3e:null 3f:["$","title",null,{"children":"Final Project | RainaAaseesProjectDataSelection | Observable"}] 45:T5b0,VegaLite({ data: {values: laPM25Data}, hconcat: [ { transform: [ {filter: {field: "traffic", gte:range[0]}}, {filter: {field: "traffic", lte:range[1]}}, ], mark: {type: "point"}, selection: { brush: {type: "interval"}, pts: {type: "single", on: "mouseover"}, }, width: 350, height: 350, encoding: { x: {field: "pm25", type: "quantitative",}, y: {field: "traffic", type: "quantitative"}, tooltip: {field: "pm25", type: "quantitative"}, color: { condition: { selection: "pts", field: "pm25", type: "quantitative" }, value: "gray", }, } }, { transform: [ {filter: {field: "traffic", gte:range[0]}}, {filter: {field: "traffic", lte:range[1]}}, {filter: {selection: "brush"}} ], mark: {type: "point"}, selection: { pts: {type: "single", on: "mouseover"}, }, width: 350, height: 350, encoding: { x: {field: "ces", type: "quantitative",}, y: {field: "traffic", type: "quantitative"}, tooltip: {field: "ces", type: "quantitative"}, color: { condition: { selection: "pts", field: "pm25", type: "quantitative" }, value: "gray", }, } }, ] })46:Ta20,vegalite({ "data": { "values": updatedData }, "params": [ { "name": "districtFilter", "bind": { "input": "select", "options": ["All"].concat(updatedData.map(d => d.Dist).filter((v, i, a) => a.indexOf(v) === i)), "labels": ["All"] } }, { "name": "dayFilter", "bind": { "input": "select", "options": ["All"].concat(updatedData.map(d => d.Day).filter((v, i, a) => a.indexOf(v) === i)), "labels": ["All"] } } ], "hconcat": [ { "transform": [ { "filter": "districtFilter === 'All' || datum.Dist === districtFilter" }, { "joinaggregate": [ { "op": "sum", "field": "Total", "as": "FinalTotal" } ] }, { "calculate": "datum.Total/datum.FinalTotal * 100", "as": "Percentage" } ], "width": 400, "height": 400, "mark": { "type": "arc", "innerRadius": 100, "tooltip": {"content": "data"} }, "encoding": { "theta": {"field": "Percentage", "type": "quantitative"}, "order": {"field": "Dist", "type": "nominal", "sort": "descending"}, "color": {"field": "Dist", "type": "nominal", "title": "District"} }, "view": {"stroke": null}, "padding": 30, "title": { "text": "Traffic by District", "align": "center", "fontSize": 25, "offset": 30 } }, { "transform": [ { "filter": "dayFilter === 'All' || datum.Day === dayFilter" }, { "joinaggregate": [ { "op": "sum", "field": "DayTotal", "as": "DayFinalTotal" } ] }, { "calculate": "datum.DayTotal/datum.DayFinalTotal * 100", "as": "Percentage" } ], "width": 400, "height": 400, "mark": { "type": "arc", "innerRadius": 100, "tooltip": {"content": "data"} }, "encoding": { "theta": {"field": "Percentage", "type": "quantitative"}, "order": {"field": "Day", "type": "nominal", "sort": "descending"}, "color": {"field": "Day", "type": "nominal", "title": "Day"} }, "view": {"stroke": null}, "padding": 30, "title": { "text": "Traffic by Weekday", "align": "center", "fontSize": 25, "offset": 30 } } ] }) 47:Tdb5,viewof mapboxMap = { let container = html`

`; mapboxgl.accessToken = "pk.eyJ1IjoiYWFzZWVzMjgiLCJhIjoiY204Nnd1dHU3MDEwZTJsb2drNjR0MWQyZiJ9.Rl6Gx1LRIZOMc3_RIzPpVA"; let map = new mapboxgl.Map({ container: container, style: "mapbox://styles/mapbox/streets-v11", center: [-118.2437, 33.9922], zoom: 8, maxZoom: 15, minZoom: 8, maxBounds: [[-119.3, 33.2], [-117.3, 34.5]] }); let heatmapVisible = true; // Default Heatmap is ON map.on("load", () => { // Traffic Layer map.addLayer({ id: "traffic", type: "line", source: { type: "vector", url: "mapbox://mapbox.mapbox-traffic-v1" }, "source-layer": "traffic", paint: { "line-width": ["interpolate", ["linear"], ["zoom"], 3, 1, 8, 4], "line-color": [ "match", ["get", "congestion"], "low", "#00FF00", "moderate", "#FFA500", "heavy", "#FF0000", "severe", "#8B0000", "#000000" ], "line-opacity": 0.8 } }); // Heatmap Layer map.addSource("heatmap-source", { type: "geojson", data: { "type": "FeatureCollection", "features": geojsonPM25.filter(d => d.properties.pm25 <= pm25Range) } }); map.addLayer({ id: "weather-heatmap", type: "heatmap", source: "heatmap-source", paint: { "heatmap-intensity": ["interpolate", ["linear"], ["zoom"], 10, 1, 15, 3], "heatmap-radius": ["interpolate", ["linear"], ["zoom"], 10, 7, 18, 12], "heatmap-color": [ "interpolate", ["linear"], ["heatmap-density"], 0, "rgba(0, 0, 255, 0)", 0.1, "rgb(0, 255, 255)", 0.3, "rgb(0, 255, 0)", 0.5, "rgb(255, 255, 0)", 1, "rgb(255, 0, 0)" ] } }); // Adding a circle Layer for Hover Popups map.addLayer({ id: "pm25-points", type: "circle", source: "heatmap-source", paint: { "circle-radius": 5, "circle-color": "black", "circle-opacity": 0.0 // Hidden - only for hover events } }); // Creating a Popup that follows the cursor let popup = new mapboxgl.Popup({ closeButton: false, closeOnClick: false, offset: [133, -555] }); map.on("mousemove", "pm25-points", (e) => { map.getCanvas().style.cursor = "pointer"; let coordinates = e.lngLat; let pm25Value = e.features[0].properties.pm25; popup.setLngLat(coordinates) .setHTML(`

