3e:null 3f:["$","title",null,{"children":"Obesity in the USA. What causes it? | Manideep Vangari | Observable"}] 45:Tc11,//Completed by Tianming usMap = { let width = 1000 let height = 800 let container = DOM.element('div', { style: `width:${width}px;height:${width/2}px` }); yield container //base layer let map = L.map(container).setView([37.8, -96], 4); let baseLayer = L.tileLayer(tile, { attribution: openStreetAttr, minZoom: 1, maxZoom: 19, }).addTo(map); L.geoJson(usStates).addTo(map); //add US areas let UsStatesLayer = L.geoJson(usStates, {style: style}) .bindTooltip(function (Layer) { return Layer.feature.properties.NAME; }).addTo(map); //add Counties areas let UsCountiesLayer = L.geoJson(usCounties, {style: style}) .bindTooltip(function (Layer) { return Layer.feature.properties.NAME; }).addTo(map); //L.geoJson(usCounties).addTo(map); //defining the legend var legend = L.control({position: 'bottomright'}); legend.onAdd = function (map) { var div = L.DomUtil.create('div', 'info legend'), grades = [20,25,30,35], labels = []; // loop through our density intervals and generate a label with a colored square for each interval for (var i = 0; i < grades.length; i++) { div.innerHTML += ' ' + grades[i] + (grades[i + 1] ? '–' + grades[i + 1] + '
' : '+'); } return div; }; //add the legend to the map legend.addTo(map); //defining the info panel var info = L.control(); info.onAdd = function (map) { this._div = L.DomUtil.create('div', 'info'); // create a div with a class "info" this.update(); return this._div; }; // method that we will use to update the control based on feature properties passed info.update = function (props) { this._div.innerHTML = '

US Obesity Rate by State

' + (props ? '' + props.name + '
' + props.obesity_rate + ' % ' : 'Hover over a state'); }; info.addTo(map); //defining the highlight layer when the mouse is hovering function highlightFeature(e) { var layer = e.target; layer.setStyle({ weight: 5, color: '#666', dashArray: '', fillOpacity: 0.7 }); //bringing the layer to font layer.bringToFront(); info.update(layer.feature.properties); } //definging the geoJson shape and applying to the map const geojson = L.geoJson(usStates, { style: style, onEachFeature: onEachFeature }).addTo(map); //defining the layer when the mouse is off of state panel function resetHighlight(e) { geojson.resetStyle(e.target); info.update(); } //defing the zoom in geature function zoomToFeature(e) { map.fitBounds(e.target.getBounds()); } //applying the hover over and off and zoom in features to the map function onEachFeature(feature, layer) { layer.on({ mouseover: highlightFeature, mouseout: resetHighlight, click: zoomToFeature }); } } 46:T4ea,Two maps show the obesity rates on county and state level, the dimensions including locations and the obesity rates. By comparing the maps on these two levels we can see the obese population distribution in general and by county. The data are from United States Census Bureau. The interactions are details on demand, names of the county and the state and the obesity rate in percentage will show up once the user hovers on the map. To answer the question, I want to show the map of the USA and show the data by county. Because I think the map is the most intuitive way to compare the data between the east and west side. The map shows the longitude and latitude data, and a colour gradient is used to show the obesity rate which adds another dimension to the chart. The higher the obesity rate is, the darker the colour is. I picked the data we need from the original datasets and transformed the data into a CSV file. Finally, I converted Obesity_Rate and Median_Household_Income to integers data type and changed the scale for Obesity_Rate from 0-100 to 0-1 since the format in vega light is in percentage. From the map, it is easy to see that the colour is darker on the east side, so we know that the obesity rate is higher on the east side of the USA.47:T5f6,The fast food restaurant data are from Kaggale and the US median household income data are from US Census Bureau. The dimensions are longitute, latitute, and the median household income by county. The interaction is details on demand, the viweri will be able to hover on the map and see the correspoding county name and the median household income. To see if there is a relation between the median household income and the number of fast food restaurants. I would like to use one point map to see the distribution of the fast food restaurant and one choropleth map to see the wealth distribution. On the point map, the longitude and latitude of the fast food restaurant show the spacial information. On the choropleth map, the colour gradient shows the third dimension of information on top of longitude and latitude which is the income. Because the original file was way too large, I cleaned the data by deleting the unnecessary indicators and then transformed it into a CSV file. On the choropleth map, I set the range of the median household income from $0 to $100000. The hues may not be distinguishable if the range is too small since the median household income of the country is around $50000. By comparing the two graphs, I can see those fast-food restaurants are more likely to be located in