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COVID-19 Deaths in the USA as of March 9th 2023

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Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","bio":""},"version":127,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"c43a3832073d59e0","login":"jonathan-tarun-rajasekaran","name":"Jonathan Tarun Rajasekaran","avatar_url":"https://avatars-next.observableusercontent.com/7236be39798128ec4d4f3c6f7847b6409a07099a3c87825dcc50c3ec2860644e","tier":null,"bio":""}],"members":[{"type":"individual","id":"c43a3832073d59e0","login":"jonathan-tarun-rajasekaran","name":"Jonathan Tarun Rajasekaran","avatar_url":"https://avatars-next.observableusercontent.com/7236be39798128ec4d4f3c6f7847b6409a07099a3c87825dcc50c3ec2860644e","tier":null,"role":"editor"},{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"A Haven Amidst the COVID-19 Pandemic: North-Western USA’s Resilience","cells":[{"id":0,"value":"# A Haven Amidst the COVID-19 Pandemic: North-Western USA’s Resilience","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":39,"value":"This is a data visualization project done by [Jonathan Tarun Rajasekaran](https://observablehq.com/d/8586f881717414c5) from Northeastern University, Oakland for the course [CS7295 - Special Topics in Data Visualization](https://johnguerra.co/classes/data_visualization_fall_2024/) under Professor [John Alexis Guerra Gomez](https://johnguerra.co/).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":61,"value":"This visualization project aims to visualize spatial data, focusing on the patterns and trends in COVID-19 cases, deaths, and incident rates (per 100,000 people). By examining this data across multiple locations, the project aims to uncover insights into how the pandemic affected different regions. This analysis seeks to offer a deeper understanding of the pandemic's impact and contribute to planning for future health crises.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"intro","database":"$undefined"},{"id":113,"value":"## Table of Contents\n\n1. [Introduction](#intro)\n2. Idioms (Visualizations)\n - [Idiom 1](#ID1)\n - [Idiom 2](#ID2)\n - [Idiom 3](#ID3)\n3. [Example Insights](#ei)\n - Example Insight 1\n - Example Insight 2\n4. [Real Insights](#ri)\n5. [Data Abstraction](#what)\n - Dataset Type\n - Data Availability\n - Attributes\n - Data Processing\n6. [Task Abstraction](#why)\n - Task 1z\n - Task 2\n - Task 2.1\n7. [Marks and Channels](#how)\n8. [Target Audience](#users)\n9. [Expressiveness and Effectiveness](#ee)\n10. [YouTube Video](#yt)\n11. [Tweet](#x)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":74,"value":"## Idiom 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":59,"value":"$31","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"## Idiom 2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID2","database":"$undefined"},{"id":53,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"## Idiom 3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID3","database":"$undefined"},{"id":41,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"## Example Insights\nExample Insight 1: \nThe region which was least affected by COVID-19 is the south-eastern part of USA.\n
\n\nExample Insight 2: There were only 2 states that had more than half a million confirmed cases - Washington and California. California had it worse with 20,000 cases per 100,000 people.\n
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ei","database":"$undefined"},{"id":63,"value":"## Real Insights\n\nInsight 1: The region which was least affected by COVID-19 is the North-Western part of the USA. (Taken from Idiom 2)\n\nInsight 2: There were 4 states which had more than half a million cases - California, New York, Texas and Florida. Florida had it the worst with 35,267 cases per 100,000 people. (Taken from Idiom 2)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ri","database":"$undefined"},{"id":25,"value":"## Data Abstraction (What?)\n\nThis is a COVID-19 dataset taken from the [GitHub Data Repository](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports_us) by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.\n\nDataset Type: A table with 18 columns (attributes) and 58 rows (records).\n
\n\nData Availability: It is a static dataset\n\nAttributes: \n
\n1. Province_State: A state in the USA. (Categorical, Geo)\n\n2. Confirmed: The number of confirmed COVID-19 cases. (Quantitative, Sequential)\n\n3. Deaths: The number of deaths due to COVID-19. (Quantitative, Sequential)\n\n4. Case_Fatality_Ratio: The ratio of number of deaths to the number of confirmed cases as a percentage(%). (Quantitative, Sequential)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"what","database":"$undefined"},{"id":76,"value":"## Task Abstraction (Why?)\n\nT1: Identify which region of the USA was least affected (lowest cases per 100,000 people). (Identify, Extremes)\n\nT2: Identify all the states which had over half a million cases. (Identify, Trends)\n - T2.1: Identify the state with the worst incident rate among those identified in T2. (Identify, Extremes)\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"why","database":"$undefined"},{"id":86,"value":"## Marks and Channels (How?)\n\nMarks: \n- Geo (Idioms 1,2 & 3)\n\nChannels: \n- Spatial Region: \"Province_State\" our geo spatial attribute is represented using spatial region on a world map. (Idioms - 1,2, & 3)
\n\n- Color Saturation: The \"Confirmed Cases\" (Idiom 1) (Quantitative, Sequential), \"Incident Rate\" (Idiom 2) (Quantitative, Sequential) and \"Deaths\" (Idiom 3) (Quantitative, Sequential) are represented using color saturation in their respective idioms.
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"how","database":"$undefined"},{"id":90,"value":"## Target Audience (Users)\n1. General Public and Community Leaders:\nCommunicating patterns and risks effectively empowers communities to take preventive actions, such as adhering to public health guidelines and understanding localized risks in their region.\n\n2. Healthcare Providers and Emergency Responders:\nUnderstanding trends in COVID-19 cases and deaths helps hospitals and emergency services anticipate demand for critical care facilities, staffing, and supplies. This data can guide planning for future health crises and ensure preparedness during surges.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"users","database":"$undefined"},{"id":96,"value":"## Expressiveness and Effectiveness\n\nI have chosen Idiom 2 as the one suited best for Tasks 1 & 2 (T1 & T2) as Idioms 1 & 3 are misleading, showing California as one of the most affected by a large margin, whereas it isn't if we take into account the population.\n\nExpressiveness: The idiom is very expressive as it the effect of the pandemic across the country (per 100,000 people) using a map.\n\nEffectiveness: The idiom clearly helps us accomplish our task of identifying which region was least affected and the states which had over half a million cases .","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ee","database":"$undefined"},{"id":102,"value":"## YouTube Video\nClick [here](https://youtu.be/xC_qWCMoJv8) to check out my YouTube video on the project!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"yt","database":"$undefined"},{"id":104,"value":"## Tweet\n\nClick [here](https://x.com/JonathanTa84074/status/1857639204685008943) to check out my tweet on the project!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"x","database":"$undefined"},{"id":5,"value":"untitled = await FileAttachment(\"03-09-2023.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"untitled","database":"$undefined"}],"files":"$W34","stdlib":"1"}} 30:"$2f:body"