2e:null 2f:["$","title",null,{"children":"U.S. citizens are over 2 times more likely to be arrested for felony crimes than undocumented immigrants | leonardo Nicoletti | Observable"}] 34:T1a9a,SMchart = { // create and select an svg element that is the size of the bars plus margins const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight+40 + margin.top*4 + margin.bottom); // append a group element and move it left and down to create space // for the left and top margins const g = svg.append("g") .attr('transform', `translate(${margin.left*1.1}, ${margin.top*5.7})`); // Title g.append("text") .attr("class", "FigTitle") .attr("x", -margin.left) .attr("y", -100) .attr("text-anchor", "left") .text("In Texas, undocumented immigrants don't drive crime rates, U.S. citizens do") g.append("line") .attr("x1", -margin.left) .attr("x2", -20) .attr("y1", -75) .attr("y2", -75) .attr("stroke", "black") g.append("text") .attr("class", "FigSubtitle") .attr("x", -margin.left) .attr("y", -60) .attr("text-anchor", "left") .text("Trends of felony crime rates by U.S. citizens, legal immigrants, and undocumented immigrants") // create a group for each small multiple const cells = g.append('g') .selectAll('g') .data(data_cl_dt) .join('g') .attr('class', d=>'cell'+d.row + d.col) // .attr('class', 'cell') .attr('transform', d => `translate(${col_dt(d.col)}, ${row_dt(d.row)})`); // add path for each category to each cell cells.append('path') // access the area generator for this category .attr('d', d => categoryToScale_dt[d.category].lineGeneratorSM(d.rates.filter(d=>d.variable == "illegal2_immigrants_crime_rate"))) .attr("stroke", colorScale("illegal2_immigrants_crime_rate")) .attr("stroke-width", 3) .attr('fill', 'None') // .on('mouseover', tip.show) // .on('mouseout', tip.hide); cells.append('path') .attr('d', d => categoryToScale_dt[d.category].lineGeneratorSM(d.rates.filter(d=>d.variable == "immigrant_crime_rate"))) .attr("stroke", colorScale("immigrant_crime_rate")) .attr("stroke-width", 3) .attr('fill', 'None'); cells.append('path') .attr('d', d => categoryToScale_dt[d.category].lineGeneratorSM(d.rates.filter(d=>d.variable == "citizen_crime_rate"))) .attr("stroke", colorScale("citizen_crime_rate")) .attr("stroke-width", 3) .attr('fill', 'None'); // Annotations g.append("text").attr("class", "annotation").attr("x", width/1.67).attr("y", -4).attr("text-anchor", "end").attr("fill", colorScale("citizen_crime_rate")).text("U.S. Citizens") g.append("text").attr("class", "annotation").attr("x", width/1.67).attr("y", 50).attr("text-anchor", "end").attr("fill", colorScale("immigrant_crime_rate")).text("Legal Immigrants") g.append("text").attr("class", "annotation").attr("x", width/1.67).attr("y", 85).attr("text-anchor", "end").attr("fill", colorScale("illegal2_immigrants_crime_rate")).text("Undocumented Immigrants") // y axis g.append("text") .attr("class", "axisAnnotation") .attr("x", 0) .attr("y", 10) .attr("text-anchor", "start") .text("Crimes per 100,000 people") // add the y axis for each cell cells.each(function(d) { const group = d3.select(this) // get the y-scale for this industry const yAxisSM = d3.axisLeft(categoryToScale_dt[d.category].y) .ticks(3) .tickSizeOuter(0); const xSM = d3.axisBottom(categoryToScale_dt[d.category].x) .ticks(4) group.call(yAxisSM).attr("id", "yAxisSM") .call(g => g.selectAll('.domain').remove()) group.append("g") .attr("transform", `translate(0,${visHeight + margin.top*2 - margin.bottom})`).call(xSM) group.append("line") .attr("x1", 0) .attr("x2", 20) .attr("y1", -25) .attr("y2", -25) .attr("stroke", "black") group.append("g").attr("class", "catLabel").append("text") // .attr("x", 110) // .attr("y", -12) // .attr("text-anchor", "middle") .attr("x", 0) .attr("y", -12) .attr("text-anchor", "start") .attr("fill", "black") .text(d.category.replace('_',' ')) .attr("font-size", 10) .attr("class", "SMTitle_dt") // Circles const groups = ["illegal2_immigrants_crime_rate", "immigrant_crime_rate", "citizen_crime_rate"] groups.forEach(function (item, index) { group.append("g").attr("class", "annotation").append("circle") // .attr("x", width-15) .attr("cx", categoryToScale_dt[d.category].x(data_dt.filter(c =>(c.variable === item) && (c.category === d.category))[6].year)) .attr("cy", categoryToScale_dt[d.category].y(data_dt.filter(c =>(c.variable === item) && (c.category === d.category))[6].value)) .attr("r", 2.7) .attr("text-anchor", "middle") .attr("fill", colorScale(item)) group.append("g").attr("class", "annotation").append("circle") // .attr("x", width-15) .attr("cx", categoryToScale_dt[d.category].x(data_dt.filter(c =>(c.variable === item) && (c.category === d.category))[0].year)) .attr("cy", categoryToScale_dt[d.category].y(data_dt.filter(c =>(c.variable === item) && (c.category === d.category))[0].value)) .attr("r", 2.7) .attr("text-anchor", "middle") .attr("fill", colorScale(item)) // .on('touchmove mousemove', console.log("entered")) // .on('mouseout', console.log("exited")); // .text("test") }) // // tooltip // const bisect = d3.bisector(d => d.date).left; // return mx => { // const date = categoryToScale_dt[d.category].x.invert(mx); // const index = bisect(data_dt, date, 1); // const a = data_dt[index - 1]; // const b = data_dt[index]; // return date - a.year > b.year - date ? b : a; // }; // const tooltip = group.append("g"); // group.on("touchmove mousemove", function() { // const { date, value } = bisect(d3.mouse(this)[0]); // tooltip.attr("transform", `translate(${categoryToScale_dt[d.category].x(date)},${categoryToScale_dt[d.category].y(value)})`) // .call( // callout, // `${value.toLocaleString(undefined, { // // style: "currency", // // currency: "USD" // })} // ${date.toLocaleString(undefined, { // month: "short", // day: "numeric", // year: "numeric" // })}` // ); // }); // group.on("touchend mouseleave", () => tooltip.call(callout, null)); }); return svg.node(); }35:T1001,html`

"When Mexico sends its people, they’re not sending their best [...] They’re sending people that have lots of problems, and they’re bringing those problems with us. They’re bringing drugs. They’re bringing crime. They’re rapists." This is how Donald Trump, former U.S. president, described Mexican immigration to the United States during his 2016 election campaign. During his presidency Donald Trump had used the same rhetoric to fuel aversion towards immigrants, tighten immigration laws, and justify expensive public projects, such as building a wall across the U.S. - Mexico border. From a public policy perspective, such measures could be justified if undocumented immigrants did indeed pose a significant threat to U.S. law and order. A recent paper published by Michael T. Light, Jingying He, and Jason P. Robey in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) debunks this claim by presenting strong evidence of a much different reality: U.S. citizens, not undocumented immigrants, are the main culprits for sustained crime rates in the United States.

Surprisingly, data about the crime rates of undocumented U.S. immigrants have been largely unavailable until very recently. Historically, most U.S. crime databases and law enforcement agencies have not recorded information by immigration status. Thus, until now, many basic questions about the criminal activity of undocumented immigrants have remained unanswered: How do their crime rates compare to those of legal immigrants and naturalised U.S. citizens? Are there specific categories of crime in which the crime rates of undocumented immigrants are higher? How have these crime rates changed over time?

Exceptionally, the Texas Department of Public Safety (DPS) not only maintains a detailed database for every jailable arrest in Texas, but it also collects information about the immigration status of every arrestee directly from the US Department of Homeland Security (DHS). In their paper, Michael T. and his colleagues have combined this DPS data to deliver the first comprehensive analysis of criminality according to immigration status. With arrest and immigration status information, the authors of this study have first categorised each Texas arrestee as a native-born US citizen, a legal immigrant, or an undocumented immigrant. They have then compared the frequency of criminal offences within each of these demographics groups across a wide range of felony crime types (i.e. violent crimes, property crimes, drugs crimes, and traffic crimes). Finally, using historical data that dates back to 2012, they have investigated how the crime rates of these different demographic groups have evolved over time.

