27:null 28:["$","title",null,{"children":"IAT 355 - Final Project | IAT 355 Workspace | Observable"}] 2d:T594,md`In September 2015, Canada joined 192 countries in committing to the United Nations’ 2030 Agenda for Sustainable Development. This agenda is a universal call to action to end poverty, protect the planet, and improve prosperity, peace, and partnership, with a focus on helping the most vulnerable first and leaving no one behind. The agenda sets out 17 Sustainable Development Goals (SDG), which cover a broad range of social, economic, and environmental issues, such as poverty, health, gender equality, sustainable economic growth, water, and climate change. Canada's target for SDG 13: Climate Action is to achieve 40% to 45% greenhouse gas emission reductions below 2005 levels by 2030, and achieve net-zero greenhouse gas emissions by 2050. To reach this target, the government is taking steps such as transitioning to zero-emission vehicles, promoting sustainable transportation options, and reducing carbon pollution from the oil and gas sector. This project is designed to help stakeholders understand the current state of Canada’s achievements as we approach 2030 in the city of Vancouver. It also seeks to aid policymakers and environmental advocates in exploring the relationship between electric vehicle adoption rates and greenhouse gas emissions, thereby enhancing their engagement with relevant research studies and reports to inform regulations and deepen their understanding of this sustainability issue.`2e:T793,VegaLite({ data: {values: vcy2023}, hconcat: [ // ─────────────── Bar Chart ─────────────── { title: "Distribution of Vehicle Registrations by Fuel Type in 2023", width: 450, height: 300, mark: {type: "bar", tooltip: true}, selection: { fuelSelect: { type: "single", fields: ["Fuel Type"], on: "click", empty: "all" } }, encoding: { x: {field: "Fuel Type", type: "nominal", sort: "-y"}, y: {aggregate: "count", type: "quantitative"}, color: { condition: { selection: "fuelSelect", field: "Fuel Type", type: "nominal", scale: {scheme: "tableau20"} }, value: "lightgray" } } }, // ─────────────── Line Chart ─────────────── { data: {values: vp_formatted_csv}, title: "Trends of Vehicle Registrations by Fuel Type (2019–2023)", width: 450, height: 300, transform: [ {filter: {selection: "fuelSelect"}} ], layer: [ { mark: {type: "line", opacity: 0.7}, encoding: { x: {field: "Vehicle Count Year", type: "ordinal", axis: {title: "Year"}}, y: {aggregate: "sum", field: "Vehicle Count", type: "quantitative"}, color: {field: "Fuel Type", type: "nominal", scale: {scheme: "tableau20"}} } }, { mark: {type: "circle", tooltip: true}, encoding: { x: {field: "Vehicle Count Year", type: "ordinal"}, y: {aggregate: "sum", field: "Vehicle Count", type: "quantitative"}, color: {field: "Fuel Type", type: "nominal", scale: {scheme: "tableau20"}} } } ] } ] }) 2f:Tb46,VegaLite({ data: { values: evi }, vconcat: [ // Top bar chart (faceted) { title: "Distribution of Zero-Emission Vehicle Registrations by Fuel Type (2019–2023)", facet: { column: { field: "Vehicle Count Year", type: "ordinal", title: "Year" } }, spec: { width: 160, height: 160, mark: { type: "bar", tooltip: true }, transform: [{ filter: { selection: "lineSelect" } }], selection: { fuelSelect: { type: "single", fields: ["Fuel Type"], on: "click", empty: "all" } }, encoding: { x: { field: "Fuel Type", type: "nominal", sort: "y" }, y: { aggregate: "sum", field: "Vehicle Count", type: "quantitative", axis: { title: "Number of Vehicles Registered" } }, color: { condition: { selection: "fuelSelect", field: "Fuel Type", type: "nominal", scale: { scheme: "category10" } }, value: "lightgray" } } } }, // Line chart below { title: "Trends of Zero-Emission Vehicle Registrations by Fuel Type (2019–2023)", width: 900, height: 400, transform: [{ filter: { selection: "fuelSelect" } }], layer: [ { mark: { type: "line", opacity: 0.7 }, selection: { lineSelect: { type: "single", fields: ["Fuel Type"], on: "click", empty: "all" } }, encoding: { x: { field: "Vehicle Count Year", type: "ordinal" }, y: { aggregate: "sum", field: "Vehicle Count", type: "quantitative", axis: { title: "Number of Vehicles Registered" } }, color: { condition: { selection: "lineSelect", field: "Fuel Type", type: "nominal", scale: { scheme: "category10" } }, value: "lightgray" } } }, { mark: { type: "circle", tooltip: true }, encoding: { x: { field: "Vehicle Count Year", type: "ordinal" }, y: { aggregate: "sum", field: "Vehicle Count", type: "quantitative" }, color: { condition: { selection: "lineSelect", field: "Fuel Type", type: "nominal", scale: { scheme: "category10" } }, value: "lightgray" } } } ] } ] }); 30:T63a,VegaLite({ data: {values: mergedGases}, title: "Trends of Greenhouse Gas Emissions by Gas Type in the Road Transport Sector (1990-2021)", hconcat: [ // Left Plot { mark: {type: "line", tooltip: {content: "hover"}}, width: 600, height: 300, selection: { brush: {type: "interval"}, bnd: { type: "single", fields: ["gas"], bind: {input: "select", options: [null, "CARBON DIOXIDE (CO2)", "METHANE (CH4)", "NITROUS OXIDE (N2O)"]} } }, encoding: { x: {field: "year", type: "ordinal", axis: {title: "Year"}}, y: {field: "megatonnes (mt CO2e)", type: "quantitative", axis: {title: "Emissions (Megatonnes)"}}, color: { condition: { selection: "bnd", field: "gas", type: "nominal", title: "Gas Type", scale: {scheme: "category10"} }, value: "gray" } } }, // Right Plot { width: 300, height: 300, transform: [ {filter: {selection: "brush"}} ], mark: {type: "point", tooltip: {content: "hover"}}, encoding: { x: {field: "year", type: "ordinal", axis: {title: "Year"}}, y: {field: "megatonnes (mt CO2e)", type: "quantitative", axis: {title: "Emissions (Megatonnes)"}}, color: { condition: { selection: "brush", field: "gas", type: "nominal", title: "Gas Type", scale: {scheme: "category10"} }, value: "gray" } } }, ] })31:T7f5,md`In reflecting on further work related to this project, there were several areas I intended to explore but ultimately could not complete. If given access to real datasets and additional time, I would seek to investigate not only greenhouse gas emissions but also expand the study to include shifts in air quality and energy consumption patterns. By doing so, I aim to uncover the broader implications of electric vehicle adoption on urban ecology and public health, thereby gaining a more holistic understanding of their impact on sustainability. Through the initial testing of my prototype with two participants, I discovered areas for improvement in the visualization design. While the interactions on the graphs were functioning as intended, the testers pointed out some critical issues that could enhance the overall effectiveness of the visualizations. One such recommendation was to ensure that the datasets used were aligned in terms of the years covered. Currently, the ICBC vehicle population dataset spans from 2019 to 2023, while the greenhouse gas emissions dataset ranges from 1990 to 2021. Harmonizing these datasets would allow for a more coherent analysis of trends. Another suggestion was to combine electric vehicle growth and greenhouse gas emissions on a single line graph, however, differences in measurement units and scales presented a challenge that hindered this integration. In the next iteration of this work, I would focus on sourcing a broader dataset that encompasses various aspects of sustainability. This would enable a more in-depth exploration of the relationships between emissions and energy consumption, which can provide a richer context for stakeholders to understand how these elements influence each other. By addressing the limitations identified during the pilot study and pursuing additional lines of inquiry, I believe the visualizations could be further improved, ultimately contributing to a more comprehensive understanding of the implications of electric vehicles on sustainability.`32:T447,VegaLite({ title: "Trends of Electric Vehicle Registrations VS Greenhouse Gas Emissions (2019-2023 & 1990-2021)", hconcat: [ // First Graph: Vehicle Registrations { data: {values: evi}, width: 420, height: 300, mark: {type: "line", opacity: 0.7}, encoding: { x: {field: "Vehicle Count Year", type: "ordinal", axis: {title: "Year"}}, y: {aggregate: "sum", field: "Vehicle Count", type: "quantitative", axis: {title: "Number of Vehicles Registered"}}, color: {field: "Fuel Type", type: "nominal", scale: {scheme: "category10"}} } }, // Second Graph: Greenhouse Gas Emissions { data: {values: mergedGases}, width: 420, height: 300, mark: {type: "line", tooltip: {content: "hover"}}, encoding: { x: {field: "year", type: "ordinal", axis: {title: "Year"}}, y: {field: "megatonnes (mt CO2e)", type: "quantitative", axis: {title: "Emissions (Megatonnes)"}}, color: {field: "gas", type: "nominal", scale: {scheme: "category10"}} } } ] })33:T4e4,md`In conclusion, the results derived from the visualizations reveal a significant contradiction to the initial hypothesis that a higher rate of electric vehicle adoption would correlate with lower greenhouse gas emission levels. This unexpected outcome suggests that other factors may be contributing to the continuing trend of greenhouse gas emissions. Specifically, while electric vehicles contribute to reducing emissions, their impact appears limited unless accompanied by a more comprehensive shift in transportation infrastructure and behaviour. The data indicates that additional measures, such as increasing the deployment of electric vehicles and phasing out gasoline-powered cars, may be necessary to achieve noteworthy reductions in greenhouse gas emissions. Furthermore, the persistent rise in carbon dioxide levels signals that the current adoption rate of electric vehicles alone has not substantially influenced overall emissions in the transport sector. Thus, a multi-faceted approach, incorporating both increased electric vehicle usage and substantial policy changes, will be essential for capturing the full potential of electric vehicles in combating climate change and achieving meaningful reductions in greenhouse gas emissions.