{"id":169,"date":"2020-02-27T19:08:15","date_gmt":"2020-02-28T00:08:15","guid":{"rendered":"https:\/\/ctdallocationstudy.com\/?page_id=169"},"modified":"2020-09-09T17:26:46","modified_gmt":"2020-09-09T22:26:46","slug":"interim-reports","status":"publish","type":"page","link":"https:\/\/ctdallocationstudy.com\/index.php\/data\/interim-reports\/","title":{"rendered":"Interim Reports"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"169\" class=\"elementor elementor-169\" data-elementor-settings=\"[]\">\n\t\t\t\t\t\t<div class=\"elementor-inner\">\n\t\t\t\t\t\t\t<div class=\"elementor-section-wrap\">\n\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3859487 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3859487\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-65a45f6\" data-id=\"65a45f6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8cbe81a elementor-widget elementor-widget-heading\" data-id=\"8cbe81a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Centerline Miles (CLM)<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6d345d0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6d345d0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0d6b75d\" data-id=\"0d6b75d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8d6ba7d elementor-widget elementor-widget-text-editor\" data-id=\"8d6ba7d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\">\n\t\t\t\t\t<p>This page provides interim analysis, analysis between the initial report and the final report, on the variable of centerline miles (CLM). Along with the TD population as measured by the 5-year American Community Survey (in table C18130), the CLM variable attempts to capture inherent demand for transportation from one Florida county relative to another. The idea behind including the CLM variable is similar in concept to the use of county square miles in the Trip &amp; Equipment Grant Program&#8217;s current allocation methodology: that some counties may have fewer residents (or TD-eligible residents), but that residents in those counties typically have farther to travel for a standard trip, whether it be to a doctor&#8217;s appointment, a job, a grocery store, etc.<\/p><p>If this idea behind support for inclusion of the CLM variable is sound, then it would be expected that its inclusion works to the benefit of smaller counties more than it does larger counties (as measured by population). A good indication that inclusion of CLM works to the benefit of smaller counties <strong>is the relationship between CLM per capita and population, not the total CLM in a county.<\/strong><\/p><p>Below is an interactive horizontal bar chart that switches between the total CLM miles and the CLM per capita in each Florida county. To switch between the two charts, click on the &#8220;CLM Total&#8221; and &#8220;CLM per Capita&#8221; located near the top of the chart.<\/p><p>\u00a0<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0798567 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0798567\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8c4d42f\" data-id=\"8c4d42f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4aa1018 elementor-widget elementor-widget-html\" data-id=\"4aa1018\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<canvas id=\"chartCLMpop\" style=\"position: relative; height: 130vh; width: 110vw;\"><\/canvas>\r\n\r\n<script type=\"text\/javascript\">\r\n\/\/ Request data using D3\r\nvar clmpopulation = d3.csv('https:\/\/raw.githubusercontent.com\/helgonio\/ctdallocationstudy\/master\/data\/public-roads\/CLM%20and%20Population%20for%20json.csv').then(makeChart);\r\n\r\nfunction makeChart(clmpop) {\r\n  \/\/ clmpop is an array of objects where each object is something like:\r\n  \/\/ {\r\n  \/\/   \"COUNTY\": \"Alachua\",\r\n  \/\/   \"YEAR\": 2018,\r\n  \/\/   \"Rural 759.554\": \"4000\"\r\n  \/\/ }\r\n  \r\n\r\n  var years = clmpop.filter(function(clmpop) { return clmpop.YEAR }).map(function(d) { return d.YEAR });\r\n  var countyLabels = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.COUNTY });\r\n  var rural = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.Rural });\r\n  var smallUrban = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Small Urban'] });\r\n  var smallUrbanized = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Small Urbanized'] });\r\n  var largeUrbanized = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Large Urbanized'] });\r\n  var population = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.Population });\r\n  var clmTotal = rural.map(function(item, index) { return Number(rural[index]) + Number(smallUrban[index]) + Number(smallUrbanized[index]) + Number(largeUrbanized[index]) });\r\n  var clmPerPop = population.map(function(item, index) { return clmTotal[index] \/ Number(population[index]) });\r\n\r\n\r\n  var chart = new Chart('chartCLMpop', {\r\n    type: 'horizontalBar',\r\n    options: {\r\n      title: {\r\n        display: true,\r\n        position: 'top',\r\n        text: 'CLM and CLM per Capita by Florida County',\r\n        fontSize: 18,\r\n        fontColor: '#111'\r\n      },\r\n      maintainAspectRatio: false,\r\n      legend: {\r\n        display: true,\r\n        position: 'top',\r\n        onClick: function(e, legendItem) {\r\n\r\n          var index = legendItem.datasetIndex;\r\n          var ci = this.chart;\r\n          var alreadyHidden = (ci.getDatasetMeta(index).hidden === null) ? true : ci.getDatasetMeta(index).hidden;\r\n\r\n          ci.data.datasets.forEach(function(e, i) {\r\n            var meta = ci.getDatasetMeta(i);\r\n\r\n            if (i !== index) {\r\n              if (!alreadyHidden) {\r\n                meta.hidden = meta.hidden === null ? !meta.hidden : null;\r\n              } else if (meta.hidden === null) {\r\n                meta.hidden = true;\r\n              }\r\n            } else if (i === index) {\r\n              meta.hidden = null;\r\n            }\r\n          });\r\n\r\n          ci.update();\r\n        }\r\n      },\r\n      scales: {\r\n        xAxes:[{\r\n            scaleLabel: {\r\n              display: true,\r\n              labelString: 'CLM',\r\n              fontSize: 16\r\n            },\r\n            ticks: {\r\n    beginAtZero: true,\r\n    userCallback: function(value, index, values) {\r\n        value = value.toString();\r\n        value = value.split(\/(?=(?:...)*$)\/);\r\n        value = value.join(',');\r\n        return value;\r\n    }\r\n}\r\n        }],\r\n        yAxes: [{\r\n            stacked: true,\r\n            }]\r\n      },\r\n       tooltips: {\r\n            mode: 'label',\r\n            intersect: false,\r\n            callbacks: {\r\n                afterTitle: function() {\r\n                    window.total = 0;\r\n                },\r\n                label: function(tooltipItem, data) {\r\n                    var datasetLabel = data.datasets[tooltipItem.datasetIndex].label;\r\n                    var datasetValue = data.datasets[tooltipItem.datasetIndex].data[tooltipItem.index];\r\n\r\n                      \/\/THIS ONE:\r\n                    datasetValue = parseFloat(datasetValue).toFixed(3);\r\n\r\n        window.total += datasetValue;\r\n                    return datasetLabel + \": \" + datasetValue.toString().replace(\/\\B(?=(\\d{3})+(?!\\d))\/g, \",\");             \r\n                }\r\n            }\r\n        },\r\n    },\r\n    data: {\r\n      labels: countyLabels,\r\n      datasets: [\r\n        {\r\n          label: \"CLM Total\",\r\n          data: clmTotal,\r\n          backgroundColor: 'rgba(0, 0, 0, 0.5)',\r\n      borderColor: 'rgba(0, 0, 0, 1.0)',\r\n      borderWidth: 1,\r\n      stack: 1\r\n        },\r\n        {\r\n          label: \"CLM per Capita\",\r\n          data: clmPerPop,\r\n          backgroundColor: 'rgba(100, 50, 100, 0.5)',\r\n      borderColor: 'rgba(100, 50, 100, 1.0)',\r\n      borderWidth: 1,\r\n      stack: 1\r\n        }\r\n      ]\r\n    }\r\n  });\r\n}\r\n\r\n  <\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e013661 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e013661\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9e935b8\" data-id=\"9e935b8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a36327b elementor-widget elementor-widget-heading\" data-id=\"a36327b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Relationship of Population Size to CLM per Capita<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d0d6fc1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d0d6fc1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6912614\" data-id=\"6912614\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b0ef0e1 