31:Tb53,// JavaScript source code weaponMap = { let container = DOM.element('div', { style: `width:${width}px;height:${width / 1.6}px` }); yield container; var mymap = L.map(container).setView([41.835832, -87.623177], 10); L.tileLayer('https://api.tiles.mapbox.com/v4/{id}/{z}/{x}/{y}.png?access_token=pk.eyJ1IjoiemVhZiIsImEiOiJjanZxdTNzOTIwbnFqNDlvejV5Z2theDh1In0.i5rEU42TIXyyy4frojTIIg', { attribution: 'Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox', maxZoom: 18, id: 'mapbox.streets', accessToken: 'pk.eyJ1IjoiemVhZiIsImEiOiJjanZxdTNzOTIwbnFqNDlvejV5Z2theDh1In0.i5rEU42TIXyyy4frojTIIg' }).addTo(mymap); var data = districtData.sort((a, b) => (a.area > b.area) ? 1 : ((b.area > a.area) ? -1 : 0)); function style(feature) { let color = "#000" if(ch3){ var iter = parseInt(feature.properties.area_numbe-1); color = colorPick(weaponData[iter].number,1100,100); } else{ var iter = parseInt(feature.properties.area_numbe) - 1; console.log(data[iter]) color = colorPick(data[iter].number,35000,1000); } return { fillColor: color, color: color, weight: 1, fillOpacity: 0.75 }; } function onEachFeature(feature, layer) { if (feature.properties.community !== "") { if(ch3){ var iter = parseInt(feature.properties.area_numbe) - 1; let value = weaponData[iter].number; // define the popup content let content = `

Area Name: ${factors[iter]["COMMUNITY AREA NAME"]}

Number of weapon violations ${value}

` layer.bindPopup(content); } else { var iter = parseInt(feature.properties.area_numbe) - 1; let value = data[iter].number; // define the popup content let content = `

Area Name: ${factors[iter]["COMMUNITY AREA NAME"]}

Number of crimes ${value}

` layer.bindPopup(content); } // bind the popup to the layer layer.on('mouseover', function (e) { this.openPopup(); }); layer.on('mouseout', function (e) { this.closePopup(); }); } } let areas = L.geoJson(areasData, { style: style, onEachFeature: onEachFeature }).addTo(mymap); } 32:T77d,map = { let container = DOM.element('div', { style: `width:${width}px;height:${width/1.6}px` }); yield container; var mymap = L.map(container).setView([41.835832, -87.623177], 10); L.tileLayer('https://api.tiles.mapbox.com/v4/{id}/{z}/{x}/{y}.png?access_token=pk.eyJ1IjoiemVhZiIsImEiOiJjanZxdTNzOTIwbnFqNDlvejV5Z2theDh1In0.i5rEU42TIXyyy4frojTIIg', { attribution: 'Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox', maxZoom: 18, id: 'mapbox.streets', accessToken: 'pk.eyJ1IjoiemVhZiIsImEiOiJjanZxdTNzOTIwbnFqNDlvejV5Z2theDh1In0.i5rEU42TIXyyy4frojTIIg' }).addTo(mymap); function style(feature) { let color = "#000" if (factors[feature.properties.community] !== "") var iter = parseInt(feature.properties.area_numbe)-1; if (selFactor === "PER CAPITA INCOME ") color = colorPick2(factors[iter][selFactor], fmax, fmin); else color = colorPick(factors[iter][selFactor], fmax, fmin); return { fillColor: color, color: color, weight: 1, fillOpacity: 0.75 }; } function onEachFeature(feature, layer) { if (factors[feature.properties.community] !== "") { var iter = parseInt(feature.properties.area_numbe)-1; let value = factors[iter][selFactor]; // define the popup content let content = `

Area Name: ${factors[iter]["COMMUNITY AREA NAME"]}

${selFactor} ${value}

` // bind the popup to the layer layer.bindPopup(content); layer.on('mouseover', function (e) { this.openPopup(); }); layer.on('mouseout', function (e) { this.closePopup(); }); } } let areas = L.geoJson(areasData ,{style: style, onEachFeature: onEachFeature}).addTo(mymap); }33:T9ea,md ` First of all it is observed that none of the values are just above 0.85 for theft per capita income and the closest to zero value is 0.009 for other offence/Prc. of housing crowded. When reviewing these numbers common accepted norms are used (Hinkle 2003). The standards used can be seen in table 2. Size of Correlation Interpretation * .90 to 1.00 (−.90 to −1.00) Very high positive (negative) correlation * .70 to .90 (−.70 to −.90) High positive (negative) correlation * .50 to .70 (−.50 to −.70) Moderate positive (negative) correlation * .30 to .50 (−.30 to −.50) Low positive (negative) correlation * .00 to .30 (.00 to −.30) negligible correlation Many results and consideration can be highlighted from this matrix. Since hardship is a general indicator. It becomes visible that more incidents of theft, crimital thresspass and deceptive practices (illigal trade) occur in areas where hardship is at lower levels. Especially theft and deceptive practices have High possitive correlation (0.77, 0.86). This may be enterpreted as more of these crimes occur in areas with more