933, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-907ccc2c-eb7e-4658-bd47-7d36b0a23dc8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-907ccc2c-eb7e-4658-bd47-7d36b0a23dc8' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>wsfevolution</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-ba7d0746-b3f2-4e8b-92d9-fdf8cdcb2377' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba7d0746-b3f2-4e8b-92d9-fdf8cdcb2377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2672ba4-17b9-4f2f-98af-22607c309425' class='xr-var-data-in' type='checkbox'><label for='data-f2672ba4-17b9-4f2f-98af-22607c309425' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>crs :</span></dt><dd>epsg:6933</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[   0,    0,    0, ..., 2012, 2010, 2010],\n",
+       "       [   0,    0,    0, ..., 2011, 2008, 1989],\n",
+       "       [   0,    0,    0, ..., 2011, 2006, 1998],\n",
+       "       ...,\n",
+       "       [   0,    0,    0, ...,    0,    0,    0],\n",
+       "       [   0,    0,    0, ...,    0,    0,    0],\n",
+       "       [   0,    0,    0, ...,    0,    0,    0]], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>idc_score</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>6 6 6 6 6 6 6 6 ... 6 6 6 6 6 6 6 6</div><input id='attrs-2319e617-3346-41f2-afe3-7295c899e2f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2319e617-3346-41f2-afe3-7295c899e2f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0ce3c60-b932-491c-9122-611bbea60ceb' class='xr-var-data-in' type='checkbox'><label for='data-f0ce3c60-b932-491c-9122-611bbea60ceb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>crs :</span></dt><dd>epsg:6933</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[6, 6, 6, ..., 6, 6, 6],\n",
+       "       [6, 6, 6, ..., 6, 6, 6],\n",
+       "       [6, 6, 6, ..., 6, 6, 6],\n",
+       "       ...,\n",
+       "       [6, 6, 6, ..., 6, 6, 6],\n",
+       "       [6, 6, 6, ..., 6, 6, 6],\n",
+       "       [6, 6, 6, ..., 6, 6, 6]], dtype=uint8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3be9fbd5-84d7-4bde-808b-57d7de4e40e3' class='xr-section-summary-in' type='checkbox'  ><label for='section-3be9fbd5-84d7-4bde-808b-57d7de4e40e3' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-22c1426c-c253-47c5-8381-83d5d5acacf7' class='xr-index-data-in' type='checkbox'/><label for='index-22c1426c-c253-47c5-8381-83d5d5acacf7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3795525.0, 3795495.0, 3795465.0, 3795435.0, 3795405.0, 3795375.0,\n",
+       "       3795345.0, 3795315.0, 3795285.0, 3795255.0,\n",
+       "       ...\n",
+       "       3751935.0, 3751905.0, 3751875.0, 3751845.0, 3751815.0, 3751785.0,\n",
+       "       3751755.0, 3751725.0, 3751695.0, 3751665.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1463))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-80510dc7-30e9-4892-9eed-f0fef6528f96' class='xr-index-data-in' type='checkbox'/><label for='index-80510dc7-30e9-4892-9eed-f0fef6528f96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3008865.0, 3008895.0, 3008925.0, 3008955.0, 3008985.0, 3009015.0,\n",
+       "       3009045.0, 3009075.0, 3009105.0, 3009135.0,\n",
+       "       ...\n",
+       "       3047205.0, 3047235.0, 3047265.0, 3047295.0, 3047325.0, 3047355.0,\n",
+       "       3047385.0, 3047415.0, 3047445.0, 3047475.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1288))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92bb2d73-3d93-4c1d-b252-cc8a49c2ec37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92bb2d73-3d93-4c1d-b252-cc8a49c2ec37' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>epsg:6933</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:       (y: 1463, x: 1288)\n",
+       "Coordinates:\n",
+       "    time          datetime64[ns] 2000-07-01T23:59:59.999500\n",
+       "  * y             (y) float64 3.796e+06 3.795e+06 ... 3.752e+06 3.752e+06\n",
+       "  * x             (x) float64 3.009e+06 3.009e+06 ... 3.047e+06 3.047e+06\n",
+       "    spatial_ref   int32 6933\n",
+       "Data variables:\n",
+       "    wsfevolution  (y, x) int32 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0\n",
+       "    idc_score     (y, x) uint8 6 6 6 6 6 6 6 6 6 6 6 6 ... 6 6 6 6 6 6 6 6 6 6 6\n",
+       "Attributes:\n",
+       "    crs:           epsg:6933\n",
+       "    grid_mapping:  spatial_ref"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#create reusable datacube query object\n",
+    "query = {\n",
+    "    'x': lon_range,\n",
+    "    'y': lat_range,\n",
+    "    'resolution':(-30, 30),\n",
+    "    'output_crs': 'epsg:6933',\n",
+    "}\n",
+    "\n",
+    "#loading the data using dc.load\n",
+    "wsf_evolution = dc.load(product='wsf_evolution',\n",
+    "                        measurements=['wsfevolution', 'idc_score'],\n",
+    "                        **query).squeeze()\n",
+    "\n",
+    "display(wsf_evolution)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a76b1d3f-3d35-4089-a8c7-c50e13c7cbfc",
+   "metadata": {},
+   "source": [
+    "### Spatial Plotting of WSF Evolution data\n",
