2e:null 2f:["$","title",null,{"children":"An Exploration of Social Vulnerability in Mississippi | Anna D | Observable"}] 34:T44d,avg = html`

` 35:T42a,bar2 = html`

` 36:T433,socioeconomic = html`

` 37:T430,disability = html`

` 38:T42e,language = html`

` 39:T42d,housing = html`

` 3a:T419,### Target Users: This insights from the visualizations along with the compared data would be most beneficial to *local*, *state*, and *federal legislatures* as well as *community organizers*. Government officials would benefit from insights about what areas their states display the most social vulnerability, as well as the federal government identifying states that may need extra support. Community organizers could leverage these insights to work within their municipalities to tackle systemic issues that may inform the persistence of these social vulnerabilities, outside of the government. ### Task Abstraction * T1: Compare the overall Social Vulnerability Scores across states * T2: Explore the score of each of the four SVI themes in the state with the highest Social Vulnerability score as well as across the counties of that state. ### Data Abstraction: * Dataset type: Table * Attribute types: Categorical, Nominal, & Ordered (Quantitative) * Availability: Static * Derivation: SVI scores (RPL themes) averaged by state and county3b:[{"id":"2fae429770dafc934394c10d5e252e3d9e7c37435bba1b9b363898f44bd46d8d3d2af9d7469d523aa4ea054afb59bf96df500ae001b756d1791853a8493a053d","href":"https://static.observableusercontent.com/files/2fae429770dafc934394c10d5e252e3d9e7c37435bba1b9b363898f44bd46d8d3d2af9d7469d523aa4ea054afb59bf96df500ae001b756d1791853a8493a053d","name":"RUCA.csv","type":"text/csv","lastModified":1648781968686.647,"size":1288862},{"id":"b51c4d067e46c093b52cc85ecd24f54cceec3b2a7d93a55f21dc846b7c18c2fd7534143e67ddedb9aef77c0bc63547dea655140ec56e506c4b89c9d17dc0aecd","href":"https://static.observableusercontent.com/files/b51c4d067e46c093b52cc85ecd24f54cceec3b2a7d93a55f21dc846b7c18c2fd7534143e67ddedb9aef77c0bc63547dea655140ec56e506c4b89c9d17dc0aecd","name":"SVI.csv","type":"text/csv","lastModified":1649975899531.247,"size":44704458}] 32:{"id":"1a258ecf0dd450bb","slug":"svi","title":"An Exploration of Social Vulnerability in Mississippi","thumbnail":"6c6150f79c4a5c07076803313996649cb9d76e4f54208a1d350a6655441f5821","access_level":"public","create_time":"$D2022-04-01T02:37:42.800Z","update_time":"$D2022-04-16T19:04:00.832Z","view_time":null,"first_public_time":"$D2022-04-14T22:43:24.863Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"1155a581d86b11e0","login":"amdaccache","name":"Anna D","avatar_url":"https://avatars-next.observableusercontent.com/3652218099fbb342d72a66863fa1181d8970d13c3f9d49199741c386a7e6f9a3","tier":"starter_2024"},"version":233,"publish_version":233,"role":null,"authored":false,"authors":[{"type":"individual","id":"b662506cf3b81797","login":"amdaccache","name":"Anna D","avatar_url":"https://avatars-next.observableusercontent.com/3652218099fbb342d72a66863fa1181d8970d13c3f9d49199741c386a7e6f9a3","tier":null}],"members":[{"type":"individual","id":"b662506cf3b81797","login":"amdaccache","name":"Anna D","avatar_url":"https://avatars-next.observableusercontent.com/3652218099fbb342d72a66863fa1181d8970d13c3f9d49199741c386a7e6f9a3","tier":null,"role":"editor"},{"type":"individual","id":"be0076fec73aa550","login":"john-guerra","name":"John Alexis Guerra Gómez","avatar_url":"https://avatars-next.observableusercontent.com/21638cee44bb0ea70798da725b57111a5d3e2acebcd14524565992f7d1594aed","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"An Exploration of Social Vulnerability in Mississippi","cells":[{"id":0,"value":"# An Exploration of Social Vulnerability in Mississippi ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3,"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/). You can watch my video summarizing the work in this notebook and insights [here](https://youtu.be/KpTy9jWz3BA). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"#### Introduction\n\nThis dataset is the **Center for Disease Control's [Social Vulnerability Index](https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html)** which utilizes census data variables (socioeconomic status, household composition, race/ethnicity/language, & housing/transportation) to develop a social vulnerability score of communities in the US. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":13,"value":"viewof table = aq.from(data).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":" #### Example Made-Up Insights:\n1. Mississippi will be the state with the highest average SVI score. This takes into account socioeconomic status, minority status/language, household composition and disability, and housing type/transportation. \n2. Minority/Language Status will be the theme that Mississippi scores the lowest in and it will score highest in Socioeconomic Status. This means Socioeconomic Status score plays a large role in MS being the state with the highest social vulnerability.