2f:T404,The analysis of Electric Vehicle Charging Station (EVCS) locations is a crucial factor in the successful adoption of electric vehicles in a given area. According to literature reviews, green infrastructure planning analyses are typically conducted using either an exact method or a heuristic optimization approach. In contrast, visual analytics have been demonstrated to be an effective tool for decision-making, particularly when dealing with complex and multivariate data. The siting of EVCS is one such example: the process of selecting suitable locations for EVCS involves GIS analysis, which can become exponentially complex due to the numerous parameters involved with multivariate and multiple-scale geospatial data used in the analysis. This notebook details the data preparation and analysis for each parameter selected for the use case study in the Dundee area. The datasets included in this notebook were chosen based on three sources: use-case scenarios, literature reviews, and existing regulations on EVCS siting.30:T844,html`

Paper	Parameters	Remarks
(Kizhakkan et al., 2019) ${reference( "https://doi.org/10.1109/ITEC-India48457.2019.ITECINDIA2019-226" )}	Review paper. Different dataset, focusing on heuristics and exact (GIS) methods	Reviews of various methods with which EVCS location analysis are conducted
(Kaya et al., 2020)${reference( "https://www.sciencedirect.com/science/article/pii/S1361920919307254" )}	19 parameters, categorized by Economic, Geographical, Energy, Social and Transportation	Using AHP and GIS analysis for decision making based on PROMETHEE and VIKOR method
(Hisoglu & Comert, 2021)	Solar radiation, slope, aspect, land use/land cover, traffic volume and proximity to the road	Utilizing Multi-criteria Decision Making (MCDM) and GIS for site selection. Solar radiation is included as an analysis parameter since the paper discuss about solar-powered EVCS
(Priefer & Steiger, 2022)	Accessibility (parking space, traffic and road); proximity to users (population density, proximity to POI, educational medical and gastronomy, public transport); Environmental impact (protected areas, water bodies); power grid (proximity to transformer stations)	Using Analytical Hierarchy Process (AHP) to factorize the weight of each parameters, then model the probable location for EVCS using GIS
(Vansola et al., 2023)	OD Data for each grid. Parameters for the grids are taken from land use maps	Considers type of vehicle and mixed traffic flow, as well as types of and EVCS based on the charging speed. Divides the charging demand by grids, and calculate the suitability for EVCS in the grid
(Banegas & Mamkhezri, 2023)	Divided into 6 categories: Geographic; Energy, CS attributes, EV characteristics; Transportation, traffic, route features; Economic; Socio-demographic; Environmental	Systematic review of EVCS Siting methodologies

`31:T475,// 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 // } ]32:[{"id":"dbc80d40bc60e6f732ec2d3ac2cbb308bb6506b81eb1274e336d42a484dabc5e484cd05d9f97bcb8f54afdb259245dec6f6fb71ee9f947260f5ba48b95ad60fa","href":"https://static.observableusercontent.com/files/dbc80d40bc60e6f732ec2d3ac2cbb308bb6506b81eb1274e336d42a484dabc5e484cd05d9f97bcb8f54afdb259245dec6f6fb71ee9f947260f5ba48b95ad60fa","name":"epc_surveys.csv","type":"text/csv","lastModified":1684502971835.762,"size":42863214},{"id":"d29660d57b6b945687e8390ed0d5a559125180232b2e6bc572586bf07dc9e90b063ed4c84eb0275a0f6d0588ee6a5ebac9e7782c7766d4aaf1f7816237e4cfc4","href":"https://static.observableusercontent.com/files/d29660d57b6b945687e8390ed0d5a559125180232b2e6bc572586bf07dc9e90b063ed4c84eb0275a0f6d0588ee6a5ebac9e7782c7766d4aaf1f7816237e4cfc4","name":"multideprivation_indicators.png","type":"image/png","lastModified":1686226866248.309,"size":63711},{"id":"c9a4795077f00fffc78abf68c12f596555ed4ada62805c1ddaa2f8f98711ba492d85be16a01b6f0a7881f8651d972fdb4387e7d3a2d67821e10f82fa9499ee86","href":"https://static.observableusercontent.com/files/c9a4795077f00fffc78abf68c12f596555ed4ada62805c1ddaa2f8f98711ba492d85be16a01b6f0a7881f8651d972fdb4387e7d3a2d67821e10f82fa9499ee86","name":"kaya_etal.png","type":"image/png","lastModified":1686133884948.535,"size":125706},{"id":"54d8740e438c43d0d99a9364f56a2d9e13c45a3d774bd389af78d626aea3d6ac43f895854e9cb5b8c6e8bee00a7b06acdd91c3fe94a0c514521e5cc5c8da3d79","href":"https://static.observableusercontent.com/files/54d8740e438c43d0d99a9364f56a2d9e13c45a3d774bd389af78d626aea3d6ac43f895854e9cb5b8c6e8bee00a7b06acdd91c3fe94a0c514521e5cc5c8da3d79","name":"existing_evcs.geojson","type":"application/geo+json","lastModified":1684227087184.204,"size":14976},{"id":"f92c60e28b0efb66df84fd22c32bb88fdc439804aa7a92da205bb779a965af61d3655634373de15331641ad95971da1a279896c9688726e9616629e2ea1