2f:T7e5,victimizations_by_income_chart = Plot.plot({ marginTop: 0, marginLeft: 140, grid: false, height: 440, width: 600, fontSize: 23, x: { label: null, 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", "$150k+" ], label: "Renters by Household Income", tickSize: 0, padding: 0.1 // labelOffset: 10 }, r: { range: [2.5, 64], domain: [174, 35800] }, color: { // legend: true, range: ["#f16122", "#15a8e0"], fontSize: 16 }, marks: [ Plot.dot(victimizations_by_income_data, { x: 1350000, dx: 20, y: "bracket", r: "cash_savings", fill: "#0d73bb", stroke: "#ffffff", title: (d) => `${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)}`, strokeWidth: 1, stroke: "#0d73bb" }), 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), fill: "#f16122", strokeWidth: 1, stroke: "#f16122" }) ], caption: html`

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

` })30:T407,renter_age_chart = Plot.plot({ marginLeft: 250, marginBottom: 50, grid: false, x: { label: "Percent of householders who rent their homes→", labelOffset: 40, tickSize: 0, domain: [0, 0.85], tickFormat: percent_format }, y: { tickSize: 0, label: null, domain: [ "15 to 24 years", "25 to 34 years", "35 to 44 years", "All Householders", "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.barX(householder_data, { x: "pct", y: "age", padding: 0.2, fill: (d) => (d.age == "All Householders" ? "#15a8e0" : "#f16122") // title: (d) => percent_format(d.pct) }), Plot.text(householder_data, { x: "pct", y: "age", text: (d) => percent_format(d.pct), dx: 17 }) ], caption: html`

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

` })31:T52d,renter_age_chart_vert = Plot.plot({ grid: false, width: chartWidth, 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.

` })32: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.

` })33:T517,renters_by_networth_chart = Plot.plot({ marginLeft: 250, marginBottom: 50, grid: false, x: { label: "Percent of households that rent their homes→", labelOffset: 40, tickSize: 0, domain: [0, 1], tickFormat: percent_format }, y: { 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.barX(renters_by_networth_data, { x: "pct_renters", y: "bracket", padding: 0.2, fill: (d) => (d.bracket == "All Householders" ? "#15a8e0" : "#f16122") // title: (d) => percent_format(d.pct_renters) }), Plot.text(renters_by_networth_data, { x: "pct_renters", y: "bracket", text: (d) => percent_format(d.pct_renters), dx: 17 }) ], 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.

` })34: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`

` })35: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.

` })36: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.

` })37:T62d0,explanation = html`

Why is this chart interactive?

The charts showing cash savings and net worth percentiles are interactive because the extreme inequalities make it impossible to show all that is meaningful about the data at the same time.

Viewing all the data highlights differences between the wealthiest Americans and the poorest 90 percent

In this chart, you can see the cash savings data for all percentiles. The main thing that stands out when you see all the data is the difference between the wealthiest households all other households. The 90th through 99th percentiles have vastly more in cash savings than households in lower percentiles. The differences between the wealthiest percentiles and everyone else make it impossible to discern smaller - but very significanty - differences among households with less wealth.

Excluding the wealthiest housholds makes clear significant differences among households with less in cash savings

This chart shows the same data, but only for households in the bottom 50 percentiles according to cash savings. By leaving the wealthiest households off the charts, significant differences between renting and owner-occupied households are now visible.

Use the ranger slider below to limit or expand the data you see. You can also show renters only, and pivot between cash savings and net worth.

`38: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.

` })39:T4c0,cash_savings_chart_tall = Plot.plot({ height: 600, marginLeft: 50, 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.

` })3a: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.

` })3b: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.

` })3c:T75a,a = 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", "$150k+" ] }, color: { domain: ["Renters", "Owner-Occupied"], range: ["#f16122", "#0d73bb"] }, symbol: { domain: ["Renters", "Owner-Occupied"], range: ["circle", "circle"], legend: true }, marks: [ Plot.ruleX( victimizations_by_tenure_and_income_bracket, Plot.groupX( { y1: "min", y2: "max" }, { y: "rate", x: "bracket", stroke: "#dddddd", strokeWidth: 2.5 } ) ), Plot.dot(victimizations_by_tenure_and_income_bracket, { y: "rate", x: "bracket", r: 5, fill: "tenure", symbol: "tenure" // title: (d) => // `${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.

