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edits urban green space
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‎Real_world_examples/Urban_green_space.ipynb‎

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@@ -86,7 +86,7 @@
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"metadata": {},
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"source": [
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"### Connect to the datacube\n",
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"Connect to the datacube so we can access DE AFrica data.\n",
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"Connect to the datacube so we can access DE Africa data.\n",
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"The `app` parameter is a unique name for the analysis which is based on the notebook file name."
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]
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},
@@ -159,7 +159,7 @@
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" <meta name="viewport" content="width=device-width,\n",
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" initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n",
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" <style>\n",
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" #map_0a0016f958e13c993f026949793cb32e {\n",
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" #map_4708e94c20100f05e3850576b4211e24 {\n",
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" position: relative;\n",
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" width: 100.0%;\n",
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" height: 100.0%;\n",
@@ -189,14 +189,14 @@
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"<body>\n",
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" \n",
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" \n",
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" <div class="folium-map" id="map_0a0016f958e13c993f026949793cb32e" ></div>\n",
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" <div class="folium-map" id="map_4708e94c20100f05e3850576b4211e24" ></div>\n",
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" \n",
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"</body>\n",
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"<script>\n",
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" \n",
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" \n",
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" var map_0a0016f958e13c993f026949793cb32e = L.map(\n",
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" "map_0a0016f958e13c993f026949793cb32e",\n",
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" var map_4708e94c20100f05e3850576b4211e24 = L.map(\n",
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" "map_4708e94c20100f05e3850576b4211e24",\n",
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" {\n",
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" center: [6.6975, -1.6286],\n",
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" crs: L.CRS.EPSG3857,\n",
@@ -205,79 +205,79 @@
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" preferCanvas: false,\n",
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" }\n",
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" );\n",
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" L.control.scale().addTo(map_0a0016f958e13c993f026949793cb32e);\n",
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" L.control.scale().addTo(map_4708e94c20100f05e3850576b4211e24);\n",
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"\n",
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" \n",
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"\n",
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" \n",
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" \n",
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" var tile_layer_94aa53f8dea5a6d1d28f4e2e47a9f6f7 = L.tileLayer(\n",
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" var tile_layer_ffc7ab65e05d97bf92cc8d8fcf9a237d = L.tileLayer(\n",
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" "https://mt1.google.com/vt/lyrs=s\\u0026x={x}\\u0026y={y}\\u0026z={z}",\n",
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" {"attribution": "http://mt0.google.com/vt/lyrs=y\\u0026hl=en\\u0026x={x}\\u0026y={y}\\u0026z={z}", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n",
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" );\n",
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" \n",
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" \n",
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" tile_layer_94aa53f8dea5a6d1d28f4e2e47a9f6f7.addTo(map_0a0016f958e13c993f026949793cb32e);\n",
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" tile_layer_ffc7ab65e05d97bf92cc8d8fcf9a237d.addTo(map_4708e94c20100f05e3850576b4211e24);\n",
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" \n",
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" \n",
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" map_0a0016f958e13c993f026949793cb32e.fitBounds(\n",
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" map_4708e94c20100f05e3850576b4211e24.fitBounds(\n",
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" [[6.4975, -1.8286], [6.8975, -1.4286]],\n",
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" {}\n",
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" );\n",
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" \n",
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" \n",
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" function geo_json_3133d596ddc276ad9109b9a6a622feee_styler(feature) {\n",
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" function geo_json_9537b00d29a42864d9f014cfad44a49e_styler(feature) {\n",
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" switch(feature.id) {\n",
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" default:\n",
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" return {"fillOpacity": 0.5, "weight": 2};\n",
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" }\n",
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" }\n",
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" function geo_json_3133d596ddc276ad9109b9a6a622feee_highlighter(feature) {\n",
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" function geo_json_9537b00d29a42864d9f014cfad44a49e_highlighter(feature) {\n",
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" switch(feature.id) {\n",
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" default:\n",
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" return {"fillOpacity": 0.75};\n",
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" }\n",
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" }\n",
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" function geo_json_3133d596ddc276ad9109b9a6a622feee_pointToLayer(feature, latlng) {\n",
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" function geo_json_9537b00d29a42864d9f014cfad44a49e_pointToLayer(feature, latlng) {\n",
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" var opts = {"bubblingMouseEvents": true, "color": "#3388ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3388ff", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 2, "stroke": true, "weight": 3};\n",
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" \n",
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" let style = geo_json_3133d596ddc276ad9109b9a6a622feee_styler(feature)\n",
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" let style = geo_json_9537b00d29a42864d9f014cfad44a49e_styler(feature)\n",
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" Object.assign(opts, style)\n",
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" \n",
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" return new L.CircleMarker(latlng, opts)\n",
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" }\n",
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"\n",
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" function geo_json_3133d596ddc276ad9109b9a6a622feee_onEachFeature(feature, layer) {\n",
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" function geo_json_9537b00d29a42864d9f014cfad44a49e_onEachFeature(feature, layer) {\n",
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" layer.on({\n",
