29:null 2a:["$","title",null,{"children":"McDonald’s and American Urbanization | Fuller, Flynn | Observable"}] 2f:T656,With urbanization and movement comes change, opportunity, and, most importantly for this project, business. That's where McDonald's comes in. McDonald's was founded in 1940 by the McDonald brothers, the business originally being a carhop-style barbecue restaurant. The brothers soon realized that most of their profits came from their hamburgers, and so they rebranded after a few months' rest, selling their burgers along with a small selection of other fixings in a Henry Ford-style quick production line. From there, McDonald's has never looked back, currently standing as the world's largest restaurant chain by revenue. Chain restaurants like McDonald's can act as signifiers for urbanization - if one pops up, that means the chain sees some kind of opportunity in the town in which it appears. The more there are, the more opportunity exists in the eyes of McDonald's, and the more McDonald's seeks to profit from the town's hungry customers. As such, I want to test the following hypothesis: ***US States with a higher urban population have, on average, more McDonald's locations compared to states with a lower urban population.***

Sources:

30:[{"id":"abf1733321d44286321e9f4d308e15bbc58bbeeb512861f58f10160291530488b2154c83f573726ae95a8a09effe29429ad9ff845b1a60072570dd534adcf85e","href":"https://static.observableusercontent.com/files/abf1733321d44286321e9f4d308e15bbc58bbeeb512861f58f10160291530488b2154c83f573726ae95a8a09effe29429ad9ff845b1a60072570dd534adcf85e","name":"us-counties-10m.json","type":"application/json","lastModified":1790554752389,"size":842143},{"id":"7b573aaf06aa0f89baa91647c90f5b2b5865502f6b53f67976dda308a3a2b24b76c57c7cba08b4ad9a616e1a19d66b51c0a5f972cf414295b0e1f14db043cf7f","href":"https://static.observableusercontent.com/files/7b573aaf06aa0f89baa91647c90f5b2b5865502f6b53f67976dda308a3a2b24b76c57c7cba08b4ad9a616e1a19d66b51c0a5f972cf414295b0e1f14db043cf7f","name":"image.png","type":"image/png","lastModified":1790564966327,"size":53643},{"id":"2b20f72760f1cb19f9542412ed920832f0d981eb1d4ed8294f5ccd56d617d74364ffc46c1edcdb5bb0fcb277a682075537756f8541e3d8cad0d4f795b6307290","href":"https://static.observableusercontent.com/files/2b20f72760f1cb19f9542412ed920832f0d981eb1d4ed8294f5ccd56d617d74364ffc46c1edcdb5bb0fcb277a682075537756f8541e3d8cad0d4f795b6307290","name":"McDonalds Locations.csv","type":"text/csv","lastModified":1790573976542,"size":888917},{"id":"65a9ac34a28f6753c6b0430bc22c3670b23a080a4743c09abf94583fdc09f09f31a1e82e2170505b7aab3830005ac28334d76ff50ff4926b8f6c031582d0a23b","href":"https://static.observableusercontent.com/files/65a9ac34a28f6753c6b0430bc22c3670b23a080a4743c09abf94583fdc09f09f31a1e82e2170505b7aab3830005ac28334d76ff50ff4926b8f6c031582d0a23b","name":"States by Urban Population and McDonalds Count.csv","type":"text/csv","lastModified":1790608858412,"size":2475}] 2d:{"id":"45f33319fc6fe626","slug":"mc-donalds-and-american-urbanization","title":"McDonald’s and American Urbanization","thumbnail":"eb55204bc1faad5cc954da66ccd917f8114c74c1921f93b780da50bd172af32c","access_level":"public","create_time":"$D2026-09-27T22:37:44.769Z","update_time":"$D2026-09-28T16:41:29.527Z","view_time":null,"first_public_time":"$D2026-09-28T12:48:52.172Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"c1840ee67d00e58d","login":"fuller-flynn","name":"Fuller, Flynn","avatar_url":"https://avatars-next.observableusercontent.com/e828411dff52a8988d00f6820779f412c6bf4ea1d1f1d01f016c00205c8ef67b","tier":"starter_2024"},"version":569,"publish_version":569,"role":null,"authored":false,"authors":[{"type":"individual","id":"9a587d59cba6991c","login":"flynn-fuller","name":"Fuller, Flynn","avatar_url":"https://avatars-next.observableusercontent.com/e828411dff52a8988d00f6820779f412c6bf4ea1d1f1d01f016c00205c8ef67b","tier":null}],"members":[{"type":"team","id":"c1840ee67d00e58d","login":"fuller-flynn","name":"Fuller, Flynn","avatar_url":"https://avatars-next.observableusercontent.com/e828411dff52a8988d00f6820779f412c6bf4ea1d1f1d01f016c00205c8ef67b","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"McDonald’s and American Urbanization","cells":[{"id":1,"value":"# McDonald's and American Urbanization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"The United States is a vast and varied country. Across its 50 states and 5 incorporated territories, the US boasts a population of roughly 342.9 million people - the third largest in the world. It's a population that's quickly urbanizing.