2f:T4ac,cash_savings_chart = Plot.plot({ marginLeft: 55, marginBottom: 35, x: { label: "percentile", labelOffset: 30, tickFormat: percent_format }, y: { label: "Cash Savings", grid: false, tickFormat: dollar_format }, color: { legend: true, domain: ["Owner-Occupied", "Renters"], range: ["#0d73bb", "#f16122"] }, marks: [ Plot.ruleY([0]), Plot.line( cash_savings_data.filter( (d) => (d.percentile < form.narrow / 100) & renters_toggle.includes(d.tenure) ), { x: "percentile", y: form.series_selection, z: "tenure", stroke: "tenure", curve: "catmull-rom", title: (d) => (d[form.series_selection] > 0) | (form.series_selection == "net worth") ? `${percent_format(d.percentile)} have less than ${dollar_format( d[form.series_selection] )} in ${form.series_selection}` : `${percent_format(d.percentile)} have no ${ form.series_selection }`, tip: true } ) ], caption: html`

ASJ analysis of the 2022 Survey of Consumer Finances.

` })30:T526,renter_age_chart_vert = Plot.plot({ grid: false, width: 800, y: { label: null, labelOffset: 40, tickSize: 0, domain: [0, 0.85], tickFormat: percent_format }, x: { tickSize: 0, domain: [ "15 to 24 years", "25 to 34 years", "35 to 44 years", "45 to 54 years", "55 to 59 years", "60 to 64 years", "65 to 74 years", "75 to 84 years", "85 years and over" ] }, marks: [ Plot.axisX({ label: null, lineWidth: 4, marginBottom: 40 }), Plot.barY(householder_data, { y: "pct", x: "age", padding: 0.2, fill: (d) => (d.age == "All Householders" ? "#15a8e0" : "#f16122") // title: (d) => percent_format(d.pct) }), Plot.text(householder_data, { y: "pct", x: "age", text: (d) => percent_format(d.pct), dy: -12 }), Plot.text(householder_data, { y: "pct", x: (d) => "60 to 64 years", text: (d) => `Average across all householders`, filter: (d, i) => i == 4, dy: -29 }), Plot.ruleY(householder_data, { y: "pct", strokeDasharray: "4,4", filter: (d) => d.age == "All Householders" }) ], caption: html`

U.S. Census Bureau, 2022 American Community Survey, Tenure by Age of Householder.

` })31:T4ec,renters_by_income_chart_vert = Plot.plot({ width: 600, marginLeft: 50, grid: false, y: { label: null, labelOffset: 40, tickSize: 0, domain: [0, 0.7], tickFormat: percent_format }, x: { tickSize: 0, label: null, domain: [ "Less than $20k", "$20k to $34,999", "$35k to $49,999", "$50k to $74,999", "$75k to $99,999", "$100k to $149,999", "$150k+" ] }, marks: [ Plot.axisX({ label: null, lineWidth: 8, marginBottom: 40 }), Plot.barY(renters_by_income_data, { y: "pct_renters", x: "bracket", padding: 0.2, fill: (d) => (d.bracket == "All Householders" ? "#15a8e0" : "#f16122") // title: (d) => percent_format(d.pct_renters) }), Plot.text(renters_by_income_data, { y: "pct_renters", x: "bracket", text: (d) => percent_format(d.pct_renters), dy: -17 }), Plot.text(householder_data, { y: "pct", x: (d) => "$100k to $149,999", text: (d) => `Average across all householders`, filter: (d, i) => i == 4, dy: -34 }), Plot.ruleY([0.35], { strokeDasharray: "4,4" }) ], caption: html`

ASJ analysis of 2022 Survey of Consumer Finances.