money, as can be seen on the Per Capita Income attribute, which has similar correlations as hardship e.g. the results seam to suggest that low income by area is a good indicator for hardship by area. Among high negative correlations it becomes aparent that the act of battery and weapons violation is less frequent in rich areas and areas with better socioeconomic conditions (-0.76, -0.81). It is interesting how burglary and theft is not alligened in terms of socioeconomic correlation. The analysis seams thus to suggest that burglary is much more independent of socioeconomic backgrounds and occurs in all layers of society represented by districts. While theft very specificly only seam to correlate when there is wealth present. That may seam intuitive, as in the thieves are drawn to the money, but assumptions could be made that the rich also guard their houses and valuables better and thus burglary spreads accross society. Finally the analysis might highlight the results of weapons violations which draws a distinct picture that percent households in poverty and unemployment (0.736822, 0.799575) has high correlation with unlawful behavior for possession and use of weapons. Oppositely low levels of hardship, e.g. good life conditions seams to strongly attribute against being exposed to weapons violations (-0.818258). We can indeed see that this is the case of southside Chicago, where Katana was born and raised.`34:T10d4,md `To gain a deeper intuitive understanding of our socioeconomic factors we used the Apriori algorithm to compute rules, or configurations of one or more variables that strongly imply a configuration for another variable. These rules can be thought of as analogues to the reasoning of how items in a supermarket are shelved - customers that purchase cereal usually purchase milk, but customers that purchase cereal may or may not necessarily purchase broccoli. So, the supermarket capitalizes on customer purchasing habits by storing cereal and milk near each other. Looking at the socioeconomic factors in Chicago, it's easy to assume that factors like lower incomes and higher unemployment predict higher hardship. Running our data through the Apriori confirmed some such rules, and generated some surprises. To utilize the Apriori algorithm, we first need to preprocess the data. This is done binarizing all continuous variables according to their median value. For example, if the median value of one variable is 0.5, then all instances where the variable is less than 0.5 are classified as zero, and all other instances are classified as 1. The binarized data is then transformed into a list containing the label names of whether a given variable is ‘on’ or ‘off’ using the provided mat2transactions function. This list is then fed into the provided apriori algorithm function. It's worth mentioning that our socioeconomic data set is very small, so these rules should really be treated more like suggestions. However, some predicted rules (see table) were consistent with our data analysis, so this proved a worthwhile overview. Some interesting rules are presented and discussed below. Socioeconomic Factor(s) Result Support Confidence * % OF HOUSEHOLDS BELOW POVERTY (HIGH), HARDSHIP INDEX (HIGH) PER CAPITA INCOME (LOW) 0.416 0.970 * % OF HOUSEHOLDS BELOW POVERTY (LOW) HARDSHIP INDEX (LOW) PER CAPITA INCOME (HIGH) 0.416 0.941 * % AGED 16+ UNEMPLOYED (HIGH) HARDSHIP INDEX (HIGH) PER CAPITA INCOME (LOW) 0.403 0.969 * PER CAPITA INCOME (LOW) HARDSHIP INDEX (HIGH) 0.481 0.949 * % AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA (LOW) % OF HOUSING CROWDED (LOW) 0.429 0.846 (LOW) and (HIGH) signify whether the binarized variable is treated as 0 or 1, respectively. This analysis was done before inverting the hardship rating. So, higher hardship implies worse socioeconomic conditions and lower hardship implies better conditions. From the rules, we find the best rules for predicting per capita income. We transcribe the first two rules: if the percentage of households below the poverty line exceed the median percentage of households under the poverty line, and the district's hardship index is above the median hardship index, we can say with ~95% assurance that the per capita income of the district will fall below the median per capita income across all districts. The converse of this rule is also true; this implies a circular generation of rules from the Apriori algorithm. Another indicator of low per capita income is high unemployment combined with high hardship. Intuitively this makes sense, as areas with high