\n\nThe graph below depicts the rising American urban population from 1960 to 2025, with each of the milestone lines representing another 50 million in urban population growth. Currently, the US sits at an urban population of around 274 million - 79.9% of the nation's population, and it's a projection that isn't likely to slow down based on the graph's upward trend across its covered timespan.\n\n(Image taken from MacroTrends.net)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"FileAttachment(\"image.png\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"$2f","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"To start, shown below are some base graphs for an initial look at the data - a graph displaying the count of every state's McDonald's location quantities, and a graph displaying each state's urban population by percentage.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"Plot.plot({\n title: \"States by # of McDonald's Locations\",\n marginBottom: 80,\n width: 1200,\n x: {\n tickRotate: -40,\n },\n y: {\n label: \"↑ # of McDonald's Locations\"\n },\n marks: [\n Plot.barY(data, {x: \"State\", y: \"# of McDonalds\", fill: \"steelblue\", sort: { x: \"y\", reverse: true, limit: 50 }}),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"const urbanMap = new Map(data.map(d => [d.State, {\n ...d,\n pct: Number(d[\"Urban Pop. (%)\"])\n}]))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"Plot.plot({\n title: \"US States by % of Urban Population\",\n width: 975,\n height: 610,\n projection: \"albers-usa\",\n color: {\n scheme: \"blues\",\n domain: [0, 100],\n label: \"Urban Population (%)\",\n legend: true\n },\n marks: [\n Plot.geo(states, {\n fill: d => urbanMap.get(d.properties.name)?.pct,\n channels: {\n State: (d) => d.properties.name,\n \"Raw Population\": (d) => urbanMap.get(d.properties.name)?.[\"Urban Pop. (Raw)\"]\n },\n tip: true\n }),\n Plot.geo(statemesh, {stroke: \"white\", strokeWidth: .5})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"With our base data, we can now combine both to create two more graphs - one to display the count of McDonald's locations, and another to display the amount of McDonald's locations per 100,000 people.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"const amountMap = new Map(data.map(d => [d.State, {\n ...d,\n pct: Number(d[\"# of McDonalds / 100,000 People\"])\n}]))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"Plot.plot({\n title: \"US States by # of McDonald's Locations per 100,000 People\",\n width: 975,\n height: 610,\n projection: \"albers-usa\",\n color: {\n scheme: \"blues\",\n domain: [0, 6],\n label: \"# of McDonalds / 100,000 People\",\n legend: true\n },\n marks: [\n Plot.geo(states, {\n fill: d => amountMap.get(d.properties.name)?.pct,\n channels: {\n State: (d) => d.properties.name,\n \"# of McDonalds Locations\": (d) => amountMap.get(d.properties.name)?.[\"# of McDonalds\"],\n \"Population\": (d) => amountMap.get(d.properties.name)?.[\"Population\"]\n },\n tip: true\n }),\n Plot.geo(statemesh, {stroke: \"white\", strokeWidth: .5})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"Now, let's put it all together. Is there an actual relation between a state's urban population and its number of McDonald's locations?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"Plot.plot({\n title: \"US States by Urban Population and Number of McDonald's Locations\",\n x: {\n type: \"log\",\n label: \"Urban Population (log)\"\n },\n y: {\n type: \"log\",\n label: \"# of McDonalds\"},\n marks: [\n Plot.dot(data, {\n x: (d) => Number(d[\"Urban Pop. (Raw)\"].replaceAll(\",\", \"\")),\n y: \"# of McDonalds\",\n stroke: \"# of McDonalds / 100,000 People\",\n channels: {State: \"State\"},\n tip: true\n }),\n Plot.link({length: 1}, {\n x1: 225000,\n x2: 38000000,\n y1: 25,\n y2: 1300,\n stroke: \"red\"\n })\n ],\n color: {\n legend: true,\n domain: [0, 6],\n scheme: \"blues\"\n }\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"In conclusion, based on the data I've gathered from each state's urban population and McDonald's locations, it's safe to say that having more McDonald's locations in your state's bounds is indicative of having a higher urban population.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"# Revisions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"## Revision #1\nWith my first graph, I initially wanted to display each McDonald's location by its latitude and longitude in a map of the US, creating a sort of map-based scatterplot with each dot representing an individual location. However, the dots were incredibly clustered, especially on the East Coast, and as such, I changed my graph to a bar chart.