2b:null 2c:["$","title",null,{"children":"BinGuru | Arpit Narechania | Observable"}] 31:T69f,viewof inputs = InputGroup({ rawDataFile: Inputs.file({label: "Upload CSV file (e.g. https://raw.githubusercontent.com/nl4dv/nl4dv/master/examples/assets/data/euro.csv)", accept: ".csv", required: false}), rawDataFileFeature: Inputs.text({label: "Feature from CSV file", placeholder: "Type here", value: null, required: false}), rawData: Inputs.textarea({label: "OR Enter comma (,) or newline (\\n) or tab (\\t) separated list of values", placeholder: "[1, 2, 3, 4, 5]", value: [1.7,1.8,1.9,3,1.8,1.3,1.9,1.9,1.7,1.4,2.3,1.7,1.8,1.3,2,1.7,2.2,2.9,1.9,1.7,1.8,1.6,2,1.1,1.8,1.8,1.7,2.4,1.9,1.6,1.3,1.4,1.8,1.6,1.4,1.5, null, null, "nan", undefined, "", "null"]}), binCount: Inputs.range([1, 10], {value: 6, step: 1, label: "Enter desired number of bins, wherever applicable."}), binExtent: Inputs.range([0, 100], {value: 10, step: 0.01, label: "Enter desired bin extent, wherever applicable."}), precision: Inputs.range([0, 5], {value: 2, step: 1, label: "Enter desired precision value."}), colorScheme: Inputs.select([].concat(["viridis", "magma", "inferno", "plasma", "cividis", "turbo", "bluegreen", "bluepurple", "greenblue", "orangered", "purplebluegreen", "purpleblue", "purplered", "redpurple", "yellowgreenblue", "yellowgreen", "yelloworangebrown", "yelloworangered", "category10", "category20", "category20b", "category20c", "dark2", "paired", "pastel1", "pastel2", "set1", "set2", "set3", "tableau10", "tableau20", "blues", "greens", "greys", "oranges", "purples", "reds", "blueorange", "brownbluegreen", "purplegreen", "pinkyellowgreen", "purpleorange", "redblue", "redgrey", "redyellowblue", "redyellowgreen", "spectral"]), {label: "Color Scheme", value: "viridis"}) });32:T546,## Credits BinGuru was created by Arpit Narechania, Alex Endert, and Clio Andris of the Georgia Tech Visualization Lab. We also thank the members of the Georgia Tech Visualization Lab for their support and constructive feedback.

## Citations ```bibTeX @InProceedings{narechania2023resiliency, author = {Narechania, Arpit and Endert, Alex and Andris, Clio}, title = {{Resiliency: A Consensus Data Binning Method}}, booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)}, pages = {55:1--55:7}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, year = {2023}, volume = {277}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, doi = {10.4230/LIPIcs.GIScience.2023.55} } ```
## License The software is available under the [MIT License](https://github.com/exploropleth/binguru/blob/master/LICENSE).
## Contact If you have any questions, feel free to [open an issue](https://github.com/exploropleth/binguru/issues/new/choose) or contact [Arpit Narechania](https://narechania.com).33:[] 2f:{"id":"0b0bb1671c785333","slug":"binguru-demo","title":"BinGuru","thumbnail":"dff714a6cba185d303b043d00dd8e2a2804ff54ad51e986064dc7edbb835fbc2","access_level":"public","create_time":"$D2023-06-28T21:10:47.091Z","update_time":"$D2026-08-14T07:24:45.693Z","view_time":null,"first_public_time":"$D2023-06-28T21:10:47.091Z","fork":false,"stars":10,"starred":false,"owner":{"type":"team","id":"e6b1236e48dcc90d","login":"arpitnarechania","name":"Arpit Narechania","avatar_url":"https://avatars-next.observableusercontent.com/f259393d88df91d8b9592f151ac96d27773593b560669ba24bb0f7f641087c57","tier":"starter_2024"},"version":1166,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"3730278dd49a6a8d","login":"arpitnarechania","name":"Arpit Narechania","avatar_url":"https://avatars-next.observableusercontent.com/c767299eedeadf5365066138ad374805050804b137ec149d50c32e3c3c56ff8d","tier":null}],"members":[{"type":"individual","id":"3730278dd49a6a8d","login":"arpitnarechania","name":"Arpit Narechania","avatar_url":"https://avatars-next.observableusercontent.com/c767299eedeadf5365066138ad374805050804b137ec149d50c32e3c3c56ff8d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"BinGuru","cells":[{"id":0,"value":"# BinGuru\n\nBinGuru is a Javascript package with an API to several established data binning / data classification methods, often used for visualizing data on choropleth maps. It also includes an implementation of a new, consensus binning method, 'Resiliency'.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":32,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":34,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1156,"value":"binguru = import('https://esm.sh/binguru@1.0.1');","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":771,"value":"import {InputGroup} from \"@sethpipho/input-group\";","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7,"value":"embed = require(\"vega-embed@6\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":28,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":30,"value":"## Specify Inputs","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":763,"value":"$31","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1097,"value":"fileData = inputs.rawDataFile ? inputs.rawDataFile.csv({typed: true}): []","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":811,"value":"rawData = inputs.rawDataFile && inputs.rawDataFileFeature ? fileData.map(val => val[inputs.rawDataFileFeature]) : inputs.rawData.split(/[,\\t\\r\\n]/).map(parseFloat);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":17,"value":"binGuruObj = new binguru.BinGuru(rawData, inputs.binCount, inputs.binExtent, inputs.precision);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":25,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":37,"value":"## Interval-based Methods\nWe define this category to include methods that bin data into a set of intervals generally using arithmetic operations along the number line.