PM2.5: ${pm25Value.toFixed(2)} µg/m³

`) .addTo(map); }); map.on("mouseleave", "pm25-points", () => { map.getCanvas().style.cursor = ""; popup.remove(); }); // Toggling Heatmap viewof heatmapToggleButton.onclick = () => { heatmapVisible = !heatmapVisible; map.setLayoutProperty("weather-heatmap", "visibility", heatmapVisible ? "visible": "none"); }; // Updating Heatmap when PM2.5 slider changes viewof pm25Range.oninput = () => { map.getSource("heatmap-source").setData({ "type": "FeatureCollection", "features": geojsonPM25.filter(d => d.properties.pm25 <= pm25Range.value) }); }; }); return container; };48:T9ff,### Design Choices: **- User interaction: Filtering →** Users can toggle the heatmap on/off and adjust the PM2.5 range using a slider, filtering the data displayed. **- Details on demand →** When hovering over a data point, a popup appears showing the precise PM2.5 value at that location. **- Multiple views →** The map integrates both a heatmap and a traffic layer, providing two perspectives on air pollution and traffic congestion. **- Using data from more than one source →** The visualization combines PM2.5 pollution data with Mapbox Traffic v1 data. **- Visualization idiom →** The heatmap effectively encodes pollution density using color gradients, while the circle markers allow for detailed inspection of individual data points. ### How Design Choices Support Analytical Questions → Our visualization was designed to explore where and how air pollution varies across Los Angeles. Each design element was chosen to directly support the analytical goals of the project. **Research Question 1:** _Where are the highest levels of air pollution in Los Angeles located?_ - Heatmap Encoding (Color Intensity - Red = Worse): The heatmap uses a gradient color scale, where darker reds indicate higher concentrations of PM2.5. This allows users to instantly identify pollution hotspots across the city with just a glance. - Circle Markers with Details on Demand: By hovering over individual data points, users can see the exact PM2.5 values, providing a more detailed understanding of localized air quality. This supports precise comparisons between areas. - Multiple Views with Traffic Integration: The map integrates Mapbox Traffic v1 as a separate visual layer, helping users visually connect high-traffic zones with high-pollution regions, supporting location-specific hypotheses. **Research Question 2:** _How does pollution intensity vary across different areas of the city?_ - User Interaction – Filtering and Slider: Users can adjust the PM2.5 range using a slider to filter out lower or higher pollution values. This interaction helps focus on specific pollution thresholds and compare patterns across different city zones. - Toggle Heatmap On/Off: This allows users to switch between raw data points and generalized pollution density, making it easier to analyze macro-level trends versus micro-level details. - Use of Multiple Data Sources: Combining PM2.5 data with traffic information helps users correlate patterns, adding depth to their analysis of pollution variation and potential causes across neighborhoods. 49:T9d4,viewof pm25Histogram = { let binsWithTitles = bins.map((bin, i) => ({ x: bin.x0, x1: bin.x1, y: bin.length, index: i, title: `Range: ${bin.x0} - ${bin.x1}\nCount: ${bin.length}` })); let chart = Plot.plot({ width: 600, height: 300, marks: [ Plot.rectY(binsWithTitles, { x: "x", x1: "x1", y: "y", fill: "steelblue", stroke: "black", strokeWidth: 0.5, title: "title" }), Plot.ruleY([0]) ], x: { label: "PM2.5 Value Range", tickFormat: (d) => `${Math.floor(d)}-${Math.ceil(d)}` }, y: { label: "Count of Data Points" } }); let wrapper = html`

`; wrapper.appendChild(chart); // Add CSS for selected bar and label const style = html``; wrapper.appendChild(style); // Click behavior for histogram bars const bars = wrapper.querySelectorAll("rect"); bars.forEach((bar, i) => { bar.style.cursor = "pointer"; bar.addEventListener("click", (e) => { // Remove "selected" from all bars bars.forEach(b => b.classList.remove("selected")); bar.classList.add("selected"); // Remove old popup const existing = wrapper.querySelector(".popup-label"); if (existing) existing.remove(); // Create new popup const popup = document.createElement("div"); popup.className = "popup-label"; popup.innerHTML = `Range: ${binsWithTitles[i].x}–${binsWithTitles[i].x1}