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":17,"value":"### T1: Mississippi is the state with the highest social vulnerability","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":94,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"{\n avg;\n var divElement = document.getElementById('viz1649983094585'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"### T2: Mississippi's Social Vulnerability Score broken down\n* Socioeconomic status (RPL Theme 1)\n* Household Composition (RPL Theme 2)\n* Minority/Language (RPL Theme 3)\n* Housing Type & Transportation (RPL Theme 4)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":170,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":206,"value":"{\n bar2;\nvar divElement = document.getElementById('viz1650130940218'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"Socioeconomic status proved to be the highest scoring in Mississippi, suggesting it plays a large role in why Mississippi is the state with the highest overall average Social Vulnerability score. Household composition was a close second. Minority/Language score was the lowest for Mississippi suggesting that it played a minimal role in Mississippi having the highest overall average Social Vulnerability Score. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":108,"value":"#### Socioeconomic Status\nThis metric factors in: \n* Below Poverty\n* Unemployed\n* Income\n* No High School Diploma","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":112,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"{\n socioeconomic;\n var divElement = document.getElementById('viz1649983749156'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"Rankin and Madison counties, the two in the middle of MS with lower scores than the counties around it directly surround the city of Jackson, the capital of Mississippi. These would be considered the suburbs of the state's hub which could suggest why they have much lower socioeconomic status scores compared to the rest of Mississippi, it's home to folks with higher paying jobs in the city, who choose to live in the suburbs. Mississippi's northwestern counties overwhelmingly score high in their socioeconomic status vulnerability. We see in the other scores this area of the state has consistently higher scores, suggesting systemic issues that could be addressed by looking at the education system, public health, and job opportunities in the area. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":120,"value":"#### Household Composition and Disability\nThis metric factors in: \n* Aged 65+\n* Aged 17 and younger\n* Civilian with a Disability\n* Single-parent households","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":130,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":132,"value":"{\n disability;\n var divElement = document.getElementById('viz1649983693362'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"#### Minority Status and Language\nThis metric factors in: \n* Minority\n* Aged 5+ who speaks English \"less than well\"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":141,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"{\n language;\n var divElement = document.getElementById('viz1649983891124'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":125,"value":"#### Housing Type & Transportation\nThis metric factors in: \n* Multi-Unit Structures\n* Mobile Homes\n* Crowding\n* No Vehicle\n* Group Quarters","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":149,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"{\n housing;\n var divElement = document.getElementById('viz1649984087879'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":220,"value":"Again we see Madison and Rankin counties with lower vulnerability score when it comes to housing type and transportation compared to the surrounding areas. One county sticks out, Issaquena, which has a clearly higher score than any other county meaning they have higher rates of mobile homes and overcrowded living as well as lack of transportation across the population. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":15,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":79,"value":"### Data Preprocessing ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":82,"value":"// filtered out Census scores that could not be computed (-999)\nSVI = \n data\n .filter(d => d.RPL_THEMES != -999.0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"viewof table2 = aq.from(SVI).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"### Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"viewof selected = navio(data) ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"// download dataset \ndata = FileAttachment(\"SVI.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"#### Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":38,"value":"import { navio } from \"@john-guerra/navio\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"import { aq, op } from '@uwdata/arquero'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"import {vl} from '@vega/vega-lite-api-v5'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"usa = 'https://cdn.jsdelivr.net/npm/us-atlas@2/us/10m.json'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3b","stdlib":"1","titling":"auto"}} 33:"$32:body"