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":39,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":21,"value":"### Equal Interval\nThis method divides the data range into the specified bin count with equal bin intervals. For example, to bin attribute with values ranging from 0 to 30 into three bins, the resultant bins will be 0-10, 10-20, and 20-30. Equal interval is best applied to familiar data ranges, such as percentages and temperature.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":42,"value":"ei = binGuruObj.equalInterval()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":56,"value":"embed(binGuruObj.visualize(ei, binguru.EQUAL_INTERVAL, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":66,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":65,"value":"### Defined Interval\nThis method divides the data range into bins based on a numeric bin interval. For example, if we want bins with an interval of 15 units for an attribute ranging from 0 to 30, then the resultant bin extents will be 0-15 and 15-30.\n\n`binInterval`: the defined interval value. Default: null (falls back to the `binInterval` defined in the class constructor.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":64,"value":"di = binGuruObj.definedInterval(0.5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":63,"value":"embed(binGuruObj.visualize(di, binguru.DEFINED_INTERVAL, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":74,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":73,"value":"### Exponential Bin Sizes\nThis method uses an exponential series (a^n) for binning. In this variation, size of each successive bin increases (or decreases) exponentially, e.g., if there are 30 data values and 4 specified bins, the resultant bin sizes will be 2, 4, 8, and 16.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":72,"value":"ebs = binGuruObj.exponentialBinSizes()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":71,"value":"embed(binGuruObj.visualize(ebs, binguru.EXPONENTIAL_BIN_SIZE, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":82,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":97,"value":"### Geometric Interval\nThis method divides the data range into a specified bin count based on bin intervals that follow a geometric series (a + ar + ar2 + ...) where a is a constant and r is the geometric coefficient. This technique is useful for binning skewed data distributions, and for balancing between highlighting changes in central and extreme values, thereby producing a result that is visually appealing and cartographically comprehensive.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":96,"value":"gi = binGuruObj.geometricInterval()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":95,"value":"embed(binGuruObj.visualize(gi, binguru.GEOMETRIC_INTERVAL, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":129,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":130,"value":"### Maximum Breaks\nThis method defines bins based on the maximum gradients of sorted data values, where a gradient is the difference between successive data values. It detects the distance between subsequent data values in a sorted sequence and based on the desired bin count, accordingly places a break point in between the pairs of values that are most stretched apart from each other.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":131,"value":"mb = binGuruObj.maximumBreaks()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":132,"value":"embed(binGuruObj.visualize(mb, binguru.MAXIMUM_BREAKS, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":167,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":169,"value":"## Statistical Methods\nThese methods rely on statistical measures and analysis of the data such as the count, mean, median, and standard deviation to determine bins.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":172,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":174,"value":"### Quantile\nThis method defines bin intervals such that their bin size is (approximately) the same. For example, if there are 100 data points and one needs 5 equal-sized bins (or quantiles), each bin would then contain 100/5=20 data points. However, maps with data binned this way can sometimes be confusing or misleading because the values that are put into the bins can be similar to or very different from one another.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":178,"value":"qa = binGuruObj.quantile()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":180,"value":"embed(binGuruObj.visualize(qa, binguru.QUANTILE, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":182,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":183,"value":"### Box Plot\nThis method typically defines six bins: four quartiles plus two classifications for data items that are more than 1.5 times the inter-quartile range (IQR) from the median, enabling quick visualization of the distribution of the data across the region being mapped, as well as identifying any outliers or areas with unusual values.