2b:null 2c:["$","title",null,{"children":"Analyzing suicides in US | Mihir Mesia | Observable"}] 31:T95d,# Analyzing suicides in US By Harshit Gajjar, Mihir Mesia
Video: [Video](https://youtu.be/plpRLAP5vD0)
Slides: [Slide](https://docs.google.com/presentation/d/1WYunTILcJPSpI4NOevOWNdu8ScHV3fOoYIE2POzNkuc/edit#slide=id.p1)
Usability Report: [Usability study (Harshit Gajjar)](https://drive.google.com/drive/folders/13KQj3usYoxOjy3mlXvMGx0muof72R4DU?usp=share_link)
Usability Report: [Usability study (Mihir Mesia)](https://docs.google.com/document/d/1kWy7G8V1ij1jm9Uq3HxG28IYXft4f6btp6cupvk9FKE/edit)
Demo: [Demo](https://harshitg24.github.io/SuicidesInUS/)
Class: [Class](https://johnguerra.co/classes/data_visualization_spring_2023/)
Instructor: [John Alexis Guerra Gomez](https://observablehq.com/@john-guerra)
Code on Github: [Code](https://github.com/HarshitG24/SuicidesInUS/tree/rls/master) ## Between Hope and Despair: Dissecting the Complex Interplay of Suicide Rates, Methods, and Gun Laws in the USA Suicide is a pressing public health concern that continues to devastate families and communities across the United States. With a rising number of individuals succumbing to this preventable tragedy, understanding the factors contributing to suicide rates becomes critical in designing effective interventions and policies. Our data visualization aims to shed light on the complex interplay between suicide rates, methods, and gun laws across different states, providing a comprehensive and comparative perspective on this crucial issue. This timely analysis will help unravel the impact of suicides across different ethnicity, gender as well as highlight variations in methods used for suicide across states. By examining the correlation between these factors, our visualization seeks to provide an evidence-based foundation for further research, policy discussions, and targeted interventions. Ultimately, our goal is to contribute to the ongoing efforts to better understand and prevent suicide, and to create a safer and more supportive environment for individuals struggling with mental health challenges. By delving into this important topic, we hope to stimulate a constructive dialogue among researchers, policymakers, mental health professionals, and the general public. Together, we can work towards reducing the incidence of suicide and improving the well-being of individuals and communities throughout the United States.32:T554,## Data Abstraction (What) **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |year|ordinal, sequential| |Derived Attributes|Type & Direction| |---|---| |rate|quantitative, sequential| * **Availability:** Static * **Time frame of Data:** 1999 - 2020 * **Data Source:** [Suicides in United States](https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=C8B84AE25B40557654EAE4E01326) ## Task Abstraction (Why) - T1: Summarize the suicide rate in US. **[Summarize, Trend]** - T2: Identify the extremes in suicide rate. **[Identify, Extreme]** ## Marks and Channels (How) * **Marks:** Point |Channel|Attribute|Using|Rationale |---|---|---|---| |Horizontal Position|rate|Express|It is a quantitative attribute but on the y-axis. Each year can have a different suicide rate, so we use position to display it, making it the most effective and expressive |Vertical Position|year|Separate, Order| It is an ordinal attribute but on the x-axis. There are multiple years which needs to be shown. so we use position to display it, making it the most effective and expressive ## Insights * From 1999 to 2020, the country experienced a shocking increase of nearly 23% in suicide rates, with the sharpest increase occurring between 2016 and 2017 when it reached its peak. * In the year 2017, suicide rate was at peak with the suicide rate of 26.75/100k33:T924,## Data Abstraction (What) **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |Adults_serious_suicide_thoughts|quantitative, sequential| |Untreated_mental_health_adult|quantitative, sequential| |States|Categorical| |Political_Affiliation|Categorical| * **Availability:** Static * **Time frame of Data:** 2022 * **Data Source:** [Suicides in United States](https://vpc.org/revealing-the-impacts-of-gun-violence/suicide/) ## Task Abstraction (Why) - T1: Identify the states with highest and lowest percentage of adults with serious thoughts of suicides. **[Identify, Extreme]** - T2: Identify the states with highest and lowest percentage of untreated adults with mental illness . **[Identify, Feature]** - T3: Summarize the correlation adult suicide rate and treatment of adults with mental health conditions. **[Summarize, Correlation]** ## Marks