`34:[{"id":"be842093c6ac8321cc622678977ca50e3abada988b1b61098a0937d234bbf98aa8f463f0b120ebad8cf5dbbd87b04b8a5b738012fd17a3cbf1764247f79f77b0","href":"https://static.observableusercontent.com/files/be842093c6ac8321cc622678977ca50e3abada988b1b61098a0937d234bbf98aa8f463f0b120ebad8cf5dbbd87b04b8a5b738012fd17a3cbf1764247f79f77b0","name":"Vehicle Population - Vehicle Policies in Force_Ful_data@3.csv","type":"text/csv","lastModified":1743799811983.382,"size":6485591},{"id":"be4f9259857e82307918e9f2aca2d28da66a8bd5fc8c1466395f7f2abeb265dac32403f0341741c62bf5789015fcb2eb9d72c8c849b12aa5927bb400d5e7b869","href":"https://static.observableusercontent.com/files/be4f9259857e82307918e9f2aca2d28da66a8bd5fc8c1466395f7f2abeb265dac32403f0341741c62bf5789015fcb2eb9d72c8c849b12aa5927bb400d5e7b869","name":"Vehicle Population - Vehicle Policies in Force_Ful_data.csv","type":"text/csv","lastModified":1743799811983.382,"size":12858908},{"id":"56634d0697c80c48b5525dbe8b5aaa23b9f40a9f4eb201dd9702dbd98eaa633eff68db3330831f5781eefd118c5191b1789224d27bb126b94aca79afcf3cc8a8","href":"https://static.observableusercontent.com/files/56634d0697c80c48b5525dbe8b5aaa23b9f40a9f4eb201dd9702dbd98eaa633eff68db3330831f5781eefd118c5191b1789224d27bb126b94aca79afcf3cc8a8","name":"bc_ghg_emissions_1990-2021.csv","type":"text/csv","lastModified":1743799811983.382,"size":98179}] 2b:{"id":"539fca02aa616c67","slug":null,"title":"IAT 355 - Final Project","thumbnail":"af03d05c07e781e8dee03713bdcfa295838e359677a9913d1d103f21c86c1caa","access_level":"public","create_time":"$D2025-04-04T20:50:11.983Z","update_time":"$D2025-04-07T21:09:26.095Z","view_time":null,"first_public_time":"$D2025-04-04T20:50:11.983Z","has_unpublished_edits":null,"fork":{"id":"0ea11bdf54af513d","slug":null,"title":"Assignment 4 Multiple Views","owner":{"id":"df98d1437f0beed5","login":"iat-355-workspace","name":"IAT 355 Workspace"}},"stars":0,"starred":false,"owner":{"type":"team","id":"df98d1437f0beed5","login":"iat-355-workspace","name":"IAT 355 Workspace","avatar_url":"https://avatars-next.observableusercontent.com/af781b10520b1b2cc1606008ab2466a993b76118ebc8ba98e443ddfefdb4eba4","tier":"starter_2024"},"version":2724,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"33de61db8f854a6b","login":"stephy-wong","name":"Stephy Wong","avatar_url":"https://avatars-next.observableusercontent.com/bad89b68eaf7d99f5840f504c66642ebcd94d71485420c75aa368b0cbaded6d0","tier":null}],"members":[{"type":"team","id":"df98d1437f0beed5","login":"iat-355-workspace","name":"IAT 355 Workspace","avatar_url":"https://avatars-next.observableusercontent.com/af781b10520b1b2cc1606008ab2466a993b76118ebc8ba98e443ddfefdb4eba4","tier":"starter_2024","role":"editor"},{"type":"individual","id":"33de61db8f854a6b","login":"stephy-wong","name":"Stephy Wong","avatar_url":"https://avatars-next.observableusercontent.com/bad89b68eaf7d99f5840f504c66642ebcd94d71485420c75aa368b0cbaded6d0","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"IAT 355 - Final Project","cells":[{"id":0,"value":"md`# IAT 355 - Final Project`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2387,"value":"md`## Table of Contents`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2390,"value":"md`\n

[Overview](#overview)

[Introduction](#introduction)

[Problem & Domain Questions](#problem)

[Data](#data)

[Analytical Question #1](#question1)

[Analytical Question #2](#question2)

[Analytical Question #3](#question3)

[Video](#video)

[Further Work](#furtherwork)

[Conclusion](#conclusion)

[References and appendices](#references)

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":97,"value":"overview = md`## Overview`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"md`As the world grapples with climate change and seeks sustainable solutions, the rise of electric vehicles (EVs) presents a promising alternative to traditional gasoline-powered cars. This project investigates the impact of electric vehicles on sustainability in British Columbia, with a particular focus on the Vancouver area.\n\nIn this project, I will create visualizations using the most current data to examine the latest developments and trends in EV production, compare the registration rates of EVs versus conventional vehicles, and explore how the adoption of EVs influences the natural environment, regarding greenhouse gas emissions. The hypothesis guiding this investigation is that a higher rate of electric vehicle adoption will correlate with lower greenhouse gas emission levels, highlighting the potential for EVs to contribute significantly to a more sustainable future.