+    "Spatial plot enable us to visualise the areas where there was development of settlements. To identify evolution of the area from 1985 to 2015.\n",
+    "\n",
+    ">The IDC score supports a proper interpretation of the WSF Evolution product.\n",
+    "**Input Data Consistency (IDC) score,which ranges from 6 to 1 with: \n",
+    "6 - very good; \n",
+    "5 - good; \n",
+    "4 - fair; \n",
+    "3 - moderate; \n",
+    "2 - low; \n",
+    "1 - very low**. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "de7578ae-5bc4-48c3-b3db-537fd0744078",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 6), sharey=True)\n",
+    "\n",
+    "#in plotting of te data zero(no-data) is marked out \n",
+    "wsf_plt = wsf_evolution.where(wsf_evolution != 0\n",
+    "                             ).wsfevolution.plot(cmap=ListedColormap(\n",
+    "    sns.color_palette('rocket_r').as_hex()), ax=ax[0], add_colorbar= False)\n",
+    "\n",
+    "ax[0].set_title('WSF Evolution from 1985 to 2015')\n",
+    " \n",
+    "divider = make_axes_locatable(ax[0])\n",
+    "cax = divider.new_vertical(size='2%', pad=0.6, pack_start = True)\n",
+    "fig.add_axes(cax)\n",
+    "fig.colorbar(wsf_plt, cax = cax, orientation = 'horizontal', label='Year')\n",
+    "\n",
+    "\n",
+    "#in plotting of the IDC score to support interpretation of the data\n",
+    "wsf_plt = wsf_evolution.where(wsf_evolution != 0\n",
+    "                             ).idc_score.plot(cmap=ListedColormap(\n",
+    "    sns.color_palette('hls', 6).as_hex()), ax=ax[1], add_colorbar= False,  vmin=1, vmax=6)\n",
+    "\n",
+    "ax[1].set_title('IDC score')\n",
+    "\n",
+    "divider = make_axes_locatable(ax[1])\n",
+    "cax = divider.new_vertical(size='2%', pad=0.6, pack_start = True)\n",
+    "fig.add_axes(cax)\n",
+    "fig.colorbar(wsf_plt, cax = cax, orientation = 'horizontal', label='IDC score')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aab6003-2358-4047-8a8c-d7ee312df82e",
+   "metadata": {},
+   "source": [
+    "From the spatial plotting above, the left image shows the evolution from 1985 to 2015, while the right image indicates the IDC score for the study area. Looking at the IDC score of **6** for the area of study, the values obtained from the WSF Evolution data can be classified as **very good**, and one can rely on it for further analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f417f51-f708-457f-ac9e-bcd281c9f0b7",
+   "metadata": {},
+   "source": [
+    "### Calculate the area of settlement footprint\n",
+    "\n",
+    "The number of pixels can be used for the area of the building if the pixel area is known. \n",
+    "Run the following cell to generate the necessary constants for performing this conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fbff6e48-b4b3-43f5-9060-8e6405bcbdfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixel_length = query[\"resolution\"][1]  # in metres\n",
+    "m_per_km = 1000  # conversion from metres to kilometres\n",
+    "area_per_pixel = pixel_length**2 / m_per_km**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "849c53b2-59f1-45b9-a83e-bf79295fb79a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Plotting of the WSF Evolution data\n",
+    "\n",
+    "Each pixel represents the year value of the WSF evolution dataset; to get the area of the evolution, the year pixel has to be counted and saved in a Pandas dataframe to enable calculation of the area. The cumulative sum of area will be calculated based on how it has increased over the period.\n",
+    "\n",
+    ">Note that there is a discrepancy between the area calculated for 2015 from the WSF 2015 product and the Evolution product. This may be because the Evolution product is derived from Landsat-5 and Landsat-7, whereas WSF 2015 and 2019 are derived from Sentinel-1 and Landsat-8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dc98a013-25cc-47a2-96d8-9667c5633a13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "evolution_period = {}\n",
+    "#running a for loop to extract year and calculate the area based on the year\n",
+    "for evolution_year in np.unique(wsf_evolution.wsfevolution.values):\n",
+    "    if evolution_year != 0:\n",
+    "        evolution_area = wsf_evolution.where(\n",
+    "            wsf_evolution.wsfevolution ==evolution_year).wsfevolution.count() * area_per_pixel\n",
+    "        evolution_period[evolution_year] = evolution_area\n",
+    "\n",
+    "#convert dict to dataframe\n",
+    "evolution_data = pd.DataFrame.from_dict(evolution_period, orient='index', dtype=float)\n",
+    "\n",
+    "#Cummulative sum of the data\n",
+    "evolution_data['evolve'] = evolution_data[0].cumsum()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12,6))\n",
+    "\n",
+    "evolution_data.evolve.plot(ax=ax, color='#B5784A')\n",
+    "\n",
+    "ax.set_ylabel(\"Area (Sq. km)\")\n",
+    "ax.set_title(f\"World Settlement Footprint \\n Evolution from 1985 to 2015 for the Study Area\")\n",
+    "ax.grid(linewidth=0.3)\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd26ea2a-c4b0-4217-a2e9-dcaf977d9fed",
+   "metadata": {},
+   "source": [