elementor-widget elementor-widget-text-editor\" data-id=\"b0ef0e1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\">\n\t\t\t\t\t<p>Below is a scatterplot that charts the relationship between the total population of each county and the CLM per capita of each county. The overall relationship is one of a strong, reverse exponential relationship between the size of a county and the CLM per capita of the county. In other words, a larger population is strongly predictive of a county having a low CLM per capita, while a smaller population is strongly predictive of a county having a high CLM per capita.&nbsp;<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6a29273 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6a29273\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0f1f4d2\" data-id=\"0f1f4d2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b18c31a elementor-widget elementor-widget-html\" data-id=\"b18c31a\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<canvas id=\"bubbleCLMpop\" style=\"position: relative; height: 120vh; width: 110vw;\"><\/canvas>\r\n\r\n<script type=\"text\/javascript\">\r\n\/\/ Request data using D3\r\nvar clmpopulation = d3.csv('https:\/\/raw.githubusercontent.com\/helgonio\/ctdallocationstudy\/master\/data\/public-roads\/CLM%20and%20Population%20for%20json.csv').then(makeChart);\r\n\r\nfunction makeChart(clmpop) {\r\n  \/\/ clmpop is an array of objects where each object is something like:\r\n  \/\/ {\r\n  \/\/   \"COUNTY\": \"Alachua\",\r\n  \/\/   \"YEAR\": 2018,\r\n  \/\/   \"Rural 759.554\": \"4000\"\r\n  \/\/ }\r\n  \r\n\r\n  var years = clmpop.filter(function(clmpop) { return clmpop.YEAR }).map(function(d) { return d.YEAR });\r\n  var countyLabels = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.COUNTY });\r\n  var rural = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.Rural });\r\n  var smallUrban = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Small Urban'] });\r\n  var smallUrbanized = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Small Urbanized'] });\r\n  var largeUrbanized = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Large Urbanized'] });\r\n  var population = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.Population });\r\n  var clmTotal = rural.map(function(item, index) { return Number(rural[index]) + Number(smallUrban[index]) + Number(smallUrbanized[index]) + Number(largeUrbanized[index]) });\r\n  var clmPerPop = population.map(function(item, index) { return clmTotal[index] \/ Number(population[index]) });\r\n\r\n  var bubbleData = clmPerPop.map(function(item, index) {\r\n    return {\r\n      x: population[index],\r\n      y: clmPerPop[index],\r\n      r: 5,\r\n      name: countyLabels[index]\r\n    }\r\n  });\r\n\r\n  var scatterChartData = {\r\n      labels: name,\r\n      datasets: [{\r\n        label: \"CLM per Capita\",\r\n        backgroundColor: 'rgba(0, 0, 100, 0.7)',\r\n        data: bubbleData,\r\n      }]\r\n    };\r\n\r\n\r\n  \/\/ start chart variable\r\n  var chart = new Chart('bubbleCLMpop', {\r\n\r\n    type: 'bubble',\r\n    data: scatterChartData,\r\n    options: {\r\n      legend: {\r\n          display: false\r\n      },\r\n      responsive: true,\r\n      title: {\r\n        display: true,\r\n        position: 'top',\r\n        text: 'CLM per Capita by Florida County',\r\n        fontSize: 18,\r\n        fontColor: '#111'\r\n      },\r\n      scales: {\r\n        xAxes: [{\r\n            scaleLabel: {\r\n              display: true,\r\n              labelString: 'Population',\r\n              fontSize: 16\r\n            },\r\n            ticks: {\r\n    beginAtZero: true,\r\n    userCallback: function(value, index, values) {\r\n        value = value.toString();\r\n        value = value.split(\/(?=(?:...)*$)\/);\r\n        value = value.join(',');\r\n        return value;\r\n    }\r\n}\r\n        }],\r\n        yAxes: [{\r\n          scaleLabel: {\r\n            display: true,\r\n            labelString: \"CLM per Capita\",\r\n            fontSize: 16\r\n          }\r\n            }]\r\n      },\r\n      tooltips: {\r\n        enabled: true,\r\n        mode: 'single',\r\n        callbacks: {\r\n          title: function(tooltipItems, data) {\r\n            return countyLabels[tooltipItems[0].index];\r\n     },\r\n          label: function(tooltipItems, data) {\r\n            var multistringText = [\"Population: \" + tooltipItems.xLabel.toString().replace(\/\\B(?