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2235,"value":"introduction = md`## Introduction`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2237,"value":"$2d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2447,"value":"problem = md`## Problem & Domain Questions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2370,"value":"md`#### Main Problem:\n\n - How does the introduction of electric vehicles impact sustainability in the Vancouver area?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1153,"value":"md`#### Analytical questions:\n1.\tWhat is the current distribution of fuel types among vehicles in Vancouver?\n\n2.\tHow has the growth of electric vehicles evolved over the years?\n\n3.\tHow has the adoption of electric vehicles influenced the overall trends in greenhouse gas emissions in the transport sector over the years?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2245,"value":"data = md`## Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2248,"value":"md`#### Datasets:\n\n1. https://catalogue.data.gov.bc.ca/dataset/icbc-vehicle-population-vehicle-policies-in-force\n2. https://catalogue.data.gov.bc.ca/dataset/british-columbia-greenhouse-gas-emissions\n\n#### Dimensions:\n- Fuel Type: nominal\n\n- Vehicle Count: quantitative\n\n- Vehicle Count Year: ordinal\n\n- Gas Type: nominal/categorical\n\n- Transport Sub-sector: nominal/categorical\n\n- Year: ordinal\n\n- Emission Rate (in megatonnes): quantitative\n\n#### Data Cleaning:\nI have manipulated the ICBC vehicle population dataset to focus exclusively on the Vancouver municipality, filtering out unrelated contexts in order to address the research question effectively. In the case of the BC greenhouse gas emissions dataset, I have reformatted the original data table as it was not structured in the desired format for my analysis. I renamed the headers and organized the data to enhance clarity and facilitate a more intuitive understanding of the emissions trends.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"md`## Import D3 and VegaLite`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1093,"value":"VegaLite = require(\"vega-embed@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":101,"value":"question1 = md`## Analytical Question #1`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"md`What is the current distribution of fuel types among vehicles in Vancouver?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"md`#### Vehicle Population Dataset`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":494,"value":"md`Dataset Link: https://catalogue.data.gov.bc.ca/dataset/icbc-vehicle-population-vehicle-policies-in-force`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"vehiclePopulationVehiclePoliciesInForce_ful_d = await FileAttachment(\"Vehicle Population - Vehicle Policies in Force_Ful_data@3.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"vehiclePopulationVehiclePoliciesInForce_ful_d","database":"$undefined"},{"id":151,"value":"vehicle_population_file = FileAttachment(\"Vehicle Population - Vehicle Policies in Force_Ful_data.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":156,"value":"raw_text = vehicle_population_file.text()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":942,"value":"md`Since the text was not correctly formatted, I have reformatted it by removing all null characters (\\x00) and replaces all tab characters (\\t) with commas (,)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":531,"value":"vehicle_population_text = raw_text.replace(/\\x00/g, '').replace(/\\t/g, ',');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":554,"value":"md`#### Data cleaning`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":872,"value":"vehicle_population_lines = vehicle_population_text.trim().split('\\n');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":947,"value":"md`Below are two functions that read in a data file into the data model and organizes it properly according to its unique key, it also verifies the data for missing and bad (out of range, out of type) values.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":557,"value":"function cleanAndOrganizeData(lines, raw_text) {\n\n // Split the text into lines and parse headers\n const headers = lines[0].split(','); // First line as headers\n\n // Initialize an array to hold the organized data\n const data = [];\n\n // Process each line of data\n for (let i = 1; i < lines.length; i++) {\n const values = lines[i].split(',');\n\n // Create an object for the current line\n const record = {};\n headers.forEach((header, index) => {\n record[header] = values[index] ? values[index].trim() : null; // Trim whitespace\n });\n\n // Verify data\n if (verifyRecord(record)) {\n data.push(record); // Push valid records\n } else {\n console.warn(`Invalid record at line ${i + 1}:`, record);\n }\n }\n\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":559,"value":"// Function to verify the record\nfunction verifyRecord(record) {\n // Get all keys from the