e4199","href":"https://static.observableusercontent.com/files/f92c60e28b0efb66df84fd22c32bb88fdc439804aa7a92da205bb779a965af61d3655634373de15331641ad95971da1a279896c9688726e9616629e2ea1e4199","name":"parking_spaces.geojson","type":"application/geo+json","lastModified":1684227099340.72,"size":17490963},{"id":"b8ac031fd6f0b1c0b0c1c8493c745cda6771d3f528ca78bbd083de01a12bf59530903a44e759b56642c56df5c129ed105fb5a09360e6deadb6473da9a90a44fd","href":"https://static.observableusercontent.com/files/b8ac031fd6f0b1c0b0c1c8493c745cda6771d3f528ca78bbd083de01a12bf59530903a44e759b56642c56df5c129ed105fb5a09360e6deadb6473da9a90a44fd","name":"streetlighting_csv.csv","type":"text/csv","lastModified":1684227066250.31,"size":953302},{"id":"1cdb35187fdd6ce245ec0b202a7bbb6fed38c556ca389068b2c7bbea7dd11c5d941c6eee15c0783a33efd317cf1a0bd875a7f2ce65595dce8eea4a01e84b08ac","href":"https://static.observableusercontent.com/files/1cdb35187fdd6ce245ec0b202a7bbb6fed38c556ca389068b2c7bbea7dd11c5d941c6eee15c0783a33efd317cf1a0bd875a7f2ce65595dce8eea4a01e84b08ac","name":"multideprivations_gridded.csv","type":"text/csv","lastModified":1684227066837.495,"size":2118850},{"id":"24c940f08ae917e72114c6918fedf4661e76b9388d75fc1a809c7185ef20e55e12be777edd4e7ba63c026349b0a476c76ea11bb14c3fd79a4a52b255092c246e","href":"https://static.observableusercontent.com/files/24c940f08ae917e72114c6918fedf4661e76b9388d75fc1a809c7185ef20e55e12be777edd4e7ba63c026349b0a476c76ea11bb14c3fd79a4a52b255092c246e","name":"parking_spaces.csv","type":"text/csv","lastModified":1684227067526.201,"size":3590637},{"id":"60cb090c3a38be9dc6c45f1c7e9dae5dce4ab7e4ab42a0c4e5a601a1064be3bb3358f9763bb2e9ddfaf5407787f4a0574aa0d176bb52b73eba093222df84424c","href":"https://static.observableusercontent.com/files/60cb090c3a38be9dc6c45f1c7e9dae5dce4ab7e4ab42a0c4e5a601a1064be3bb3358f9763bb2e9ddfaf5407787f4a0574aa0d176bb52b73eba093222df84424c","name":"conservation_area.geojson","type":"application/geo+json","lastModified":1684227088682.352,"size":356736},{"id":"662c8ba7564627443ef41b176ac0d0333bf48d885c11c53decdc51cfba4b04373d5c907883ee8593ae688a87ff8f10cbb4cf95f495157062f55993bf6f29d4c2","href":"https://static.observableusercontent.com/files/662c8ba7564627443ef41b176ac0d0333bf48d885c11c53decdc51cfba4b04373d5c907883ee8593ae688a87ff8f10cbb4cf95f495157062f55993bf6f29d4c2","name":"multideprivations.geojson","type":"application/geo+json","lastModified":1684227089919.158,"size":1177945},{"id":"db8cdfa9b84241f87431791319c9245c1f5d64956ee57df5403305885c0c316b25eff333c7acb294da10a907cfd58e63c382c45a1c98954d538be438d2a62cf6","href":"https://static.observableusercontent.com/files/db8cdfa9b84241f87431791319c9245c1f5d64956ee57df5403305885c0c316b25eff333c7acb294da10a907cfd58e63c382c45a1c98954d538be438d2a62cf6","name":"existing_evcs.csv","type":"text/csv","lastModified":1684227064203.325,"size":3406},{"id":"b984a9b3d504c49d0655ee22c562260be14e1865f027534f2ede11623e2723ef5ae620864ef350b64e721f859b30d1b27f527c0e46d589de78ea9b8d61411374","href":"https://static.observableusercontent.com/files/b984a9b3d504c49d0655ee22c562260be14e1865f027534f2ede11623e2723ef5ae620864ef350b64e721f859b30d1b27f527c0e46d589de78ea9b8d61411374","name":"gas_electricity_consumption.geojson","type":"application/geo+json","lastModified":1684227088676.835,"size":874288},{"id":"f1f8b83f897d6877142d2fce9afd74c0ceeb3f9851a09c3eb1793d6dfce1ee6f8ed7dea4c441d418242175de70755a022aab0d10dbd9cbfe6626d605ebfebdd8","href":"https://static.observableusercontent.com/files/f1f8b83f897d6877142d2fce9afd74c0ceeb3f9851a09c3eb1793d6dfce1ee6f8ed7dea4c441d418242175de70755a022aab0d10dbd9cbfe6626d605ebfebdd8","name":"transformer_loading_gridded.csv","type":"text/csv","lastModified":1684227064671.268,"size":310333},{"id":"468412de797134d39f24a68692918bad7fa581bcecf43f3f1a14dbf0c65985b036b584c020fd53b7078998ee5db112759bd4745fbdf33abb4c940fab25e68610","href":"https://static.observableusercontent.com/files/468412de797134d39f24a68692918bad7fa581bcecf43f3f1a14dbf0c65985b036b584c020fd53b7078998ee5db112759bd4745fbdf33abb4c940fab25e68610","name":"gas_electricity_gridded.csv","type":"text/csv","lastModified":1684227064305.793,"size":354442},{"id":"1fb7e0676c98121bb3490143c9f6508ab269a7c79ae4c5ff6728c62e975a5518c6d49128e3033658a230ec2fd4b65930c2101966045fec76040e9f28e128558e","href":"https://static.observableusercontent.com/files/1fb7e0676c98121bb3490143c9f6508ab269a7c79ae4c5ff6728c62e975a5518c6d49128e3033658a230ec2fd4b65930c2101966045fec76040e9f28e128558e","name":"streetlighting.geojson","type":"application/geo+json","lastModified":1684227093318.536,"size":5116915},{"id":"dfca5e191eb9bac6f4661ecb578d553ba477a0c81140a1b5d2250d4c3fb11939d46793220a4528d951887744fce6bc629ce9e68a82276e678bb2c0f7188ed632","href":"https://static.observableusercontent.com