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Mentality in China","cells":[{"id":0,"value":"md`# Maternal Mentality in China`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":684,"value":"md`## 1 Maternal Mortality Rate (1/100,000) in China from 2010 to 2018\n### 1.1 An Overview: Urban-Rural Gap`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":690,"value":"{\n const urban_rural1 = vl.markArea({opacity: 0.5})\n .data(mortality_data_china)\n .encode(\n vl.x().fieldT(\"Year\").timeUnit(\"year\").title(\"2010-2018\"),\n vl.y().mean(\"Maternal\").stack(null).title(\"Maternal Mortality Rate\"),\n vl.color().fieldN(\"Urban-Rural\").scale({scheme: \"goldorange\"}),\n )\n\nconst urban_rural2 = vl.markLine({strokeWidth: 3, interpolate: 'monotone'})\n .data(mortality_data_china)\n .encode(\n vl.x().fieldT(\"Year\").timeUnit(\"year\"),\n vl.y().fieldQ('Maternal'),\n vl.color().fieldN('Urban-Rural').legend(null),\n vl.tooltip().fieldN('Urban-Rural')\n )\n .width(700)\n .height(500)\n\n return vl.layer(urban_rural1, urban_rural2).render();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":772,"value":"md`### 1.2 An Overview: Provincial Gap`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1632,"value":"md`Date: 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.render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":695,"value":"md`### 1.3 Urban-Rural Gap at Provincial Level`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1130,"value":"viewof selectLocation = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1125,"value":"vl.markBar()\n .data(mortality_data_province)\n .transform(vl.filter(\"datum.Location == '\" + selectLocation + \"'\"))\n .encode(vl.y().fieldN(\"Urban_Rural\"), \n vl.x().fieldO(\"Year\"),\n vl.color().mean(vl.repeat(\"column\")).scale({scheme: \"lightgreyred\"}).title(\"Maternal Mortality Rate\"),\n vl.tooltip().mean(vl.repeat(\"column\")))\n .width(400)\n .height(120)\n .repeat({column: [\"Mortality\"]})\n .resolve({scale: {color: 'independent'}})\n\n .render()\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1279,"value":"md`## 2 Major Causes of Maternal Mortality \n### 2.1 Mortality Rate of Major Causes (1/100,000) from 2010 to 2019`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1994,"value":"{ \n const totalrate = vl.markBar()\n .data(maternal_data_china)\n .encode(vl.y().fieldQ(\"Value\").title(\"Mortality Rate\"),\n vl.x().fieldN(\"Year\"),\n vl.color().fieldN(\"Causes\").scale({scheme: \"inferno\"}).title(\"Major Causes\"),\n vl.tooltip().fieldQ(\"Value\")) \n .width(360)\n .height(360)\n return vl.layer(totalrate)\n .facet({column: vl.field(\"Location\")})\n .data(maternal_data_china)\n .render()\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1438,"value":"md`### 2.2 Major Causes for % of Total Maternal Mortality in 2019\n#### 2.2.1 From Urban-Rural Perspective`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2040,"value":"vl.markBar()\n .data(mortality_data_2019)\n .width(580)\n .height(400)\n .encode(\n vl.y().fieldN(\"Causes\").title(\"Major Causes in urban/rural area\"),\n vl.x().fieldQ(\"Value\").title(\"% of Total Maternal Mortality\"),\n vl.color().fieldQ(\"Value\").scale({scheme: \"lightorange\"}).title(\"% of Total Maternal Mortality\"),\n vl.tooltip().fieldQ(\"Value\"))\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":712,"value":"md`#### 2.2.2 From Provincial Perspective`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2249,"value":"viewof illness = DOM.select([\"Obstetrical Bleeding\", \"Gestational Hypertension\", \"Medical Complications\", \"Amniotic Fluid_Embolism\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2236,"value":"choropleth = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(360,20)\")\n .append(() => \n legend({\n color: color3,\n title: data.title,\n width: 260,\n tickFormat: \".1f\"\n })\n );\n\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(China_p.