record\n const keys = Object.keys(record);\n\n // Loop through each key to verify the value\n for (const key of keys) {\n // Check if the value is missing or not a number\n if (!record[key]) {\n return false; // If any field is invalid, return false\n }\n }\n\n return true; // All fields are valid\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":951,"value":"md`Call the function`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":561,"value":"vp_formatted_csv = cleanAndOrganizeData(vehicle_population_lines, raw_text);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":657,"value":"md`## Visualizations`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1784,"value":"md`### Visualization #1`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1787,"value":"md`#### Visualization Idiom:\n\nCircle View`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1794,"value":"md`#### Data Dimensions:\n\nx-axis: Fuel Type (nominal/categorical)\n\ny-axis: Vehicle Count (quantitative)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2015,"value":"md`#### Design Brief:\n\nA circle view chart that illustrates the distribution of vehicle registrations for each fuel type from 2019 to 2023. The visualization allows users to filter the data points by year.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2277,"value":"md`#### User Manual:\n\n- Hover over the data points to view the exact counts through the tooltip.\n\n- Slide the control to set minimum and maximum year ranges.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1650,"value":"md`#### Filtering`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1414,"value":"import {rangeSlider} from \"@mootari/range-slider\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1411,"value":"viewof range = rangeSlider({\n min: d3.min(vp_formatted_csv, d => d[\"Vehicle Count Year\"]),\n max: d3.max(vp_formatted_csv, d => d[\"Vehicle Count Year\"]),\n value: this ? this.value:[0,5],\n title: \"Year\",\n description: 'filtering by year',\n step: 1\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1430,"value":"VegaLite({\n data: {values: vp_formatted_csv},\n\n title: \"Distribution of Vehicle Registrations by Fuel Type (2019-2023)\",\n\n transform: [\n {filter: {field: \"Vehicle Count Year\", gte:range[0]}},\n {filter: {field: \"Vehicle Count Year\", lte:range[1]}}\n ],\n\n mark: {type: \"point\", tooltip: {content: \"vp_formatted_csv\"}, size: 72},\n\n selection: {\n circle: {type: \"single\", on: \"mouseover\"}\n },\n\n width: 800,\n height: 500,\n\n encoding: {\n x: {field: \"Fuel Type\", type: \"nominal\", sort: \"-y\"},\n y: {field: \"Vehicle Count\", type: \"quantitative\", axis: {title: \"Number of Vehicles Registered (per record)\"}},\n color: {\n condition: {\n selection: \"circle\",\n field: \"Vehicle Count Year\", type: \"Ordinal\", scale: {scheme: \"set1\"}\n },\n value: \"gray\"\n }\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1768,"value":"md`### Visualization #2`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1137,"value":"md`Filter the Vehicle Count Year to 2023 to focus on the most current data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1127,"value":"vcy2023 = vp_formatted_csv.filter(d => d[\"Vehicle Count Year\"] === \"2023\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1314,"value":"md`#### Visualization Idiom:\n\nBar Chart & Line Graph`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":891,"value":"md`#### Data Dimensions:\n\nLeft plot:\n\nx-axis: Fuel Type (nominal/categorical)\n\ny-axis: Count of Records for each Fuel Type (quantitative)\n\nRight plot:\n\nx-axis: Year (ordinal, range: 2019-2023)\n\ny-axis: Sum of Vehicle Count (quantitative)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2012,"value":"md`#### Design Brief:\n\nA set of interconnected graphs that illustrate the current 2023 distribution of vehicle registrations for each fuel type and the trend of vehicle registration by fuel type from 2019 to 2023. The visualization utilizes concatenation and brushing and linking techniques, featuring bar selection and tooltips for user interaction. The second line graph aims to provide a more detailed view of the first plot, allowing users to explore the trend of a specific fuel type across a five-year range.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2260,"value":"md`#### User Manual:\n\n- Hover over the bars and data points to view the exact counts through the tooltip.\n\n- Click on the bars on the left plot to view the corresponding trends of vehicle registrations on the right plot.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1680,"value":"md`#### Bar Selection + Tooltip + Brushing and linking`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2477,"value":"$2e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2061,"value":"md`#### Answers to Research Question #1\n\nThe visualizations show that gasoline-powered vehicles are the most registered type in 2023, with approximately 5,500 registrations. Diesel vehicles follow as the second most common, with around 2,300 registrations, while electric vehicles and other fuel types have significantly fewer registrations, which may indicate that gasoline still remains a strong preference among vehicle owners in Vancouver.