/files/dfca5e191eb9bac6f4661ecb578d553ba477a0c81140a1b5d2250d4c3fb11939d46793220a4528d951887744fce6bc629ce9e68a82276e678bb2c0f7188ed632","name":"conservation_area_gridded.csv","type":"text/csv","lastModified":1684227064752.574,"size":225086},{"id":"e852a5e41959cdb3d156ffb7584ec6bfce5a2d8f1e8d00ea39a154cd22eb266e0daab1f60b5694f28fe85738d840cd124535d32cd54f38a6af9319651ea2254a","href":"https://static.observableusercontent.com/files/e852a5e41959cdb3d156ffb7584ec6bfce5a2d8f1e8d00ea39a154cd22eb266e0daab1f60b5694f28fe85738d840cd124535d32cd54f38a6af9319651ea2254a","name":"accessibilities_gridded.csv","type":"text/csv","lastModified":1684227065219.553,"size":403615},{"id":"7ca0480944a51f1c5e424b6156d665d7e9bad00fb5bb7f716a19644ec769c706ce028143a67153f83d6b3481cc95a9336a91100ebd2dd24cca89027d850912fb","href":"https://static.observableusercontent.com/files/7ca0480944a51f1c5e424b6156d665d7e9bad00fb5bb7f716a19644ec769c706ce028143a67153f83d6b3481cc95a9336a91100ebd2dd24cca89027d850912fb","name":"transformer_loading.geojson","type":"application/geo+json","lastModified":1684227087832.846,"size":415404},{"id":"8ff5ebc38eb1411734a1378e1451765d6c59e88a33a4d167db57a97c595e8eb7f372279bb978cd4e2a824c0eb05fae3b7025f584e0e0895d61333efcb8190159","href":"https://static.observableusercontent.com/files/8ff5ebc38eb1411734a1378e1451765d6c59e88a33a4d167db57a97c595e8eb7f372279bb978cd4e2a824c0eb05fae3b7025f584e0e0895d61333efcb8190159","name":"accessibilities.geojson","type":"application/geo+json","lastModified":1684227089714.137,"size":889551}] 2d:{"title":"EVCS Analytics - Data Processing","cells":[{"id":0,"value":"# EVCS Analytics - Data Processing","pinned":false,"hidden":false,"mode":"md"},{"id":304,"value":"This notebook outlines the pre-processing of data for Electric Vehicle Charging Station (EVCS) placement in the Dundee area. The reasons for choosing each dataset are explained, as well as the steps taken to process and combine the data for visual analysis. The results are presented and discussed in subsequent notebooks. Understanding these datasets and the reasoning behind EVCS decision-making will help make informed decision about where to locate EVCS.","pinned":false,"hidden":false,"mode":"md"},{"id":315,"value":"toc()","pinned":false,"hidden":false,"mode":"ojs"},{"id":337,"value":"## Background","pinned":false,"hidden":false,"mode":"md"},{"id":339,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":591,"value":"## Identifying Parameters for EVCS Siting Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":611,"value":"### What parameters to use?","pinned":false,"hidden":false,"mode":"md"},{"id":624,"value":"Decisions on what parameters to use for EVCS Siting analysis can be broken down into three sources: 1) Reviews from previous literature discussing EVCS location analysis; 2) Regulations and rulings regarding the siting of EVCS; and 3) Use-case scenarios from clients who have the need to identify possible locations for EVCS.\n\nBased on literature studies, the analysis itself falls into two categories: heuristic and exact methods (GIS analysis). Both methodologies employ different sets of parameters to support decision-making. This notebook will look further into the latter.","pinned":false,"hidden":false,"mode":"md"},{"id":613,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":629,"value":"md`Banegas and Mamkhezri (2023)${reference(\n \"https://doi.org/10.1007/s11356-023-27383-6\"\n)} conducted a systematic reviews on these parameters, which also aligns with Kaya et. al. (2020)${reference(\n \"https:doi.org/10.1109/ITEC-India48457.2019.ITECINDIA2019-226\"\n)} as shown below. These parameters depict possible locations that can be used to place an EV charging site. \n\n${await FileAttachment(\"kaya_etal.png\").image()}\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":774,"value":"md`Other references for selecting parameters of EVCS location are based on existing regulations and guidelines, such as this ${reference(\n \"https://www.enwl.co.uk/get-connected/apply-for-a-new-connection/electric-vehicles2/single-ev/\"\n)}, this ${reference(\n \"https://energysavingtrust.org.uk/wp-content/uploads/2020/10/Local-Authority-Guidance-Positioning-chargepoints.pdf\"\n)}, this ${reference(\n \"https://www.london.gov.uk/sites/default/files/the_london_plan_2021.pdf\"\n)} and this ${reference(\n \"https://www.oref.co.uk/wp-content/uploads/2016/07/20160726-Charging-Infrastructure-Design-Guide-V1.3.3.pdf\"\n)}.