` })3d: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.

` })3e:T495,#### Table 1: 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.

3f: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.

` })40:Td11,// These cells are slightly modified from @mkfreeman addTooltips = (chart, styles) => { const stroke_styles = { stroke: "blue", "stroke-width": 3 }; const fill_styles = { fill: "blue", opacity: 0.5 }; // Workaround if it's in a figure const type = d3.select(chart).node().tagName; let wrapper = type === "FIGURE" ? d3.select(chart).select("svg") : d3.select(chart); // Workaround if there's a legend.... const svgs = d3.select(chart).selectAll("svg"); if (svgs.size() > 1) wrapper = d3.select([...svgs].pop()); wrapper.style("overflow", "visible"); // to avoid clipping at the edges // Set pointer events to visibleStroke if the fill is none (e.g., if its a line) wrapper.selectAll("path").each(function (data, index, nodes) { // For line charts, set the pointer events to be visible stroke if ( d3.select(this).attr("fill") === null || d3.select(this).attr("fill") === "none" ) { d3.select(this).style("pointer-events", "visibleStroke"); if (styles === undefined) styles = stroke_styles; } }); if (styles === undefined) styles = fill_styles; const tip = wrapper .selectAll(".hover") .data([1]) .join("g") .attr("class", "hover") .style("pointer-events", "none") .style("text-anchor", "middle"); // Add a unique id to the chart for styling const id = id_generator(); // Add the event listeners d3.select(chart).classed(id, true); // using a class selector so that it doesn't overwrite the ID wrapper.selectAll("title").each(function () { // Get the text out of the title, set it as an attribute on the parent, and remove it const title = d3.select(this); // title element that we want to remove const parent = d3.select(this.parentNode); // visual mark on the screen const t = title.text(); if (t) { parent.attr("__title", t).classed("has-title", true); title.remove(); } // Mouse events parent .on("pointerenter pointermove", function (event) { const text = d3.select(this).attr("__title"); const pointer = d3.pointer(event, wrapper.node()); if (text) tip.call(hover, pointer, text.split("\n")); else tip.selectAll("*").remove(); // Raise it // d3.select(this).raise(); // Keep within the parent horizontally const tipSize = tip.node().getBBox(); if (pointer[0] + tipSize.x < 0) tip.attr( "transform", `translate(${tipSize.width / 2}, ${pointer[1] + 7})` ); else if (pointer[0] + tipSize.width / 2 > wrapper.attr("width")) tip.attr( "transform", `translate(${wrapper.attr("width") - tipSize.width / 2}, ${ pointer[1] + 7 })` ); }) .on("pointerout", function (event) { tip.selectAll("*").remove(); // Lower it! // d3.select(this).lower(); }); }); // Remove the tip if you tap on the wrapper (for mobile) wrapper.on("touchstart", () => tip.selectAll("*").remove()); // Define the styles chart.appendChild(html`","pinned":false,"hidden":false,"mode":"html"},{"id":287,"value":"title_cash_savings = htl.html`

People in the poorest households endure the most violence and have very little cash to use in an emergency.

\n\n

Number of violent victimizations of renters by household income, with
Median cash savings of renters by household income

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":746,"value":"title_population_and_violence = md`#### People who rent their homes bear a disproportionate share of violence.\n*Renters in US population vs renters as victims of violence*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":720,"value":"pop_vs_victimizations_chart = Plot.plot({\n marginLeft: 250,\n marginBottom: 50,\n grid: false,\n\n x: {\n label: null,\n labelOffset: 40,\n tickSize: 0,\n domain: [0, 0.75],\n tickFormat: percent_format\n },\n y: {\n label: null,\n domain: [\n \"Renters as Percent of Population\",\n \"Percent of Violence Borne by Renters\"\n ]\n },\n\n marks: [\n Plot.barX(pop_vs_victimizations, {\n x: \"value\",\n y: \"var\",\n padding: 0.2,\n fill: \"#f16122\",\n class: \"bar\",\n sort: true\n // title: (d) => percent_format(d.value)\n }),\n Plot.text(pop_vs_victimizations, {\n x: \"value\",\n y: \"var\",\n text: (d) => percent_format(d.value),\n dx: 17\n })\n ],\n caption: html`