+    ">It is worth noting that past Landsat-5/7 availability considerably varies across the world and over time. Independently from the implemented approach, this might then result in a lower quality of the final product where few/no scenes have been collected. The **IDC score** supports a proper interpretation of the WSF evolution product."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a4f079-ba3d-4e49-842f-545fc4f77f06",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Animation of WSF Evolution data from 1985 to 2015\n",
+    "The cell below plots out the dataset and shows the animation of how the building footprint has evolved over the years from 1985 to 2015."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "33658821-ea4e-4814-a5dd-2f5205b3d8c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/gif": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evolution_year = evolution_data.index.values\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "#Generating the color scheme based on the number of years\n",
+    "lenyear=len(evolution_year)\n",
+    "palette = list(reversed(sns.color_palette(\"rocket\", lenyear).as_hex()))\n",
+    "wsf = []\n",
+    "color_index = 0\n",
+    "for i in range(1, len(evolution_year) + 1):\n",
+    "    evo_list= (evolution_year[:i])\n",
+    "    color = palette[:color_index]\n",
+    "    wsf_plt = wsf_evolution.where(\n",
+    "        wsf_evolution.isin(evo_list)).wsfevolution.plot(cmap=ListedColormap(color),\n",
+    "                                                 add_colorbar= False, ax=ax)\n",
+    "    color_index += 1\n",
+    "    t = ax.annotate(evolution_year[i-1], (1,1), xycoords='axes pixels', size='x-large',\n",
+    "                    weight='bold') \n",
+    "    wsf.append([wsf_plt, t])\n",
+    "\n",
+    "ax.set_title(f'World Settlement Footprint \\n Evolution from 1985 to 2015 for the Study Area')\n",
+    "\n",
+    "divider = make_axes_locatable(ax)\n",
+    "cax = divider.new_vertical(size='2%', pad=0.6, pack_start = True)\n",
+    "fig.add_axes(cax)\n",
+    "fig.colorbar(wsf_plt, cax = cax, orientation = 'horizontal')\n",
+    "\n",
+    "ani = animation.ArtistAnimation(fig=fig, artists=wsf, interval=500, blit=True)\n",
+    "ani.save('wsf.gif')\n",
+    "plt.close()\n",
+    "\n",
+    "Image(filename='wsf.gif')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ff4648c-cc89-44c2-9f40-11d1ec0fa1a2",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Conclusion\n",
+    "\n",
+    "The World Settlement Footprint offers a knowledge base that can help researchers, governmental organizations, and other stakeholders, such as urban planners, better understand how urbanization is happening and, concurrently, put in place sustainable urban development strategies for informed policy decisions at local and national levels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4abefee4-6671-4c34-bc54-9d83c33da944",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**Note**\n",
+    "\n",
+    "To run for different area go to cell **Analysis parameters**, change the **lat** and **lon** values in the **define_area_function**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a900d484-1ebe-4ffa-a7ca-23d2aab47a16",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "#### Referencing\n",
+    "\n",
+    "Mapping our human footprint from space(Accessed on 2023 August). ESA - Mapping Our Human Footprint From Space. https://www.esa.int/Applications/Observing_the_Earth/Mapping_our_human_footprint_from_space\n",
+    "\n",
+    "Mattia Marconcini, Annekatrin Metz-Marconcini, Thomas Esch and Noel Gorelick. Understanding Current Trends in Global Urbanisation - The World Settlement Footprint Suite. GI_Forum 2021, Issue 1, 33-38 (2021) https://austriaca.at/0xc1aa5576%200x003c9b4c.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bb9a661-6d5c-4b62-9d0f-7b7f07038f9e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "---\n",
+    "\n",
+    "## Additional information\n",
+    "\n",
+    "**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). \n",
+    "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n",
+    "\n",
+    "**Contact:** If you need assistance, please post a question on the [Open Data Cube Slack channel](http://slack.opendatacube.org/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n",
+    "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n",
+    "\n",
+    "**Compatible datacube version:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f9095cb1-fe4a-4cb4-88bc-67be83fee519",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.8.15\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(datacube.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9b1cbc2-1fc2-4ac6-8e9c-0dba1a615e27",
+   "metadata": {},
+   "source": [
+    "**Last tested:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "67bf3ff4-d076-4bcf-a409-fc24a4f8c957",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2023-10-31'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datetime import datetime\n",
+    "datetime.today().strftime('%Y-%m-%d')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}