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"Plot.plot({\n title: \"US McDonald's Locations\",\n projection: \"albers-usa\",\n marks: [\n Plot.geo(statemesh, {strokeWidth: 0.5}),\n Plot.dot(mcdonaldsdata, {\n x: \"Longitude\",\n y: \"Latitude\",\n channels: {Store: \"Store\", City: \"City\", State: \"State\"},\n tip: true\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"## Revision #2\nWith my initial urban population percentage map, I initially displayed the scale as going from the lowest point (35.1, Vermont) to the highest (94.2, California). This could, however, lead to a bit of a misleading graph in which people might think that Vermont is highly under-urbanized at face value, so I edited the scale to go from 0-100% instead.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"Plot.plot({\n title: \"US States by % of Urban Population\",\n width: 975,\n height: 610,\n projection: \"albers-usa\",\n color: {\n scheme: \"blues\",\n domain: [35.1, 94.2],\n label: \"Urban Population (%)\",\n legend: true\n },\n marks: [\n Plot.geo(states, {\n fill: d => urbanMap.get(d.properties.name)?.pct,\n channels: {\n State: (d) => d.properties.name,\n \"Raw Population\": (d) => urbanMap.get(d.properties.name)?.[\"Urban Pop. (Raw)\"]\n },\n tip: true\n }),\n Plot.geo(statemesh, {stroke: \"white\", strokeWidth: .5})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"value":"## Revision #3\nAfter fixing the scale of my urban population percentage graph, I found that the scale for my # of McDonald's locations per 100,000 people graph could also be misleading, as it only goes from 2 to 6. As such, that scale has been fixed, too.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"Plot.plot({\n title: \"US States by # of McDonald's Locations per 100,000 People\",\n width: 975,\n height: 610,\n projection: \"albers-usa\",\n color: {\n scheme: \"blues\",\n domain: [2, 6],\n label: \"# of McDonalds / 100,000 People\",\n legend: true\n },\n marks: [\n Plot.geo(states, {\n fill: d => amountMap.get(d.properties.name)?.pct,\n channels: {\n State: (d) => d.properties.name,\n \"# of McDonalds Locations\": (d) => amountMap.get(d.properties.name)?.[\"# of McDonalds\"],\n \"Population\": (d) => amountMap.get(d.properties.name)?.[\"Population\"]\n },\n tip: true\n }),\n Plot.geo(statemesh, {stroke: \"white\", strokeWidth: .5})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"## Revision #4\nWith my initial scatterplot, I found that the data appeared very clustered around the 0-5 million Urban Population range, with some heavy outliers in the 20-35 million Urban Population range, causing the plot to look rather bare on the right. I decided to do a log scale to try to assuage this issue.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"Plot.plot({\n title: \"US States by Urban Population and Number of McDonald's Locations\",\n x: {\n type: \"linear\",\n label: \"Urban Population\"\n },\n y: {\n type: \"linear\",\n label: \"# of McDonalds\"},\n marks: [\n Plot.dot(data, {\n x: (d) => Number(d[\"Urban Pop. (Raw)\"].replaceAll(\",\", \"\")),\n y: \"# of McDonalds\",\n stroke: \"# of McDonalds / 100,000 People\",\n channels: {State: \"State\"},\n tip: true\n })\n ],\n color: {\n legend: true,\n domain: [2, 6],\n scheme: \"blues\"\n }\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"## Revision #5\nWith the log scale making the data appear more uniform, I then added a red \"median\" line to represent a baseline log-McDonalds Count scale.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"Plot.plot({\n title: \"US States by Urban Population and Number of McDonald's Locations\",\n x: {\n type: \"log\",\n label: \"Urban Population (log)\"\n },\n y: {\n type: \"log\",\n label: \"# of McDonalds\"},\n marks: [\n Plot.dot(data, {\n x: (d) => Number(d[\"Urban Pop. (Raw)\"].replaceAll(\",\", \"\")),\n y: \"# of McDonalds\",\n stroke: \"# of McDonalds / 100,000 People\",\n channels: {State: \"State\"},\n tip: true\n })\n ],\n color: {\n legend: true,\n domain: [2, 6],\n scheme: \"blues\"\n }\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"mcdonaldsdata = FileAttachment(\"McDonalds Locations.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"data = FileAttachment(\"States by Urban Population and McDonalds Count.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"us = FileAttachment(\"us-counties-10m.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"states = topojson.feature(us, us.objects.states).features","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"statemesh = topojson.mesh(us, us.objects.states)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W30","stdlib":"2","titling":"auto"}} 2e:"$2d:body"