2f:T4bb,pvPotential = ( await FileAttachment("lsoa_rooftop_pv@2.geojson").json() ).features .filter((d) => !d.properties["LSOA11NM"].includes("South Cambridgeshire")) .map((d) => { const code = d.properties.LSOA11CD; const type = showTotals ? "sum" : "mean"; const pv_totalbuildings = d.properties.total_buildings; const pv_suitable = d.properties.suitable_buildings; const pv_suitable_pct = d.properties.percentage_suitable; const pv_annualgen = showTotals ? d.properties["sum_annualgeneration"] : d.properties["mean_annualgeneration"]; const pv_roofarea = showTotals ? d.properties["sum_roofareasuitable"] : d.properties["mean_roofareasuitable"]; const pv_arraysize = showTotals ? d.properties["sum_arraysizerecomn"] : d.properties["mean_arraysizerecomn"]; return { code, type, pv_totalbuildings, pv_suitable, pv_suitable_pct, pv_annualgen, pv_roofarea, pv_arraysize }; }, {}) // .map((d) => [ // d.properties.LSOA11CD, // showTotals ? "sum" : "mean", // showTotals // ? d.properties["Annual Generation [kWh]_sum"] // : d.properties["Annual Generation [kWh]_mean"] // ])30:T48c,airPumpPotential = ( await FileAttachment("lsoa_airsource_heatpump.geojson").json() ).features .filter((d) => !d.properties["LSOA11NM"].includes("South Cambridgeshire")) .map((d) => { const code = d.properties.LSOA11CD; const type = showTotals ? "sum" : "mean"; const ashp_totalbuildings = d.properties.total_buildings; const ashp_suitable = d.properties.suitable_buildings; const ashp_suitable_pct = d.properties.percentage_suitable; const ashp_heatpumpsize = showTotals ? d.properties["sum_heatpumpsize"] : d.properties["mean_heatpumpsize"]; const ashp_peakheatdemand = showTotals ? d.properties["sum_peakheatdemand"] : d.properties["mean_peakheatdemand"]; return { code, type, ashp_totalbuildings, ashp_suitable, ashp_suitable_pct, ashp_heatpumpsize, ashp_peakheatdemand }; }, {}) // to make it as array instead of dictionary // .map((d) => [ // d.properties.LSOA11CD, // showTotals ? "sum" : "mean", // showTotals // ? d.properties["Recommended Heat Pump Size [kW]_sum"] // : d.properties["Recommended Heat Pump Size [kW]_mean"] // ])31:T464,groundPumpPotential = ( await FileAttachment("lsoa_groundsource_heatpump.geojson").json() ).features .filter((d) => !d.properties["LSOA11NM"].includes("South Cambridgeshire")) .map((d) => { const code = d.properties.LSOA11CD; const type = showTotals ? "sum" : "mean"; const gshp_totalbuildings = d.properties.total_buildings; const gshp_suitable = d.properties.suitable_buildings; const gshp_suitable_pct = d.properties.percentage_suitable; const gshp_heatpumpsize = showTotals ? d.properties["sum_heatpumpsize"] : d.properties["mean_heatpumpsize"]; const gshp_peakheatdemand = showTotals ? d.properties["sum_peakheatdemand"] : d.properties["mean_peakheatdemand"]; return { code, type, gshp_totalbuildings, gshp_suitable, gshp_suitable_pct, gshp_heatpumpsize, gshp_peakheatdemand }; }, {}) // .map((d) => [ // d.properties.LSOA11CD, // showTotals ? "sum" : "mean", // showTotals // ? d.properties["Recommended Heat Pump Size [kW]_sum"] // : d.properties["Recommended Heat Pump Size [kW]_mean"] // ])32:T9eb,insulationPotential = ( await FileAttachment("lsoa_insulated_potentials@1.geojson").json() ).features .filter((d) => !d.properties["LSOA11NM"].includes("South Cambridgeshire")) .map((d) => { const code = d.properties.LSOA11CD; const type = showTotals ? "sum" : "percentage"; const insulation_totalbuildings = d.properties.total_buildings; const insulation_insulatedbuildings = showTotals ? d.properties.insulated_cavitywall + d.properties.insulated_externalwall + d.properties.insulated_roof + d.properties.insulated_underfloor : Math.round( ((d.properties.insulated_cavitywall / insulation_totalbuildings + d.properties.insulated_externalwall / insulation_totalbuildings + d.properties.insulated_roof / insulation_totalbuildings + d.properties.insulated_underfloor / insulation_totalbuildings) / 4) * 100, 2 ); const insulation_epcABCD = showTotals ? d.properties.count_EPC_Rating_A + d.properties.count_EPC_Rating_B + d.properties.count_EPC_Rating_C + d.properties.count_EPC_Rating_D : Math.round( ((d.properties.count_EPC_Rating_A / insulation_totalbuildings + d.properties.count_EPC_Rating_B / insulation_totalbuildings + d.properties.count_EPC_Rating_C / insulation_totalbuildings + d.properties.count_EPC_Rating_D / insulation_totalbuildings) / 8) * 100, 2 ); const insulation_epcEFGUnknown = showTotals ? d.properties.count_EPC_Rating_E + d.properties.count_EPC_Rating_F + d.properties.count_EPC_Rating_G + d.properties.count_EPC_Rating_Unknown : Math.round( ((d.properties.count_EPC_Rating_E / insulation_totalbuildings + d.properties.count_EPC_Rating_F / insulation_totalbuildings + d.properties.count_EPC_Rating_G / insulation_totalbuildings + d.properties.count_EPC_Rating_Unknown / insulation_totalbuildings) / 8) * 100, 2 ); return { code, type, insulation_totalbuildings, insulation_insulatedbuildings, insulation_epcABCD, insulation_epcEFGUnknown }; }, {}) // .map((d) => [ // d.properties.LSOA21CD, // d.properties.LSOA21NM, // showTotals ? "sum" : "mean", // showTotals // ? d.properties["Wall Area [m^2]_sum"] // : d.properties["Wall Area [m^2]_mean"] // ])33:T45f,// Join a geojson with list of objects having an identifier (id) column function attrJoinGeoJSON(geojson, listsOfObjects) { const mergedFeatures = []; const seenFeatureCodes = new Set(); // Track unique ids turf.featureEach(geojson, (feature) => { const code = feature.properties.LSOA11CD; if (!seenFeatureCodes.has(code)) { // Only process each code once seenFeatureCodes.add(code); const matchingObjects = []; // Iterate through lists and collect all matching objects listsOfObjects.forEach((list) => { const matchingObject = list.find((obj) => obj.code === code); if (matchingObject) { matchingObjects.push(matchingObject); } }); // Merge properties from all matching objects matchingObjects.forEach((matchingObject) => { Object.assign(feature.properties, matchingObject); }); mergedFeatures.push(feature); // console.log(mergedFeatures); } }); // Create new GeoJSON with merged features const mergedGeoJSON = turf.featureCollection(mergedFeatures); return mergedGeoJSON; }34:T44c,// if we know the id column beforehand function joinByCode(listsOfDictionaries, excludedCode = -123456) { const allUPRNs = new Set(); const allColumns = new Set(); const mergedDictionaries = {}; // Collect all UPRNs and columns listsOfDictionaries.forEach((list) => { list.forEach((dict) => { const code = dict.code; if (code !== excludedCode) { allUPRNs.add(code); Object.keys(dict).forEach((key) => allColumns.add(key)); } }); }); allColumns.delete("type"); // Create empty objects with all columns for each UPRN allUPRNs.forEach((code) => { mergedDictionaries[code] = {}; allColumns.forEach((column) => (mergedDictionaries[code][column] = null)); }); // Merge properties from each list, filling missing values listsOfDictionaries.forEach((list) => { list.forEach((dict) => { const code = dict.code; if (code !== excludedCode) { for (const column in dict) { mergedDictionaries[code][column] = dict[column]; } } }); }); return Object.values(mergedDictionaries); }35:[{"id":"85f8da80b48c4be442b1cec9c31c23be527d96384724fd51bafa6329e46ec17b0fd95f89dbe268f0d31d171860ab16cc58082ef9450ab6d988e91427ebf9f45a","href":"https://static.observableusercontent.com/files/85f8da80b48c4be442b1cec9c31c23be527d96384724fd51bafa6329e46ec17b0fd95f89dbe268f0d31d171860ab16cc58082ef9450ab6d988e91427ebf9f45a","name":"lsoa_airsource_heatpump.geojson","type":"application/geo+json","lastModified":1706189100928.363,"size":1887492},{"id":"413ba7663db8ff9e2ae51833a555052e2c84e5c5b72e3b878ccca932f7562eda0009111f4e12ef84b7eb17bc1c6bae95fbc2200b8ab0879a7eb7006b3dea9b23","href":"https://static.observableusercontent.com/files/413ba7663db8ff9e2ae51833a555052e2c84e5c5b72e3b878ccca932f7562eda0009111f4e12ef84b7eb17bc1c6bae95fbc2200b8ab0879a7eb7006b3dea9b23","name":"lsoa_groundsource_heatpump.geojson","type":"application/geo+json","lastModified":1706189101256.049,"size":1887549},{"id":"cbabb42746b12634b6cc9f81991522f68c9fe670ff2c0504dba672978e385c7f31f06980659d052