and Channels (How) * **Marks:** Points |Channel|Attribute|Using|Rationale |---|---|---|---| |Position|State||As it is a grid map the state needs to positioned on the map accordingly, so that it becomes easy to identify states according to their position on the map. |Color Hue|Political_Affiliation|Express| It is a categorical attribute that separated two political parties "democrat" and "republicans". So using color hue according to the state's political affiliation makes it most effective and expressive |Area|Adults_serious_suicide_thoughts,Untreated_mental_health_adult|| It is a quantitative attribute, so higher the percentage of adults for both the map bigger the area of the state. ## Insights - A revealing grid map chart of the U.S. in 2022 highlights varying percentages of adults with serious suicidal thoughts across states. Utah, Alaska, and Ohio showed the highest rates, while Georgia, Texas, and North Carolina reported the lowest rates. - Hawaii, Georgia, and California have the highest percentages of adults with mental health conditions who were untreated, while Vermont, Minnesota, and Wisconsin report the lowest - The states which had comparatively lower population of adults with serious thoughts suicide like California and Texas had very high percentage of adults with mental health conditions who where untreated.This contrast provides a basis for a compelling narrative exploring social, economic, and healthcare factors in these regions34:T788,## Data Abstraction (What) **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |year|ordinal, sequential| |occupation|categorical| |gender|categorical| |Derived Attributes|Type & Direction| |---|---| |rate|quantitative, sequential| * **Availability:** Static * **Time frame of Data:** 2012 and 2015 * **Data Source:** [Workplace suicides in United States](https://www.cdc.gov/mmwr/volumes/67/wr/mm6745a1.htm?s_cid=mm6745a1_w) ## Task Abstraction (Why) - T1: Identify the occupations with highest suicide rate in males for given year [Identify, Extreme] - T2: Identify the occupations with highest suicide rate in females for given year [Identify, Extreme] ## Marks and Channels (How) * **Marks:** Line |Channel|Attribute|Using|Rationale |---|---|---|---| |Horizontal Position|rate|Express|It is a quantitative attribute but on the y-axis. Each gender for different occupation can have a different suicide rate, so we use position to display it, making it the most effective and expressive |Vertical Position|occupation|Separate, Order| It is a categorical attribute but on the x-axis. There are multiple occupation which needs to be shown. so we use position to display it, making it the most effective and expressive |Length|rate|Express|It is a quantitative attribute to denote suicide rate per 100k. More the length, higher the suicide rate. This helps user see visually which occupations saw higher suicide rate for male/female in the year 2012/2015 ## Insights * Male workers in the construction and extraction industries had the highest suicide rates in both years, and education, training and library occupation saw the least suicide rate in years 2012 and 2015. * Female workers in the arts, design, entertainment, sports, and media industries had the highest suicide rates in years of 2012 and 2015, while education, training and library occupation saw the least suicide rate in years 2012 and 201535:T940,## Data Abstraction (What) * **Dataset Type:** Table (converted to network) |Attributes|Type & Direction| |---|---| |Race|categorical| |State|categorical| |Deaths|quantitative, sequential| |Derived Attributes|Type & Direction| |---|---| |value|quantitative, sequential| * **Availability:** Static * **Time frame of Data:** 2016 - 2020 * **Data Source:** [Suicides in United States](https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=C8B84AE25B40557654EAE4E01326) ## Task Abstraction (Why) - T1: Summarize the state as blue or red. **[Summarize, Feature]** - T2: Summarize the suicide trends in the red and blue states **[Summarize, Feature]** - T3: Identify the race/ethnicity with highest number of suicide cases. **[Identify, Extreme]** ## Marks and Channels (How) * **Marks:** Point, Line |Channel|Attribute|Using|Rationale |---|---|---|---| |Position|State, Race|-|State" and "Race" is a categorical attribute and they are represented by node. The best way to show red and blue states and race/ethnicity is to use the position. The nodes can be aligned as the outcome of the force simulation algorithm and we can place the nodes depending upon forces applied. |Color Hue|State, Race|separate| "State" and "Race" is a categorical