ASJ analysis of the 2022 National Crime Victimization Survey.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1723,"value":"pop_vs_victimizations_chart_vert = Plot.plot({\n marginLeft: 50,\n marginBottom: 50,\n grid: false,\n\n y: {\n label: null,\n labelOffset: 40,\n tickSize: 0,\n domain: [0, 0.75],\n tickFormat: percent_format\n },\n x: {\n label: null,\n domain: [\n \"Renters as Percent of Population\",\n \"Percent of Violence Borne by Renters\"\n ]\n },\n\n marks: [\n Plot.barY(pop_vs_victimizations, {\n y: \"value\",\n x: \"var\",\n padding: 0.2,\n fill: \"#f16122\",\n sort: true\n // title: (d) => percent_format(d.value)\n }),\n Plot.text(pop_vs_victimizations, {\n y: \"value\",\n x: \"var\",\n text: (d) => percent_format(d.value),\n dy: -17\n })\n ],\n caption: html`

ASJ analysis of the 2022 National Crime Victimization Survey.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1403,"value":"title_householder_age = md`#### Younger people rent their homes at the highest rates.\n*Percent of householders who rent, by age*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1537,"value":"Add line going across all bars. Create additional vertical copies.","pinned":false,"hidden":false,"mode":"md"},{"id":1397,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1599,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1410,"value":"title_renters_by_income = md`#### Households with lower incomes rent at the highest rates.\n*Percent of households that rent, by household income*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1408,"value":"renters_by_income_chart = Plot.plot({\n marginLeft: 250,\n marginBottom: 50,\n grid: false,\n\n x: {\n label: \"Percent of households that rent their homes→\",\n\n labelOffset: 40,\n tickSize: 0,\n domain: [0, 0.7],\n tickFormat: percent_format\n },\n y: {\n tickSize: 0,\n label: null,\n domain: [\n \"Less than $20k\",\n \"$20k to $34,999\",\n \"$35k to $49,999\",\n \"$50k to $74,999\",\n \"$75k to $99,999\",\n \"$100k to $149,999\",\n \"$150k+\"\n ]\n },\n\n marks: [\n Plot.barX(renters_by_income_data, {\n x: \"pct_renters\",\n y: \"bracket\",\n padding: 0.2,\n fill: (d) => (d.bracket == \"All Householders\" ? \"#15a8e0\" : \"#f16122\")\n // title: (d) => percent_format(d.pct_renters)\n }),\n Plot.text(renters_by_income_data, {\n x: \"pct_renters\",\n y: \"bracket\",\n text: (d) => percent_format(d.pct_renters),\n dx: 17\n })\n ],\n caption: html`

ASJ analysis of 2022 Survey of Consumer Finances.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1540,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1438,"value":"title_renters_by_networth = md`#### Households with the least wealth rent at the highest rates.\n*Percent of households that rent, by net worth*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1440,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1585,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1460,"value":"title_renters_by_race = md`#### Householders who are Black and Latino rent at almost twice the rate of White householders\n*Percent of households that rent, by race and ethnicity of householder*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1463,"value":"renters_by_race_chart = Plot.plot({\n marginLeft: 250,\n marginBottom: 50,\n grid: false,\n\n x: {\n label: \"Percent of households that rent their homes→\",\n\n labelOffset: 40,\n tickSize: 0,\n domain: [0, 0.55],\n tickFormat: percent_format\n },\n y: {\n tickSize: 0,\n label: null,\n domain: [\"Black\", \"Latino\", \"White\"]\n },\n\n marks: [\n Plot.barX(renters_by_race_data, {\n x: \"pct_renters\",\n y: \"race\",\n padding: 0.2,\n fill: (d) => (d.bracket == \"All Householders\" ? \"#15a8e0\" : \"#f16122\")\n // title: (d) => percent_format(d.pct_renters)\n }),\n Plot.text(renters_by_race_data, {\n x: \"pct_renters\",\n y: \"race\",\n text: (d) => percent_format(d.pct_renters),\n dx: 17\n })\n ],\n caption: html`