b2ba8b1676d98eed5ea06226738fa8d8d9384c5ea5aadecd5","href":"https://static.observableusercontent.com/files/cbabb42746b12634b6cc9f81991522f68c9fe670ff2c0504dba672978e385c7f31f06980659d052b2ba8b1676d98eed5ea06226738fa8d8d9384c5ea5aadecd5","name":"lsoa_insulated_potentials@1.geojson","type":"application/geo+json","lastModified":1706189384243.217,"size":2019346},{"id":"9e2c2afaa4d7813a052145b1151d2ba6fb48b6ebb9c2280a1f3fc990d05eb0141e06048e9bf83c5d6a1caaaad601dff8f5831151cf7825afd170e37efde96639","href":"https://static.observableusercontent.com/files/9e2c2afaa4d7813a052145b1151d2ba6fb48b6ebb9c2280a1f3fc990d05eb0141e06048e9bf83c5d6a1caaaad601dff8f5831151cf7825afd170e37efde96639","name":"cambridge_boundary.geojson","type":"application/geo+json","lastModified":1706510818041.304,"size":41847},{"id":"869a10f23fc63aaeb8612d652743d097579606bbb40c3cac3475901b178dd217675d7ebc529f9e9a3547e37165025edf16c25658fbd0974ee3f257fdbfd4d374","href":"https://static.observableusercontent.com/files/869a10f23fc63aaeb8612d652743d097579606bbb40c3cac3475901b178dd217675d7ebc529f9e9a3547e37165025edf16c25658fbd0974ee3f257fdbfd4d374","name":"lsoa_multideprivation.geojson","type":"application/geo+json","lastModified":1706259090195.229,"size":2188409},{"id":"41cb4dda437e342d3c3f1c111ecfe47ce48b9419c9e2aa359d8cc4bce372261967fc83780cf12a96c9c7040eb71f96ee294262ca458d5c0ed0ea6f4860a1e81c","href":"https://static.observableusercontent.com/files/41cb4dda437e342d3c3f1c111ecfe47ce48b9419c9e2aa359d8cc4bce372261967fc83780cf12a96c9c7040eb71f96ee294262ca458d5c0ed0ea6f4860a1e81c","name":"lsoa_rooftop_pv@2.geojson","type":"application/geo+json","lastModified":1706191194395.194,"size":1899982},{"id":"b6cabb770d416d3e9c2cedce80e0aab1cfd058a51efeddadbb7b3bc74624edce2a6d7c472297638c1996f20bf521c177a28d78b83923f9a9ea622f0bf435ff0c","href":"https://static.observableusercontent.com/files/b6cabb770d416d3e9c2cedce80e0aab1cfd058a51efeddadbb7b3bc74624edce2a6d7c472297638c1996f20bf521c177a28d78b83923f9a9ea622f0bf435ff0c","name":"grids_as_csv.csv","type":"text/csv","lastModified":1706266916992.475,"size":424720},{"id":"2bf4e91c4fef8c94866ecda3338ca1be2ed740a79dd2830d8010e7c972b4f9470d335710425b6554e064c190c35e50623ef7aad48ec3a085f528b8d87d6503ab","href":"https://static.observableusercontent.com/files/2bf4e91c4fef8c94866ecda3338ca1be2ed740a79dd2830d8010e7c972b4f9470d335710425b6554e064c190c35e50623ef7aad48ec3a085f528b8d87d6503ab","name":"LSOA_2011_fuel_poverty.geojson","type":"application/geo+json","lastModified":1706113644755.528,"size":2107940},{"id":"e4305e849cde35e8ac95ef4b79284fb8b4646858b38d79c8498d99b496d607fb12b5e5764c0d678c4b9bfda8511c5636a5184fc465af6f29f710370a149f0ceb","href":"https://static.observableusercontent.com/files/e4305e849cde35e8ac95ef4b79284fb8b4646858b38d79c8498d99b496d607fb12b5e5764c0d678c4b9bfda8511c5636a5184fc465af6f29f710370a149f0ceb","name":"LSOA_2011_domestic_electricity.geojson","type":"application/geo+json","lastModified":1706113645291.132,"size":2119861},{"id":"039d299bf8ec5af5627cd859ae7b102aba2212f8ecc9de37b4551258b6d63a0f8138ad0a95efeb3585796f1be4a0801b03f165310cf0641e37765d116473e6c3","href":"https://static.observableusercontent.com/files/039d299bf8ec5af5627cd859ae7b102aba2212f8ecc9de37b4551258b6d63a0f8138ad0a95efeb3585796f1be4a0801b03f165310cf0641e37765d116473e6c3","name":"LSOA_2011_domestic_gas.geojson","type":"application/geo+json","lastModified":1706113645391.526,"size":2124193},{"id":"10cdc848a92efd7cecfab266bbeb2ba663e420a3bfb04ade69fbab2d6213dcec7517a7dd90b675ff9f7663777f574ea5ce5220fda47123ec65738b651865f487","href":"https://static.observableusercontent.com/files/10cdc848a92efd7cecfab266bbeb2ba663e420a3bfb04ade69fbab2d6213dcec7517a7dd90b675ff9f7663777f574ea5ce5220fda47123ec65738b651865f487","name":"cambridge_lsoa_boundary_2011.geojson","type":"application/geo+json","lastModified":1706114021931.69,"size":2095294}] 2d:{"title":"Cambridge LSOA Level Data Processing","cells":[{"id":0,"value":"# Cambridge LSOA Level Data Processing","pinned":false,"hidden":false,"mode":"md"},{"id":912,"value":"This notebook documents data