Count: ${binsWithTitles[i].y}`; wrapper.appendChild(popup); // Position popup above bar const rect = bar.getBoundingClientRect(); const wrapperRect = wrapper.getBoundingClientRect(); popup.style.left = `${rect.left + rect.width / 2 - wrapperRect.left}px`; popup.style.top = `${rect.top - wrapperRect.top - 8}px`; // Filter geojsonPM25 based on selected range const selectedRange = binsWithTitles[i]; const filteredGeoJson = geojsonPM25.filter(d => d.properties.pm25 >= selectedRange.x && d.properties.pm25 <= selectedRange.x1 ); }); }); return wrapper; } 4a:T74a,### Research Question Addressed: - What is the distribution of PM2.5 pollution levels across Los Angeles? ### Design Choices: **- User interaction: Filtering →** Users can click on a bar to single out a range and view its specific value. **- Details on demand →** When hovering over a bar, a popup appears showing the precise PM2.5 range and count at that bar. ### How Design Choices Support Analytical Question → **User Interaction – Filtering by Bar Click:** Clicking on a specific bar allows users to filter the map view to only display locations where PM2.5 concentrations fall within the selected range. This makes it easy to isolate and investigate specific pollution levels, supporting a targeted exploration of the data. **Details on Demand – Hover Tooltip:** Hovering over a bar displays a tooltip with the exact PM2.5 range and the number of instances (e.g., how many locations or readings fall in that range). This supports a precise understanding of the distribution without overwhelming the visual space. **Visual Encoding – Bar Height = Frequency:** The height of each bar encodes the frequency of measurements within that PM2.5 range, giving users a clear sense of which pollution levels are most common across the dataset. **Unimplemented Section of the Design→** We aimed to link the histogram and heatmap so users could click a bar in the histogram to filter the heatmap by a specific PM2.5 range, showing which areas correspond to that pollution level. Additionally, hovering over a bar was intended to highlight matching regions on the heatmap. However, due to issues with event handling and rendering, the full synchronization between the two visualizations was not achieved. The heatmap did not update dynamically with bar selection, and the range highlighting feature was partially developed but not fully implemented.4b:T4e2,VegaLite({ data: {values: laPM25Data}, hconcat: [ { mark: {type: "point"}, selection: { brush: {type: "interval"}, pts: {type: "single", on: "mouseover"}, }, width: 350, height: 350, encoding: { x: {field: "population", type: "quantitative",}, y: {field: "traffic", type: "quantitative"}, tooltip: {field: "population", type: "quantitative"}, color: { condition: { selection: "pts", field: "population", type: "quantitative" }, value: "gray", }, } }, { transform: [ {filter: {selection: "brush"}} ], mark: {type: "point"}, selection: { pts: {type: "single", on: "mouseover"}, }, width: 350, height: 350, encoding: { x: {field: "cardio", type: "quantitative",}, y: {field: "traffic", type: "quantitative"}, tooltip: {field: "cardio", type: "quantitative"}, color: { condition: { selection: "pts", field: "population", type: "quantitative" }, value: "gray", }, } }, ] })4c:[{"id":"05897513d92524d7414ddee16daa8ba1237598eafcb78ca3411103b5ac8bf8e8412d6b4c330876de491e9b8796484addbe87649d1f1f127a8dd264cff42a4f23","href":"https://static.observableusercontent.com/files/05897513d92524d7414ddee16daa8ba1237598eafcb78ca3411103b5ac8bf8e8412d6b4c330876de491e9b8796484addbe87649d1f1f127a8dd264cff42a4f23","name":"calenviroscreen40resultsdatadictionary_F_2021.csv","type":"text/csv","lastModified":1744172300890.143,"size":2953084},{"id":"2998d7c14d34eb56783866eeb4fe21b5719e41c7a37c7d22fac2ffd9546b67bc7a5cdc74e09097a4bad0391e110a326ba870bfec20c02fd4909447dde7fe8aa8","href":"https://static.observableusercontent.com/files/2998d7c14d34eb56783866eeb4fe21b5719e41c7a37c7d22fac2ffd9546b67bc7a5cdc74e09097a4bad0391e110a326ba870bfec20c02fd4909447dde7fe8aa8","name":"LADOT_Traffic_Counts_Summary.csv","type":"text/csv","lastModified":1744172300890.143,"size":432879}] 43:{"id":"4184723d97a64aab","slug":null,"title":"Final Project","thumbnail":"a1171e3e07e6eb852887ff90e494479f632661f4678d443d1e40d4b4067aaaef","access_level":"public","create_time":"$D2025-04-09T04:18:20.890Z","update_time":"$D2025-04-09T04:50:57.530Z","view_time":null,"first_public_time":"$D2025-04-09T05:02:15.129Z","has_unpublished_edits":null,"fork":{"id":"2ba93eb69a1c626a","slug":null,"title":"Final