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":609,"value":"### Categorizing the data","pinned":false,"hidden":false,"mode":"md"},{"id":7,"value":"## Data Exploration","pinned":false,"hidden":false,"mode":"md"},{"id":19,"value":"### All datasets","pinned":false,"hidden":false,"mode":"md"},{"id":23,"value":"### General Overview/LSOA Level Datasets","pinned":false,"hidden":false,"mode":"md"},{"id":45,"value":"The datasets below are gridded data in LSOA level in Dundee City that are aggregated to 100 m grids. Descriptions for each dataset are explained in the following cells:","pinned":false,"hidden":false,"mode":"md"},{"id":643,"value":"#### Accessibilities","pinned":false,"hidden":false,"mode":"md"},{"id":650,"value":"md`Accessibilities refer to the ease of reaching opportunities such as jobs, services, and activities. Although different from mobility, which depicts how many activities and movements there are in the area, accessibilities give a global picture of the potential for movement. Hence, it can be a good indication of how likely the residents are to use EV charging sites if they are available in the area.\n\nThe dataset for accessibilities is obtained from Urban Big Data Centre (UBDC) ${reference(\n \"http://ubdc.gla.ac.uk/dataset/public-transport-accessibility-indicators-2022\"\n)} and pre-processed to depict only the accessibility that is available for 15 minutes driving from each LSOA.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1167,"value":"viewof acc_map = Inputs.select(accessibilities_csv.columns, {\n value: \"acc_employment\",\n label: \"Choropleth variable: \"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":848,"value":"{\n await visibility();\n return quickmap(accessibilities, {\n attrib: acc_map,\n choropleth: {\n numClass: 4,\n method: \"jenks\",\n colorScheme: \"OrRd\"\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":38,"value":"accessibilities_csv = FileAttachment(\"accessibilities_gridded.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":711,"value":"The array below stores selected variables from the above accessibilities to use for the visualization. For each variables, a 'group' key and 'weight' key is assigned, storing the values of each layer's group and weight, respectively. ","pinned":false,"hidden":false,"mode":"md"},{"id":713,"value":"// Comments explain the parameters and sub-parameters in Banegas (2023)\n// Geographic > Proximity to workspaces\naccessibilities_variables = [\n {\n layer: \"acc_employment\",\n group: \"overview\",\n weight: 1\n },\n {\n layer: \"acc_gp\",\n group: \"overview\",\n weight: 1\n },\n {\n layer: \"acc_hospitals\",\n group: \"overview\",\n weight: 1\n },\n {\n layer: \"acc_school\",\n group: \"overview\",\n weight: 1\n },\n {\n layer: \"acc_supermarket\",\n group: \"overview\",\n weight: 1\n },\n {\n layer: \"acc_urbancentre\",\n group: \"overview\",\n weight: 1\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1174,"value":"Correlation heatmap for the above variables:","pinned":false,"hidden":false,"mode":"md"},{"id":1158,"value":"viewof fields = Inputs.checkbox(accessibilities_csv.columns, {\n label: \"fields\",\n value: accessibilities_csv.columns.filter((c) =>\n accessibilities_csv.find((d) => typeof d[c] === \"number\")\n )\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1152,"value":"Plot.plot({\n marginLeft: 100,\n label: null,\n color: { scheme: \"rdylbu\", pivot: 0, legend: true, label: \"correlation\" },\n marks: [\n Plot.cell(correlations, { x: \"a\", y: \"b\", fill: \"correlation\" }),\n\n Plot.text(correlations, {\n x: \"a\",\n y: \"b\",\n text: (d) => d.correlation.toFixed(2),\n fill: (d) => (Math.abs(d.correlation) > 0.6 ? \"white\" : \"black\")\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1154,"value":"correlations = d3.cross(fields, fields).map(([a, b]) => ({\n a,\n b,\n correlation: corr(\n Plot.valueof(accessibilities_csv, a),\n Plot.valueof(accessibilities_csv, b)\n )\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":645,"value":"#### Gas and Electricity","pinned":false,"hidden":false,"mode":"md"},{"id":695,"value":"Gas and electricity at LSOA level are also an indication of how the area currently consumes non-renewable energy. Although the variable is not explicitly mentioned in the research papers, the dataset and use case is based on an actual requirements on EVCS location analysis in Dundee City. ","pinned":false,"hidden":false,"mode":"md"},{"id":1176,"value":"viewof gas_map = Inputs.select(gas_electricity_csv.columns, {\n value: \"gas_mean_consumption\",\n label: \"Choropleth variable: \"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":858,"value":"{\n await visibility();\n return quickmap(gas_electricity_consumption, {\n attrib: gas_map,\n choropleth: {\n numClass: 4,\n method: \"jenks\",\n colorScheme: \"BuPu\"\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":41,"value":"gas_electricity_csv = FileAttachment(\"gas_electricity_gridded.