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", \"1\")\n .attr(\"fill\", d => color3(data.get(d.properties.ID_number.toString())))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => d.properties.ID + \": \" + data.get(d.properties.ID_number.toString())) ;\n\n return svg.node();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2183,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2185,"value":"topojson = require(\"topojson-client@3\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2187,"value":"json = FileAttachment(\"China_adm-1@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2194,"value":"China_p = topojson.feature(json, json.objects.China_adm)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2196,"value":"test = China_p.features[3].properties.ID_number","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2198,"value":"\ncsv_data = {\n switch (illness){\n case 'Obstetrical Bleeding':\n default:\n return d3.csvParse(await FileAttachment(\"China_province.csv\").text(),({id, Obstetrical_Bleeding}) => [id, Obstetrical_Bleeding])\n case 'Gestational Hypertension':\n return d3.csvParse(await FileAttachment(\"China_province.csv\").text(),({id, Gestational_Hypertension}) => [id, Gestational_Hypertension])\n case 'Medical Complications':\n return d3.csvParse(await FileAttachment(\"China_province.csv\").text(),({id, Medical_Complications}) => [id, Medical_Complications])\n case 'Amniotic Fluid_Embolism':\n return d3.csvParse(await FileAttachment(\"China_province.csv\").text(),({id, Amniotic_Fluid_Embolism}) => [id, Amniotic_Fluid_Embolism])\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2200,"value":"data = {\n switch (illness){\n case 'Obstetrical Bleeding':\n default:\n return Object.assign(new Map(csv_data), {title: \"Obstetrical_Bleeding in China, 2010\"})\n case 'Gestational Hypertension':\n return Object.assign(new Map(csv_data), {title: \"Gestational Hypertension in China, 2010\"})\n case 'Medical Complications':\n return Object.assign(new Map(csv_data), {title: \"Medical Complications in China, 2010\"})\n case 'Amniotic Fluid_Embolism':\n return Object.assign(new Map(csv_data), {title: \"Amniotic Fluid_Embolism in China, 2010\"})\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2202,"value":"population_d = Array.from(csv_data.values(),d => d[1])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2204,"value":"//Create a classification method (I use natural breaks since there are so many extreme values in data and it isn't influenced by extreme values and group up the extreme values together)\nsimple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2206,"value":"//import Kmeans threshold\nnaturalbreaks = simple.ckmeans(population_d, 6).map(v => v.pop())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2208,"value":"YlGnBu = [d3.color(\"#fef0d9\"), d3.color(\"#fdd49e\"), d3.color(\"#fdbb84 \"),d3.color(\"#fc8d59\"),d3.color(\"#e34a33\"),d3.color(\"#b30000\")]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2210,"value":"color = d3.scaleThreshold()\n .domain(naturalbreaks)\n .range(YlGnBu)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2212,"value":"color2 = d3.scaleQuantize()\n .domain(d3.extent(population_d)) // pass only the extreme values to a scaleQuantize’s domain\n .range(YlGnBu)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2214,"value":"color3 =d3.scaleQuantile()\n .domain(population_d) // pass the whole dataset to a scaleQuantile’s domain\n .range(YlGnBu);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2216,"value":"width = 975","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2218,"value":"height = 710","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2220,"value":"margin = 100;","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2222,"value":"//Rotate the map sets the longitude of origin for our Albers projection.\nprojection = d3.geoTransverseMercator().rotate([-110,0]).fitExtent([[65, 80], [width, height]], China_p);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2224,"value":"//Using a path generator to project geometry onto the map\npath = d3.geoPath().projection(projection);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2226,"value":"color4 = d3.scaleQuantile()\n .domain(population_d)\n .range(d3.quantize(d3.interpolateHcl(\"#60c96e\", \"#4d4193\"), 6))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2228,"value":"import {legend} from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":836,"value":"md`## mortality_file_china`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"import {vl} from '@vega/vega-lite-api'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":817,"value":"mortality_file_china = FileAttachment(\"mortality_china2@2.csv\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"mortality_text_china = mortality_file_china.text()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"mortality_data_china = d3.csvParse(mortality_text_china, d3.autoType)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"import {printTable} from '@uwdata/data-utilities'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":706,"value":"printTable(mortality_data_china.slice(0, 10))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":838,"value":"md`## 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