Contrary to conservative narratives, it turns out that U.S. citizens and legal immigrants have markedly higher crime rates in comparison to undocumented immigrants across a broad range of crime types. In fact, this study reveals that "relative to undocumented immigrants, U.S. citizens are over 2 times more likely to be arrested for violent crimes, 2.5 times more likely to be arrested for drug crimes, and over 4 times more likely to be arrested for property crimes”. Moreover, while the undocumented immigrant population in Texas has tripled in the past few years, its crime rates have either remained steady or gradually decreased over most categories of crime type during the same time frame. However, the causation behind this striking relationship between felony crime and undocumented status is not explained in the presented analysis.

Americans have every right to want a safe country to live in. They may want to consider, however, that former President Donald Trump was wrong about undocumented immigrants. A detailed investigation into US government records could have reassured the former president that undocumented immigrants are, in fact, neither bringing a substantial amount of crime nor drugs to the U.S. It appears that in the end, Mexico has always been sending their best.`36:T14a0,chart = { const svg = d3.create("svg") .style("cursor", "default") .attr("viewBox", [0, 0, width_w, height_w+70]) // Title svg.append("text") .attr("class", "FigTitle") .style("font-size", 12) .attr("x", 0) .attr("y", 10) .attr("text-anchor", "left") // .text(crimeType==="all"? "In Texas, U.S. citizens are " + US_vs_illegal_prob + "% more likely to commit a crime than undocumented immigrants": "In Texas, U.S. citizens are " + US_vs_illegal_prob + "% more likely to commit a " + crimeType + " crime than undocumented immigrants") .text(crimeType==="all"? "In Texas, U.S. citizens are " + US_vs_illegal_prob_charge + "% more likely to commit a crime than undocumented immigrants": "In Texas, U.S. citizens are " + US_vs_illegal_prob_charge + "% more likely to commit a " + crimeType + " crime than undocumented immigrants") svg.append("line") .attr("x1",0) .attr("x2",20) .attr("y1", 20) .attr("y2", 20) .attr("stroke", "black") svg.append("text") .attr("class", "FigSubtitle") .attr("x", 0) .attr("y", 35) .style("font-size", 8.8) .attr("text-anchor", "left") .text("Proportion of felony crime rates for U.S. citizens, legal immigrants, and undocumented immigrants. Each square represents 1000 individuals.") const g = svg.append("g") .attr("transform", `translate(0,70)`) .selectAll(".waffle") .data(waffles) .join("g") .attr("class", "waffle") if (!whole) { const numPerRow = Math.floor(width_w / (waffleSize + padding.x)); g.attr("transform", (d, i) => { var r = Math.floor(i / numPerRow); var c = i - r * numPerRow; return `translate(${c*1.5 * (waffleSize + padding.x)},${r * (waffleSize + padding.y)})` }); } const cellSize = scale.bandwidth(); const half = cellSize / 2; const cells = g.append("g") // .selectAll(options.shape) .selectAll("rect") .data(d => d) // .join(options.shape) .join("rect") .attr("fill", d => d.index === -1 ? "#ddd" : color(d.index)); if (isRect) { cells.attr("x", d => scale(d.x)) .attr("y", d => whole ? 0 : scale(d.y)) .attr("rx", 2).attr("ry", 2) .attr("width", cellSize).attr("height", cellSize) } else { cells.attr("cx", d => scale(d.x) + half) .attr("cy", d => whole ? 0 : scale(d.y) + half) .attr("r", half); } if (whole) { cells.append("title").text(d => { const cd = chartData[d.index]; // return `${cd.territory}\n${toCurrency(cd.profit)} (${cd.ratio.toFixed(1)}%)`; return `${cd.variable.replace('_',' ').replace('_',' ').replace('_',' ').replace('2','')}\n${Math.round(cd.value)} crimes per 100,000 people (${cd.ratio.toFixed(1)}%)`; }); cells.transition() .duration(d => d.y * 100) .ease(d3.easeBounce) .attr(isRect ? "y" : "cy", d => scale(d.y) + (isRect ? 