processing for visualising Multicriteria Decision Analysis (MCA) of decarbonisation data in Cambridge, UK. The visualisation aim to act as a tight coupling of geovisualisation and MCA by facilitating parameter weighting and discovery through [gridded-glyphmaps](https://observablehq.com/@aidans/slippy-glyphmaps). This notebook in particular elaborates on what the data are, and how it leads to the weighting process used in subsequent visualisation in gridded-glyphmap. ","pinned":false,"hidden":false,"mode":"md"},{"id":9,"value":"## Data","pinned":false,"hidden":false,"mode":"md"},{"id":349,"value":"For this Cambridge example, we use building level data, aggregated into each LSOA (Lower Layer Super Output Areas). Each dataset models the potentials for decarbonization in each buildings in the LSOA. We use both summarised values and normalised mean values of each select attributes, which can be selected using the checkbox below: ","pinned":false,"hidden":false,"mode":"md"},{"id":89,"value":"viewof showTotals = Inputs.toggle({ label: \"Show Totals?\", value: false })","pinned":false,"hidden":false,"mode":"ojs"},{"id":701,"value":"viewof table = Inputs.table(combined_variables)","pinned":false,"hidden":false,"mode":"ojs"},{"id":595,"value":"viewof attrChoropleth = Inputs.select(\n Object.keys(lsoaData.features[11].properties),\n {\n value: \"pv_suitable\",\n label: \"Select attribute on map: \"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":597,"value":"quickmap(lsoaData, {\n attrib: attrChoropleth,\n title: \"Cambridge LSOA Data: \" + attrChoropleth,\n height: 600,\n fullscreen: false,\n choropleth: {\n numClass: 6,\n method: \"quantile\",\n colorScheme: \"OrRd\"\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":281,"value":"### Photovoltaic Potentials","pinned":false,"hidden":false,"mode":"md"},{"id":355,"value":"This dataset represent the suitability of each property for rooftop PV installation, aggregated to LSOA level. The higher the number for this potential means the higher the potential for decarbonisation.","pinned":false,"hidden":false,"mode":"md"},{"id":363,"value":"html`${toHtmlTable(pvPotential, pvDescriptions)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":403,"value":"We choose the **pv_annualgen** to represent the potential of PV installation within each LSOA. This represent how much electricity can be generated from all buildings in each LSOA for every year. This value contributes linearly to decarbonisation strategy, as higher annual generation means more efficient energy produced. ","pinned":false,"hidden":false,"mode":"md"},{"id":334,"value":"### Air-source and Ground-source Heat Pump","pinned":false,"hidden":false,"mode":"md"},{"id":407,"value":"The suitability of a property for an air source or ground source heat pump, aggregated to LSOA level. Heat pumps extract heat from the air or ground and transfer it to the houses, using significantly less energy than traditional boilers. More properties suitable for Air Source or Ground source heatpumps means higher potential for decarbonisation.","pinned":false,"hidden":false,"mode":"md"},{"id":418,"value":"The description for both air source heat pump (_ashp_) and ground source heat pump (_gshp_) is as follow:","pinned":false,"hidden":false,"mode":"md"},{"id":894,"value":"html`${toHtmlTable(airPumpPotential, ashpDescriptions)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":112,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":133,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":431,"value":"In both cases, we use the **ashp/ghsp_suitable_pct** to represent the heat pump installation potential in each LSOA. This represents how many buildings in each LSOA that are categorised as suitable for airsource/groundsource heatpump. This value is linear to decarbonisation potential, as higher number of suitable buildings for airsource/groundsource heatpump means more energy efficient houses. ","pinned":false,"hidden":false,"mode":"md"},{"id":337,"value":"### Insulation Potentials","pinned":false,"hidden":false,"mode":"md"},{"id":434,"value":"The