34:["$","title",null,{"children":"A Deep Dive into Social Vulnerability and Prevalence of Obesity in California | Anna D | Observable"}] 3c:T466,bar = html`

Prevalence of Obesity in Men and Women across California counties

` 3d:T480,scatter = html`

Prevalence of Obesity and average Socioeconomic Status Score across California counties

` 3e:T494,scatter2 = html`

Prevalence of Obesity and average Housing Composition & Disability Score across California counties

` 3f:T487,scatter3 = html`

Prevalence of Obesity and average Minority & Language Score across California counties

` 40:T491,scatter4 = html`

Prevalence of Obesity and average Housing Type & Transportation Score across California counties

` 41:T452,mapF = html`

Prevalence of obesity in Females in California

` 42:T452,mapM = html`

Prevalence of obesity in Males in California

` 43:[] 38:{"id":"4eaba8683e02207a","slug":"a-deep-dive-into-social-vulnerability-and-prevalence-of-obe","title":"A Deep Dive into Social Vulnerability and Prevalence of Obesity in California","thumbnail":"1df641fb212359045224aad7015eb9ccc3b20ca7595bf293b25b4f61379960bf","access_level":"public","create_time":"$D2022-05-05T09:46:10.011Z","update_time":"$D2022-05-05T19:25:16.026Z","view_time":null,"first_public_time":"$D2022-05-05T11:12:53.908Z","has_unpublished_edits":null,"fork":{"id":"b5ad04cc1335fe10","slug":"social-vulnerability-and-rates-of-obesity-a-comparison-acr","title":"Social Vulnerability and Prevalence of Obesity: A Comparison across States","owner":{"id":"1155a581d86b11e0","login":"amdaccache","name":"Anna D"}},"stars":0,"starred":false,"owner":{"type":"team","id":"1155a581d86b11e0","login":"amdaccache","name":"Anna D","avatar_url":"https://avatars-next.observableusercontent.com/3652218099fbb342d72a66863fa1181d8970d13c3f9d49199741c386a7e6f9a3","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":317,"publish_version":317,"role":null,"authored":false,"authors":[{"type":"individual","id":"b662506cf3b81797","login":"amdaccache","name":"Anna D","avatar_url":"https://avatars-next.observableusercontent.com/3652218099fbb342d72a66863fa1181d8970d13c3f9d49199741c386a7e6f9a3","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"b662506cf3b81797","login":"amdaccache","name":"Anna D","avatar_url":"https://avatars-next.observableusercontent.com/3652218099fbb342d72a66863fa1181d8970d13c3f9d49199741c386a7e6f9a3","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"a70cb5596dbfe2cf","slug":"cs7295-project","title":"CS7295 Project","owner":{"id":"1155a581d86b11e0","login":"amdaccache","name":"Anna D"}},"type":"notebook","body":{"titling":"auto","title":"A Deep Dive into Social Vulnerability and Prevalence of Obesity in California","cells":[{"id":0,"value":"# A Deep Dive into Social Vulnerability and Prevalence of Obesity in California","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"By [Anna Daccache](https://observablehq.com/@amdaccache) for Professor John Alexis Guerra Gómez' Spring 2022 section of [CS7295: Special Topics in Data Visualization](https://johnguerra.co/classes/data_visualization_spring_2022/).\n\n\n[Tweet](https://twitter.com/annadaccache97/status/1522294967544082433)\n\n\n[Youtube Video](https://www.youtube.com/watch?v=2B3o78HiwwY&feature=youtu.be)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":106,"value":"### Example Insight \n\nAcross counties in California, areas with the highest socioeconomic vulnerability will have the highest prevalence of obesity. \n * This would take into account county (categorical) and obesity prevalence (quantitative) from the IHME dataset and scores from the SVI dataset (ordinal).\n * This will serve as a comparison of SVI and obesity across California and allow for any patterns across social vulnerability metrics and health outcomes to be revealed. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":288,"value":"### Introduction \n[In California, 27.1% of the population is obese.](https://letsgethealthy.ca.gov/goals/living-well/reducing-adult-obesity/#:~:text=California%20has%20the%20highest%20obesity,estimated%20at%20%2415.2%20billion%20annually.&text=By%202012%2C%2021%20of%20California's,of%20the%20counties%20in%202001) Obesity is an underlying health condition that was shown to inform hospitalization and deaths in COVID-19 patients. Understanding how social vulnerability, specifically socioeconomic status and obesity may relate is important in being better prepared for preventative public health action in the face of another global health emergency. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":215,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":216,"value":"{\n bar;\n var divElement = document.getElementById('viz1651749726284'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":305,"value":"* Marks: bars (lines)\n* Channels: length for magnitude & color for identity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":300,"value":"Here we are able to compare prevalence of obesity amongst male and female populations across the counties of California. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":221,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":222,"value":"{\n scatter;\n var divElement = document.getElementById('viz1651750205103'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":308,"value":"* Marks: circles (points)\n* Channels: position on common scale for magnitude (horizontal and vertical) & color for identity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":292,"value":"This scatter plot delineates a very clear upward trend, more so than across the general states, particularly when looking at female prevalence of obesity by socioeconomic status but also for male prevalence of obesity. What this tells us is that there is indeed a correlation between the prevalence of obesity in a population and socioeconomic vulnerability: areas with higher socioeconomic status scores (more vulnerable) will have higher % of their population be obese. 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