attribute so it is represented with color hue. The best way to show red and blue states and race/ethnicity is to use color hue. The colors determines if it is a red/blue state or race/ethnicity. |Area|value|express| "Value" is a quantitative attribute so it is represented with Area. The best way to see determine number of suicide cases across different red and blue states a is to use Area in a Force Simulation (Node-Link). The Node-Link idiom will help us summarize the states as red or blue and show the number of suicide cases for each state. ## Insights * Suicides among Asians were found in only blue states and one red state that is Texas. * suicides among American Indian or Alaskan Native were mostly found in red states and one blue state that is new mexico. * California has highest suicide rates in the country and maximum suicides were linked with Whites as compared to other race/ethnicity. * White was the only race/ethnicity which had suicides in all the states and other races such as Asians, Black or African American didn't report suicide cases in all the states from the timeframe of 2016-202036:T68a,## Data Abstraction (What) * **Dataset Type:** Table (converted to network) |Attributes|Type & Direction| |---|---| |Race|categorical| |State|categorical| |Deaths|quantitative, sequential| |Derived Attributes|Type & Direction| |---|---| |value|quantitative, sequential| * **Availability:** Static * **Time frame of Data:** 1999 - 2020 * **Data Source:** [Suicides in United States](https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=C8B84AE25B40557654EAE4E01326) ## Task Abstraction (Why) - T1: Summarize the suicide trends among different race/ethnicity in US. **[Summarize, Feature]** - T2: Identify the trends in suicide rate among different race/ethnicity. **[Identify, Trend]** ## Marks and Channels (How) * **Marks:** Point |Channel|Attribute|Using|Rationale |---|---|---|---| |Horizontal Position|year|Express|It is a quantitative attribute but on the y-axis. Each year can have a different suicide rate, so we use position to display it, making it the most effective and expressive |Vertical Position|rate|Separate, Order| It is an ordinal attribute but on the x-axis. There are multiple years which needs to be shown. so we use position to display it, making it the most effective and expressive |Color Hue|race|Separate, Order| It is a categorical attribute. There are multiple race/ethnicity which needs to be shown so we use color hue to separate them, making it the most effective and expressive ## Insights * From 1999 to 2020, Native Americans experienced the highest suicide rates among all racial groups, followed by Whites. * Black and Asian populations consistently displayed the lowest suicide rates as compared to other suicide rates.37:T6bd,## Data Abstraction (What) **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |Suicide_Percentage|quantitative, sequential| |Method_Suicide|categorical| |Derived Attributes|Type & Direction| |---|---| |Suicide_Percentage|quantitative, sequential| * **Availability:** Static * **Time frame of Data:** 2020 * **Data Source:** [Suicides in United States](https://www.cdc.gov/suicide/suicide-data-statistics.html) ## Task Abstraction (Why) - T1: Summarize the distribution of most common methods of committing suicide. **[Summarize, Distribution]** - T2: Identify the most common method of committing suicide. **[Identify, Extreme]** ## Marks and Channels (How) * **Marks:** Arcs |Channel|Attribute|Using|Rationale |---|---|---|---| |Angular Position|Suicide_Percentage|Express|It is a quantitative attribute so the start and end angles of each arc represent the respective proportions of the data, so we use angular position to display it, making it the most effective and expressive |Color Hue|method|NA| It is a categorical attribute, and according to the task we need to distinguish the methods of committing suicide. So we use color use, making it the most effective and expressive. ## Insights - In 2020, firearms were involved in approximately 52.8% of all suicide deaths in the United States , making it the most common method by far. The second most prevalent method was suffocation, which accounted for approximately 27.2% of suicides. Poisoning came in third place, contributing to roughly 12% of all suicide deaths. - In 2020, more than half of the suicide (52.8%) were committed using firearms. This is indicates that the ease of accessibility to firearms plays a significant role in suicides.38:T5dc,## Data Abstraction (What) **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |Suicide_Rates|quantitative, sequential| |Method_Suicide|categorical| |Gender|categorical| * **Availability:** Static * **Time frame of Data:** 2000-2020 * **Data Source:** [Suicides in United States](https://vpc.org/revealing-the-impacts-of-gun-violence/suicide/) ## Task Abstraction (Why) - T1: Summarize the trends of most common methods of committing suicide for both males and females. **[Summarize, Trends]** - T2: Identify the most common method of committing suicide for both males and females. **[Identify, Extreme]** - T3: Compare the trends of most common methods of committing suicide between males and females. **[Identify, Extreme]** ## Marks and Channels (How) * **Marks:** Point |Channel|Attribute|Using|Rationale |---|---|---|---| |Vertical Position|Suicide_Rates|Express|It is a quantitative attribute but on the y-axis. Each year can have a different suicide rate, so we use position to display it, making it the most effective and expressive |Horizontal Position|year|Separate, Order| It is an ordinal attribute but on the x-axis. There are multiple years which needs to be shown. so we use position to display it, making it the most effective and expressive |Color Hue|Method|Separate, Order| It is a categorical attribute. There are different methods of committing suicides which needs to be shown so we use color hue to separate them, making it the most effective and expressive.39:T8a9,## Data Abstraction (What) **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |Suicide_Percentages|quantitative, sequential| |Gun_Possessing_Household|quantitative, sequential| |States|categorical| |Political_affiliation|categorical| * **Availability:** Static * **Time frame of Data:** 2020 * **Data Source:** [Suicides in United States](https://vpc.org/revealing-the-impacts-of-gun-violence/suicide/) ## Task Abstraction (Why) - PT1: Identify the correlation between percentage of households possessing guns vs suicide rates for different states. **[Identify, Correlation]** - ST1: Identify the political affiliation of the states which had higher suicide rates and higher guns possession. **[Identify, Feature]** - PT2: Identify the states with highest suicide rate and the states with lowest suicide rate and their corresponding percentage of household possessing guns. **[Identify, Extreme]** ## Marks and Channels (How) * **Marks:** Point, Line |Channel|Attribute|Using|Rationale |---|---|---|---| |Vertical Position|Suicide_Rates|Express|It is a quantitative attribute but on the y-axis.The suicide rates varies with the percentage of guns possessing household, making it the most effective and expressive |Horizontal Position|Guns_Possessing_Household|Express| It is a quantitative attribute but on the x-axis. The suicide rates varies with the percentage of guns possessing household, making it the most effective and expressive. |Color Hue|States|| It is a categorical attribute that differentiates states between democrat and republican. So color hue is the most expressive and effective for it. ## Insights - The data reveals a significant regression coefficient of 0.6, indicating a positive correlation between the number of households owning guns and suicide rates per 100k population. In other words, states with higher levels of gun ownership tend to have higher suicide rates. - Republican states tended to have higher rates of gun possession and suicide, as compared to Democratic states. Notably, Wyoming, a Republican state, exhibited the highest rates of both gun possession and suicide, while Democratic-leaning New Jersey displayed the lowest rates in these categories.3a:Tb13,## Data Abstraction (What) * **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |Ten-Year Age Groups|ordinal, sequential| |method|categorical| |state|categorical| |race|categorical| |Derived Attributes|Type & Direction| |---|---| |suicide rate|quantitative, sequential| |Political affiliation|categorical| * **Availability:** Static * **Time frame of Data:** 2016 - 2020 * **Data Source:** [Suicides in United States](https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=C8B84AE25B40557654EAE4E01326) ## Task Abstraction (Why) * T1: Compare the suicide cases across red and blue states for different methods of suicide **[Compare, Feature]** * T2: Identify the Age Group with highest cases of suicide for given method of suicide for age groups **[Identify, Outliers]** * T3: Identify the race/ethnicity with highest cases of suicide for given method of suicide **[Identify, Outliers]** ## Marks and Channels (How) * **Marks:** Area |Channel|Attribute|Using|Rationale |---|---|---|---| |Color Saturation|suicide rate|Express|"Suicide rate" is a quantitative attribute