ASJ analysis of 2022 Survey of Consumer Finances.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":840,"value":"title_cash_savings_percentiles = md`\n\n
\n
\n\nThis interactive tool highlights the economic precarity of tenants. One in 4 renting households has less than $200 in cash on hand, and 42% have less than $1,000. \n\n
\n
\n\n\n#### Renting households have very little cash on hand to use in case of an emergency.\n*Household cash savings percentiles, renters vs. people in owner-occupied units, lowest 50% only*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":816,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1544,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":966,"value":"\n\n\n\n@Emily: Ignore the \"Why is this chart interactive\" section\n\n*this is the same chart but interactive. you can look at either cash savings or net worth, and you can see all the data instead of just the bottom half*","pinned":false,"hidden":false,"mode":"md"},{"id":1129,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":782,"value":"#### Household cash savings and net worth percentiles, renters vs. people in owner-occupied units","pinned":false,"hidden":false,"mode":"md"},{"id":1668,"value":"viewof form = Inputs.form(\n {\n narrow: Inputs.range([25, 100], {\n label: \"Change the maximum percentile to zoom or expand\",\n step: 1,\n value: 51\n }),\n series_selection: Inputs.select([\"cash savings\", \"net worth\"], {\n label: \"See cash savings or net worth\"\n }),\n renters_only: Inputs.toggle({ label: \"Show renters only\" })\n },\n {\n template: (inputs) => htl.html`

\n ${Object.values(inputs)}\n

`\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":784,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1725,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":599,"value":"#### People who rent endure more violence than people in owner-occupied housing across racial and ethnic categories.\n*Victims of violence per 10k people, renters vs. people in owner-occupied units*","pinned":false,"hidden":false,"mode":"md"},{"id":1253,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":601,"value":"title_tenure_by_offense_type = md`#### People who rent endure more violence of all types than people in owner-occupied housing.\n*Violent victimization rates per 10k people by offense, renters vs people in owner-occupied units*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1238,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":1344,"value":"title_income_and_tenure_chart = md`#### Across all income levels, renters are more vulnerable to violence.\n*Violent victimization rates per 10k people by household income, renters vs people in owner-occupied units*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1324,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":1512,"value":"title_age_and_tenure_chart = md`#### Across all age groups, renters are more vulnerable to violence.\n*Violent victimization rates per 10k people by age, renters vs people in owner-occupied units*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1509,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":1470,"value":"title_victimization_among_evicted = md`#### More than 40% of people who were evicted in the past 12 months were violently victimized.\n*Percent of people who were victims of violence, by recent experience of eviction*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1472,"value":"victimization_among_evicted_chart = Plot.plot({\n marginLeft: 250,\n marginBottom: 50,\n grid: false,\n\n x: {\n label: null,\n labelOffset: 40,\n tickSize: 0,\n domain: [0, 0.5],\n tickFormat: percent_format\n },\n y: {\n label: null,\n domain: [\n \"Recent Experience of Eviction\",\n \"No Recent Experience of Eviction\"\n ]\n },\n\n marks: [\n Plot.barX(victimization_among_evicted_data, {\n x: \"pct\",\n y: \"pop\",\n padding: 0.2,\n fill: (d) =>\n d.pop == \"No Recent Experience of Eviction\" ? \"#0d73bb\" : \"#f16122\",\n sort: true\n // title: (d) => percent_format(d.value)\n }),\n Plot.text(victimization_among_evicted_data, {\n x: \"pct\",\n y: \"pop\",\n text: (d) => percent_format(d.pct),\n stroke: (d) =>\n d.pop == \"No Recent Experience of Eviction\" ? \"#0d73bb\" : \"#f16122\",\n strokeWidth: 1,\n dx: 17\n })\n ],\n caption: html`