` })32:T62c,renters_by_networth_chart_vert = Plot.plot({ marginLeft: 50, width: 800, grid: false, y: { label: null, labelOffset: 40, tickSize: 0, domain: [0, 1], tickFormat: percent_format }, x: { tickSize: 0, label: null, domain: [ "In Debt", "$0 to $9,999", "$10k to $49,999", "$50k to $99,999", "$100k to $199,999", "$200k to $299,999", "$300k to $499,999", "$500k to $999,999", "$1M to $2M", "$2M+" ] }, marks: [ Plot.axisX({ label: null, lineWidth: 5, marginBottom: 40 }), Plot.barY(renters_by_networth_data, { y: "pct_renters", x: "bracket", padding: 0.2, fill: (d) => (d.bracket == "All Householders" ? "#15a8e0" : "#f16122") // title: (d) => percent_format(d.pct_renters) }), Plot.text(householder_data, { y: "pct", x: (d) => "$200k to $299,999", text: (d) => `Average across all householders`, filter: (d, i) => i == 4, dy: -28 }), Plot.ruleY([0.35], { strokeDasharray: "4,4" }), Plot.text(renters_by_networth_data, { y: "pct_renters", x: "bracket", text: (d) => percent_format(d.pct_renters), dy: -10 }) ], caption: html`

ASJ analysis of 2022 Survey of Consumer Finances. These net worth groupings roughly correspond with net worth deciles: $0 with the 8th; $10k with the 18th; $50k with the 30th; $100k with the 39th; $200k with the 51st, $300k with the 59th, $500k with the 70th, $1M with the 81st, and $2M with the 90th.

` })33:T53a,cash_savings_chart_annotated = Plot.plot({ marginLeft: 50, marginBottom: 30, x: { label: "percentile", tickFormat: percent_format, labelOffset: 30 }, y: { label: "Cash Savings", grid: false, tickFormat: dollar_format }, color: { legend: true, domain: ["Owner-Occupied", "Renters"], range: ["#0d73bb", "#f16122"] }, marks: [ Plot.ruleY([0]), Plot.line( cash_savings_data.filter( (d) => (d.percentile < 51 / 100) & renters_toggle.includes(d.tenure) ), { x: "percentile", y: "cash savings", z: "tenure", stroke: "tenure", curve: "catmull-rom", title: (d) => d["cash savings"] > 0 ? `${percent_format(d.percentile)} have less than ${dollar_format( d["cash savings"] )}` : `${percent_format(d.percentile)} have no cash savings`, tip: true } ), Plot.tip([`25% of renting households have less than $200`], { x: 0.25, y: 200, anchor: "bottom", lineWidth: 10 }), Plot.tip([`42% of renting households have less than $1,000`], { x: 0.42, y: 1000, anchor: "bottom", lineWidth: 10 }) ], caption: html`

ASJ analysis of the 2022 Survey of Consumer Finances.

` })34:T41d,cash_savings_chart_for_pdf_not_annotated = Plot.plot({ marginLeft: 50, marginBottom: 30, x: { label: "percentile", tickFormat: percent_format, labelOffset: 30 }, y: { label: "Cash Savings", grid: false, tickFormat: dollar_format }, color: { legend: true, domain: ["Owner-Occupied", "Renters"], range: ["#0d73bb", "#f16122"] }, marks: [ Plot.ruleY([0]), Plot.line( cash_savings_data.filter( (d) => (d.percentile < 51 / 100) & renters_toggle.includes(d.tenure) ), { x: "percentile", y: "cash savings", z: "tenure", stroke: "tenure", curve: "catmull-rom", title: (d) => d["cash savings"] > 0 ? `${percent_format(d.percentile)} have less than ${dollar_format( d["cash savings"] )}` : `${percent_format(d.percentile)} have no cash savings`, tip: true } ) ], caption: html`

ASJ analysis of the 2022 Survey of Consumer Finances.