counties that have a higher median income. Because the population density is different in each county, I cannot conclude if there is a correlation between the number of fast food restaurant and the median household income.48:T493,{ //Completed by Manideep const brush = vl.selectInterval().encodings("x"); const click = vl.selectPoint(); const click2 = vl.selectPoint(); const brushChart = vl.markCircle() .data(data_parsed) .encode( vl.x().field('Obesity_Rate').bin({step: 1.5}), vl.y().fieldQ('Median_Income').scale({domain: [40000, 90000]}), vl.tooltip([{field: "State"}, {field: "Obesity_Rate", title: "Obesity Rate"}, {field: "Median_Income", title: "Median Income"}]), vl.opacity().if(brush, vl.value(0.9)).value(0.1), // vl.opacity().if(selectRange, vl.value(0.9)).value(0.1) ) .params(brush) .width(190) .height(190) ////////////////////////////////////////////////////// const reactChart = vl.markBar() .data(data_parsed) //.params(selectRange) .transform( vl.filter(brush) ) .encode( vl.x().field("State"), vl.y().sum("Num_Restaurants"), vl.tooltip([{field: "Num_Restaurants", title: "Number of Restaurants"}]), //vl.opacity().if(click2, vl.value(0.75)).value(0.05) ) // .params(selectRange) // .params(click2) .height(250) .width(600); return vl.vconcat(brushChart,reactChart) .render() }49:T45c,This visualization uses data from more than one source, which includes data from the US Census Bureau and the number of quick serve restaurants from Kaggle. Multiple data dimensions are also used, such as median income, states, and the number of restaurants. I combined these two results into one data set, so it would be easier to parse. Details can be seen from the tooltip easily, and also the user can brush the bar chart to see how the selected states compare to each other. Two views are included, which are the scatterplot and the bar chart. The bar graph was used to show the massive spikes in restaurant count per state, and the brushing of the scatterplot allows the user to compare multiple states to these spikes. From this visualization, it can be seen that states that have a higher number of restaruants (i.e. California) don't always have a higher obesity rate percentage. However, states that have more restaurants AND a lower median income (i.e. Pennsylvania), do have higher obesity rates. This suggests that the differentiating factor for higher obesity rates could be due to lower median income.4a:T411,The user can click on one of the bar charts to see the corresponding changes in the scatterplot. From this visualization, the user can observe how ranges of income affect the obesity rate of that state. If we click on both the "50k-60k" and "80k-90k" bars, the scatterplot suggests that states that have a lower median income have higher obesity rates. This pattern can be observed through the other income ranges as well. Similarily, it can also be seen that obesity rates decrease as median household income increases. The data also suggests that the price range where obesity rates are the highest is between $50k-$60k. This could mean that the differenciating factor for higher obesity rates could income, rather than the number of restaurants the state has. This type of visualization was chosen to show the income trends more clearly, and the color coding of each price range makes it easier to differenciate how a state is categorized on the scatterplot chart. The scatterplot chart was chosen to show the numerous state information. 4b:T5d2,## Further Work **Tianming:** I intended to show the obesity data on county level on the leaflet map but then I realized that it would be difficult to populate 3134 entries of data manually into the Json file. I should’ve collected the obesity rate datasets in the past ten years so it would be more clear to see the change and offer a new perspective for the user. The bias I want to adjust in the future is that the population density is different in each state and more people live in the east side. I would like to continue working on this in the future. **Manideep:** I intended to combine both state and county data into my visualizations instead of only using state data. My initial idea was to include a top-layer which shows the information per state (current visualizations) but clicking/brushing/menu clicking the specific state would populate another linked visualization to show the data values of all the counties that fall into the selected state. On one of my visualizations I decided to use the brushing functionality on a data type (state) that wasn’t ordinal in nature and wouldn’t work the best. I found this out by Professor Lyn and our TA Tiffany by receiving comments on an assignment submission. I changed this design decision by implementing the brushing functionality to the scatter plot graph. In the next version I would try to implement this, and maybe we can also include how the prevalence/frequency of fast food ads relates to obesity rates as well. 