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":725,"value":"Selected variables are as follow:","pinned":false,"hidden":false,"mode":"md"},{"id":944,"value":"gas_electricity_variables = [\n {\n layer: \"gas_mean_consumption\",\n group: \"overview\",\n weight: 1\n },\n {\n layer: \"elec_mean_consumption\",\n group: \"overview\",\n weight: 1\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":647,"value":"#### Multideprivations","pinned":false,"hidden":false,"mode":"md"},{"id":734,"value":"md`The dataset for multideprivation is obtained from Scottish Index of Multiple Deprivation (SIMD) ${reference(\n \"https://www.gov.scot/collections/scottish-index-of-multiple-deprivation-2020/\"\n)}. This dataset consists of several different categories or indicators, such as population, income, employment, health, crime, etc., at the LSOA level. These indicators are then subsequently categorised into sub-indicators, which are used to gauge the level of deprivation in the area. The following table elaborates the description for each of these indicators: \n\n\n${await FileAttachment(\"multideprivation_indicators.png\").image()}\n\nAgain, the data represented here is at the LSOA level, which depicts multiple variables related to deprivation in the given area. Some variables is selected for the purpose of analysis based on use case requirements and parameters mentioned in Banegas et. al. (2023)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1182,"value":"viewof simd_map = Inputs.select(multideprivations_csv.columns, {\n value: \"simd2020_income_count\",\n label: \"Choropleth variable: \"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":863,"value":"{\n await visibility();\n return quickmap(multideprivations, {\n attrib: simd_map,\n choropleth: {\n numClass: 4,\n method: \"jenks\",\n colorScheme: \"OrRd\"\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"multideprivations_csv = FileAttachment(\"multideprivations_gridded.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":740,"value":"Some of the sub-indicators above are selected for the visualisation variables. Judgements for this selection is based on the parameters mentioned in previous studies, in particular Banegas (2023) as mentioned above:","pinned":false,"hidden":false,"mode":"md"},{"id":952,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1186,"value":"viewof fields_multideprivation = Inputs.checkbox(\n multideprivations_csv.columns,\n {\n label: \"fields\",\n value: multideprivations_csv.columns.filter((c) =>\n multideprivations_csv.find((d) => typeof d[c] === \"number\")\n )\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1194,"value":"Plot.plot({\n marginLeft: 200,\n width: 1200,\n label: null,\n color: { scheme: \"rdylbu\", pivot: 0, legend: true, label: \"correlation\" },\n marks: [\n Plot.cell(correlations_multideprivations, {\n x: \"a\",\n y: \"b\",\n fill: \"correlation\"\n }),\n\n Plot.text(correlations_multideprivations, {\n x: \"a\",\n y: \"b\",\n text: (d) => d.correlation.toFixed(2),\n fill: (d) => (Math.abs(d.correlation) > 0.6 ? \"white\" : \"black\")\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1190,"value":"correlations_multideprivations = d3\n .cross(fields_multideprivation, fields_multideprivation)\n .map(([a, b]) => ({\n a,\n b,\n correlation: corr(\n Plot.valueof(multideprivations_csv, a),\n Plot.valueof(multideprivations_csv, b)\n )\n }))","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"### Aggregated Datasets","pinned":false,"hidden":false,"mode":"md"},{"id":59,"value":"Aggregated datasets are more detailed than LSOA, but not detailed enough for siting EV Charging Point location. Both the transformer loading area and conservation area indicates polygon which cover multiple LSOAs.","pinned":false,"hidden":false,"mode":"md"},{"id":685,"value":"#### Transformer Loading","pinned":false,"hidden":false,"mode":"md"},{"id":692,"value":"Transformer loading relates to how the EVCS will be powered, and what level of power is available. This should give a picture on what kinds of EVCS can be put into the location, since different types of EVCS requires different level of power.","pinned":false,"hidden":false,"mode":"md"},{"id":1199,"value":"viewof transformer_map = Inputs.select(transformer_loading_csv.columns, {\n value: \"transformer_loading\",\n label: \"Choropleth variable: \"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":867,"value":"{\n await visibility();\n return quickmap(transformer_loading, {\n attrib: transformer_map,\n choropleth: {\n numClass: 4,\n method: \"jenks\",\n colorScheme: \"OrRd\"\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":47,"value":"transformer_loading_csv = FileAttachment(\"transformer_loading_gridded.csv\").csv(\n { typed: true }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":783,"value":"Variable selection is