` })35:T6a3,prevalence_chart_labels_vert = Plot.plot({ marginLeft: 50, marginBottom: 50, grid: false, color: { domain: ["Renters", "Owner-Occupied"], range: ["#f16122", "#0d73bb"] }, symbol: { domain: ["Renters", "Owner-Occupied"], range: ["circle", "circle"], legend: true }, y: { label: "Victims of violence per 10k people", labelOffset: 50, domain: [0, 250], tickSize: 0 }, x: { label: null, tickSize: 0, domain: ["Latino", "Black", "White", "Overall"] }, marks: [ Plot.ruleX( prevalence_data, Plot.groupX( { y1: "min", y2: "max" }, { y: "rate", x: "race", stroke: "#dddddd", strokeWidth: 2.5 } ) ), Plot.dot(prevalence_data, { y: "rate", x: "race", r: 5, fill: "tenure", symbol: "tenure", title: (d) => `${d.race} ${d.tenure}:\n ${round( d.rate )} people per 10k \n experienced violence in 2022` // sort: { y: "x", reverse: true } }), Plot.text( prevalence_data.filter((d) => d.tenure == "Renters"), { y: "rate", x: "race", text: (d) => d3.format(".0f")(d.rate), dy: -17 } ), Plot.text( prevalence_data.filter((d) => d.tenure == "Owner-Occupied"), { y: "rate", x: "race", text: (d) => d3.format(".0f")(d.rate), dy: 17 } ), Plot.ruleY([0]), Plot.axisX({ label: null, lineWidth: 8, marginBottom: 40, labelOffset: 40 }) ], caption: html`

ASJ analysis of the 2022 National Crime Victimization Survey.

` })36:T7f3,victimizations_by_offense = Plot.plot({ marginLeft: 40, marginBottom: 55, grid: false, y: { labelOffset: 50, tickSize: 0, domain: [0, 250], label: "Violent victimizations per 10k people" }, x: { label: null, tickSize: 0 }, symbol: { domain: ["Renters", "Owner-Occupied"], range: ["circle", "circle"], legend: true }, color: { domain: ["Renters", "Owner-Occupied"], range: ["#f16122", "#0d73bb"] }, marks: [ Plot.ruleX( violent_incidents_by_type_and_tenure_data, Plot.groupX( { y1: "min", y2: "max" }, { y: "rate", x: "label", stroke: "#dddddd", strokeWidth: 2.5 } ) ), Plot.dot(violent_incidents_by_type_and_tenure_data, { y: "rate", x: "label", r: 5, fill: "tenure", symbol: "tenure", // title: (d) => // `${d.label} \n ${d.tenure}\n ${round(d.rate)} victimizations per 10k`, sort: { x: "y", reverse: false } }), Plot.text( violent_incidents_by_type_and_tenure_data.filter( (d) => d.tenure == "Renters" ), { y: "rate", x: "label", text: (d) => d3.format(".0f")(d.rate), dy: -17 } ), Plot.text( violent_incidents_by_type_and_tenure_data.filter( (d) => (d.tenure == "Owner-Occupied") & (d.rate > 20) ), { y: "rate", x: "label", text: (d) => d3.format(".0f")(d.rate), dy: 17 } ), Plot.text( violent_incidents_by_type_and_tenure_data.filter( (d) => (d.tenure == "Owner-Occupied") & (d.rate < 20) ), { y: "rate", x: "label", text: (d) => d3.format(".0f")(d.rate), dx: 17 } ), Plot.axisX({ label: null, lineWidth: 8, marginBottom: 40, labelOffset: 40 }), Plot.ruleY([0]) ], caption: html`

ASJ analysis of the 2022 National Crime Victimization Survey.