Project","owner":{"id":"bd3cefe984a576b6","login":"simon-fraser-university12","name":"Simon Fraser University"}},"stars":0,"starred":false,"owner":{"type":"team","id":"a86a8af704bf86c4","login":"simon-fraser-university6","name":"RainaAaseesProjectDataSelection","avatar_url":"https://avatars-next.observableusercontent.com/fa5aa4132807d02d13add4196d68d9102a662528362204a3ee7f497d778f5178","tier":"starter_2024","bio":"","following":false},"version":379,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"9b03a980a60dde5a","login":"akr22","name":"Akr22","avatar_url":"https://avatars-next.observableusercontent.com/7fb9887f54444b692b6784015042a5cbf9cef724d40112b55bc01e5899f3feba","tier":null,"bio":"","following":false}],"members":[{"type":"individual","id":"9b03a980a60dde5a","login":"akr22","name":"Akr22","avatar_url":"https://avatars-next.observableusercontent.com/7fb9887f54444b692b6784015042a5cbf9cef724d40112b55bc01e5899f3feba","tier":null,"role":"editor"},{"type":"team","id":"a86a8af704bf86c4","login":"simon-fraser-university6","name":"RainaAaseesProjectDataSelection","avatar_url":"https://avatars-next.observableusercontent.com/fa5aa4132807d02d13add4196d68d9102a662528362204a3ee7f497d778f5178","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Final Project","cells":[{"id":0,"value":"# Final Project","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"_Hasrat Buttar (301554205) & Aasees Raina (301554205)_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"____________________","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":276,"value":"## Problem","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":278,"value":"This report explores the correlation between daily **traffic volume and air pollution levels in Los Angeles**, California. Gasoline-powered vehicles remain one of the leading contributors to urban air pollution, emitting large amounts of pollutants like fine particulate matter (PM2.5). In a sprawling city like Los Angeles, famous for its car culture and congested roadways, this issue is particularly pronounced.\n\nThe goal of our project is to visualize and analyze patterns that **link traffic density with corresponding pollution levels** across different regions of Los Angeles. By using real-world datasets on vehicle traffic and air quality, we aim to reveal how human mobility impacts environmental health. We hope our findings can inform ongoing conversations around urban planning, sustainable transportation, and public health.\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":291,"value":"## Scope","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":293,"value":"This project is designed to help people understand how vehicular traffic contributes to air pollution in urban environments, focusing specifically on Los Angeles. By visualizing the spatial and temporal relationships between traffic congestion and pollution levels, we hope to raise awareness and encourage data-driven decisions on issues such as transportation policy, infrastructure development, and climate change mitigation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":281,"value":"## User Groups","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":283,"value":"- **General Public:** Commuters who are concerned about their contribution to air pollution on a daily basis. \n- **Environmental Researchers & Activists:** People who want to understand pollution trends to advocate for change.\n- **Policy Makers:** Officials who need specified insights to better design policies and pollution control measures. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":295,"value":"These stakeholders are in a position to use the insights from our visualizations to better understand the impact of vehicle emissions and potentially explore interventions, such as promoting public transportation or redesigning traffic flow. In addition, we aim to make the data accessible and engaging for environmentally conscious citizens who wish to better understand their city’s environmental footprint.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":302,"value":"## Research Questions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":304,"value":"To guide our visualization and design choices, we have defined several key questions that our tool aims to address:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":306,"value":"- **How does the volume of daily traffic correlate with PM2.5 pollution levels (Particulate Matter that is 2.5 micrometers or smaller in diameter) in different neighborhoods of Los Angeles?