unemployment typically suffer economically. Combining high unemployment with a 'hard' district, one can claim with 96% certainty that the district is below the median per capita income. The overall best indicator for hardship index was found to be 'Per capita income'. The best indicator was found by finding the maximum sum of the support and confidence values for rules predicting hardship index. Combining the support and confidence values ensures the greatest 'spread' across the data set to purport an actual rule instead of coincidental pairings. This rule makes sense, and it's interesting that the overall best indicator for 'hardness' of a district is the amount of wealth the district has. The final rule was unexpected. One would think that graduating from high school wouldn't correlate too heavily with whether or not one lives in a crowded home. It makes sense that, if one graduates from high school, then they are more likely to eventually move somewhere away from home compared to someone who did not graduate. It's possible that this rule could be misattributed due to the low quantity of data, but it nonetheless provides an interesting thought.`35:T7b2,chart = { var margin = {top: 0, right: 30, bottom: 30, left: 30}, width = 700 - margin.left - margin.right, height = 700 - margin.top - margin.bottom; var svg = d3.select("#bars") .append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + 65 + "," +25+ ")"); var crimes = extractCrimes(coefficients, "CRIME"); var xb = d3.scaleBand() .domain(crimes) .range([margin.left, width - margin.right]) .padding(0.1) var yb = d3.scaleLinear() .domain([0, 270000]) .range([550, margin.top]) var xAxis = g => g .attr("transform", `translate(0,${height - 120})`) .call(d3.axisBottom(xb).tickSizeOuter(0)) .selectAll("text") .attr("y", 0) .attr("x", 9) .attr("dy", ".35em") .attr("transform", "rotate(90)") .style("text-anchor", "start"); var y = d3.scaleLinear() .domain([0, d3.max(crimesData, d => d.number)]) .range([400,0]) /* svg.append("g") .call(d3.axisLeft(y));*/ var yAxis = g => g .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(yb)) .call(g => g.select(".domain").remove()) var tip = d3tip() .attr('class', 'd3-tip-bar') .style("visibility","visible") .offset([-20, 0]) .html(function(d) { return "Number: " + parseInt(d.number); }); tip(svg.append("g")); console.log(xb("THEFT")) svg.append("g") .attr("fill", "darkcyan") .selectAll("rect") .data(crimesData) .join("rect") .attr("x", d => xb(d.crime)) .attr("y", d => yb(d.number)) .attr("height", d => yb(0) - parseInt(yb(d.number))) .attr("width", xb.bandwidth()) .on('mouseover', tip.show) .on('mouseout', tip.hide); svg.append("g") .call(xAxis); svg.append("g") .call(yAxis); }36:T7da,districtChart = { var margin = {top: 0, right: 0, bottom: 30, left: 0}, width = 1350 - margin.left - margin.right, height = 700 - margin.top - margin.bottom; var svg = d3.select("#districts") .append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + 40 + "," +25+ ")"); var areas = extractCrimes(factors, "COMMUNITY AREA NAME"); var xb = d3.scaleBand() .domain(areas) .range([margin.left, width - margin.right]) .padding(0.1) var yb = d3.scaleLinear() .domain([0, 67000]) .range([550, margin.top]) var xAxis = g => g .attr("transform", `translate(0,${height - 120})`) .call(d3.axisBottom(xb).tickSizeOuter(0)) .selectAll("text") .attr("y", 0) .attr("x", 9) .attr("dy", ".35em") .attr("transform", "rotate(90)") .style("text-anchor", "start"); var y = d3.scaleLinear() .domain([0, d3.max(districtData, d => d.number)]) .range([400,0]) /* svg.append("g") .call(d3.axisLeft(y));*/ var yAxis = g => g .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(yb)) .call(g => g.select(".domain").remove()) var tip = d3tip() .attr('class', 'd3-tip-bar') .style("visibility","visible") .offset([-20, 0]) .html(function(d) { return "Number: " + parseInt(d.number); }); tip(svg.append("g")); console.log(xb("THEFT")) svg.append("g") .attr("fill", "darkcyan") .selectAll("rect") .data(districtData) .join("rect") .attr("x", d => xb(areas[parseInt(d.area)-1])) .attr("y", d => yb(d.number)) .attr("height", d => yb(0) - parseInt(yb(d.number))) .attr("width", xb.bandwidth()) .on('mouseover', tip.show) .on('mouseout', tip.hide); svg.append("g") .call(xAxis); svg.append("g") .call(yAxis); }37:T7e2,// set the dimensions and margins of the graph { var margin = {top: 0, right: 30, bottom: 0, left: 30}, width = 600 - margin.left - margin.right, height = 800 - margin.top - margin.bottom; // append the svg object to the body of the page var svg = d3.select("#my_dataviz") .append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + 128 + "," + 130 + ")"); // Labels of row and columns var dim_1 = extractCrimes(coefficients, "CRIME"); var dim_2 = chartX; // Build X scales and axis: var x = d3.scaleBand() .range([ 0, 408 ]) .domain(dim_2) .padding(0.04); //svg.append("g") // .attr("transform", "translate(0," + "-height"+")") // .call(d3.axisBottom(x)) svg.append("g") .attr("class", "x axis") .attr("transform", "translate(0," + "-height"+")") .call(d3.axisTop(x)) .selectAll("text") .attr("y", 0) .attr("x", 9) .attr("dy", ".35em") .attr("transform", "rotate(-90)") .style("text-anchor", "start"); var tip = d3tip() .attr('class', 'd3-tip') .style("visibility","visible") .offset([-20, 0]) .html(function(d) { return "Value: " + parseFloat(d.value).toFixed(3); }); tip(svg.append("g")); // Build X scales and axis: var y = d3.scaleBand() .range([ 600, 0 ]) .domain(dim_1) .padding(0.04); svg.append("g") .call(d3.axisLeft(y)); svg.selectAll() .data(coefficientsChartData) .enter() .append("rect") .attr("x", function(d) { return x(d.x) }) .attr("y", function(d) { return y(d.y) }) .attr("rx", 3) .attr("ry", 3) .attr("width", x.bandwidth() ) .attr("height", y.bandwidth() ) .attr("class","box") .style("fill", function(d) { return colorPickHeat(d.value)} ) .on('mouseover', tip.show) .on('mouseout', tip.hide); }38:[] 2f:{"title":"THE DEATH OF SCHOOLGIRL KATANA PETERSON, 2012","cells":[{"id":938,"value":"md `# THE DEATH OF SCHOOLGIRL KATANA PETERSON, 2012 \n
\n## A STATISTICAL NARRATIVE ON GUNVIOLENCE AND HOW SOCIOECONOMICS MATTER`","pinned":false,"hidden":false,"mode":"ojs"},{"id":934,"value":"md `This is the gatheered work of Ioannis Koutsoukidis (s180615), Ian Mindich (s186244) and Maalthe Baungaard (s153338) in the course Social Data Analysis and Vizualisation 02806 in 2019. The project was planned and carried out to plan a narrative, select data, sort it, analyze it and visualize it to the common user. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":940,"value":"md `## VIDEO OF THE PROEJCT`","pinned":false,"hidden":false,"mode":"ojs"},{"id":941,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":943,"value":"md `## INTRODUCTION\n\nIn 2012 Katana from south side districts in Chicago got shot and murdered on open street. Her family wondered and said \"It wasn't fair\". In light of this tragic event, which unfortunately happens way too often, we wish to take you on a story through Chicago riding on the Data of socioeconomics and Crime. Particularly we investigate crime with weapon violations, as Katana Peterson was shot, however the project exspands beyond that and correlate socioeconomics with crime, to see if income, employment and education matters. The notebook will share some trends and allow the user to explore. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":958,"value":"md `## DATASETS\n\nWe've used two datasets. One describes the socioeconomics of chicago accumulated from 2008-2012 the other is all crime incidence recordings data from 2001-2019 wherein we've used the 2008-2012. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":959,"value":"md `
\n# AN OVERVIEW OF CHICAGOS CRIME\n\nLets talk about Chicago and crime. The graph below shows the number of crimes committed in diffrent categories. This is over a four year period, but it highlight that theft, battery and crimial damage is some of the key crimes to take care of when walking the city, so take care! Where are the weapons you may ask? The walkthrough of the data showed that many of the crimes contain guns, but guns are secondary. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":962,"value":"md `## CRIMES CHICAGO 2008-2012 IN NUMBERS`","pinned":false,"hidden":false,"mode":"ojs"},{"id":686,"value":"html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":964,"value":"md `## CRIMES IN CHICAGO 2008-2012 PER COMMUNITY AREA`","pinned":false,"hidden":false,"mode":"ojs"},{"id":804,"value":"html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":966,"value":"md `## LETS TAKE IT TO A MAP!\n\nNow its visible where crime happens if one knows Chicago, but why not enter and explore independently. The map below shows all crimes in Chicago. The more red an area is the more crimes took place, oppositely the more green an area is the the less likely crime is to happens if one live in that district. Try hower over an area and you can see the total number!