energy efficiency status and insulation improvement opportunities for domestic properties based on an assessment of existing building fabric.","pinned":false,"hidden":false,"mode":"md"},{"id":450,"value":"html`${toHtmlTable(insulationPotential, insulationDescriptions)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":454,"value":"We pick the **insulation_epcABCD** variable to represent the potential of decarbonisation. Lower EPC ratings (E, F, G or unknown) means higher energy bills and a greater risk of fuel poverty. Higher EPC rating means more houses with better energy savings.","pinned":false,"hidden":false,"mode":"md"},{"id":339,"value":"### Electricity and Gas Demand","pinned":false,"hidden":false,"mode":"md"},{"id":456,"value":"Annual gas and electricity consumption in a year.","pinned":false,"hidden":false,"mode":"md"},{"id":465,"value":"html`${toHtmlTable(gasUse, gasDescription)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":142,"value":"electricityUse = (\n await FileAttachment(\"LSOA_2011_domestic_electricity.geojson\").json()\n).features\n .filter((d) => d.properties[\"Local authority\"] === \"Cambridge\")\n .map((d) => {\n const code = d.properties[\"LSOA code\"];\n const type = showTotals ? \"sum\" : \"mean\";\n const electricity_use = showTotals\n ? d.properties[\"Total \\nconsumption\\n(kWh)\"]\n : d.properties[\"Mean \\nconsumption\\n(kWh per meter)\"];\n return { code, type, electricity_use };\n }, {})\n// .map((d) => [\n// d.properties[\"LSOA code\"],\n// showTotals ? \"sum\" : \"mean\",\n// showTotals\n// ? d.properties[\"Total \\nconsumption\\n(kWh)\"]\n// : d.properties[\"Mean \\nconsumption\\n(kWh per meter)\"]\n// ])","pinned":false,"hidden":false,"mode":"ojs"},{"id":167,"value":"gasUse = (\n await FileAttachment(\"LSOA_2011_domestic_gas.geojson\").json()\n).features\n .filter((d) => d.properties[\"Local authority\"] === \"Cambridge\")\n .map((d) => {\n const code = d.properties[\"LSOA code\"];\n const type = showTotals ? \"sum\" : \"mean\";\n const gas_use = showTotals\n ? d.properties[\"Total \\nconsumption\\n(kWh)\"]\n : d.properties[\"Mean \\nconsumption\\n(kWh per meter)\"];\n return { code, type, gas_use };\n }, {})\n// .map((d) => [\n// d.properties[\"LSOA code\"],\n// showTotals ? \"sum\" : \"mean\",\n// showTotals\n// ? d.properties[\"Total \\nconsumption\\n(kWh)\"]\n// : d.properties[\"Mean \\nconsumption\\n(kWh per meter)\"]\n// ])","pinned":false,"hidden":false,"mode":"ojs"},{"id":468,"value":"We use total or mean gas and electricity consumptions (**gasUse** and **electricityUse**) to indicate decarbonisation potentials.","pinned":false,"hidden":false,"mode":"md"},{"id":344,"value":"### Fuel Poverty","pinned":false,"hidden":false,"mode":"md"},{"id":476,"value":"Estimates of the level of the inability to afford to the property adequately heated.","pinned":false,"hidden":false,"mode":"md"},{"id":483,"value":"html`${toHtmlTable(fuelPoverty, fuelDescription)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":222,"value":"fuelPoverty = (\n await FileAttachment(\"LSOA_2011_fuel_poverty.geojson\").json()\n).features\n .filter((d) => !d.properties[\"LA Name\"].includes(\"South Cambridgeshire\"))\n .map((d) => {\n const code = d.properties[\"LSOA Code\"];\n const type = \"mean\";\n const fuel_poverty =\n 100 - d.properties[\"Proportion of households fuel poor (%)\"]; // normalise the value so it's proportional to decarbonisation strategy\n return { code, type, fuel_poverty };\n }, {})","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"The Proportion of households fuel poor (%) (**fuelPoverty**) depicts the percentage of fuel poor households, which negatively affects decarbonisation potentials. Therefore, we calculate the inverse (100-percentage) for our final calculation.","pinned":false,"hidden":false,"mode":"md"},{"id":489,"value":"### Deprivations","pinned":false,"hidden":false,"mode":"md"},{"id":501,"value":"The English indices of deprivation measure relative deprivation in small areas in England called lower-layer