based on existing literature discussing suitable transformer loads for different types of charging stations (e.g., Vansola (2023)). In principle, the higher the ","pinned":false,"hidden":false,"mode":"md"},{"id":985,"value":"transformer_variables = [\n {\n layer: \"transformer_loading\",\n group: \"aggregated\",\n weight: 1\n },\n {\n layer: \"transformer_spareload\",\n group: \"aggregated\",\n weight: 1\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":687,"value":"#### Conservation Area","pinned":false,"hidden":false,"mode":"md"},{"id":690,"value":"Based on Mamkhrezi (2023), the conservation areas represent boundaries within which the EVCS is not allowed to be located. Hence, this dataset depicts locations that should be avoided by decision makers to put the EVCS.","pinned":false,"hidden":false,"mode":"md"},{"id":879,"value":"{\n await visibility();\n return quickmap(conservation_area, {\n attrib: \"conservation_area\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"conservation_area_csv = FileAttachment(\"conservation_area_gridded.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":784,"value":"There is only a single variable that needs to be selected for the conservation area: whether it is inside the conservation area or not. However, Banegas and Mamkhrezi (2023) mentioned about the proximity to conservation areas as one of the parameters, which might be more suitable for future iterations.","pinned":false,"hidden":false,"mode":"md"},{"id":786,"value":"// Comments explain the parameters and sub-parameters in Banegas (2023)\nconservationarea_variables = [\n {\n layer: \"conservation_area\",\n group: \"aggregated\",\n weight: 1\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"### Detailed Datasets","pinned":false,"hidden":false,"mode":"md"},{"id":71,"value":"Detailed datasets are not aggregated: they are presented as is. Therefore, the location that is shown in these datasets represents the actual coordinates where the parking space, existing EVCS, streetlights, or houses are located.","pinned":false,"hidden":false,"mode":"md"},{"id":666,"value":"#### Parking Spaces","pinned":false,"hidden":false,"mode":"md"},{"id":763,"value":"Based on some regulations available in the UK for EVCS location, parking space is one of the variables that has a direct impact on whether the proposed location is suitable for EVCS siting or not. It is also one of the use-case requirements in Dundee City, since an EVCS can be put in available parking spaces, which relates to decision making for the analysis.","pinned":false,"hidden":false,"mode":"md"},{"id":1212,"value":"Plot.plot({\n projection: \"identity\", // Set the projection\n marks: [Plot.geo(parking_spaces, { stroke: \"black\" })]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":883,"value":"// {\n// await visibility();\n// return quickmap(parking_spaces, {\n// attrib: \"parkable_area\",\n// heatmap: { radius: 60, blur: 35 }\n// });\n// }","pinned":true,"hidden":false,"mode":"ojs"},{"id":61,"value":"parking_spaces_csv = FileAttachment(\"parking_spaces.csv\")\n .csv({ typed: true })\n .then((d) => {\n return d.filter((value) => value[\"parkable_area\"] !== -9999);\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":798,"value":"Variables selected are the available parking spaces and parkable area for these spaces: ","pinned":false,"hidden":false,"mode":"md"},{"id":791,"value":"// Comments explain the parameters and sub-parameters in Banegas (2023)\nparkingspaces_variables = [\n {\n layer: \"parking_sp\",\n group: \"detailed\",\n weight: 1\n },\n {\n layer: \"parkable_area\",\n group: \"detailed\",\n weight: 1\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":668,"value":"#### Existing EVCS Location","pinned":false,"hidden":false,"mode":"md"},{"id":765,"value":"Similar to parking spaces, the existing EVCS location is also another factor that directly relates to where to put an EV charging station. Previous research such as Zafar et.al.(2021) and Yi et. al.(2023) investigated the optimisation problem where a new location for an EV charging station should not be closer to an existing one. However, another factor, such as 24-hour access and the rated output of the existing charging station, might be another variable to investigate as well.","pinned":false,"hidden":false,"mode":"md"},{"id":886,"value":"// {\n// await visibility();\n// return quickmap(existing_evcs);\n// }","pinned":true,"hidden":false,"mode":"ojs"},{"id":63,"value":"existing_evcs_csv = FileAttachment(\"existing_evcs.