ASJ analysis of Add Health Waves III and IV.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1495,"value":"title_evictions_among_victims = md`#### People who were victimized in the past 12 months experienced eviction in the same period at 2.6 times the rate of people who were not victimized.\n*People evicted per 10,000 in the past 12 months, by recent experience of victimization*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1483,"value":"evictions_among_victimized_chart = Plot.plot({\n marginLeft: 250,\n marginBottom: 50,\n grid: false,\n\n x: {\n label: \"Evictions per 10k people\",\n labelOffset: 40,\n tickSize: 0,\n domain: [0, 200]\n },\n y: {\n label: null,\n domain: [\"Recent Victimization\", \"No Recent Victimization\"]\n },\n\n marks: [\n Plot.barX(eviction_among_victims, {\n x: \"value\",\n y: \"pop\",\n padding: 0.2,\n fill: \"#f16122\",\n sort: true\n // title: (d) => percent_format(d.value)\n }),\n Plot.text(eviction_among_victims, {\n x: \"value\",\n y: \"pop\",\n text: (d) => d.value,\n dx: 17\n })\n ],\n caption: html`

ASJ analysis of Add Health Waves III and IV.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":934,"value":"$3e","pinned":false,"hidden":false,"mode":"md"},{"id":1191,"value":"#### Table 2: Owner-occupied households by household income, violent victimizations, and violent victimizations per 10k people\n
\n\n\n| Household Income | Violent Victimizations | Violent Victimizations per 10k People \n| --------- | ---------- | -------- | \n|Less than $20k|173,860|163\n|$20k to $34,999|369,910|209\n|$35k to $49,999|332,280|152\n|$50k to $74,999|456,320|142\n|$75k to $99,999|401,160|134\n|$100k to $149,999|490,540|133\n|$150k+|733,040|178\n","pinned":false,"hidden":false,"mode":"md"},{"id":289,"value":"#### Most violent victimizations of renters occur in or near their homes.\n*Violent victimizations of renters, by location of incident*","pinned":false,"hidden":false,"mode":"md"},{"id":103,"value":"at_home_chart = Plot.plot({\n x: {\n tickFormat: percent_format,\n label: \"Percent of violent victimizations\",\n labelOffset: 30\n },\n y: { tickSize: 0, label: null },\n marginBottom: 50,\n marginLeft: 100,\n color: {\n domain: [\n \"At home\",\n \"within 1 mile\",\n \"within 5 miles\",\n \"between 5 and 50 miles\",\n \"further than 50 miles\"\n ],\n range: [\"#08519c\", \"#3182bd\", \"#6baed6\", \"#f06224\", \"#f68e63\"],\n legend: true,\n reverse: false\n },\n marks: [\n Plot.barX(at_home_data.slice(0, 3), {\n x: \"pct\",\n y: \"near\",\n fill: \"where\",\n title: (d) =>\n `${percent_format(\n d.cum_pct\n )} of victimizations occur \\n ${d.where.toLowerCase()}`\n }),\n Plot.barX(at_home_data.slice(3, 5), {\n x: (d) => d.pct,\n y: \"near\",\n fill: \"where\",\n title: (d) =>\n `${percent_format(d.pct)} of victimizations occur \\n ${d.where}`\n })\n ],\n caption: html`

ASJ analysis of the 2022 NCVS.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1315,"value":"#### More than half of all violent victimizations of renters are not reported to police.\n*Percent of renters' victimizations not reported to the police, by offense*","pinned":false,"hidden":false,"mode":"md"},{"id":1216,"value":"reported_to_police_chart_alt = Plot.plot({\n marginLeft: 250,\n marginBottom: 50,\n grid: false,\n\n x: {\n label: \"Percent of victimizations not reported to police→\",\n\n labelOffset: 40,\n tickSize: 0,\n domain: [0, 0.75],\n tickFormat: percent_format\n },\n y: {\n tickSize: 0,\n label: null,\n domain: [\n \"rape/sexual assault\",\n \"simple assault or assault without injury\",\n \"all offenses\",\n \"aggravated assault\",\n \"robbery\"\n ]\n },\n\n marks: [\n Plot.barX(pct_reported_to_police, {\n x: (d) => 1 - d.pct_reported_to_police,\n y: \"label\",\n padding: 0.2,\n fill: (d) => (d.label == \"all offenses\" ? \"#0d73bb\" : \"#f16122\")\n // title: (d) => percent_format(d.pct_reported_to_police)\n }),\n Plot.text(pct_reported_to_police, {\n x: (d) => 1 - d.pct_reported_to_police,\n y: \"label\",\n text: (d) => percent_format(1 - d.pct_reported_to_police),\n dx: 17\n })\n ],\n caption: html`

ASJ analysis of the 2022 NCVS.