super output areas (LSOAs). The index of multiple deprivation is the most widely used of these indices. Data Source is from Ministry of Housing, Communities & Local Government. \n\nWe picked the Index of Multiple Deprivation (IMD) Decile (**depriv_index**) which proportionally affect decarbonisation potential (meaning higher score means less deprived, hence more potential for decarbonisation). Deciles are calculated by ranking the 32,844 small areas in England from most deprived to least deprived and dividing them into 10 equal groups. These range from the most deprived 10 per cent of small areas nationally to the least deprived 10 per cent of small areas nationally. In this case, the value closer to '**1**' represents more deprived.","pinned":false,"hidden":false,"mode":"md"},{"id":723,"value":"html`${toHtmlTable(deprivations, deprivDescription)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":492,"value":"deprivations = (\n await FileAttachment(\"lsoa_multideprivation.geojson\").json()\n).features\n .filter(\n (d) =>\n !d.properties[\"Local Authority District name (2019)\"].includes(\n \"South Cambridgeshire\"\n )\n )\n .map((d) => {\n const code = d.properties[\"LSOA code (2011)\"];\n const type = \"sum\";\n // const depriv_totalbuildings = d.properties.total_buildings;\n const depriv_index =\n d.properties[\n \"Index of Multiple Deprivation (IMD) Decile (where 1 is most dep\"\n ];\n\n return {\n code,\n type,\n depriv_index\n };\n }, {})","pinned":false,"hidden":false,"mode":"ojs"},{"id":910,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":506,"value":"## Select and Normalise Data","pinned":false,"hidden":false,"mode":"md"},{"id":721,"value":"Based on the data above, we aim to have two versions of the data: merged at LSOA level, and discretised into grids for gridded-glyphmap visualisation. The following code performs the tasks and returns both LSOA boundaries with the dataset and 100m gridded data.","pinned":false,"hidden":false,"mode":"md"},{"id":916,"value":"### Data Selection and Normalisation","pinned":false,"hidden":false,"mode":"md"},{"id":731,"value":"This function merges all the data above:","pinned":false,"hidden":false,"mode":"md"},{"id":798,"value":"combined_variables = mergeOnColumn(\n [\n pvPotential,\n airPumpPotential,\n groundPumpPotential,\n insulationPotential,\n electricityUse,\n gasUse,\n fuelPoverty,\n deprivations\n ],\n \"code\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":733,"value":"We select only parameters that we need, as elaborated above.","pinned":false,"hidden":false,"mode":"md"},{"id":735,"value":"selected_parameters = [\n // \"code\",\n \"pv_annualgen\", //\tTotal or mean expected annual electricity generation of the PV array of all buildings in the LSOA\n \"ashp_suitable_pct\", // Total or mean modelled peak building heat demand of the property, aggregated to LSOA level\n \"gshp_suitable_pct\", // Total or mean modelled peak building heat demand of the property, aggregated to LSOA level\n \"insulation_epcABCD\", // Total number or percentage of buildings within the LSOA with EPC rating as A, B, C or D\n \"electricity_use\", // Total or mean consumption of electricity (kWh per meter) in given period of time\n \"gas_use\", // Total or mean consumption of gas in given period of time\n \"fuel_poverty\", // Proportion of households fuel poor (%) within the LSOA\n \"depriv_index\" // Index of Multiple Deprivation (IMD) Decile\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":753,"value":"selected_data = _.map(combined_variables, (obj) => {\n return _.pick(obj, [\"code\", ...selected_parameters]);\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":851,"value":"selected_data","pinned":false,"hidden":false,"mode":"ojs"},{"id":789,"value":"Next, we need to normalise the data to be used in the visualisation. We normalised the data based on the actual data range, or in some cases, based on a custom range (e.g., the deprivation index should be between 1 to 10). ","pinned":false,"hidden":false,"mode":"md"},{"id":873,"value":"normalized_data = normalizeData(selected_data, selected_parameters, {\n depriv_index: [0, 10] // custom range for deprivation index\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":952,"value":"normalized_data","pinned":false,"hidden":false,"mode":"ojs"},{"id":914,"value":"### Gridded and LSOA level data","pinned":false,"hidden":false,"mode":"md"},{"id":828,"value":"For choropleth maps, we merged the normalised data to LSOA geographic boundary. We can use this for futher computation with LSOA as the enumeration unit.","pinned":false,"hidden":false,"mode":"md"},{"id":508,"value":"lsoaData = turf.featureCollection(\n attrJoinGeoJSON(cambridge_lsoa_boundary_2011, [\n normalized_data\n ]).features.filter(\n (d) => !d.properties[\"LSOA11NM\"].includes(\"South Cambridgeshire\")\n )\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1074,"value":"","pinned":true,"hidden":false,"mode":"sql","output":"","database":"null"},{"id":807,"value":"We performed spatial join on the data to the predefined grid based on LSOA Code. The grids was created with 100 m intervals across cambridge. This variable can be used in gridded-glyphmap library for gridded data visualisation.","pinned":false,"hidden":false,"mode":"md"},{"id":968,"value":"griddedGeoJSON = spatialJoin(lsoaData, grids_100m)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1037,"value":"griddedData = {\n const gridded = [];\n griddedGeoJSON.features.map((d) => {\n gridded.push(d.properties);\n });\n return gridded;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":938,"value":"### Using the Data","pinned":false,"hidden":false,"mode":"md"},{"id":945,"value":"To use the data:\n\n`import {gridedData, lsoaData, griddedGeoJSON, selected_parameters } from \"0e9c91c84c2d055a\"` \n","pinned":false,"hidden":false,"mode":"md"},{"id":918,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":7,"value":"## Dependencies","pinned":false,"hidden":false,"mode":"md"},{"id":604,"value":"### Boundary and Grid Data","pinned":false,"hidden":false,"mode":"md"},{"id":823,"value":"Geographic LSOA boundary of Cambridge ","pinned":false,"hidden":false,"mode":"md"},{"id":160,"value":"cambridge_lsoa_boundary_2011 = turf.featureCollection(\n await FileAttachment(\"cambridge_lsoa_boundary_2011.geojson\").json()\n).features\n// .filter((d) => !d.properties[\"LSOA11NM\"].includes(\"South Cambridgeshire\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":825,"value":"Gridded data of Cambridge LSOA Generated using TurfJS with 100 m intervals.","pinned":false,"hidden":false,"mode":"md"},{"id":986,"value":"grids_100m = {\n var extent = turf.bbox(cambridge_boundary);\n var cellSide = 100;\n var options = {\n units: \"meters\",\n mask: turf.multiPolygon(cambridge_boundary.features[0].geometry.coordinates)\n };\n\n var grid = turf.pointGrid(extent, cellSide, options);\n\n // Add lat and long attributes to each point\n turf.featureEach(grid, (feature) => {\n const coords = feature.geometry.coordinates;\n feature.properties.lat = coords[1];\n feature.properties.long = coords[0];\n });\n\n return grid;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":399,"value":"### Libraries","pinned":false,"hidden":false,"mode":"md"},{"id":600,"value":"import { quickmap } from \"@danylaksono/quickmap\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":83,"value":"import {\n // deprivation,\n socioEconomic,\n energyUseTimeseries\n} from \"a6e085c4aeddfd86\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":988,"value":"cambridge_boundary = FileAttachment(\"cambridge_boundary.