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":806,"value":"As mentioned above, the variables selected for analysis are 24-hours accessibility and rated output from the existing evcs.","pinned":false,"hidden":false,"mode":"md"},{"id":802,"value":"existingevcs_variables = [\n {\n layer: \"access24Hours\",\n group: \"detailed\",\n weight: 1\n },\n {\n layer: \"connector1RatedOutputKW\",\n group: \"detailed\",\n weight: 1\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":671,"value":"#### Existing Streetlight location","pinned":false,"hidden":false,"mode":"md"},{"id":809,"value":"Existing streetlights is one of the parameters that is based on previous research, as there is no previous research that includes this parameter. However, based on use-case requirements in Dundee City, streetlight location is required to aid decision makers in positioning EVCS away from it, since it is preferred to have the charging station away from any existing electrical installation. ","pinned":false,"hidden":false,"mode":"md"},{"id":889,"value":"// {\n// await visibility();\n// return quickmap(steetlighting);\n// }","pinned":false,"hidden":false,"mode":"ojs"},{"id":66,"value":"streetlighting_csv = FileAttachment(\"streetlighting_csv.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":815,"value":"There are no particular parameters that are available for this layer, since the only requirement is the location itself. In order to include the data into further analysis, the pole_no variable is used as the variable.","pinned":false,"hidden":false,"mode":"md"},{"id":826,"value":"streetlight_variable = [\n {\n layer: \"pole_no\",\n group: \"detailed\",\n weight: 1\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":675,"value":"#### EPC Surveys","pinned":false,"hidden":false,"mode":"md"},{"id":892,"value":"// {\n// await visibility();\n// let data = geo.coords2geo(epc_surveys_csv, {\n// lat: \"lat\",\n// lon: \"long\"\n// });\n// return quickmap(data);\n// }","pinned":false,"hidden":false,"mode":"ojs"},{"id":819,"value":"Energy Performance Certificate (EPC) provides information about the energy efficiency of a building. Although no prior studies discuss about using this parameter for EVCS location analysis, the inclusion of this parameter is based on use-case study, which requires information about existing energy distribution in the study area.","pinned":false,"hidden":false,"mode":"md"},{"id":243,"value":"epc_surveys_csv = FileAttachment(\"epc_surveys.csv\")\n .csv({ typed: true })\n .then((d) => {\n return d.map((row) => ({\n long: row[\"LONGITUDE\"],\n lat: row[\"LATITUDE\"],\n prime_energy_indicator: row[\"Primary Energy Indicator (kWh/m²/year)\"],\n potential_epc: row[\"Potential Energy Efficiency Rating\"],\n co2_emission: row[\"CO2 Emissions Current Per Floor Area (kg.CO2/m²/yr)\"]\n }));\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":823,"value":"In this case, some variables which might indicates energy consumption in the house are selected. For example, A lower Primary Energy Indicator value indicates that the building is more energy efficient and uses less primary energy. A high value in CO2 Emissions indicates that the building is emitting a large amount of CO2 per square meter of floor area per year. This means that the building is not very energy efficient and is likely using a lot of energy from sources that produce CO2 emissions, such as burning fossil fuels. Arguably, location with houses that have lower energy consumption might indicate that the area is more perceptible to adopting electric vehicle, and thus the location is suitable as a electric vehicle charging station.","pinned":false,"hidden":false,"mode":"md"},{"id":830,"value":"epc_variables = [\n {\n layer: \"prime_energy_indicator\",\n group: \"detailed\",\n weight: 1\n },\n {\n layer: \"potential_epc\",\n group: \"detailed\",\n weight: 1\n },\n {\n layer: \"co2_emission\",\n group: \"detailed\",\n weight: 1\n }\n]","pinned":true,"hidden":false,"mode":"ojs"},{"id":250,"value":"## Data Processing","pinned":false,"hidden":false,"mode":"md"},{"id":896,"value":"Based on the elaborated explanation above, we can conclude that there are 27 variables that we can use for an analysis of EVCS location suitability in Dundee City:","pinned":false,"hidden":false,"mode":"md"},{"id":703,"value":"variables = {\n let variables = [].concat(\n accessibilities_variables,\n gas_electricity_variables,\n multideprivation_variables,\n transformer_variables,\n conservationarea_variables,\n parkingspaces_variables,\n existingevcs_variables,\n streetlight_variable,\n epc_variables\n );\n return variables;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1019,"value":"We can merge all data into a single variable, having all the keys and values from the composing dataset, with identical lat and long columns","pinned":false,"hidden":false,"mode":"md"},{"id":1013,"value":"all_data = {\n let alldata = [].concat(\n accessibilities_csv,\n gas_electricity_csv,\n