=(\\d{3})+(?!\\d))\/g, \",\")];\r\n            multistringText.push(\"CLM per Capita: \" + tooltipItems.yLabel.toFixed(3));\r\n            return multistringText;\r\n          }\r\n        }\r\n      }\r\n    }\r\n\r\n    \r\n  }); \/\/ end chart variable\r\n}\r\n\r\n  <\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a83b3f5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a83b3f5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b41f377\" data-id=\"b41f377\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-edfb31e elementor-widget elementor-widget-heading\" data-id=\"edfb31e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Modeling the Omission of the CLM Variable in Model 1<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f179ffc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f179ffc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f974f41\" data-id=\"f974f41\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-38319a6 elementor-widget elementor-widget-text-editor\" data-id=\"38319a6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\">\n\t\t\t\t\t<p>To visualize the impact of leaving out the CLM variable in a statewide model, below is another scatterplot that charts the relationship between the size of a county&#8217;s population and the change in total allocation between Model 1 from the initial report and the same model, but with CLM taken out as a variable and only using TD population as a variable for capturing inherent demand.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bb3fe9a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bb3fe9a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4f6e710\" data-id=\"4f6e710\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fd97681 elementor-widget elementor-widget-html\" data-id=\"fd97681\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<table style=\"margin-left:auto;margin-right:auto;\">\r\n  <tr style=\"background-color: black; color: white;\">\r\n    <th>Variable<\/th>\r\n    <th>Model 1 Weight<\/th>\r\n    <th>Model 1 (no CLM) Weight<\/th>\r\n  <\/tr>\r\n  <tr>\r\n    <td>TD Population<\/td>\r\n    <td>12.5%<\/td>\r\n    <td>25%<\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td>Centerline Miles<\/td>\r\n    <td>12.5%<\/td>\r\n    <td>0%<\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td>Invoiced Trips<\/td>\r\n    <td>25%<\/td>\r\n    <td>25%<\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td>19-20 Allocation<\/td>\r\n    <td>50%<\/td>\r\n    <td>50%<\/td>\r\n  <\/tr>\r\n\r\n<\/table>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-842069e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"842069e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c50a3ca\" data-id=\"c50a3ca\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4073f20 elementor-widget elementor-widget-html\" data-id=\"4073f20\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<canvas id=\"bubbleCLMmodel\" style=\"position: relative; height: 120vh; width: 110vw;\"><\/canvas>\r\n\r\n<script type=\"text\/javascript\">\r\n\/\/ Request data using D3\r\nvar clmpopulation = d3.csv('https:\/\/raw.githubusercontent.com\/helgonio\/ctdallocationstudy\/master\/data\/public-roads\/CLM%20and%20Population%20for%20json.csv').then(makeChart);\r\n\r\nfunction makeChart(clmpop) {\r\n  \/\/ clmpop is an array of objects where each object is something like:\r\n  \/\/ {\r\n  \/\/   \"COUNTY\": \"Alachua\",\r\n  \/\/   \"YEAR\": 2018,\r\n  \/\/   \"Rural 759.554\": \"4000\"\r\n  \/\/ }\r\n  \r\n\r\n  var years = clmpop.filter(function(clmpop) { return clmpop.YEAR }).map(function(d) { return d.YEAR });\r\n  var countyLabels = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.COUNTY });\r\n  var rural = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.Rural });\r\n  var smallUrban = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Small Urban'] });\r\n  var smallUrbanized = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Small