` })37:T79c,victimizations_by_tenure_and_income = Plot.plot({ marginLeft: 40, marginBottom: 55, grid: false, y: { tickSize: 0, domain: [0, 800], label: "Violent victimizations per 10k people" }, x: { label: "Household Income", tickSize: 0, domain: [ "Less than $20k", "$20k to $34,999", "$35k to $49,999", "$50k to $74,999", "$75k to $99,999", "$100k to $149,999", "$150k+" ] }, color: { domain: ["Renters", "People in Owner-Occupied Units"], range: ["#f16122", "#0d73bb"] }, symbol: { domain: ["Renters", "People in Owner-Occupied Units"], range: ["circle", "circle"], legend: true }, marks: [ Plot.ruleX( victimizations_by_tenure_and_income_bracket, Plot.groupX( { y1: "min", y2: "max" }, { y: "rate", x: "bracket", stroke: "#dddddd", strokeWidth: 2.5 } ) ), Plot.dot(victimizations_by_tenure_and_income_bracket, { y: "rate", x: "bracket", r: 5, fill: "tenure", symbol: "tenure" // title: (d) => // `${d.label} \n ${d.tenure}\n ${round(d.rate)} victimizations per 10k`, }), Plot.text( victimizations_by_tenure_and_income_bracket.filter( (d) => d.tenure == "Renters" ), { y: "rate", x: "bracket", text: (d) => d3.format(".0f")(d.rate), dy: -17 } ), Plot.text( victimizations_by_tenure_and_income_bracket.filter( (d) => d.tenure == "Owner-Occupied" ), { y: "rate", x: "bracket", text: (d) => d3.format(".0f")(d.rate), dy: 17 } ), Plot.axisX({ label: null, lineWidth: 8, marginBottom: 40, labelOffset: 40 }), Plot.ruleY([0]) ], caption: html`

ASJ analysis of the 2022 National Crime Victimization Survey.

` })38:T6cb,victimizations_by_tenure_and_age_chart = Plot.plot({ marginLeft: 40, marginBottom: 55, grid: false, y: { tickSize: 0, domain: [0, 600], label: "Violent victimizations per 10k people" }, x: { label: "Age", tickSize: 0, domain: [ "12 - 17", "18 - 24", "25 - 34", "35 - 44", "45 - 54", "55 - 64", "65+" ] }, color: { domain: ["Renters", "Owner-Occupied"], range: ["#f16122", "#0d73bb"] }, symbol: { domain: ["Renters", "Owner-Occupied"], range: ["circle", "circle"], legend: true }, marks: [ Plot.ruleX( victimizations_by_age_data, Plot.groupX( { y1: "min", y2: "max" }, { y: "rate", x: "age", stroke: "#dddddd", strokeWidth: 2.5 } ) ), Plot.dot(victimizations_by_age_data, { y: "rate", x: "age", r: 5, fill: "tenure", symbol: "tenure" // title: (d) => // `${d.label} \n ${d.tenure}\n ${round(d.rate)} victimizations per 10k`, }), Plot.text( victimizations_by_age_data.filter((d) => d.tenure == "Renters"), { y: "rate", x: "age", text: (d) => d3.format(".0f")(d.rate), dy: -17 } ), Plot.text( victimizations_by_age_data.filter((d) => d.tenure == "Owner-Occupied"), { y: "rate", x: "age", text: (d) => d3.format(".0f")(d.rate), dy: 17 } ), Plot.axisX({ label: null, lineWidth: 8, marginBottom: 40, labelOffset: 40 }), Plot.ruleY([0]) ], caption: html`

ASJ analysis of the 2022 National Crime Victimization Survey.

` })39:T48c,#### Renting households by household income, violent victimizations, and financial situation | Household Income | Number of Households | Violent Victimizations | Violent Victimizations per 10k People | Risk Ratio | Median Cash Savings | Median Rent| % Paycheck to Paycheck | | --------- | ---------- | -------- | --------| --------| --------| |Less than $20k|9.6M|1,213,840|690|4.6|$174|$666|84% |$20k to $34,999|7.1M| 725,830|442|2.9|$582|$772|88% |$35k to $49,999|6.2M| 456,010|289|1.9|$1,470|$930|84% |$50k to $74,999|8.1M| 526,740|325|2.2|$3,860|$1,140|71% |$75k to $99,999|5.2M| 252,670|244|1.6|$6,200|$1,480|61% |$100k to $149,999|5.1M| 284,410|318|2.1|$13,020|$1,700|48% |$150k+|3.9M| 208,360|310|2.1|$34,840|$2,500|23%

The risk ratio shows how many times higher the victimization rate is than that of people living in owner-occupied households with more than $75,000 in income. The number of households is reported based on the U.S. Census Bureau's 2022 American Community Survey, Table B25118. ASJ analysis of the 2022 National Crime Victimization Survey and 2022 Survey of Consumer Finances.