**\n\n- **Where are the highest levels of pollution in Los Angeles located?**\n\n- **How does pollution intensity vary across different areas of the city?**\n\n- **How does traffic affect PM2.5 levels (Particulate Matter that is 2.5 micrometers or smaller in diameter)?**\n\n- **How does traffic affect the CalEnviroScore (CES - a measure used by the California Environmental Protection Agency to assess the environmental vulnerabilities of different communities across California)**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":308,"value":"These questions not only shape our data selection and visualization strategies, but also reflect the real-world concerns of stakeholders aiming to build more livable, sustainable cities.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":317,"value":"## Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":319,"value":"Our project combines data from these sources:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":321,"value":"**Air Pollution Data (PM2.5):**\n- Sourced from the State of California OEHHA via their Maps & Data - OEHHA data. This dataset provided an array of data regarding traffic in the United States. This dataset provided us with daily average concentrations of PM2.5 (fine particulate matter) measured across various monitoring stations in Los Angeles County, which was crucial for our visualizations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":328,"value":"**Traffic Data:**\n- We used traffic volume data from the MapBox Library . The 'Mapbox Traffic v1 | Tilesets | Mapbox Docs | Mapbox', is a Mapbox-provided vector tileset that can be used to display real-time traffic conditions on a map. This was used on our Heatmap visualization to portray traffic conditions in realtime. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":331,"value":"**Traffic Data 2:**\n\n- Data.gov: The U.S. government's open data portal, providing public access to datasets from various federal agencies across multiple domains, including science, health, and the environment. We sourced traffic data and temporal data in Los Angeles from this website. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":297,"value":"_____________________","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"# Visualization 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"### Importing and connecting Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"data = FileAttachment(\"calenviroscreen40resultsdatadictionary_F_2021.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"### Filtering data so that it only has records located in Los Angeles","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":23,"value":"laData = data.filter(d => d[\"California County\"] === \"Los Angeles\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"### Mapping only necessary dimensions into a new data array","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":59,"value":"laPM25Data = laData.map (d => ({\n latitude: d.Latitude,\n longitude: d.Longitude,\n traffic: d.Traffic,\n trafficPerc: d[\"Traffic Pctl\"],\n approxLoc: d[\"Approximate Location\"],\n ces: d[\"CES 4.0 Percentile\"],\n CES: d[\"CES 4.0 Percentile Range\"],\n pm25: d[\"PM2.5\"],\n cardio: d[\"Cardiovascular Disease Pctl\"],\n population: d[\"Total Population\"]\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"### Importing d3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"### Importing VegaLite","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":38,"value":"VegaLite = require(\"vega-embed@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"### Importing libraries necessary for dynamic slider","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":45,"value":"import {rangeSlider} from \"@mootari/range-slider\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"import {slider, menu, checkbox} from \"@jheer/dom-utilities\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"value":"### Dynamic slider that filters by traffic per square kilometer","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"viewof range = rangeSlider({\n min: d3.min(laPM25Data, d => d.traffic),\n max: d3.max(laPM25Data, d => d.traffic),\n value: this ? this.value:[0,7000],\n title: \"Traffic\",\n description: 'filtering by traffic'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":235,"value":"### Research Questions Explored - ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":237,"value":"1) How does traffic affect pm2.5 levels (particles that are a major component of air pollution)?