Urbanized'] });\r\n  var largeUrbanized = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Large Urbanized'] });\r\n  var population = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d.Population });\r\n  var modelChange = clmpop.filter(function(clmpop) { return clmpop.YEAR == 2018 }).map(function(d) { return d['Model Difference %'] });\r\n  var clmTotal = rural.map(function(item, index) { return Number(rural[index]) + Number(smallUrban[index]) + Number(smallUrbanized[index]) + Number(largeUrbanized[index]) });\r\n  var clmPerPop = population.map(function(item, index) { return clmTotal[index] \/ Number(population[index]) });\r\n\r\n  var bubbleData = clmPerPop.map(function(item, index) {\r\n    return {\r\n      x: population[index],\r\n      y: modelChange[index],\r\n      r: 5,\r\n      name: countyLabels[index]\r\n    }\r\n  });\r\n\r\n  var scatterChartData = {\r\n      labels: name,\r\n      datasets: [{\r\n        label: \"Change in Allocation\",\r\n        backgroundColor: 'rgba(100, 0, 0, 0.7)',\r\n        data: bubbleData,\r\n      }]\r\n    };\r\n\r\n\r\n  \/\/ start chart variable\r\n  var chart = new Chart('bubbleCLMmodel', {\r\n\r\n    type: 'bubble',\r\n    data: scatterChartData,\r\n    options: {\r\n        legend: {\r\n            display: false\r\n        },\r\n      responsive: true,\r\n      title: {\r\n        display: true,\r\n        position: 'top',\r\n        text: 'Change in Allocations for Model 1 by Eliminating CLM',\r\n        fontSize: 18,\r\n        fontColor: '#111'\r\n      },\r\n      scales: {\r\n        xAxes: [{\r\n            scaleLabel: {\r\n              display: true,\r\n              labelString: 'Population',\r\n              fontSize: 16\r\n            },\r\n            ticks: {\r\n    beginAtZero: true,\r\n    userCallback: function(value, index, values) {\r\n        value = value.toString();\r\n        value = value.split(\/(?=(?:...)*$)\/);\r\n        value = value.join(',');\r\n        return value;\r\n    }\r\n}\r\n        }],\r\n        yAxes: [{\r\n          scaleLabel: {\r\n            display: true,\r\n            labelString: \"Change in Allocation\",\r\n            fontSize: 16\r\n          },\r\n          ticks: {\r\n    beginAtZero: true,\r\n    userCallback: function(value, index, values) {\r\n        value = value.toFixed(2);\r\n        value = value.toString() + '%';\r\n        return value;\r\n    }\r\n}\r\n            }]\r\n      },\r\n      tooltips: {\r\n        enabled: true,\r\n        mode: 'single',\r\n\r\n        callbacks: {\r\n          title: function(tooltipItems, data) {\r\n            return countyLabels[tooltipItems[0].index];\r\n     },\r\n          label: function(tooltipItems, data) {\r\n            var multistringText = [\"Population: \" + tooltipItems.xLabel.toString().replace(\/\\B(?=(\\d{3})+(?!\\d))\/g, \",\")];\r\n            multistringText.push(\"Change in Allocation: \" + tooltipItems.yLabel.toFixed(2) + '%');\r\n            return multistringText;\r\n          }\r\n        }\r\n      }\r\n    }\r\n\r\n    \r\n  }); \/\/ end chart variable\r\n}\r\n\r\n  <\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Centerline Miles (CLM) This page provides interim analysis, analysis between the initial report and the final report, on the variable of centerline miles (CLM). Along with the TD population as measured by the 5-year American Community Survey (in table C18130), the CLM variable attempts to capture inherent demand for transportation &hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":16,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"_links":{"self":[{"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/pages\/169"}],"collection":[{"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/comments?post=169"}],"version-history":[{"count":123,"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/pages\/169\/revisions"}],"predecessor-version":[{"id":1026,"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/pages\/169\/revisions\/1026"}],"up":[{"embeddable":true,"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/pages\/16"}],"wp:attachment":[{"href":"https:\/\/ctdallocationstudy.com\/index.php\/wp-json\/wp\/v2\/media?parent=169"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}