3a:T5b5,at_home_chart_vert = Plot.plot({ y: { tickFormat: percent_format, label: "Percent of violent victimizations", labelOffset: 30 }, x: { tickSize: 0, label: null }, marginBottom: 50, marginLeft: 50, marginTop: 50, color: { domain: [ "At home", "within 1 mile", "within 5 miles", "between 5 and 50 miles", "further than 50 miles" ], range: ["#bb410c", "#f16122", "#f6946a", "#0d73bb", "#259cf0"], legend: true, reverse: false }, marks: [ Plot.barY(at_home_data.slice(0, 3), { y: "pct", x: "near", fill: "where", title: (d) => `${percent_format( d.cum_pct )} of victimizations occur \n ${d.where.toLowerCase()}` }), Plot.barY(at_home_data.slice(3, 5), { y: (d) => d.pct, x: "near", fill: "where", title: (d) => `${percent_format(d.pct)} of victimizations occur \n ${d.where}` }), Plot.text( at_home_data.filter((d) => d.where == "within 5 miles"), { y: (d) => d.cum_pct, x: "near", text: (d) => percent_format(d.cum_pct), dy: -12 } ), Plot.text( at_home_data.filter((d) => d.where == "further than 50 miles"), { y: (d) => d.cum_pct - 0.79, x: "near", text: (d) => percent_format(d.cum_pct - 0.79), dy: -12 } ) ], caption: html`

ASJ analysis of the 2022 NCVS.

` })3b:T7db,victimizations_by_income_chart = Plot.plot({ marginTop: 0, marginLeft: 140, grid: false, height: 600, fontSize: 23, x: { label: "Number of Violent Victimizations Median Cash Savings ", labelOffset: -540, ticks: 0, domain: [0, 1700000], labelArrow: "none" }, y: { domain: [ "Less than $20k", "$20k to $34,999", "$35k to $49,999", "$50k to $74,999", "$75k to $99,999", "$100k to $149,999", "$150k+" ], label: "Renters by Household Income", tickSize: 0, padding: 0.8 // labelOffset: 10 }, r: { range: [2.5, 64], domain: [174, 35800] }, color: { // legend: true, range: ["#f16122", "#15a8e0"], fontSize: 16 }, marks: [ Plot.dot(victimizations_by_income_data, { x: 1350000, dx: 20, y: "bracket", r: "cash_savings", fill: "#0d73bb", stroke: "#ffffff", title: (d) => `${dollar_format( d.cash_savings )} \n is the median cash savings for \nrenters in households \n making ${d.bracket.toLowerCase()}` }), Plot.text(victimizations_by_income_data, { x: 1350000, dx: 20 + 68, y: "bracket", r: "cash_savings", textAnchor: "start", text: (d) => `${dollar_format(d.cash_savings)}` }), Plot.barX(victimizations_by_income_data, { x: "n_victimizations", y: "bracket", padding: 0.2, fill: "#f15a2c", title: (d) => `${round( Math.ceil(d.n_victimizations / 10) * 10 )} victimizations \n experienced by renters in households making ${d.bracket.toLowerCase()}` }), Plot.text(victimizations_by_income_data, { x: "n_victimizations", y: "bracket", dx: 4, textAnchor: "start", text: (d) => d3.format(".3s")(d.n_victimizations) }) ], caption: html`

ASJ analysis of the 2022 NCVS and 2022 Survey of Consumer Finances.