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":239,"value":"2) How does traffic affect the CalEnviroScore (CES - measure used by the California Environmental Protection Agency to assess the environmental vulnerabilities of different communities across California)?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":222,"value":"______________________","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":68,"value":"# Visualization 2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":150,"value":"### Importing and connecting data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":70,"value":"file = FileAttachment(\"LADOT_Traffic_Counts_Summary.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"### Extracting data within file","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":76,"value":"trafficFile = file.text()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"### Data is being formatted accordingly into an array using csvParse","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":80,"value":"trafficData = d3.csvParse(trafficFile, d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"### Importing vegaLite","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":84,"value":"vegalite = require(\"@observablehq/vega-lite\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"### Sum of each districts' total traffic is calculated.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":88,"value":"sumCR = trafficData.reduce(function(sum,d){\n let total = ( d.Dist === \"CR\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"sumEV = trafficData.reduce(function(sum,d){\n let total = ( d.Dist === \"EV\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"sumHW = trafficData.reduce(function(sum,d){\n let total = ( d.Dist === \"HW\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"sumSR = trafficData.reduce(function(sum,d){\n let total = ( d.Dist === \"SR\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"sumWR = trafficData.reduce(function(sum,d){\n let total = ( d.Dist === \"WR\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"sumWV = trafficData.reduce(function(sum,d){\n let total = ( d.Dist === \"WV\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"### Sum of each weekdays' total traffic is calculated.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":102,"value":"sumMon = trafficData.reduce(function(sum,d){\n let total = ( d.Day === \"MON\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"sumTue = trafficData.reduce(function(sum,d){\n let total = ( d.Day === \"TUE\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"sumWed = trafficData.reduce(function(sum,d){\n let total = ( d.Day === \"WED\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":108,"value":"sumThu = trafficData.reduce(function(sum,d){\n let total = ( d.Day === \"THU\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"sumFri = trafficData.reduce(function(sum,d){\n let total = ( d.Day === \"FRI\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"sumSat = trafficData.reduce(function(sum,d){\n let total = ( d.Day === \"SAT\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":114,"value":"sumSun = trafficData.reduce(function(sum,d){\n let total = ( d.Day === \"SUN\") ? d.Total: 0;\n return sum+ total;\n},0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"### newData array is made empty","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":120,"value":"newData = trafficData.map(function(d,i){\n if (d.Dist === \"CR\") return 0;\n if (d.Dist === \"EV\") return 0;\n if (d.Dist === \"HW\") return 0;\n if (d.Dist === \"SR\") return 0;\n if (d.Dist === \"WR\") return 0;\n if (d.Dist === \"WV\") return 0;\n}).filter(d => d != 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"### All of the sums of the Districts and Weekdays are added to the updatedData array.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":126,"value":"updatedData = newData.concat({Dist: \"CR\", Total: sumCR},{Dist: \"EV\", Total: sumEV},{Dist: \"HW\", Total: sumHW}, {Dist: \"SR\", Total: sumSR}, {Dist: \"WR\", Total: sumWR}, {Dist: \"WV\", Total: sumWV}, {Day: \"Monday\", DayTotal: sumMon}, {Day: \"Tuesday\", DayTotal: sumTue}, {Day: \"Wednesday\", DayTotal: sumWed}, {Day: \"Thursday\", DayTotal: sumThu}, {Day: \"Friday\", DayTotal: sumFri}, {Day: \"Saturday\", DayTotal: sumSat}, {Day: \"Sunday\", DayTotal: sumSun})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":241,"value":"### Research Questions Explored - ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":243,"value":"1) How does traffic vary by neighbourhood in Los Angeles?