0 : half)); svg.transition().delay(550) .on("end", drawLegend); } else { g.append("text") .attr("class", "waffleText") .style("font-size", 10) .style("font-weight", "bold") .attr("dy", "1.5em") .attr("text-anchor", "middle") .attr("fill", (d, i) => color(i)) .attr("transform", `translate(${waffleSize / 2},-20)`) .text((d, i) => `${chartData[i].ratio.toFixed(0)}%`);// of ${crimeType} crimes g.append("g") .attr("transform", `translate(0,${waffleSize + padding.y / 2.5})`) .call(g => g.append("text").attr("class", "waffleText").text((d, i) => crimeType + " crimes committed by " + chartData[i].variable.replace('_',' ').replace('_',' ').replace('_',' ').replace('2','').replace('crime rate',''))) .call(g => g.append("text").attr("class", "waffleText").style("font-weight", 700).attr("dy", "1em").text((d, i) => Math.round(chartData[i].value)+" per 100,000 people")); } // return svg.node(); var legend; function drawLegend() { legend = svg.selectAll(".legend") .data(chartData.map(d => d.variable)) .join("g") .attr("opacity", 1) .attr("transform", (d, i) => `translate(${waffleSize + 15},${i * 19.8})`) .on("mouseover", highlight) .on("mouseout", restore); legend.append("rect") .attr("transform", `translate(0,70)`) .attr("rx", 2).attr("ry", 2) .attr("width", cellSize).attr("height", cellSize) .attr("fill", (d, i) => color(i)); legend.append("text") .attr("transform", `translate(0,70)`) .attr("dx", 25) .attr("dy", 7) .attr("class", "waffleText") .style("font-size", 8.8) .attr("alignment-baseline", "hanging") .text((d, i) => `${crimeType + " crimes committed by " + d.replace('_',' ').replace('_',' ').replace('_',' ').replace('2','').replace('crime rate','')} (${chartData[i].ratio.toFixed(1)}%)`); } function highlight(e, d, restore) { const i = legend.nodes().indexOf(e.currentTarget); cells.transition().duration(500) .attr("fill", d => d.index === i ? color(d.index) : "#ccc"); } function restore() { cells.transition().duration(500).attr("fill", d => color(d.index)) } }37:T1881,{ // create and select an svg element that is the size of the bars plus margins const svg = d3.create('svg') .attr('width', visWidth+10 + margin.left + margin.right) .attr('height', visHeight + margin.top*4 + margin.bottom); // append a group element and move it left and down to create space // for the left and top margins const g = svg.append("g") .attr('transform', `translate(${margin.left}, ${margin.top*4})`); // Title // g.append("text") // .attr("class", "FigTitle") // .attr("x", -margin.left) // .attr("y", -55) // .attr("text-anchor", "left") // .text("In Texas, undocumented immigrants don't drive crime rates. U.S. citizens do") // create a group for each small multiple const cells = g.append('g') .selectAll('g') .data(data_cl) .join('g') .attr('class', d=>'cell'+d.row + d.col) // .attr('class', 'cell') .attr('transform', d => `translate(${col(d.col)}, ${row(d.row)})`); // add path for each category to each cell cells.append('path') // access the area generator for this category .attr('d', d => categoryToScale[d.category].lineGeneratorSM(d.rates.filter(d=>d.variable == "illegal2_immigrants_crime_rate"))) .attr("stroke", colorScale("illegal2_immigrants_crime_rate")) .attr("stroke-width", 4) .attr('fill', 'None'); cells.append('path') .attr('d', d => categoryToScale[d.category].lineGeneratorSM(d.rates.filter(d=>d.variable == "immigrant_crime_rate"))) .attr("stroke", colorScale("immigrant_crime_rate")) .attr("stroke-width", 4) .attr('fill', 'None'); cells.append('path') .attr('d', d => categoryToScale[d.category].lineGeneratorSM(d.rates.filter(d=>d.variable == "citizen_crime_rate"))) .attr("stroke", colorScale("citizen_crime_rate")) .attr("stroke-width", 4) .attr('fill', 'None'); // Annotations g.append("text") .attr("class", "annotation") .attr("x", width-30) .attr("y", -4) .attr("text-anchor", "end") .attr("fill", colorScale("citizen_crime_rate")) .text("U.S. Citizens") g.append("text") .attr("class", "annotation") .attr("x", width-30) .attr("y", 40) .attr("text-anchor", "end") .attr("fill", colorScale("immigrant_crime_rate")) .text("Legal Immigrants") g.append("text") .attr("class", "annotation") .attr("x", width-30) .attr("y", 115) .attr("text-anchor", "end") .attr("fill", colorScale("illegal2_immigrants_crime_rate")) .text("Undocumented Immigrants") // y axis g.append("text") .attr("class", "annotation") .attr("x", 0) .attr("y", 10) .attr("text-anchor", "start") .text("Crime per 100,000 people") // add the y axis for each cell cells.each(function(d) { const group = d3.select(this) // get the