so it is represented with color saturation. The best way to see trends and patterns in suicide rate across different states and age group for a given method of suicide is to use color saturation in a Heatmap. The heatmap will help us analyze the patterns in suicide cases. The saturation of color implies higher or smaller count. Darker color here denotes that suicide rate are high and light color denotes that suicide rate are low. The color saturation at a glance help us see the trends and patterns in suicide cases in US states. |Horizontal Position (x-axis)|Ten-Year Age Groups|Separate, Order, Align| It is an ordinal attribute but on the x-axis. There are multiple age-groups which needs to be shown. so we use position to display it, making it the most effective and expressive. |Vertical Position (y-axis)|state|Separate, Align| There are 50 states, so the position is the most expressive and effective best to distinguish them. ## Insights: * For firearm as the method of commiting suicide, older people 85+ years had an higher suicide rate per 1 million such as wyoming, Montana, which is republican state and nevada which is a democrat state. On the other hand, younger people (15-34 years) had a higher suicide rate as compared to older population. * For suicide method as fall, hawaii had a higher suicide rate among other states in US, the age group 45-54 was affected the most and white race had higher suicide rate in hawaii as compared to other states and among other races * For suicide method as suffocation, younger people (15-24 years) had a higher suicide rate in the state of south dakota * For suicide method as drowning, asian pacific islanders had a higher suicide rate among different races in whole US.3b:Ta05,## Data Abstraction (What) **Dataset Type:** Table |Attributes|Type & Direction| |---|---| |Firearm_death_rate|quantitative, sequential| |Race/Ethnicity|Categorical| |Reason_of_Death|Categorical| |Gender|Categorical| * **Availability:** Static * **Time frame of Data:** 2020 * **Data Source:** [Suicides in United States](https://vpc.org/revealing-the-impacts-of-gun-violence/suicide/) ## Task Abstraction (Why) - T1: Compare the homicide death by firearms with suicide death by firearms for both males and females **[Compare,Features]** - T2: Identify the reason of deaths by firearms which had the highest death rate per 100k **[Identify, Extreme]** - T3: Summarize the suicide deaths by firearms between males and females. **[Summarize, Feature]** ## Marks and Channels (How) * **Marks:** Line |Channel|Attribute|Using|Rationale |---|---|---|---| |Horizontal_Position|Ethnicity|Separate, Order| As the task was to compare the death rates for different races, placing different races on the horizontal axis makes it easier to identify the races, hence for the given tasks it makes it most effective and expressive. |Vertical_Position|Death_rate|Express| It is a quantitative attribute that shows the death rate per 100k. So each ethnicity have a different death rate for each reason of death by firearm. Thus vertical position makes it most effective and expressive. |Color Hue|Reason_of_Death|| It is a categorical attribute which shows different reasons of firearm death. Thus color hue makes it most expressive and effective in identify the feature. |Length|Death_rate|| It is a quantitative attribute that shows the death rate per 100k. So higher the death rate , bigger the length. Thus making it most effective and expressive. ## Insights - Among males the mortality rate from guns was higher than the mortality from homicide. In females the mortality rate from guns for homicide and suicide were identical, indicating that firearms were used more for suicide than homicide. - White females were more likely to use guns for suicide than for homicide, while the opposite trend was observed among females of other ethnicities. Among males in the country, gun deaths in 2020 were more frequently due to suicide rather than homicide, except for Black and Hispanic populations.These trends indicate a significant divergence in the reasons for gun-related deaths between males and females, as well as among various racial groups. This trends shows that restricting accessibility to guns has the potential to significantly impact suicide mortality rates 3c:T6a7,## Data Abstraction (What) **Dataset Type:** Table (converted to map) |Attributes|Type & Direction| |---|---| |state|categorical| |vet_rate|quantitative, sequential| |Derived Attributes|Type & Direction| |---|---| |Regions|categorical| |avg_vet_rate|quantitative, sequential| * **Availability:** Static * **Time frame of Data:** 2005-2011 * **Data Source:** [Veteran suicides in United States](https://www.kaggle.com/datasets/residentmario/us-veteran-suicides) ## Task Abstraction (Why) * T1: Summarize the color of US state based on regions **[Summarize, Feature]** * T2: Summarize the veteran suicide rate in all US state **[Summarize, Feature]** * T3: Identify the regions with highest number of veterans suicide rate **[Identify, Extreme]** ## Marks and Channels (How) * **Marks:** Points |Channel|Attribute|Using|Rationale |---|---|---|---| |Position|State||As it is a grid map the state needs to positioned on the map accordingly, so that it becomes easy to identify states according to their position on the map. |Color Hue|region|seperate| It is a categorical attribute that separated US states in different regions. So using color hue makes it most effective and expressive |Area|avg_vet_rate|express| It is a quantitative attribute, so higher the suicide rate per 100, higher the area of the state. ## Insights * Suicides rate among veterans was highest in the US states in the West and among the mountain states such as Montana, Nevada, Arizona, Colorado * Suicides rate among veterans was lowest in the US states in the Northeast such New Jersey, New York * Montana has highest veteran suicide rates in the country and Massachusetts and Nebraska has lowest veteran suicide rate 3d:[{"id":"4d6914d80c7d4803c5baf65521f480a48e96a34cec440ad345efeec7ed68e5fa0096af55c714185f14c4cb356a0cb045aa6ff987770a8d8ded6c336b53d888d8","href":"https://static.observableusercontent.com/files/4d6914d80c7d4803c5baf65521f480a48e96a34cec440ad345efeec7ed68e5fa0096af55c714185f14c4cb356a0cb045aa6ff987770a8d8ded6c336b53d888d8","name":"Screen Shot 2023-04-04 at 10.25.44 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version","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":417,"value":"### New Version \n* Users were having trouble determining where to hover, so points were added for making it easy for them\n* Axis labels made bigger","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":420,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":201,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":173,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":837,"value":"## Insight 2: Untreated Mental Health Conditions Put Adults with Serious Thoughts of Suicide at Risk (by Mihir Mesia)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":209,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":512,"value":"## Visual Exploration\n![Screenshot 2023-04-25 at 4.03.26 PM.png](${await FileAttachment(\"Screenshot 2023-04-25 at 4.03.26 PM.png\").url()})\n\nFirst I tried to find a correlation between adults with serious thoughts of suicide and adults with mental health condition who where untreated. After plotting the graph, it was difficult to find the correlation over the entire distributions. So I decided to plot a grid map to identify the states that could be outliers in both the cases.\n\n### Iteration 1\n![Screenshot 2023-04-25 at 4.06.27 PM.png](${await FileAttachment(\"Screenshot 2023-04-25 at 4.06.27 PM.png\").url()})\n\n** Problems **\n* The tooltip was too big and was covering a big section of the graph.\n\n### Final\n\n![Screenshot 2023-04-25 at 4.08.38 PM.png](${await FileAttachment(\"Screenshot 2023-04-25 at 4.08.38 PM.png\").url()})","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":142,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":455,"value":"## Insight 3: Arts, Design, Entertainment, Sports and Media among the top to report workplace suicides in US among males and females (by Harshit Gajjar)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":452,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":458,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":146,"value":"## Insight 4: White populations have higher suicide rates in red states compared to blue states in US from 2016-2020 (by Harshit Gajjar)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":35,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":232,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":334,"value":"## Iterations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":339,"value":"### Iteration 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":344,"value":"key = FileAttachment(\"Screen Shot 2023-04-04 at 7.22.35 PM.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"FileAttachment(\"Screen Shot 2023-04-25 at 1.21.19 PM.png\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":347,"value":"- Made a node-link visualization, to show suicide cases among different race and ethnicity across blue/red states.