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":245,"value":"2) How does traffic vary by weekday in Los Angeles?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":154,"value":"__________________","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":152,"value":"# Visualization 3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":161,"value":"### Importing Mapbox","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":163,"value":"mapboxgl = require(\"mapbox-gl\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":167,"value":"### Converting LaPM25Data to GeoJSON format\n\n- The data was converted to GeoJSON format which is JavaScript's Object Notation (JSON) for encoding geographic data structures. This was done for later use when combining Pollution and Traffic visualizations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":165,"value":"geojsonPM25 = laPM25Data\n .map(d => ({\n type: \"Feature\",\n geometry: {\n type: \"Point\",\n coordinates: [d.longitude, d.latitude]\n },\n properties: {\n pm25: d.pm25\n }\n }));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"### Slider Filter\n- This code creates an interactive range slider for selecting PM2.5 values between 9 and 16. The selected value (pm25Range) updates in real-time and can be used to filter the heatmap or control other visual elements dynamically.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":339,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"viewof pm25Range = Inputs.range([9, 16], {\n label: \"PM2.5 Amount\", \n step: 0.01, \n value: 16\n});\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":174,"value":"### Toggle Filter\n- This code creates a button labeled \"Toggle Heatmap\" that allows users to show or hide the heatmap layer when clicked. The `heatmapToggleButton` value updates based on user interaction.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":176,"value":"viewof heatmapToggleButton = Inputs.button(\"Toggle Heatmap\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":178,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":184,"value":"__________________________","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":370,"value":"### Research questions Answered → \n1. Where are the highest levels of air pollution in Los Angeles located?\n2. How does pollution intensity vary across different areas of the city?\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":361,"value":"### Encodings:\n\nColor intensity on the heatmap represents PM2.5 concentration (red = worse).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":345,"value":"$48","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":206,"value":"_______________________","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":186,"value":"# Visualization 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":190,"value":"### Extracting all PM2.5 values from geojsonPM25 into an array","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":188,"value":"pm25Values = geojsonPM25.map(feature => feature.properties.pm25);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"### D3 histogram generator\n- Defines a domain from 0 to 16 (PM2.5 value range).\n- Sets 16 bins, meaning PM2.5 values will be grouped into 16 equal intervals.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":193,"value":"histogram = d3.histogram()\n .domain([0, 16]) \n .thresholds(16);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"### Declaring bins\n- This code generates a histogram by applying the d3.histogram() function to the pm25Values array. It divides the data into bins based on the PM2.5 value range (from 0 to 16) and calculates the count of data points within each bin.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":200,"value":"bins = histogram(pm25Values);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":210,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"$4a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":230,"value":"______________","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":232,"value":"# Visualization 5","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":228,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":252,"value":"### Research Questions Explored - ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":270,"value":"1) Does PM2.5 levels have an impact on the cardiovascular health on the population of the community?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W4c","stdlib":"1"}} 44:"$43:body"