The avatar for @danylaksono
Dany Laksono
Geospatial Visual Analytics
Public
Edited
Jan 23, 2024
Importers
19
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// Comments explain the parameters and sub-parameters in Banegas (2023)
// Geographic > Proximity to workspaces
accessibilities_variables = [
{
layer: "acc_employment",
group: "overview",
weight: 1
},
{
layer: "acc_gp",
group: "overview",
weight: 1
},
{
layer: "acc_hospitals",
group: "overview",
weight: 1
},
{
layer: "acc_school",
group: "overview",
weight: 1
},
{
layer: "acc_supermarket",
group: "overview",
weight: 1
},
{
layer: "acc_urbancentre",
group: "overview",
weight: 1
}
]
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gas_electricity_variables = [
{
layer: "gas_mean_consumption",
group: "overview",
weight: 1
},
{
layer: "elec_mean_consumption",
group: "overview",
weight: 1
}
]
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// Comments explain the parameters and sub-parameters in Banegas (2023)
multideprivation_variables = [
{
layer: "simd2020_Total_population", // Socio-demographic > Total population
group: "overview",
weight: 1
},
{
layer: "simd2020_income_count", // Economic > Household income
group: "overview",
weight: 1
},
{
layer: "simd2020_employment_rate", //Socio-demographic > Employment
group: "overview",
weight: 1
},
{
layer: "simd2020_University", // Socio-demographic > educational level
group: "overview",
weight: 1
},
// {
// layer: "simd2020_drive_primary", // Geographic > Proximity to libraries/schools/postoffice
// group: "overview",
// weight: 1
// },
// {
// layer: "simd2020_drive_petrol", // Geographic > Proximity to petrol station
// group: "overview",
// weight: 1
// },
// {
// layer: "simd2020_drive_retail", // Geographic > Proximity to retail
// group: "overview",
// weight: 1
// },
// {
// layer: "simd2020_PT_retail", // Geographic > Proximity to retail
// group: "overview",
// weight: 1
// }
]
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transformer_variables = [
{
layer: "transformer_loading",
group: "aggregated",
weight: 1
},
{
layer: "transformer_spareload",
group: "aggregated",
weight: 1
}
]
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// Comments explain the parameters and sub-parameters in Banegas (2023)
conservationarea_variables = [
{
layer: "conservation_area",
group: "aggregated",
weight: 1
}
]
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Plot.plot({
projection: "identity", // Set the projection
marks: [Plot.geo(parking_spaces, { stroke: "black" })]
})
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// {
// await visibility();
// return quickmap(parking_spaces, {
// attrib: "parkable_area",
// heatmap: { radius: 60, blur: 35 }
// });
// }
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parking_spaces_csv = FileAttachment("parking_spaces.csv")
.csv({ typed: true })
.then((d) => {
return d.filter((value) => value["parkable_area"] !== -9999);
})
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// Comments explain the parameters and sub-parameters in Banegas (2023)
parkingspaces_variables = [
{
layer: "parking_sp",
group: "detailed",
weight: 1
},
{
layer: "parkable_area",
group: "detailed",
weight: 1
}
]
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// {
// await visibility();
// return quickmap(existing_evcs);
// }
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existing_evcs_csv = FileAttachment("existing_evcs.csv").csv({typed: true})
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existingevcs_variables = [
{
layer: "access24Hours",
group: "detailed",
weight: 1
},
{
layer: "connector1RatedOutputKW",
group: "detailed",
weight: 1
}
]
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// {
// await visibility();
// return quickmap(steetlighting);
// }
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streetlight_variable = [
{
layer: "pole_no",
group: "detailed",
weight: 1
}
]
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// {
// await visibility();
// let data = geo.coords2geo(epc_surveys_csv, {
// lat: "lat",
// lon: "long"
// });
// return quickmap(data);
// }
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epc_surveys_csv = FileAttachment("epc_surveys.csv")
.csv({ typed: true })
.then((d) => {
return d.map((row) => ({
long: row["LONGITUDE"],
lat: row["LATITUDE"],
prime_energy_indicator: row["Primary Energy Indicator (kWh/m²/year)"],
potential_epc: row["Potential Energy Efficiency Rating"],
co2_emission: row["CO2 Emissions Current Per Floor Area (kg.CO2/m²/yr)"]
}));
})
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epc_variables = [
{
layer: "prime_energy_indicator",
group: "detailed",
weight: 1
},
{
layer: "potential_epc",
group: "detailed",
weight: 1
},
{
layer: "co2_emission",
group: "detailed",
weight: 1
}
]
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variables = {
let variables = [].concat(
accessibilities_variables,
gas_electricity_variables,
multideprivation_variables,
transformer_variables,
conservationarea_variables,
parkingspaces_variables,
existingevcs_variables,
streetlight_variable,
epc_variables
);
return variables;
}
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all_data = {
let alldata = [].concat(
accessibilities_csv,
gas_electricity_csv,
multideprivations_csv,
transformer_loading_csv,
conservation_area_csv,
parking_spaces_csv,
existing_evcs_csv,
streetlighting_csv,
epc_surveys_csv
);
return alldata;
}
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accessibilities = FileAttachment("accessibilities.geojson").json()
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conservation_area = FileAttachment("conservation_area.geojson").json()
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existing_evcs = FileAttachment("existing_evcs.geojson").json()
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gas_electricity_consumption = FileAttachment(
"gas_electricity_consumption.geojson"
).json()
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multideprivations = FileAttachment("multideprivations.geojson").json()
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parking_spaces = FileAttachment("parking_spaces.geojson").json()
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steetlighting = FileAttachment("streetlighting.geojson").json()
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transformer_loading = FileAttachment("transformer_loading.geojson").json()
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// https://en.wikipedia.org/wiki/Correlation#Sample_correlation_coefficient
function corr(x, y) {
const n = x.length;
if (y.length !== n)
throw new Error("The two columns must have the same length.");
const x_ = d3.mean(x);
const y_ = d3.mean(y);
const XY = d3.sum(x, (_, i) => (x[i] - x_) * (y[i] - y_));
const XX = d3.sum(x, (d) => (d - x_) ** 2);
const YY = d3.sum(y, (d) => (d - y_) ** 2);
return XY / Math.sqrt(XX * YY);
}
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// maplibregl = require("maplibre-gl")
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heatLayer = require("leaflet.heat").catch(() => L.heatLayer)
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providers = (L, require("leaflet-providers").catch(() => L.tileLayer.provider))
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<link href='https://watergis.github.io/maplibre-gl-legend/maplibre-gl-legend.css' rel='stylesheet' />
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<link href="https://unpkg.com/maplibre-gl@2.1.9/dist/maplibre-gl.css" rel="stylesheet" />
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