multideprivations_csv,\n transformer_loading_csv,\n conservation_area_csv,\n parking_spaces_csv,\n existing_evcs_csv,\n streetlighting_csv,\n epc_surveys_csv\n );\n return alldata;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1003,"value":"Hence, in order to use the data in another notebook, this import syntax can be used to load individual dataset:\n\n```\nimport {accessibilities_csv, gas_electricity_csv, multideprivations_csv, transformer_loading_csv, conservation_area_csv,\nparking_spaces_csv, existing_evcs_csv, streetlighting_csv, epc_surveys_csv} from '684573e863e1549b'\n```\n\nto import all the data, we can use:\n\n``` import { all_data } from '684573e863e1549b' ```\n\n\nSubsequently, you might also want to import the list of variables for selection purposes:\n\n``` import { variables } from '684573e863e1549b' ```","pinned":false,"hidden":false,"mode":"md"},{"id":596,"value":"references = md`## References\n\nList of references used in this notebook:\n\n

${DOM.text(\n d.url.replace(/^http[s]\\:\\/\\//, \"\")\n )}`\n)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":605,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":9,"value":"## Dependencies","pinned":false,"hidden":false,"mode":"md"},{"id":90,"value":"### GeoJSON Datasets","pinned":false,"hidden":false,"mode":"md"},{"id":92,"value":"accessibilities = FileAttachment(\"accessibilities.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":94,"value":"conservation_area = FileAttachment(\"conservation_area.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":97,"value":"existing_evcs = FileAttachment(\"existing_evcs.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":100,"value":"gas_electricity_consumption = FileAttachment(\n \"gas_electricity_consumption.geojson\"\n).json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":104,"value":"multideprivations = FileAttachment(\"multideprivations.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":106,"value":"parking_spaces = FileAttachment(\"parking_spaces.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":109,"value":"steetlighting = FileAttachment(\"streetlighting.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":112,"value":"transformer_loading = FileAttachment(\"transformer_loading.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":189,"value":"### Libraries and Functions","pinned":false,"hidden":false,"mode":"md"},{"id":1156,"value":"// https://en.wikipedia.org/wiki/Correlation#Sample_correlation_coefficient\nfunction corr(x, y) {\n const n = x.length;\n if (y.length !== n)\n throw new Error(\"The two columns must have the same length.\");\n const x_ = d3.mean(x);\n const y_ = d3.mean(y);\n const XY = d3.sum(x, (_, i) => (x[i] - x_) * (y[i] - y_));\n const XX = d3.sum(x, (d) => (d - x_) ** 2);\n const YY = d3.sum(y, (d) => (d - y_) ** 2);\n return XY / Math.sqrt(XX * YY);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1062,"value":"import { quickmap } from \"2f801113fca991a3\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":593,"value":"import { reference, data as ref_data } from \"@mbostock/reference\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"T = require(\"@tidyjs/tidy/dist/umd/tidy.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"// maplibregl = require(\"maplibre-gl\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":310,"value":"import { toc } from \"@jonfroehlich/collapsible-toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1143,"value":"heatLayer = require(\"leaflet.heat\").catch(() => L.heatLayer)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1043,"value":"L = {\n const L = await require(\"leaflet@1.9.4/dist/leaflet.js\");\n if (!L._style) {\n const href = await require.resolve(\"leaflet@1.9.4/dist/leaflet.css\");\n document.head.appendChild(\n (L._style = html``)\n );\n }\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1026,"value":"providers = (L, require(\"leaflet-providers\").catch(() => L.tileLayer.provider))","pinned":true,"hidden":false,"mode":"ojs"},{"id":254,"value":"### Stylesheets","pinned":false,"hidden":false,"mode":"md"},{"id":320,"value":"style = html`\nFont Selection\n\n\n\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":175,"value":"","pinned":true,"hidden":false,"mode":"html"},{"id":13,"value":"","pinned":true,"hidden":false,"mode":"html"},{"id":289,"value":"## Addendum","pinned":false,"hidden":false,"mode":"md"},{"id":291,"value":"This link might provide additional existing EVCS location that is missing from the current dataset, and might be useful for future iterations with the data\n\nhttps://wazeopedia.waze.com/wiki/UnitedKingdom/Electric_Vehicle_Charging_Station#Access_type","pinned":false,"hidden":false,"mode":"md"}],"files":"$W32","stdlib":"1","titling":"auto"}