y-scale for this industry const yAxisSM = d3.axisLeft(categoryToScale[d.category].y) .ticks(3) .tickSizeOuter(0); const xSM = d3.axisBottom(categoryToScale[d.category].x) // .ticks(3) // .tickSizeOuter(0); // const xSM = g => g.attr("transform", `translate(0,${20 - margin.bottom})`).call(d3.axisBottom(categoryToScale[d.category].x) // .ticks(3) // .tickSizeOuter(0)); group.call(yAxisSM).attr("id", "yAxisSM") .call(g => g.selectAll('.domain').remove()) // .call(xSM) group.append("g") .attr("transform", `translate(0,${visHeight + margin.top*2 - margin.bottom})`).call(xSM) // .call(g => g.selectAll('.domain').remove()) group.append("line") .attr("x1", 0) .attr("x2", 20) .attr("y1", -25) .attr("y2", -25) .attr("stroke", "black") group.append("g").attr("class", "catLabel").append("text") // .attr("x", 110) // .attr("y", -12) // .attr("text-anchor", "middle") .attr("x", 0) .attr("y", -12) .attr("text-anchor", "start") .attr("fill", "black") .text(d.category.replace('_',' ') + " Crime") .attr("font-size", 10) .attr("class", "SMTitle_dt") // Add Annotation of percent change to each Line const groups = ["illegal2_immigrants_crime_rate", "immigrant_crime_rate", "citizen_crime_rate"] groups.forEach(function (item, index) { group.append("g").attr("class", "annotation").append("text") // .attr("x", width-15) .attr("x", categoryToScale[d.category].x(data.filter(c =>(c.variable === item) && (c.category === d.category))[6].year)+16) .attr("y", categoryToScale[d.category].y(data.filter(c =>(c.variable === item) && (c.category === d.category))[6].value)) .attr("text-anchor", "middle") .attr("fill", colorScale(item)) .text(Math.sign(PCGroupsCat.filter(c=>(c.group=== item)&(c.category === d.category))[0]['pct_change']) === -1?PCGroupsCat.filter(c=>(c.group=== item)&(c.category === d.category))[0]['pct_change']+"%":"+"+PCGroupsCat.filter(c=>(c.group=== item)&(c.category === d.category))[0]['pct_change']+"%") group.append("g").attr("class", "annotation").append("circle") // .attr("x", width-15) .attr("cx", categoryToScale[d.category].x(data.filter(c =>(c.variable === item) && (c.category === d.category))[6].year)) .attr("cy", categoryToScale[d.category].y(data.filter(c =>(c.variable === item) && (c.category === d.category))[6].value)) .attr("r", 3.5) .attr("text-anchor", "middle") .attr("fill", colorScale(item)) group.append("g").attr("class", "annotation").append("circle") // .attr("x", width-15) .attr("cx", categoryToScale[d.category].x(data.filter(c =>(c.variable === item) && (c.category === d.category))[0].year)) .attr("cy", categoryToScale[d.category].y(data.filter(c =>(c.variable === item) && (c.category === d.category))[0].value)) .attr("r", 3.5) .attr("text-anchor", "middle") .attr("fill", colorScale(item)) }) }); return svg.node(); }38:T1eef,PCGroupsCat = [{ group: "illegal2_immigrants_crime_rate", category: "Property", pct_change: Math.round((data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Property"))[6].value - data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Property"))[0].value)/ data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Property"))[0].value*100), }, { group: "illegal2_immigrants_crime_rate", category: "Violent", pct_change: Math.round((data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Violent"))[6].value - data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Violent"))[0].value)/ data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Violent"))[0].value*100), }, { group: "illegal2_immigrants_crime_rate", category: "Drug", pct_change: Math.round((data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Drug"))[6].value - data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Drug"))[0].value)/ data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Drug"))[0].value*100), }, { group: "illegal2_immigrants_crime_rate", category: "Traffic", pct_change: Math.round((data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Traffic"))[6].value - data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Traffic"))[0].value)/ data.filter(d => (d.variable === "illegal2_immigrants_crime_rate") && (d.category === "Traffic"))[0].value*100), }, { group: "immigrant_crime_rate", category: "Property", pct_change: Math.round((data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Property"))[6].value - data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Property"))[0].value)/ data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Property"))[0].value*100), }, { group: "immigrant_crime_rate", category: "Violent", pct_change: Math.round((data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Violent"))[6].value - data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Violent"))[0].value)/ data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Violent"))[0].value*100), }, { group: "immigrant_crime_rate", category: "Drug", pct_change: Math.round((data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Drug"))[6].value - data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Drug"))[0].value)/ data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Drug"))[0].value*100), }, { group: "immigrant_crime_rate", category: "Traffic", pct_change: Math.round((data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Traffic"))[6].value - data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Traffic"))[0].value)/ data.filter(d => (d.variable === "immigrant_crime_rate") && (d.category === "Traffic"))[0].value*100), }, { group: "citizen_crime_rate", category: "Property", pct_change: Math.round((data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Property"))[6].value - data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Property"))[0].value)/ data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Property"))[0].value*100), }, { group: "citizen_crime_rate", category: "Violent", pct_change: Math.round((data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Violent"))[6].value - data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Violent"))[0].value)/ data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Violent"))[0].value*100), }, { group: "citizen_crime_rate", category: "Drug", pct_change: Math.round((data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Drug"))[6].value - data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Drug"))[0].value)/ data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Drug"))[0].value*100), }, { group: "citizen_crime_rate", category: "Traffic", pct_change: Math.round((data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Traffic"))[6].value - data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Traffic"))[0].value)/ data.filter(d => (d.variable === "citizen_crime_rate") && (d.category === "Traffic"))[0].value*100), } ]39:Ta70,styles = html``3a:Te52,html`

Methods
- data: Texas Computerized Criminal History (CCH) database provided by the Texas Department of Public Safety. CCH database contains case processing information for every jailable arrest in the state of Texas, cross referenced with immigration status dataset from the Texas DPS to gather info about immigration status of each arrestee in texas since 2011
- categorize each arrestee into 3 cats: 1) native-born US citizens, 2) legal immigrants, and 3) undocumented immigrants
- "Combined with the fact that we have complete information for every jailable arrest in Texas, our dataset is ideal for comparing the criminality between undocumented immigrants, legal immigrants, and native-born citizens. Simply put, no other data source in the United States could accomplish this task with the same degree of breadth, rigor, and detail."
- "Estimates of the undocumented population come from the Center for Migration Studies (CMS), one of the most reliable, respected, and peer-reviewed sources on the undocumented immigrant population (22, 23)".
- "First, we offer a detailed contemporary assessment of the comparative criminality between native-born US citizens, legal immigrants, and undocumented immigrants."--> aggregate crime rates by group from 2012 to 2018
- "Second, going beyond general differences in crime, we calculate comparative crime rates across multiple offense types."
- "Last, we examine the overall and relative crime trends among the undocumented."
- "Using visual plots, linear regressions, and time series techniques (augmented Dickey–Fuller [ADF] tests), we provide a longitudinal assessment of the relative involvement of undocumented immigrants in crime."