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" mouseout: function(e) {\n",
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" if(typeof e.target.setStyle === "function"){\n",
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" geo_json_3133d596ddc276ad9109b9a6a622feee.resetStyle(e.target);\n",
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" geo_json_9537b00d29a42864d9f014cfad44a49e.resetStyle(e.target);\n",
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" }\n",
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" },\n",
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" mouseover: function(e) {\n",
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" if(typeof e.target.setStyle === "function"){\n",
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" const highlightStyle = geo_json_3133d596ddc276ad9109b9a6a622feee_highlighter(e.target.feature)\n",
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" const highlightStyle = geo_json_9537b00d29a42864d9f014cfad44a49e_highlighter(e.target.feature)\n",
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" e.target.setStyle(highlightStyle);\n",
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" }\n",
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" },\n",
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" });\n",
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" };\n",
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" var geo_json_3133d596ddc276ad9109b9a6a622feee = L.geoJson(null, {\n",
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" onEachFeature: geo_json_3133d596ddc276ad9109b9a6a622feee_onEachFeature,\n",
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" var geo_json_9537b00d29a42864d9f014cfad44a49e = L.geoJson(null, {\n",
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" onEachFeature: geo_json_9537b00d29a42864d9f014cfad44a49e_onEachFeature,\n",
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" \n",
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" style: geo_json_3133d596ddc276ad9109b9a6a622feee_styler,\n",
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" pointToLayer: geo_json_3133d596ddc276ad9109b9a6a622feee_pointToLayer,\n",
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" style: geo_json_9537b00d29a42864d9f014cfad44a49e_styler,\n",
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" pointToLayer: geo_json_9537b00d29a42864d9f014cfad44a49e_pointToLayer,\n",
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" });\n",
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"\n",
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" function geo_json_3133d596ddc276ad9109b9a6a622feee_add (data) {\n",
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" geo_json_3133d596ddc276ad9109b9a6a622feee\n",
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" function geo_json_9537b00d29a42864d9f014cfad44a49e_add (data) {\n",
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" geo_json_9537b00d29a42864d9f014cfad44a49e\n",
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" .addData(data);\n",
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" }\n",
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" geo_json_3133d596ddc276ad9109b9a6a622feee_add({"bbox": [-1.8286, 6.4975, -1.4286, 6.8975], "features": [{"bbox": [-1.8286, 6.4975, -1.4286, 6.8975], "geometry": {"coordinates": [[[-1.4286, 6.4975], [-1.4286, 6.8975], [-1.8286, 6.8975], [-1.8286, 6.4975], [-1.4286, 6.4975]]], "type": "Polygon"}, "id": "0", "properties": {"agglosID": 1}, "type": "Feature"}], "type": "FeatureCollection"});\n",
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" geo_json_9537b00d29a42864d9f014cfad44a49e_add({"bbox": [-1.8286, 6.4975, -1.4286, 6.8975], "features": [{"bbox": [-1.8286, 6.4975, -1.4286, 6.8975], "geometry": {"coordinates": [[[-1.4286, 6.4975], [-1.4286, 6.8975], [-1.8286, 6.8975], [-1.8286, 6.4975], [-1.4286, 6.4975]]], "type": "Polygon"}, "id": "0", "properties": {"agglosID": 1}, "type": "Feature"}], "type": "FeatureCollection"});\n",
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"\n",
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" \n",
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" \n",
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" geo_json_3133d596ddc276ad9109b9a6a622feee.bindTooltip(\n",
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" geo_json_9537b00d29a42864d9f014cfad44a49e.bindTooltip(\n",
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" function(layer){\n",
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" let div = L.DomUtil.create('div');\n",
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" \n",
@@ -301,13 +301,13 @@
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" ,{"className": "foliumtooltip", "sticky": true});\n",
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" \n",
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" \n",
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" geo_json_3133d596ddc276ad9109b9a6a622feee.addTo(map_0a0016f958e13c993f026949793cb32e);\n",
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" geo_json_9537b00d29a42864d9f014cfad44a49e.addTo(map_4708e94c20100f05e3850576b4211e24);\n",
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" \n",
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"</script>\n",
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"&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
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],
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"text/plain": [
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"<folium.folium.Map at 0x7fd1f76f9ea0>"
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"<folium.folium.Map at 0x7fb9a8712bf0>"
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]
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},
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"execution_count": 3,
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},
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"source": [
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"### Perform zonal statistics using ESA Worldcover (2020 & 2021)\n",
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"This section of the notebook performs zonal statistics on urban agglomeration polygons using ESA WorldCover data from 2020 and 2021. It processes each polygon individually, extracting the relevant data for each year, masking the data to the polygon's boundary, and then calculating frequency histograms of land cover classes within that area. These histograms provide insights into the distribution of different land cover types within the agglomeration. The results are stored for further analysis and comparison."
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"This section of the notebook performs zonal statistics on urban agglomeration polygons using ESA WorldCover data from 2020 and 2021. It processes each polygon individually using a loop, extracting the relevant data for each year, masking the data to the polygon's boundary, and then calculating frequency histograms of land cover classes within that area. These histograms provide insights into the distribution of different land cover types within the agglomeration. The results are stored for further analysis and comparison."
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]
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},
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{
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c8df1f17bfe943e28aa570bcfca09529",
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"model_id": "4828920bdf504105949953a91b2ca9e7",
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"version_major": 2,
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"version_minor": 0
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},
@@ -976,7 +976,7 @@
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{
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"data": {
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"text/plain": [
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"'2025-01-27'"
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"'2025-01-28'"
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]
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},
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"execution_count": 10,

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