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":510,"value":"question2 = md`## Analytical Question #2`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":514,"value":"md`How has the growth of electric vehicles evolved over the years?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":517,"value":"md`#### Vehicle Population Dataset`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":520,"value":"md`Using the same dataset as Visualization #1: https://catalogue.data.gov.bc.ca/dataset/icbc-vehicle-population-vehicle-policies-in-force`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":709,"value":"md`## Visualizations`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1251,"value":"md`Filter the data to focus on electric vehicles and hybrid vehicles only`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":662,"value":"evi = vp_formatted_csv.filter(d => \n // (d[\"Electric Vehicle Indicator\"] === \"Yes\" || d[\"Hybrid Vehicle Indicator\"] === \"Yes\") &&\n (d[\"Fuel Type\"] === \"Electric\" || d[\"Fuel Type\"] === \"Gasoline Electric\") || d[\"Fuel Type\"] === \"Hydrogen\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1811,"value":"md`### Visualization #1`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1817,"value":"md`#### Visualization Idiom:\n\nBar Charts & Line Graph`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1827,"value":"md`#### Data Dimensions:\n\nTop plot:\n\nx-axis: Fuel Type (nominal/categorical)\n\ny-axis: Sum of Vehicle Count (quantitative)\n\nBottom plot:\n\nx-axis: Year (ordinal, range: 2019-2023)\n\ny-axis: Sum of Vehicle Count (quantitative)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2697,"value":"md`#### Design Brief:\n\nA set of interconnected graphs that depicts the trends of zero-emission vehicle registrations by fuel type from 2019 to 2023. The visualization utilizes concatenation and brushing and linking techniques, featuring bar selection and tooltips for user interaction. The second line graph aims to provide a more detailed view of the first plot, allowing users to explore the trend of a specific fuel type across a five-year range. It also employs layering techniques and offers point selection and tooltips for user interaction.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2280,"value":"md`#### User Manual:\n\n- Hover over the bars and data points to view the exact counts through the tooltip.\n\n- Click on the bars on the top plot to view the corresponding trends of zero-emission vehicle registrations on the bottom plot.\n\n- Click on the lines on the bottom plot to view the corresponding distribution of zero-emission vehicle registrations on the top plot.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1632,"value":"md`#### Multiple Views & Line Graph (Faceting + Bar Selection + Point Selection + Tooltip + Layering)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2663,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2077,"value":"md`#### Answers to Research Question #2\n\nThe visualizations illustrate that, while electric vehicles are not yet as prevalent as traditional gasoline-powered cars, they have experienced significant growth from 2019 to 2023. The trend line for electric vehicles shows a steep upward trajectory, which indicates a rapid increase in registrations over the years. This suggests a growing acceptance and adoption of electric vehicles within the market, signaling a shift towards more sustainable transportation options.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":473,"value":"question3 = md`## Analytical Question #3`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":476,"value":"md`How has the adoption of electric vehicles influenced the overall trends in greenhouse gas emissions in the transport sector over the years?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":362,"value":"md`#### GHG emission Dataset`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":365,"value":"md`Dataset Link: https://catalogue.data.gov.bc.ca/dataset/british-columbia-greenhouse-gas-emissions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"bc_ghg_emissions_by_economic_sector_by_gas_199020 = await FileAttachment(\"bc_ghg_emissions_1990-2021.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"bc_ghg_emissions_by_economic_sector_by_gas_199020","database":"$undefined"},{"id":34,"value":"ghg_file = FileAttachment(\"bc_ghg_emissions_1990-2021.