4c:[{"id":"8f1bad80cbb2cbd1fab19ff7c96675befd1ecbdd41f2757ea2e0ace1df4c6bb4ec21d4fbacd09a9537963a53508c158b3538cb12310c08aba8c29da14ea7f03d","href":"https://static.observableusercontent.com/files/8f1bad80cbb2cbd1fab19ff7c96675befd1ecbdd41f2757ea2e0ace1df4c6bb4ec21d4fbacd09a9537963a53508c158b3538cb12310c08aba8c29da14ea7f03d","name":"usStates.json","type":"application/json","lastModified":1670385259845.357,"size":307199},{"id":"285e4052ffeff388e8180e148d7ea93938f732e64f7f01fd881dcd15c2bd5a68a16ab9aed0c9a3176901e3baa139caf210ad53bb0fe583df564f9a6afbf266de","href":"https://static.observableusercontent.com/files/285e4052ffeff388e8180e148d7ea93938f732e64f7f01fd881dcd15c2bd5a68a16ab9aed0c9a3176901e3baa139caf210ad53bb0fe583df564f9a6afbf266de","name":"gz_2010_us_050_00_5m.json","type":"application/json","lastModified":1670385259845.357,"size":5996982}] 43:{"id":"2ae699d3ac27ee15","slug":null,"title":"Obesity in the USA. What causes it?","thumbnail":"2a73556059dae0e86b5ce018d3fda6f66503e739296aa8d0dcb375fa131a9ce4","access_level":"public","create_time":"$D2022-12-07T03:54:19.845Z","update_time":"$D2022-12-12T03:44:28.617Z","view_time":null,"first_public_time":"$D2022-12-07T03:54:19.845Z","has_unpublished_edits":null,"fork":{"id":"35ccf1eb6d3535ba","slug":null,"title":"IAT355 Assignment 4 & 5 Project Visualizations (Group 7, Tianming Liu & Manideep Vangari)","owner":{"id":"43ce8365533aacde","login":"liutianming07","name":"Tianming Liu"}},"stars":0,"starred":false,"owner":{"type":"team","id":"214cb1016147f554","login":"manideepvangari","name":"Manideep Vangari","avatar_url":"https://avatars-next.observableusercontent.com/b54d74af4916a44574b006d3643d10705cd62c0126990aa8c0571c83f3af54ee","tier":"starter_2024","bio":"","following":false},"version":1049,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"2288390d16796f22","login":"manideepvangari","name":"Manideep Vangari","avatar_url":"https://avatars-next.observableusercontent.com/b54d74af4916a44574b006d3643d10705cd62c0126990aa8c0571c83f3af54ee","tier":null,"bio":"","following":false}],"members":[{"type":"individual","id":"2288390d16796f22","login":"manideepvangari","name":"Manideep Vangari","avatar_url":"https://avatars-next.observableusercontent.com/b54d74af4916a44574b006d3643d10705cd62c0126990aa8c0571c83f3af54ee","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Obesity in the USA. What causes it?","cells":[{"id":0,"value":"# Obesity in the USA. What causes it?\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":984,"value":"#### By Tianming Liu and Manideep Vangari\n#### Fall 2022\n#### IAT 355 ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":995,"value":"## Introduction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":989,"value":"Most of us have heard or seen jokes about Americans being fat on social media and on the news. At first, we just glossed over this problem, taking it as a joke. But the more we looked at it, the more we realized that this was an actual issue that should be addressed. After brainstorming the possible reasons for why the USA have such comparatively high obesity rates, we thought of an overriding factor: income. \n\nWith this overarching reason in mind, we have also used geographic locations and the number of fast food restaurants in hopes to discover the main reason behind high obesity rates in the USA. This project is designed to show the potential factors that affect obesity rates of the different areas in the USA. We are using the datasets of median household income (Census Bureau), number of fast food restaurants (Kaggle), obesity rate of states (Statista) and obesity rate of counties (Census Bureau) to see if there are relationships among the factors.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1000,"value":"## Domain Questions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1004,"value":"Our domain question relates to how income affects obesity rates in the USA. Sub-questions include: \n\n- Is the obesity rate in the east side of the USA higher than on the west side?\n- What is the relation between the number of fast food restaurants and the median\nhousehold income in each county? \n- How does the number of restaurants and median income of a state relate to its obesity \nrate?\n- Is there a relationship between obesity percentages and median household income?\n- Do obesity percentages increase/decrease as household income increases/decreases?\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1009,"value":"## Data Dimensions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1011,"value":"We used the dataset from the US Census Bureau and Kaggle. The data dimensions we used from the **Census Bureau** dataset includes the obesity rate per county, obesity rate per state, median income per county, and median income per state. These dimensions are all ratio data types. \n\nThe data dimension used from **Kaggle** is the number of restaurants per county, and the data dimension used from **Statista** is the number of restaurants per state. These two dimensions are ratio data types as well.\n\nWe have combined these data dimensions into our own separately created datasets to make the parsing process easier. However, we have not manipulated any of the data values themselves.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"## Is the obesity rate in the east side of the USA higher than on the west side?