`","pinned":false,"hidden":false,"mode":"ojs"},{"id":976,"value":"md `## CRIME MAP`","pinned":false,"hidden":false,"mode":"ojs"},{"id":834,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":970,"value":"md `In the study of Katana Peterson we focus on guncrimes, go tick the box below, and you can see how the distribute over the map.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":894,"value":"viewof ch3 = checkbox({\n options: [{ value: true, label: \"Show weapon violations only\" }],\n value: \"toggle\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":972,"value":"md `Now some quick observations from the map. It is clearly visible how south, eastside chicago is exposed to more crime. The west is mostly green. As we select guncrimes, it becomes visible that there are certain areas that are more exposed than others, especially south-central and mid east chicago. Indeed we notice that Katana Peterson was from southside Chicago, indeed where a high portion of the red areas, the gun crimes, are. Already now we may perceive that just due to present crimerates Katana was exposed. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":980,"value":"md `## MAP OF HARDSHIP FACTORS (SOCIOECONOMICS)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":982,"value":"md ` With an representation of where the gun crimes happen, its time to understand the socioeconomics. This time there is a bit more freedom. Choose whichever socioeconomic factor you may like, and see where it is most present. One can even see the numbers in percentage if an area is hovered over. Just note if you pick income, you will see the actual PER CAPITA INCOME, of the district averaged 2008-2012.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":267,"value":"viewof selFactor = select({\n title: \"Socioeconomic factor\",\n options: factors.columns.slice(2,9),\n value: factors.columns[2]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":985,"value":"md ` From the socioeconomic map several factors emerge. Its clear that the high-income is based on the north eastside. While the rest of the hardship patterns seams to follow the crime map, some more the weapon violence map and some more the all crime map. It however seams that there is a pattern. One may state the assumption that hardship and though living conditions / development indeed increases the crime rates and thereby gun-Shootings. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":987,"value":"md `# TO UNDERSTAND THE DATA`","pinned":false,"hidden":false,"mode":"ojs"},{"id":992,"value":"md ` ## Connecting socioeconomics and crime. \n\nThe next visualization is a little bit more complicated. It describes connection between crime and socioeconomics. The more red a field is, the more it indicates that there is a connection between the crime and the socioeconomic factor. Is a field oppositely green, then it highlights that the two are oppositely correlated. As an example Weapons violation is strongly negatively correlated with Income in a given area. That meanst that the data expresses that if an area is rich with high income, then people are less likely to be exposed to gun-crimes and violation. Note this is not causality, but merely an indication shown in the data. If a square is brown then it means there is poor correlation. The close to 1 a number is the more correlated. The closer to -1 the less correlated and the closer to 0 then it is not correlated. `\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":465,"value":"html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":996,"value":"md ` # OUR ANALYSIS`","pinned":false,"hidden":false,"mode":"ojs"},{"id":994,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1002,"value":"md `# A BIT OF ASSOCIATION MINING TO ROUND THINGS UP`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1000,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":998,"value":"md `# CONCLUSION`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1005,"value":"md `The project have designed a narrative, selected data, cleared the data, analyzed it and presented it. It is now visible that socioeconomic factors according to the correlation we found had an impact on the death of Katana Peterson, and while correlation is not causality the results seam to indicate this might the case. Others may judge and find their own conclusions in the visualization and that is the power of this project.