29:null 2a:["$","title",null,{"children":"Assignment 2: Classification and Colors | miahboyle | Observable"}] 2f:T518,This histogram represents the normalized variable of population percent under the 185% poverty line in each census tract of Minnesota. This data is not normally distributed, and is more right skewed, with the majority of the data between the values 0 and 0.4. This will have different effects depending on which classfication scheme is chosen. Quantile and Quantize (equal interval) do not account for the data's distribution along the number line, and assumes a normal distribution of the data. This could cause classes with very large ranges or classes that contain the majority of the observations, respectively. The Jenks natural breaks classification scheme does take the distribution into account when determining the most logical breaks within the data. Since the data distribution is unimodal (one peak on the histogram) and continuous, a color scheme for sequential data is most appropriate. This could either be a single or multi-hued color scheme. The color schemes selected were from colorbrewer2.org from their options for sequential data with three data classes. All color schemes except for the unclassed linear scale were multi-hued. I personally prefer these for sequential data because they allow each class to be more easily distinguishable compared to a single hued color scheme. 30:T5a4,function showScaleGrouping(data, scales) { const margins = { left: 130 }; const x = d3 .scaleLinear() .domain(d3.extent(data)) .range([margins.left, width - 5]); const r = 3; const rectHeight = 10; const chart = svg` `; return chart; }31:[{"id":"7f63cd60bd780fb581d9a0869fc0fa88e96e3b42d72a56a1b4dd371f15cfb3cd74b48884b9b96cade4aec2653338f9a9a264ff95ed638e2327f10a3350c6ce82","href":"https://static.observableusercontent.com/files/7f63cd60bd780fb581d9a0869fc0fa88e96e3b42d72a56a1b4dd371f15cfb3cd74b48884b9b96cade4aec2653338f9a9a264ff95ed638e2327f10a3350c6ce82","name":"Alaska.json","type":"application/json","lastModified":1676910339835.422,"size":25543},{"id":"cebcca49f80541efc6820907fd1b3096d5c95574ddbbdac4d57fe6bc342d89026ccd851d16cc186eee115565d800dfe8cb3589069d3d4dd16fdcbcc75db6dd3b","href":"https://static.observableusercontent.com/files/cebcca49f80541efc6820907fd1b3096d5c95574ddbbdac4d57fe6bc342d89026ccd851d16cc186eee115565d800dfe8cb3589069d3d4dd16fdcbcc75db6dd3b","name":"Alaska@1.csv","type":"text/csv","lastModified":1676910339835.422,"size":8290},{"id":"4aa7e48b949a98b8157d622739a944eb54737fc32234ebdaead8b7ff59e85ae2dc49e7c2e8a76eb22de400d6163436b88e1423ce3a0d10783128df2af0d265f3","href":"https://static.observableusercontent.com/files/4aa7e48b949a98b8157d622739a944eb54737fc32234ebdaead8b7ff59e85ae2dc49e7c2e8a76eb22de400d6163436b88e1423ce3a0d10783128df2af0d265f3","name":"ej_mpca_census (1).csv","type":"text/csv","lastModified":1676910655761.061,"size":724287},{"id":"0c0112dd23b698c4f7c6bd5fb78205869ca7b3d57ef92bdb921465a872e25dab510e0ba4c7420c9ca7872213fdce6d2fa828f5630b7ae669c3f9c80d8d923497","href":"https://static.observableusercontent.com/files/0c0112dd23b698c4f7c6bd5fb78205869ca7b3d57ef92bdb921465a872e25dab510e0ba4c7420c9ca7872213fdce6d2fa828f5630b7ae669c3f9c80d8d923497","name":"MinnesotaCensusCAFO_ExportFeature.json","type":"application/json","lastModified":1676910656053.07,"size":6330014}] 2d:{"id":"6e24c727b19b022d","slug":null,"title":"Assignment 2: Classification and Colors","thumbnail":"f5ea5dcd5152b156aef5de6feb88b7471f3cabb0930cc9360fc3c4bdf524cd7c","access_level":"public","create_time":"$D2023-02-20T16:25:39.835Z","update_time":"$D2023-02-27T18:09:56.871Z","view_time":null,"first_public_time":"$D2023-02-20T16:25:39.835Z","fork":{"id":"c68fb87dffeeb9bc","slug":"second-exercise-on-observable","title":"Second Exercise on Observable: Classification and Colors","owner":{"id":"22130e106df70095","login":"geoviz","name":"Geographic Visualization (GEOG 3540) - University of Iowa"}},"stars":0,"starred":false,"owner":{"type":"team","id":"a4763cc1d142b3c4","login":"miahboyle","name":"miahboyle","avatar_url":"https://avatars-next.observableusercontent.com/422b5b3cc75b43abebc201c491f98eeefe1edd5f9db0d7e0630a5e3e0d07b784","tier":"starter_2024"},"version":563,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"d59253cef89da81f","login":"miahboyle","name":"miahboyle","avatar_url":"https://avatars-next.observableusercontent.com/422b5b3cc75b43abebc201c491f98eeefe1edd5f9db0d7e0630a5e3e0d07b784","tier":null}],"members":[{"type":"individual","id":"d59253cef89da81f","login":"miahboyle","name":"miahboyle","avatar_url":"https://avatars-next.observableusercontent.com/422b5b3cc75b43abebc201c491f98eeefe1edd5f9db0d7e0630a5e3e0d07b784","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Assignment 2: Classification and Colors","cells":[{"id":0,"value":"md`# Assignment 2: Classification and Colors\nMiah Boyle`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6,"value":"topojson = require(\"topojson-client@3\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":10,"value":"MN_CAFO = FileAttachment(\"MinnesotaCensusCAFO_ExportFeature.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":12,"value":"census_features = topojson.feature(MN_CAFO, MN_CAFO.objects.MinnesotaCensusCAFO_ExportFeature)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":18,"value":"csv_data = d3.csvParse(await FileAttachment(\"ej_mpca_census (1).csv\").text(),({tractce, totpov, pop185x}) => [tractce, +pop185x/+totpov])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":400,"value":"csv_data_objects = Object.assign((d3.csvParse(await FileAttachment(\"ej_mpca_census (1).csv\").text(), d3.autoType)).map(({tractce, totpov, pop185x}) => ({tract: +tractce, popPctUnderPov: +pop185x/+totpov})))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":415,"value":"viewof bins = Inputs.range([0, 20], {step: 1, label: \"Bins\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":394,"value":"Plot.plot({\n marks: [\n Plot.rectY(csv_data_objects, Plot.binX({y: \"count\"}, {x: \"popPctUnderPov\", thresholds: bins})),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":480,"value":"$2f","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":25,"value":"pctPov = Array.from(csv_data.values(), d => d[1]).filter(d => d >= 0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":28,"value":"data = Object.assign(new Map(csv_data), {title: \"Percent population under 185% poverty level\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":174,"value":"md`# Linear Scale (Unclassed)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":242,"value":"linear = d3.scaleLinear()\n .domain(d3.extent(pctPov))\n .range([\"white\", \"#1c9099\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":199,"value":"chart(numericSort(pctPov), linear)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":542,"value":"numericSort(pctPov)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":267,"value":"md`# Quantile Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":272,"value":"quantile = d3.scaleQuantile()\n .domain(pctPov)\n .range([\"#ece2f0\", \"#a6bddb\", \"#1c9099\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":274,"value":"chart(numericSort(pctPov), quantile)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":484,"value":"The quantile classification method places the same number of observations in each class, making it easy to compare maps, but does not account for the skewed distribution. This will cause the cutpoints for each class to be irregular, with the last class having a very large range. \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":323,"value":"md`# Jenks Natural Breaks Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":373,"value":"naturalbreaks = simple.ckmeans(pctPov, 3).map(v => v.pop())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":319,"value":"jenks = d3.scaleThreshold()\n .domain(naturalbreaks)\n .range([\"#fee8c8\", \"#fdbb84\", \"#e34a33\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":330,"value":"chart(numericSort(pctPov), jenks)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":488,"value":"The natural breaks classification method does account for the distribution of the data when determining the most logical breaks, which minimizes the differences between data values within the same class and maximizes the difference between classes. This classification results in the majority of the data being within the first two classes and only a few observation within the last one. \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":280,"value":"md`# Equal Interval Classification (Quantize)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":276,"value":"quantize = d3.scaleQuantize()\n .domain([d3.min(pctPov),d3.max(pctPov)])\n .range([\"#edf8b1\", \"#7fcdbb\", \"#2c7fb8\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":282,"value":"chart(numericSort(pctPov), quantize)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":492,"value":"The equal interval classification method has each classs taking up the same space along the number line. Although this allows for an easily interpretabe map with understandable cutpoints, it does not account for the skewed distribution of the data. Because of this, we see that the majority of the observations will fall within the first class and the number of observations will become progressively smaller as the ranges for each class increase towards the right tail of the histogram. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":295,"value":"md`# Threshold`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":289,"value":"threshold = d3.scaleThreshold()\n .domain([0.25, 0.5, 1])\n .range([\"#e7e1ef\", \"#c994c7\", \"#dd1c77\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":297,"value":"chart(numericSort(pctPov), threshold)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":498,"value":"The threshold method is a manual method for selecting the cutpoints for each class. The cutpoints above were chosen by eye to be aware of the skewed distribution, have ranges that would make sense to a map user, and to bring attention to the outliers with percentages higher than 50% below the poverty line. \n\nThis is sometimes helpful to be able to manually determine cutpoints, especially when you are using a divergent color scheme and need to specify and specific critical break in your data. However, these are more subjective compared to the other classification methods presented above. \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":355,"value":"showScaleGrouping(pctPov, {\n scaleQuantile: quantile,\n scaleThreshold: threshold,\n scaleJenks: jenks,\n scaleQuantize: quantize,\n scaleQuantizeNice: quantize.copy().nice()\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":129,"value":"md`# Annex`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":261,"value":"function numericSort(x) {\n return (\n x\n // ensure the array is not changed in-place\n .slice()\n // comparator function that treats input as numeric\n .sort((a, b) => a - b)\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":201,"value":"function chart(data, scale) {\n const w = 10, \n cols = Math.floor(Math.min(800, width) / w),\n lines = Math.ceil(1700 / cols);\n const chart = d3\n .create(\"svg\")\n .attr(\"width\", cols * w)\n .attr(\"height\", lines * w);\n\n chart\n .append(\"g\")\n .attr(\"transform\", \"translate(2,2)\")\n .attr(\"style\", \"stroke:black; fill:white;\")\n .selectAll(\"rect\")\n .data(data)\n .join(\"rect\")\n .attr(\"width\", w - 3)\n .attr(\"height\", w - 3)\n .attr(\"x\", (_, i) => w * (i % cols))\n .attr(\"y\", (_, i) => w * ((i / cols) | 0))\n .style(\"fill\", d => (scale ? scale(d) : \"#ddd\"));\n return chart.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":530,"value":"const w = 10, means the size of the squares within the chart \n cols = Math.floor(Math.min(800, width) / w), increase the number to take advantage of the full width of the page\n lines = Math.ceil(1200 / cols); increase number to expand the number of rows\n\nif you want all observations to be displayed-> extend your line number since it does not have a limitation like your column number","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":313,"value":"simple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":357,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W31","stdlib":"1","titling":"auto"}} 2e:"$2d:body"