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":976,"value":"obesityData = ObesityAndIncome","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":978,"value":"obesityDataParsed = { \n for (let i=0;i\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":561,"value":"//Completed by Tianming\nvl.markGeoshape({stroke: '#aaa', strokeWidth: 0.25})\n .data(vl.topojson(usa).feature('counties'))\n .transform(\n vl.lookup('id').from(vl.data(obesityDataParsed).key('id').fields('County','Obesity_Rate'))\n )\n .encode(\n vl.color().fieldQ('Obesity_Rate').scale({domain: [0, 0.6], clamp: true}).legend({format: '%'}),\n //vl.tooltip().fieldN('County'),\n vl.tooltip([{field:\"County\"}, {field:\"Obesity_Rate\", format:\".0%\",title:\"Obesity Rate\"}])\n )\n .title('US Obesity Rate by County')\n .project(vl.projection('albersUsa'))\n .width(900).height(500)\n .config({view: {stroke: null}})\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":204,"value":"### Reflection\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":206,"value":"$46","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":212,"value":"## What is the relation between the number of fast food restaurants and the median household income in each county?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":25,"value":"fastFoodData = FastFood","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":708,"value":"{ // Completeted by Tianming\n \n //const selection = vl.selectPoint(); //We create a selection instance which indicates that we want a selection defined over a point value\n return vl.markSquare({size: 1, opacity: 1})\n .data(fastFoodData)\n //.param(selection)\n .encode(\n vl.longitude().fieldQ('Lon'),\n vl.latitude().fieldQ('Lat') \n )\n .project(vl.projection('albersUsa'))\n .width(900)\n .height(500)\n .title(\"Fast Food Restaurant Distribution in the US\")\n .config({view: {stroke: null}})\n .render()\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":214,"value":"// Completed by Tianming\n vl.markGeoshape({stroke: '#aaa', strokeWidth: 0.25})\n .data(vl.topojson(usa).feature('counties'))\n .transform(\n vl.lookup('id').from(vl.data(obesityDataParsed).key('id').fields('County','Median_Household_Income'))\n )\n .encode(\n vl.color().fieldQ('Median_Household_Income').scale({domain: [0, 100000], clamp: true}).legend({format:'$K'}),\n vl.tooltip([{field:\"County\"},{field:\"Median_Household_Income\",format:\"$K\", title:\"Median Household Income\"}])\n )\n .title('US Median Household Income by County')\n .project(vl.projection('albersUsa'))\n .width(900).height(500)\n .config({view: {stroke: null}})\n .render() \n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":258,"value":"## Reflection\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":263,"value":"$47","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":642,"value":" ## How does the number of restaurants and median income of a state relate to its obesity rate?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":620,"value":"data_parsed = {\n\n for(let i=0; iOpenStreetMap'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"--- CSS --- \n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":372,"value":"function getColor(d) {\n return d >= 35 ? '#08519c' :\n d >= 30 ? '#3182bd' :\n\t d >= 25 ? '#6baed6' :\n\t d >= 20 ? '#bdd7e7' :\n\t '#eff3ff';\n }","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"function style(feature) {\n return {\n fillColor: getColor(feature.properties.obesity_rate),\n weight: 2,\n opacity: .5,\n color: 'white',\n dashArray: '3',\n fillOpacity: 0.7\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"## Libraries ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"d3 = require(\"https://d3js.org/d3.v5.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":613,"value":"vegalite = require(\"@observablehq/vega-lite@0.1\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":455,"value":"mutable selectedMap = [\"warmgreys\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":616,"value":"VegaLite = require('vega-lite@2.0.0-beta.2/build/vega-lite.min.js')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":457,"value":"heatLayer = L, require('leaflet.heat').catch(() => L.heatLayer)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"## Resources","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":200,"value":"#### Cartographic Visualization, https://observablehq.com/@uwdata/cartographic-visualization#unemp\n\n#### GEOJSON AND KML DATA FOR THE UNITED STATES, https://eric.clst.org/tech/usgeojson/\n\n#### [IAT355] Tutorial 2: Working with data in d3 and Vega | SOLUTION, https://observablehq.com/@krispybird/iat355-tutorial-2-working-with-data-in-d3-and-vega-solution\n\n#### [IAT355] Tutorial 7: Maps & Geocoding with Vega-Lite and Leaflet 🍂| SOLUTION, https://observablehq.com/@krispybird/iat355-tutorial-7-maps-solution\n\n#### Interactive Choropleth Map, https://leafletjs.com/examples/choropleth/\n\n#### Rankings Data & Documentation, https://www.countyhealthrankings.org/explore-health-rankings/rankings-data-documentation\n\n#### US Restaurant Dataset (County), https://www.kaggle.com/datasets/michaelbryantds/us-restaurants\n#### US Restaurant Dataset (State), https://www.statista.com/statistics/825076/number-of-lsr-s-by-us-state/\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W4c","stdlib":"1"}} 44:"$43:body"