\n**Problems:**\n* The viz is very crowded and states name were are not clearly visible.\n* The links were not clearly visible to all nodes\n* The hover only showed suicide rate and not state or race/ethnicity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":358,"value":"### Iteration","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":360,"value":"FileAttachment(\"Screen Shot 2023-04-04 at 7.22.35 PM.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":408,"value":"FileAttachment(\"Screen Shot 2023-04-25 at 2.29.23 PM.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":411,"value":"FileAttachment(\"Screen Shot 2023-04-25 at 2.33.43 PM.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":366,"value":"- In the version 3, modified the viz the lines are clear visible and also spacing between nodes increased and on hover users can see the state/race name
\n**Insights:**\n- Insight 1: White population have higher suicide rate in red states.\n- Insight 2: Red states recorded higher rates compared to blue states.\n- Insight 3: American Indian or Alaska Native race have suicides in red states and only 1 blue state of new mexico.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":171,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":152,"value":"## Insight 5: American Indian or Alaska Native have significantly higher suicide rates over last 2 decades in US (by Harshit Gajjar)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":427,"value":"### Old version","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":38,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":429,"value":"### New Version\n* Users were having trouble determining where to hover, so points were added for making it easy for them\n* Axis labels made bigger","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":431,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":251,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":169,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":181,"value":"## Insight 6: More than half of the suicides in the US were committed using firearms (by Mihir Mesia)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":28,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":211,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":167,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":183,"value":"## Insight 7: Firearms Dominate Male Suicides, While Poisoning Prevails Among Females (by Mihir Mesia)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":378,"value":"## Iteration 1\n![xxDqNL1xLxC67dymMyXbUV0afNTVRZ8_XxvEeWhMX5X-Zg4lDVZzEi-FK58OBt6nxzi56OkhrMGBTf72zYERdE5o10Iy7LT4yN8RaB5FofZTLglj96BbQ85-j7ab.png](${await FileAttachment(\"xxDqNL1xLxC67dymMyXbUV0afNTVRZ8_XxvEeWhMX5X-Zg4lDVZzEi-FK58OBt6nxzi56OkhrMGBTf72zYERdE5o10Iy7LT4yN8RaB5FofZTLglj96BbQ85-j7ab.png\").url()})\n\nI wanted to analyse the trends in suicides rates for past two decades for different methods. I tried the stacked bar chart, and due to the align problem it became difficult to compare the trends among different methods.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":381,"value":"## Iteration 2\n\n![i15eYvDHku_W2y1UcR4_diVZ9wY-IF_xSjWBfOYlKvBB5HcUVL0gM0-Zs6yE1qRCcGOmJ1TI1ZaQhCgc0pap_Li8DA2opMyQN-9CsTyYCYF0tK7GEjmfuZpGEa4X.png](${await FileAttachment(\"i15eYvDHku_W2y1UcR4_diVZ9wY-IF_xSjWBfOYlKvBB5HcUVL0gM0-Zs6yE1qRCcGOmJ1TI1ZaQhCgc0pap_Li8DA2opMyQN-9CsTyYCYF0tK7GEjmfuZpGEa4X.png\").url()})\n\nTo solve the problem faced in the previous iteration, I tried the line chart. Though I was able to see the trends clearly, but the visualization was for the combined value of both the genders.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":383,"value":"## Iteration 3(Selected)\n\n![5pVu43OdRDNYgod1TwVpreIsYR7wxqu1-MRCKkG7rqmXE5OdwVI66qdQhzOZBGqfv3J-4wmWa8ZYMKpdBrfR-qMZqymOyZeqQTJi99_D16F1xKvr6q7IRLJpvq45.png](${await FileAttachment(\"5pVu43OdRDNYgod1TwVpreIsYR7wxqu1-MRCKkG7rqmXE5OdwVI66qdQhzOZBGqfv3J-4wmWa8ZYMKpdBrfR-qMZqymOyZeqQTJi99_D16F1xKvr6q7IRLJpvq45.png\").url()}), ![i15eYvDHku_W2y1UcR4_diVZ9wY-IF_xSjWBfOYlKvBB5HcUVL0gM0-Zs6yE1qRCcGOmJ1TI1ZaQhCgc0pap_Li8DA2opMyQN-9CsTyYCYF0tK7GEjmfuZpGEa4X@1.png](${await FileAttachment(\"i15eYvDHku_W2y1UcR4_diVZ9wY-IF_xSjWBfOYlKvBB5HcUVL0gM0-Zs6yE1qRCcGOmJ1TI1ZaQhCgc0pap_Li8DA2opMyQN-9CsTyYCYF0tK7GEjmfuZpGEa4X@1.png\").url()})\n\nI realised that the two different line charts for both the genders would be more insightful, so I did two line charts showing trends in suicide per 100k for various method for both the gender.