2c:null 2d:["$","title",null,{"children":"A Warning for Adults about Robbery in Central LA | Kasi Viswanath | Observable"}] 32:T97c,svg = { const svg = d3 .create("svg") .attr("viewBox", [0, 0, width, height - margin.bottom]); const g = svg.append("g").attr("transform", `translate(${margin.left},10)`); const scalefn = (data_) => d3 .scaleLinear() .domain(d3.extent(data_.map((d) => d.count))) .range(["green", "red"]); g.append("g") .call(d3.axisBottom(x)) .attr("transform", `translate( 0, ${height - margin.right - margin.left})`); g.append("g").call(d3.axisLeft(y)); let genders = ["M", "F", "X"], xAttr = "ageGroup", yAttr = "area", agegroups = Object.values(ageGroups); genders.map((gender, i) => agegroups.map((agegroup) => { let datum = genderAgeSexDataCount .concat(missingData) .filter((d) => d["gender"] === gender && d["ageGroup"] == agegroup); // return g // .selectAll(".rect") // .data(datum) // .join("rect") // .attr("x", (d) => i * 20 + x.bandwidth() / 3 + x(d["ageGroup"])) // .attr("y", (d) => y.bandwidth() / 2 - 5 + y(d["area"])) // .attr("width", (d) => 10) // .attr("height", (d) => 10) // .attr("style", (c) => `fill:${scale(datum)(c.count)}`) // .call((d) => d.append("title").text((v) => `count:${v["count"]}`)); // }) let wordCloud = html``; g.selectAll(".svg") .data(datum) .enter() .append( (d) => html`${createSvg( gender, scalefn(datum)(d.count), `transform:translate(${ i * 20 + x.bandwidth() / 2 - 25 + x(d["ageGroup"]) }px,${y.bandwidth() / 2 - 20 + y(d["area"])}px)` )}` ) .call((d) => d .append("title") .text((v) => `Gender:${v["gender"]} Cases:${v["count"]}`) ) .on("click", (evt, d) => { mutable selection = d; }); }) ); let datum = areaAgeDataCount.concat(missingData); g.selectAll(".rect") .data(datum) .join("rect") .attr("x", (d) => x.bandwidth() / 2 - 30 + x(d["ageGroup"])) .attr("y", (d) => y.bandwidth() / 2 - 20 + y(d["area"])) .attr("width", (d) => 60) .attr("height", (d) => 35) .attr("style", (c) => `stroke:${scale(c.count)};fill:None`) .call((d) => d.append("title").text((v) => `Total Cases:${v["count"]}`)); return svg.node(); }33:T100c,## Made-up Insights (Hypothesis) - Compared to other areas in LA, the most popular tourist destinations are likely to have a higher number of crime victims among adults than among all other age groups. [**Selected**] * **Considerations** * I will need enough data of all the crime incidents in LA. * I need to iterate through all the records and group all the age group by area. * In terms of the idiom (visualization), I would need to display the summation of all incidents by each area with respect to ageGroup. Plotting a Isotype chart helps to summarize the incidents by area with respect to ageGroup and a wordcloud of crime descriptions could provide further insight into the nature of the most common types of incidents. - Areas with higher population density may experience a higher incidence of property crime, such as theft and vandalism. * **Considerations** - I will need to collect data on population density and property crime incidents in different areas. - To analyze the data, I may need to calculate the crime rate (number of incidents per capita) for each area to account for differences in population size. - A scatter plot may be useful to visualize the relationship between population density and property crime rate. I could also use color-coding to differentiate between different types of property crime. ## Target Audience - Law enforcement agencies responsible for maintaining public safety in LA. - Local government officials and policymakers who are responsible for developing policies and programs to address crime in the city. - Academic researchers and students who are interested in crime trends and patterns in LA. ## Data Abstraction(What): - Dataset type: Table - Dataset Availability: Static - Attributes: - Vict Age - Quantitative (sequential orientation) - This attribute describes the age of the victim - Vict Sex - Categorical - This attribute describes the gender of the victim - AREA NAME - Categorical - This attribute describes the area in which the crime reported - Crm Cd Desc - Categorical - This attribute describes the crime of the reported incident - Derived Attribute: - Total number of cases - derived from grouping by area, victim age and gender ## Task Abstraction(Why): - T1 : Summarize the number of crimes for each area with respect to agegroup [Summarize,Distribution] - T2 : Summarize the number of crimes for each area with respect to age group and gender [Summarize,Distribution] - T3 : Summarize the type of crime that were committed in the particular area with respect to age group and gender [Summarize,Extreme] ## Idiom Marks and Channers(How): - Mark:- Area - Channel : - Luminance: To identify total cases in LA with respect to age group and gender - Tasks: T1 - Arrange: Order - Expressiveness : The tasks expresses the total cases reported in the LA - Effectiveness: The Luminance indicates if it is a safer or dangerous region - Shape: To differentiate gender - Tasks: T1 - Arrange: Separate - Expressiveness: The tasks expresses the gender of the cases reported in the LA - Effectiveness: The shape indicates the gender - Size: To identify the frequent types of crime that was committed - Tasks: T2 - Arrange: Separate,align - Expressiveness: The tasks expresses the frequent types of cases reported in the LA - Effectiveness: The size indicates the frequency of crimes reported ## Actual Insight - The Visualization indicates that a higher number of crimes were committed against adults in the Central region of LA, compared to other age groups. This highlights the need for increased awareness and preventative measures among this demographic to reduce the risk of victimization. - Pickpocketing cases in LA are more common than other types of theft or crime, which is concerning as it can happen in crowded public places and go unnoticed by victims. 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Because computer screen has more room horizontally, and there is a need for more room to list the mutiple LA regions.","create_time":"$D2023-02-23T03:40:56.445Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"750bcf843c0f1afd","login":"zychen","name":"Zhenyu Chen","avatar_url":"https://avatars-next.observableusercontent.com/bd7f5338dd83e2af26a89fd5f8b3b8f2e371ac3333067f03ed98213201b91502","tier":null}}],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":{"id":"14a0961462b84603","slug":"dataviz","title":"DataViz","owner":{"id":"b760ea566a896d7d","login":"kasivisu4","name":"Kasi Viswanath"}},"type":"notebook","body":{"titling":"auto","title":"A Warning for Adults about Robbery in Central LA","cells":[{"id":0,"value":"# A Warning for Adults about Robbery in Central LA\n\n\nHomework 4: Extracting insights from Multidimensional Data\n- Created by [Kasi Viswanath Vandanapu](https://vandanapuk.sites.northeastern.edu/)\n- Class [CS 7295: Special Topics Course / Information Visualization](https://johnguerra.co/classes/data_visualization_spring_2023/)\n- Professor Name : [John Alexis Guerra Gómez](https://johnguerra.co)\n- [Tweet](https://twitter.com/kasi_vandanapu/status/1628490358501048320)\n- [Dataset](https://catalog.data.gov/dataset/crime-data-from-2020-to-present)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":89,"value":"## Introduction\n\nLA is a major city in Southern California with a population exceeding 4 million. As in other large urban areas, crime is an ongoing challenge in LA, with incidents of theft, assault, and other crimes occurring frequently. To gain a better understanding of the nature and scope of crime in LA, data is collected from [data.gov](https://catalog.data.gov/dataset/crime-data-from-2020-to-present) and have selected on Crimes reported in Jan for my analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1209,"value":"Legend(scale, { title: \"Total Number of Cases Registered\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1372,"value":"### Isotype Chart of LA Crimes Area wise w.r.t Gender and Age Group","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":293,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1342,"value":"**Note** : Click on the gender icon to view types of crime reported for area and age-group specific","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1115,"value":"wordCloud = {\n if (!selection) {\n return;\n }\n return html`\n

Crime Word Cloud in ${selection[\"area\"]} Area, AgeGroup: ${\n selection[\"ageGroup\"]\n } , Gender : ${selection[\"gender\"]}

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\n${WordCloud(\n ageGroupsData\n .filter(\n (d) =>\n d[\"Vict Sex\"] == selection[\"gender\"] &&\n d[\"AREA NAME\"] === selection[\"area\"] &&\n d[\"Age Group\"] === selection[\"ageGroup\"]\n )\n .map((d) => d[\"Crm Cd Desc\"])\n)}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1377,"value":"### Analysis Video\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1297,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1383,"value":"","pinned":true,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1249,"value":"width = 475","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1223,"value":"scale = d3\n .scaleLinear()\n .domain(d3.extent(areaAgeDataCount.concat(missingData).map((d) => d.count)))\n .range([\"green\", \"red\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1200,"value":"x = d3\n .scaleBand()\n .domain(Object.values(ageGroups))\n .range([0, width - margin.right - margin.left])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1202,"value":"y = d3\n .scaleBand()\n .domain(\n data\n .map((d) => d[\"AREA NAME\"])\n .sort()\n .reverse()\n )\n .range([height - margin.top - margin.bottom, 0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":668,"value":"female_svg = (await FileAttachment(\"female_svg_resized1.svg\").text())\n .split(\"\\n\")\n .slice(1)\n .join(\"\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":683,"value":"male_svg = (await FileAttachment(\"male_svg_resized1@2.svg\").text())\n .split(\"\\n\")\n .slice(1)\n .join(\"\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":688,"value":"trans_svg = (await FileAttachment(\"trans_svg_resized1.svg\").text())\n .split(\"\\n\")\n .slice(1)\n .join(\"\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":670,"value":"createSvg = (gender, color, style) => {\n console.log({ style });\n let target;\n if (gender === \"M\") {\n target = html`${male_svg}`;\n d3.select(target).attr();\n } else if (gender === \"F\") {\n target = html`${female_svg}`;\n } else {\n target = html`${trans_svg}`;\n }\n\n target.style = style;\n let elements = Array.from(target.querySelectorAll(\"path\")).slice(1);\n for (var i = 0; i < elements.length; i++) elements[i].style.fill = color;\n return target;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":622,"value":"missingData = {\n let res = [];\n let genders = [\"M\", \"F\", \"X\"],\n agegroups = Object.values(ageGroups),\n areas = Array.from(d3.group(genderAgeSexDataCount, (d) => d.area).keys());\n\n areas.forEach((area) =>\n agegroups.forEach((agegroup) =>\n genders.forEach((gender) => {\n if (\n genderAgeSexDataCount.filter(\n (d) =>\n d[\"area\"] === area &&\n d[\"ageGroup\"] === agegroup &&\n d[\"gender\"] === gender\n ).length === 0\n ) {\n res.push({\n area,\n gender,\n ageGroup: agegroup,\n count: 0\n });\n }\n })\n )\n );\n return res;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1121,"value":"mutable selection = ({\n area: \"Central\",\n gender: \"M\",\n ageGroup: \"Adulthood [20-59]\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":355,"value":"height = 900","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":352,"value":"margin = ({ left: \"60\", right: \"60\", top: \"60\", bottom: \"60\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":244,"value":"### Data Preprocessing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1184,"value":"areaAgeData = d3.flatRollup(\n ageGroupsData,\n (v) => v.length,\n (d) => d[\"AREA NAME\"],\n (d) => d[\"Age Group\"]\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1186,"value":"areaAgeDataCount = Array.from(areaAgeData, function (entry) {\n return {\n area: entry[0],\n ageGroup: entry[1],\n count: entry[2]\n };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"genderAgeSexData = d3.flatRollup(\n ageGroupsData,\n (v) => v.length,\n (d) => d[\"AREA NAME\"],\n (d) => d[\"Vict Sex\"],\n (d) => d[\"Age Group\"]\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":366,"value":"genderAgeSexDataCount = Array.from(genderAgeSexData, function (entry) {\n return {\n area: entry[0],\n gender: entry[1],\n ageGroup: entry[2],\n count: entry[3]\n };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":346,"value":"### Discard the records not having gender and assign Age Group","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":316,"value":"ageGroupsData = JSON.parse(\n JSON.stringify(data.filter((d) => d[\"Vict Sex\"] != null && d[\"Vict Age\"] > 0))\n).map((d) => {\n d[\"Age Group\"] = getAgeGroup(d[\"Vict Age\"]);\n return d;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":322,"value":"getAgeGroup = (d) => {\n let keys = Object.keys(ageGroups);\n for (let key of keys) {\n let [lower, upper] = eval(key);\n if (d >= lower && d <= upper) {\n return ageGroups[key];\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":300,"value":"// Age groups\nageGroups = ({\n \"[0, 10]\": \"Childhood [0-10]\",\n \"[11, 19]\": \"Adolescence [11-19]\",\n \"[20, 59]\": \"Adulthood [20-59]\",\n \"[60, 100]\": \"OldAge [60+]\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"data = FileAttachment(\"LA_crime_dataset_jan_2023.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1046,"value":"import { WordCloud } from \"@d3/word-cloud\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1206,"value":"import { Legend, Swatches } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1"}} 31:"$30:body"