` })3c:T58b,reported_to_police_chart_vert = Plot.plot({ marginLeft: 50, marginBottom: 50, grid: false, y: { label: "Percent of victimizations not reported to police", labelOffset: 40, tickSize: 0, domain: [0, 0.9], tickFormat: percent_format }, x: { tickSize: 0, label: null, domain: [ "robbery", "aggravated assault", "simple assault or assault without injury", "rape/sexual assault" ] }, marks: [ Plot.axisX({ label: null, lineWidth: width / 150, marginBottom: 40 }), Plot.barY(pct_reported_to_police, { y: (d) => 1 - d.pct_reported_to_police, x: "label", padding: 0.2, fill: (d) => (d.label == "all offenses" ? "#0d73bb" : "#f16122") }), Plot.text(pct_reported_to_police, { y: (d) => 1 - d.pct_reported_to_police, x: "label", text: (d) => percent_format(1 - d.pct_reported_to_police), dy: -12 }), Plot.text(pct_reported_to_police, { y: (d) => 1 - d.pct_reported_to_police, x: (d) => "aggravated assault", text: (d) => `59% average across all offense types`, filter: (d, i) => i == 4, dy: 3, dx: -60 }), Plot.ruleY(pct_reported_to_police, { y: (d) => 1 - d.pct_reported_to_police, strokeDasharray: "4,4", filter: (d) => d.label == "all offenses" }) ], caption: html`

ASJ analysis of the 2022 NCVS.

` })3d:T738,style = html``","pinned":true,"hidden":false,"mode":"ojs"},{"id":52,"value":"dollar_format = d3.format(\"$,.0f\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":60,"value":"round = d3.format(\",.0d\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":346,"value":"$3e","pinned":true,"hidden":false,"mode":"ojs"},{"id":348,"value":"// Function to position the tooltip\nhover = (tip, pos, text) => {\n const side_padding = 10;\n const vertical_padding = 5;\n const vertical_offset = 15;\n\n // Empty it out\n tip.selectAll(\"*\").remove();\n\n // Append the text\n tip\n .style(\"text-anchor\", \"middle\")\n .style(\"pointer-events\", \"none\")\n .attr(\"transform\", `translate(${pos[0]}, ${pos[1] + 7})`)\n .selectAll(\"text\")\n .data(text)\n .join(\"text\")\n .style(\"dominant-baseline\", \"ideographic\")\n .text((d) => d)\n .attr(\"y\", (d, i) => (i - (text.length - 1)) * 15 - vertical_offset)\n .style(\"font-weight\", (d, i) => (i === 0 ? \"normal\" : \"normal\"));\n\n const bbox = tip.node().getBBox();\n\n // Add a rectangle (as background)\n tip\n .append(\"rect\")\n .attr(\"y\", bbox.y - vertical_padding)\n .attr(\"x\", bbox.x - side_padding)\n .attr(\"width\", bbox.width + side_padding * 2)\n .attr(\"height\", bbox.height + vertical_padding * 2)\n .style(\"fill\", \"white\")\n .style(\"stroke\", \"#d3d3d3\")\n .lower();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":350,"value":"// To generate a unique ID for each chart so that they styles only apply to that chart\n// from mkfreeman\nid_generator = () => {\n var S4 = function () {\n return (((1 + Math.random()) * 0x10000) | 0).toString(16).substring(1);\n };\n return \"a\" + S4() + S4();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":632,"value":"tooltipPlugin = (Plot) => {\n const { plot } = Plot;\n Plot.plot = ({ tooltip, ...options }) => addTooltips(plot(options), tooltip);\n return Plot;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":352,"value":"// automatically add tooltips to every Plot in a notebook\n// this allows users to do import { Plot } from \"@mkfreeman/plot-tooltip\nPlot = tooltipPlugin(await require(\"@observablehq/plot\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":480,"value":"violent_incidents_by_type_and_tenure_data.filter((d) => d.offense_type == 4)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1132,"value":"// https://getbootstrap.com/docs/4.1/getting-started/introduction/#css\n//html`css `","pinned":true,"hidden":false,"mode":"ojs"},{"id":1183,"value":"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W3f","stdlib":"1","titling":"auto"}