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":385,"value":"## Insights\n\n- Firearm was the most common method of committing suicide in males and the trends for it has been increasing while for suffocation and posinoing there has been slight decline after 2017. While in females poisoning was the most common method till 2015 , after which the firearm became the top method. There has been significant increase in suicides by suffocation among females\n\n- Among males the firearm was the most common and it’s trend has been increasing over the years, while in case of females poisoning was the most common method till 2015, after which it showed and decline and it was overtaken by firearms.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":288,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":185,"value":"## Insight 8: States with greater gun ownership had higher correlation with suicide rate (by Mihir Mesia).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":47,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":300,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":496,"value":"## Iteration 1\n![Screenshot 2023-04-25 at 3.17.26 PM.png](${await FileAttachment(\"Screenshot 2023-04-25 at 3.17.26 PM.png\").url()})\n\n- Made a scatter plot that shows correlation between household that possessed guns vs the suicide rate for each state.
\n**Problems:**\n* The axis labels were not clear about what the viz is about\n* Need to show the correlation line as it is showing a correlation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":501,"value":"## Iteration 2\n![Screenshot 2023-04-25 at 3.20.24 PM.png](${await FileAttachment(\"Screenshot 2023-04-25 at 3.20.24 PM.png\").url()})\n\n\n**Changes:**\n* Added the correlation line and the value of R^2.\n* Added the color hue that separates democrat and republican states, as it will give more insights\n* Changed the axis labels.
\n\n**Problems:**\n* The axis label where not readable when the graph was exported to the website\n* The value of R^2 was not easily visible and it was bit far from the line.\n* Some of the points were small size so it were partially covered by the correlation line","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":503,"value":"## Final Iteration\n\n![Screenshot 2023-04-25 at 3.23.34 PM.png](${await FileAttachment(\"Screenshot 2023-04-25 at 3.23.34 PM.png\").url()})\n\n**Changes:**\n* Increased the font size of axis labels so that it is easily visible.\n* Increased the size of scatter points so that even if they get partially covered by the line they are visible.\n* Changed the position of R^2 and increased its font size, so that it is easily visible.
\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":165,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":155,"value":"## Insight 9: Suicides by firearm was higher in blue states than red states in US (by Harshit Gajjar)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":436,"value":"### Old Version\nThe problems are:\n* Data not normalized","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"hm = FileAttachment(\"Screen Shot 2023-04-04 at 10.25.44 PM.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":439,"value":"### new version\nUpdates here:\n* Suicide rate is normalized per million\n* Users have control to see suicide rates by age group and by race/ethnicity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":442,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":260,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":163,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":161,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":834,"value":"## Insight 10: Guns are used more for suicide than homicide (by Mihir Mesia)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":388,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":451,"value":"$3b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":475,"value":"## Insight 11: Western US States Report Higher Rates of Veteran Suicides (by Harshit Gajjar)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":473,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":479,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W3d","stdlib":"1","titling":"auto"}} 30:"$2f:body"