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1732,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1221,"value":"reasons_not_reported_table = md`|Most important reason for not reporting | Number of victimizations | Percent (among cases where a reason was recorded) |\n|-------------------------------------|\n|Police not think important|141,150| 16%\n|Personal or private matter|137,150 | 16% \n|Fear of reprisal or retaliation|100,350 | 12%\n|Police not effective| 77,980 | 9%\n|Inconvenient to report| 73,690 | 9%`","pinned":false,"hidden":false,"mode":"ojs"},{"id":322,"value":"| Household Income | Violent Victimizations | Median Cash Savings | Median Rent|\n| --------- | ---------- | -------- | --------| \n|Less than $20k|1,213,840|$174|$666\n|$20k to $34,999| 725,830|$582|$772\n|$35k to $49,999| 456,010|$1,470|$930\n|$50k to $74,999| 526,740|$3,860|$1,140\n|$75k to $99,999| 252,670|$6,200|$1,480\n|$100k to $149,999| 284,410|$13,020|$1,700\n|$150k+| 208,360|$34,840|$2,500\n\n\n\n

ASJ analysis of the 2022 National Crime Victimization Survey and 2022 Survey of Consumer Finances.

","pinned":false,"hidden":false,"mode":"md"},{"id":56,"value":"## Data","pinned":false,"hidden":false,"mode":"md"},{"id":1326,"value":"victimizations_by_tenure_and_income_bracket = FileAttachment(\n \"victimizations by tenure and income bracket.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":81,"value":"violent_incidents_by_type_and_tenure_data = FileAttachment(\n \"violent_victimization_by_tenure_and_offense@4.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":66,"value":"victimizations_by_income_data = FileAttachment(\n \"victimizations_by_income_with_scf_data@6.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":73,"value":"dv_data = FileAttachment(\n \"victimizations involving DV as a percentage of all victimizations by tenure@2.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":105,"value":"at_home_data = FileAttachment(\"at home by tenure.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":123,"value":"prevalence_data = FileAttachment(\"prevalence by race and tenure@2.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1802,"value":"prevalence_data","pinned":true,"hidden":false,"mode":"ojs"},{"id":780,"value":"cash_savings_data = FileAttachment(\"financial data by percentile@2.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":676,"value":"problem_and_help_data = FileAttachment(\"problem_and_help_data@1.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":722,"value":"pop_vs_victimizations = [\n { var: \"Renters as Percent of Population\", value: 0.32 },\n { var: \"Percent of Violence Borne by Renters\", value: 0.56 }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1198,"value":"pct_reported_to_police = FileAttachment(\n \"pct of victimizations reported to police - renters.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1391,"value":"householder_data = [\n {\n age: \"15 to 24 years\",\n pct: \"0.833968844\"\n },\n {\n age: \"25 to 34 years\",\n pct: \"0.580943369\"\n },\n {\n age: \"35 to 44 years\",\n pct: \"0.388510714\"\n },\n {\n age: \"All Householders\",\n pct: \"0.348206059\"\n },\n {\n age: \"45 to 54 years\",\n pct: \"0.300963533\"\n },\n {\n age: \"55 to 59 years\",\n pct: \"0.25896576\"\n },\n {\n age: \"60 to 64 years\",\n pct: \"0.236641696\"\n },\n {\n age: \"65 to 74 years\",\n pct: \"0.208917224\"\n },\n {\n age: \"75 to 84 years\",\n pct: \"0.206738061\"\n },\n {\n age: \"85 years and over\",\n pct: \"0.285136938\"\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1415,"value":"renters_by_income_data = FileAttachment(\"renters by income.csv\").csv({\n typed: true\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1443,"value":"renters_by_networth_data = FileAttachment(\"renters by networth@2.csv\").csv({\n typed: true\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1756,"value":"chartWidth = width > 800 ? 