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":977,"value":"// spatial join by location using turf's `within`\nfunction spatialJoin(polygons, points) {\n const joinedFeatures = [];\n\n turf.featureEach(polygons, (polygon) => {\n const pointsWithin = turf.pointsWithinPolygon(points, polygon);\n pointsWithin.features.forEach((point) => {\n Object.assign(point.properties, polygon.properties);\n });\n\n joinedFeatures.push(...pointsWithin.features);\n });\n\n return turf.featureCollection(joinedFeatures);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":870,"value":"function normalizeData(data, keysToNormalize, ranges) {\n // Default to using data extent if no custom range\n const defaultRanges = keysToNormalize.reduce((acc, key) => {\n acc[key] = d3.extent(data, (d) => d[key]);\n return acc;\n }, {});\n\n // Use custom ranges if provided\n const scales = keysToNormalize.map((key) => {\n const domain = ranges[key] || defaultRanges[key];\n return d3.scaleLinear().domain(domain).range([0, 100]);\n });\n\n // Normalize the data using the scales\n const normalizedData = data.map((d) => ({\n ...d, // Keep original properties\n ...keysToNormalize.reduce(\n (acc, key) => ({\n ...acc,\n [key]: parseFloat(\n scales[keysToNormalize.indexOf(key)](d[key]).toFixed(2)\n )\n }),\n {}\n )\n }));\n\n return normalizedData;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":514,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":653,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":795,"value":"function mergeOnColumn(datasets, columnName) {\n let result = {};\n\n datasets.forEach((dataset) => {\n dataset.forEach((row) => {\n let id = row[columnName];\n if (!result[id]) {\n result[id] = {};\n }\n Object.keys(row).forEach((key) => {\n result[id][key] = row[key];\n });\n });\n });\n\n return Object.values(result);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"turf = require(\"@turf/turf@latest\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":396,"value":"### Helper Descriptions","pinned":false,"hidden":false,"mode":"md"},{"id":368,"value":"function toHtmlTable(data, descriptions) {\n const tableHead =\n \"NameDescriptions\";\n\n const keys = Object.keys(data[0]);\n const tableBody =\n \"\" +\n keys\n .map((key, i) => `${key}${descriptions[i]}`)\n .join(\"\") +\n \"\";\n\n return \"\" + tableHead + tableBody + \"

\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":372,"value":"pvDescriptions = [\n \"LSOA code 2011\",\n \"Sum or Mean\",\n \"Total number of buildings within the LSOA\",\n \"Total number of buildings within the LSOA which are suitable for rooftop PV installation\",\n \"Percentage of buildings suitable for PV Installation in the LSOA\",\n \"Total or mean expected annual electricity generation of the PV array of all buildings in the LSOA\",\n \"Total or mean rooftop area suitable for PV Installation in the LSOA\",\n \"Total or mean maximum array size of all buildings in the LSOA\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":421,"value":"ashpDescriptions = [\n \"LSOA code 2011\",\n \"Sum or Mean\",\n \"Total number of buildings within the LSOA\",\n \"Total number of buildings within the LSOA which are suitable for air source or ground source heat pump installation\",\n \"Percentage of buildings suitable for air source/ground source heat pump installation in the LSOA\",\n \"Total or mean recommended heat pump size, aggregated to LSOA level\",\n \"Total or mean modelled peak building heat demand of the property, aggregated to LSOA level\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":436,"value":"insulationDescriptions = [\n \"LSOA code 2011\",\n \"Sum or Percentage\",\n \"Total number of buildings within the LSOA\",\n \"Total number or percentage of buildings within the LSOA which are insulated\",\n \"Total number or percentage of buildings within the LSOA with EPC rating as A, B, C or D\",\n \"Total number or percentage of buildings within the LSOA with EPC rating as E, F, G or Unknown\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":462,"value":"gasDescription = [\n \"LSOA code 2011\",\n \"Sum or Percentage\",\n \"Total or mean consumption (kWh per meter)\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":479,"value":"fuelDescription = [\n \"LSOA code 2011\",\n \"Sum or Percentage\",\n \"Proportion of households fuel poor (%)\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":727,"value":"deprivDescription = [\n \"LSOA code 2011\",\n \"Sum or Percentage\",\n \"Index of Multiple Deprivation (IMD) Decile\"\n]","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}