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":946,"value":"md `### References:\n\nLink to socioeconomic data: [Link](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2)\n\nLink to crime data: [Link](https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present-Dashboard/5cd6-ry5g)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1100,"value":"md ` > \n> \n> `","pinned":false,"hidden":false,"mode":"ojs"},{"id":651,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":802,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":510,"value":"chartX = [\"% CROWDED\", \"% BELOW POVERTY\", \"16+ UNEMPLOYED\", \"25+ NO DIPLOMA\", \"UNDER 18 OR OVER 64\", \"PER CAPITA INCOME \", \"HARDSHIP INDEX\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":469,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":590,"value":"html` `","pinned":false,"hidden":false,"mode":"ojs"},{"id":538,"value":"coefficientsChartData = {\n var extractedCoeffs = []\n var temp = []\n coefficients.columns.slice(1,8).forEach(function(column) {for (var i = 0; i<13;++i) extractedCoeffs.push({x:chartX[coefficients.columns.indexOf(column)-1], y:coefficients[i][\"CRIME\"], value: coefficients[i][column]})})\n return extractedCoeffs;\n}\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":326,"value":"function extractCrimes(arr, column) {\n\tfunction reduction(previousValue, currentValue) {\n\t\tpreviousValue.push(currentValue[column]);\n\t\treturn previousValue;\n\t}\n\n\treturn arr.reduce(reduction, []);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":147,"value":"function extractColumn(arr, column) {\n\tfunction reduction(previousValue, currentValue) {\n\t\tpreviousValue.push(parseFloat(currentValue[column]));\n\t\treturn previousValue;\n\t}\n\n\treturn arr.reduce(reduction, []);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":183,"value":"d3i = require(\"d3-interpolate\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":209,"value":"fmax = extractColumn(factors,selFactor).reduce(function(a, b) {\n return Math.max(a, b)})\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":226,"value":"fmin = extractColumn(factors,selFactor).reduce(function(a, b) {\n return Math.min(a, b)})","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"function colorPick(value, max, min){\n return colorAux(normalize(value, max, min))}","pinned":false,"hidden":false,"mode":"ojs"},{"id":634,"value":"function colorPick2(value){\n return colorAux2(normalize(value, fmax, fmin))}","pinned":false,"hidden":false,"mode":"ojs"},{"id":573,"value":"function colorPickHeat(value){\n return colorHeatAux(normalize(value, 1, -1))}","pinned":false,"hidden":false,"mode":"ojs"},{"id":601,"value":"colorHeatAux = d3i.interpolateRgb(\"seagreen\",\"firebrick\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":93,"value":"colorAux = d3i.interpolateRgb(\"forestgreen\", \"darkred\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":636,"value":"colorAux2 = d3i.interpolateRgb(\"darkred\", \"forestgreen\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"function normalize(val, max, min) { return (val - min) / (max - min); }","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"areasData = await d3.json(\"https://data.cityofchicago.org/api/geospatial/cauq-8yn6?method=export&format=GeoJSON\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"factors = Object.assign(await d3.csv(\"https://gist.githubusercontent.com/kikilolohu/025969837b8a5d41312336fe95966648/raw/7bf2f564f16fac3e70afdcd2666dcf05f6efa471/socioeconomic.csv\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":313,"value":"coefficients = Object.assign(await d3.csv(\"https://gist.githubusercontent.com/zeaf/f4bbe9361371f8b735adea43166fafab/raw/112dbc8bf06ec52728b0cbab085b0914c964189b/coeffs2.csv\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":648,"value":"crimesData = Object.assign(await d3.csv(\"https://gist.githubusercontent.com/zeaf/49b9a5f4a5e5ec5a26551ea59a2738eb/raw/9cb338429fe8b68507c9667fd88c6b9e96b405b5/crimes.csv\"), ({crime:crime, number:number}) => ({crime:crime, number:+number}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":794,"value":"districtData = Object.assign(await d3.csv(\"https://gist.githubusercontent.com/zeaf/0cabbfd83dd06bb09070c6392e4218a3/raw/42196bb307a313258efbb952f64a8b820a2d5869/district.csv\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":868,"value":"weaponData = Object.assign(await d3.csv(\"https://gist.githubusercontent.com/zeaf/692809158fbbfe1f1591948921afc3d0/raw/6f61e0037c85351de77503155c842759ecffcc2b/weapon.csv\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":344,"value":"d3tip = require(\"d3-tip\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"L = require(\"leaflet\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":271,"value":"import {checkbox, select} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}