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"ghg_text = ghg_file.text()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"ghg = d3.csvParse(ghg_text, d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"md`#### Data cleaning`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":857,"value":"ghg_lines = ghg_text.trim().split('\\n');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":954,"value":"md`Call the cleanAndOrganizeData() function defined above to do data cleaning on ghg dataset`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":862,"value":"ghg1_formatted_csv = cleanAndOrganizeData(ghg_lines, ghg_text);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":957,"value":"md`Filter dataset to only include TRANSPORT sector`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"// Filter dataset to only include TRANSPORT sector\nghg_cleaned = ghg.filter(d => d.subsector_level1 === \"TRANSPORT\" && d[\"2021\"] !== \"NA\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":482,"value":"md`#### Reformatting`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":485,"value":"md`Since the original data was not in the desired format, I decided to reformat it with new headers for improved clarity.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1028,"value":"md`P.S. Make sure to run each line before the code can execute properly.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":300,"value":"rows = ghg_cleaned","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":304,"value":"// !!!!!!!!!! P.S. Run each line before the code can run properly -------------------------------------------------->>>>>>>>>>>>>>>>>>>\n// array to store new headers\noutput = []","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"// !!!!!!!!!! P.S. Run each line before the code can run properly -------------------------------------------------->>>>>>>>>>>>>>>>>>>\nrows.forEach(row => {\n const gas = row.gas;\n const sector = row.sector;\n const subsectorLevel1 = row.subsector_level1;\n const subsectorLevel2 = row.subsector_level2;\n\n // Loop through each year and construct new rows\n for (let year = 1990; year <= 2021; year++) {\n const value = row[year]; // Assuming the yearly values are properties on the row object\n // output.push(`${gas},${sector},${subsectorLevel1},${subsectorLevel2},${year},${value}`);\n output.push([gas, sector, subsectorLevel1, subsectorLevel2, year, value]);\n }\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":306,"value":"// !!!!!!!!!! P.S. Run each line before the code can run properly -------------------------------------------------->>>>>>>>>>>>>>>>>>>\n// define new headers\noutput.push(`gas,sector,subsector_level1,subsector_level2,year,megatonnes (mt CO2e)`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":312,"value":"ghg_formattedOutput = output.join('\\n');","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":329,"value":"ghg_formatted_csv = d3.csvParse(ghg_formattedOutput, d3.autoType)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":377,"value":"md`## Visualizations`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1830,"value":"md`### Visualization #1`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2303,"value":"md`Filter the year to 2021 to focus on the most current data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2137,"value":"ghg2021 = ghg_formatted_csv.filter(d => (d[\"year\"] === 2021))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1856,"value":"md`#### Visualization Idiom:\n\nBar Graphs`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1859,"value":"md`#### Data Dimensions:\n\nx-axis: Gas Type (nominal/categorical)\n\ny-axis: Emission in megatonnes (mt CO2e) (quantitative)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2036,"value":"md`#### Design Brief:\n\nA set of multiple bar charts that illustrate greenhouse gas emissions by gas type for various modes of transportation in 2021. The visualization provides tooltips for user interaction.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2294,"value":"md`#### User Manual:\n\nHover over the bars to view the exact counts through the tooltip.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1599,"value":"md`#### Multiple Views (Faceting + Tooltip)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1470,"value":"VegaLite({\n data: {values: ghg2021},\n\n title: \"Greenhouse Gas Emissions by Gas Type for Various Modes of Transportation in 2021\",\n \n facet: {column: {field: \"subsector_level2\", type: \"nominal\", title: \"Modes of Transport\"}},\n \n spec: {\n width: 120,\n height: 170,\n mark: {type: \"bar\", tooltip: true},\n encoding: {\n x: {field: \"gas\", type: \"nominal\", axis: {title: \"Gas Type\"}},\n y: {field: \"megatonnes (mt CO2e)\", type: \"quantitative\", bin: \"true\", axis: {title: \"Emissions (Megatonnes)\"}},\n color: {\n field: \"gas\",\n type: \"nominal\",\n scale: {scheme: \"category10\"},\n legend: {title: \"Gas Type\"}\n }\n }\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1833,"value":"md`### Visualization #2`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2329,"value":"md`Separate the gas type into categories`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1949,"value":"co2 = ghg_formatted_csv.filter(d => d.subsector_level2 === \"Road Transport\" && d.gas === \"CARBON DIOXIDE (CO2)\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1961,"value":"ch4 = ghg_formatted_csv.filter(d => d.subsector_level2 === \"Road Transport\" && d.gas === \"METHANE (CH4)\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1964,"value":"n2o = ghg_formatted_csv.filter(d => d.subsector_level2 === \"Road Transport\" && d.gas === \"NITROUS OXIDE (N2O)b\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1968,"value":"mergedGases = [\n ...co2.map(d => ({...d, gas: \"CARBON DIOXIDE (CO2)\"})),\n ...ch4.map(d => ({...d, gas: \"METHANE (CH4)\"})),\n ...n2o.map(d => ({...d, gas: \"NITROUS OXIDE (N2O)\"}))\n];","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1984,"value":"md`#### Visualization Idiom:\n\nLine Graph & Circle View`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1987,"value":"md`#### Data Dimensions:\n\nx-axis: Year (ordinal)\n\ny-axis: Emission in megatonnes (mt CO2e) (quantitative)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2042,"value":"md`#### Design Brief:\n\nA set of interconnected graphs that illustrate the trend of greenhouse gas emissions by gas type in the road transport sector from 1990 to 2021. The visualization utilizes concatenation and brushing and linking techniques, featuring interval selection and tooltips for user interaction. The second circular plot aims to provide a zoomed-in version of the first plot, allowing users to explore specific sections of the graph in greater detail. Additionally, users have the option to use a binding tool to highlight specific fuel types.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2306,"value":"md`#### User Manual:\n\n- Hover over the bars to view the exact counts through the tooltip.\n\n- Select a specific portion of the left plot to filter the data displayed in the right plot.\n\n- Filter the gas types by selecting them from a dropdown menu.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1990,"value":"md`#### Brushing and Linking (Interval Selection + Binding + Concatination + Tooltip)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1885,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2081,"value":"md`#### Answers to Research Question #3\n\nThe visualizations reveal that despite significant decreases in methane (CH₄) and nitrous oxide (N₂O) emissions since 2004 within the road transport sector, the greater portion of GHG emission, carbon dioxide (CO₂), is constantly rising since 1990. This ongoing increase in CO₂ emissions suggests that the introduction of electric vehicles may not have had a substantial impact on reducing overall greenhouse gas emissions, or a more comprehensive shift towards electric vehicles is necessary to achieve substantial reductions in emissions from gasoline-powered cars.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2338,"value":"md`#### More visualizations\n\nhttps://observablehq.com/d/0ea11bdf54af513d`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2714,"value":"video = md`## Video`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2716,"value":"md`https://youtu.be/O5_r7A1i4kQ`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2420,"value":"furtherwork = md`## Further Work\n\n#### + Feasibility Pilot`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2359,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2190,"value":"conclusion = md`## Conclusion`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2171,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2333,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":428,"value":"references = md`## References and appendices`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":497,"value":"md`Canada, E. and S. D. (2023, January 4). Sustainable Development Goal 13: Climate action. Www.canada.ca. https://www.canada.ca/en/employment-social-development/programs/agenda-2030/climate-action.html\n\nClimate Action Secretariat. (2021). Data Catalogue. Gov.bc.ca. https://catalogue.data.gov.bc.ca/dataset/british-columbia-greenhouse-gas-emissions\n\nInsurance Corporation of British Columbia. (2023). Data Catalogue. Gov.bc.ca. https://catalogue.data.gov.bc.ca/dataset/icbc-vehicle-population-vehicle-policies-in-force\n\nIwand, F. (2023, February 21). Observable. Observable. https://observablehq.com/@mootari/range-slider\n\nMutasim, A. K. (2025, March 7). Observable. Observable. https://observablehq.com/d/2a3b75b6406d44e1\n\nMutasim, A. K. (2025, March 21). Observable. Observable. https://observablehq.com/@aunnoy/iat-355-4-geovis\n\nRuíz, F. A. (2024, November 19). Observable. Observable. https://observablehq.com/@fer-aguirre/data-cleaning\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 2c:"$2b:body"