findings
- "Foreshadowing our results, we find that undocumented immigrants have considerably lower crime rates than native-born citizens and legal immigrants across a range of criminal offenses, including violent, property, drug, and traffic crimes. We also report no evidence that undocumented criminality has become more prevalent in recent years across any crime category." - "Relative to undocumented immigrants, US-born citizens are over 2 times more likely to be arrested for violent crimes, 2.5 times more likely to be arrested for drug crimes, and over 4 times more likely to be arrested for property crimes." - Study finds that, while the undocumented population in Texas has tripled over the past few years, undocumented crime has gradually decreased.

context
- "there are compelling theoretical reasons to think that undocumented immigrants may have divergent effects on violent and nonviolent crime. Most notably, lacking legal status limits their legitimate economic opportunities, and thus, undocumented immigrants may turn to illegitimate economic pursuits"

methodology, conclusions and limitations in clear, concise, non-technical English intended for a general audience; and that you provide sufficient context for readers to understand the broader issue/trend/debate this research informs, and how it should change their thinking about that topic.

Today, the U.S. Federal Government spends more on immigration-related law enforcement than all other major criminal law enforcement agencies combined.

have analyzed a non-public dataset from the Texas Department of Public Safety (DPS). The focus on Texas, the second-largest immigrant population in the United States, 4.8 million foreign-born individuals of which estimate 1.6 million are undocumented. texas, among the states that spend the most in law enforcement related to immigration. `3b:[{"id":"bc6a2eb05c24adba094cd7ad5b08eade3f15f33a0410e233c1652105c1694e3b0e4f370bb061d5680167200993df18e2f365deae40e851210ad2e7bcae0fefc8","href":"https://static.observableusercontent.com/files/bc6a2eb05c24adba094cd7ad5b08eade3f15f33a0410e233c1652105c1694e3b0e4f370bb061d5680167200993df18e2f365deae40e851210ad2e7bcae0fefc8","name":"crimeNativity_tidy@2.csv","type":"text/csv","lastModified":1614461133570.971,"size":26593},{"id":"e54f473b3bbce45837af44c1a33840e3a972558b374f1a60e3062f2a9e4efe157d8e9b5558a2a790786c3aab91e2a3f031c22da76da9f5fcccd330da8918fa5c","href":"https://static.observableusercontent.com/files/e54f473b3bbce45837af44c1a33840e3a972558b374f1a60e3062f2a9e4efe157d8e9b5558a2a790786c3aab91e2a3f031c22da76da9f5fcccd330da8918fa5c","name":"crimeNativity_dt_tidy.csv","type":"text/csv","lastModified":1614554331411.939,"size":84864}] 32:{"id":"0d9cf9f489f8311e","slug":"u-s-citizens-are-over-2-times-more-likely-to-be-arrested-for-fe","title":" U.S. citizens are over 2 times more likely to be arrested for felony crimes than undocumented immigrants","thumbnail":"dc8984ce80e3a0eabc4f614a07cb814a2542942fb2f6d6ebda99124924f53258","access_level":"public","create_time":"$D2021-02-27T00:51:37.821Z","update_time":"$D2021-04-08T01:24:18.164Z","view_time":null,"first_public_time":"$D2021-03-15T23:37:54.213Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"405a122b348425fd","login":"lnicoletti","name":"leonardo Nicoletti","avatar_url":"https://avatars-next.observableusercontent.com/36092ac2cd0189ba9d5c4621680d6b2ef55948e600102b0814f4135b9cec7068","tier":"starter_2024"},"version":1846,"publish_version":1846,"role":null,"authored":false,"authors":[{"type":"individual","id":"8280c368bc7eaac0","login":"lnicoletti","name":"leonardo Nicoletti","avatar_url":"https://avatars-next.observableusercontent.com/36092ac2cd0189ba9d5c4621680d6b2ef55948e600102b0814f4135b9cec7068","tier":null}],"members":[{"type":"individual","id":"8280c368bc7eaac0","login":"lnicoletti","name":"leonardo Nicoletti","avatar_url":"https://avatars-next.observableusercontent.com/36092ac2cd0189ba9d5c4621680d6b2ef55948e600102b0814f4135b9cec7068","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":" U.S. citizens are over 2 times more likely to be arrested for felony crimes than undocumented immigrants","cells":[{"id":0,"value":"html`

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