800 : width","pinned":true,"hidden":false,"mode":"ojs"},{"id":1458,"value":"renters_by_race_data = [\n {\n race: \"White\",\n pct_renters: \"0.28\"\n },\n {\n race: \"Black\",\n pct_renters: \"0.53\"\n },\n {\n race: \"Latino\",\n pct_renters: \"0.5\"\n }\n]","pinned":true,"hidden":false,"mode":"ojs"},{"id":1474,"value":"victimization_among_evicted_data = [\n {\n pop: \"Recent Experience of Eviction\",\n pct: 0.43\n },\n {\n pop: \"No Recent Experience of Eviction\",\n pct: 0.22\n }\n]","pinned":true,"hidden":false,"mode":"ojs"},{"id":1486,"value":"eviction_among_victims = [\n {\n pop: \"Recent Victimization\",\n value: 191\n },\n {\n pop: \"No Recent Victimization\",\n value: 74\n }\n]","pinned":true,"hidden":false,"mode":"ojs"},{"id":1506,"value":"victimizations_by_age_data = FileAttachment(\"victimizations by age@2.csv\").csv({\n typed: true\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":891,"value":"## Local logic","pinned":false,"hidden":false,"mode":"md"},{"id":893,"value":"renters_toggle = {\n if (form.renters_only == true) {\n return \"Renters\";\n } else {\n return `\"Renters\", \"Owner-Occupied\"`;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":54,"value":"## Dependencies","pinned":false,"hidden":false,"mode":"md"},{"id":50,"value":"percent_format = d3.format(\".0%\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":387,"value":"html``","pinned":true,"hidden":false,"mode":"ojs"},{"id":262,"value":"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":"$40","pinned":true,"hidden":false,"mode":"ojs"},{"id":348,"value":"// Function to position the tooltip\nhover = (tip, pos, text) => {\n const side_padding = 10;\n const vertical_padding = 5;\n const vertical_offset = 15;\n\n // Empty it out\n tip.selectAll(\"*\").remove();\n\n // Append the text\n tip\n .style(\"text-anchor\", \"middle\")\n .style(\"pointer-events\", \"none\")\n .attr(\"transform\", `translate(${pos[0]}, ${pos[1] + 7})`)\n .selectAll(\"text\")\n .data(text)\n .join(\"text\")\n .style(\"dominant-baseline\", \"ideographic\")\n .text((d) => d)\n .attr(\"y\", (d, i) => (i - (text.length - 1)) * 15 - vertical_offset)\n .style(\"font-weight\", (d, i) => (i === 0 ? \"normal\" : \"normal\"));\n\n const bbox = tip.node().getBBox();\n\n // Add a rectangle (as background)\n tip\n .append(\"rect\")\n .attr(\"y\", bbox.y - vertical_padding)\n .attr(\"x\", bbox.x - side_padding)\n .attr(\"width\", bbox.width + side_padding * 2)\n .attr(\"height\", bbox.height + vertical_padding * 2)\n .style(\"fill\", \"white\")\n .style(\"stroke\", \"#d3d3d3\")\n .lower();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":350,"value":"// To generate a unique ID for each chart so that they styles only apply to that chart\n// from mkfreeman\nid_generator = () => {\n var S4 = function () {\n return (((1 + Math.random()) * 0x10000) | 0).toString(16).substring(1);\n };\n return \"a\" + S4() + S4();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":632,"value":"tooltipPlugin = (Plot) => {\n const { plot } = Plot;\n Plot.plot = ({ tooltip, ...options }) => addTooltips(plot(options), tooltip);\n return Plot;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":352,"value":"// automatically add tooltips to every Plot in a notebook\n// this allows users to do import { Plot } from \"@mkfreeman/plot-tooltip\nPlot = tooltipPlugin(await require(\"@observablehq/plot\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":480,"value":"violent_incidents_by_type_and_tenure_data.filter((d) => d.offense_type == 4)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1132,"value":"// https://getbootstrap.com/docs/4.1/getting-started/introduction/#css\n//html`css `","pinned":true,"hidden":false,"mode":"ojs"},{"id":1183,"value":"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W41","stdlib":"1","titling":"auto"}