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":184,"value":"bp = binGuruObj.boxPlot()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":185,"value":"embed(binGuruObj.visualize(bp, binguru.BOXPLOT, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":190,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":191,"value":"### Percentile\nThis common method defines bin intervals based on percentiles (%), e.g., \\[0,1), [1,10), [10,50), [50,90), [90,99), [99,100\\].","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":192,"value":"pe = binGuruObj.percentile()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":193,"value":"embed(binGuruObj.visualize(pe, binguru.PERCENTILE, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":210,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":211,"value":"### Standard Deviation (2 variants)\nThis method measures how spread out a dataset is compared to its mean. Each bin will contain data points that are within a certain number of standard deviations (σ) from the mean (μ) of the data, usually at intervals of 1.0σ or 0.5σ. \n\nVariant 1: In this variation, bins are defined on either side of the mean; generally, there is no central bin and the number of bins is even; if odd number of bins are specified, then one side of the mean may be unbalanced.\n\nVariant 2: In this variation there is one central bin (μ±0.5σ ) and additional bins at μ±1.0σ intervals beyond this central bin, for a total odd number of bins.\n\n`meanAsBinBoundary`:boolean: true for Variant 1, false for Variant 2; Default: true\n\n`sdfactor`:numeric = number of σs around the mean. Default = 1. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":212,"value":"sd1 = binGuruObj.standardDeviation(true, 1)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":213,"value":"embed(binGuruObj.visualize(sd1, binguru.STANDARD_DEVIATION, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1082,"value":"sd2 = binGuruObj.standardDeviation(false, 1)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1086,"value":"embed(binGuruObj.visualize(sd2, binguru.STANDARD_DEVIATION, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":230,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":267,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":269,"value":"## Iterative Methods\nThis category includes methods that use iterative algorithms to separate the data into meaningful and distinct bins, while minimizing the loss of information or predictive accuracy. The process typically involves starting with an initial set of bin breaks, and then iteratively refining them based on a criteria or optimization function.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":275,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":276,"value":"### Natural Breaks (Fisher Jenks)\nThis method defines bins such that they are as homogenous as possible within themselves and as heterogeneous as possible between themselves. It does so by minimizing the within-bin variance and maximizing the between-bin variance, thereby creating natural groupings of data that are more meaningful and interpretable.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":277,"value":"fj = binGuruObj.fisherJenks()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":278,"value":"embed(binGuruObj.visualize(fj, binguru.FISHER_JENKS, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":538,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":539,"value":"### CK-Means\nCKMeans is a dynamic programming algorithm for optimal one-dimensional clustering.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":540,"value":"ckm = binGuruObj.ckMeans()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":541,"value":"embed(binGuruObj.visualize(ckm, binguru.CK_MEANS, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":558,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":559,"value":"### Head Tail Breaks\nThis method defines bins by recursively splitting data around the iterative means until the distributions within each of the bins no longer displays a heavy-tailed distribution, i.e., there is a balance between the number of smaller and larger values assigned to each bin. This method is particularly effective for data with a heavy-tailed distribution (e.g., power law), as it recognizes that there may be far more objects of smaller (than larger) magnitudes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":560,"value":"ht = binGuruObj.headTailBreaks()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":561,"value":"embed(binGuruObj.visualize(ht, binguru.HEAD_TAIL_BREAKS, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":587,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":589,"value":"## Human-Centered Methods\nBecause humans are the ultimate consumers of binning methods, there ought to be methods that leverage not just the statistical aspects of the mapped attribute but also data semantics and the legibility and interpretability of the resulting bins, motivating a human-centered category.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":591,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":594,"value":"### Pretty Breaks\nThis method rounds each bin break up or down into pretty values that are easier to read, understand, and explain, e.g., \\[0-10\\] instead of \\[0-9.364\\]. These bin intervals are defined based on the data values, user preferences, desired number of bins, and rounding factor.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":684,"value":"pb = binGuruObj.prettyBreaks()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":688,"value":"embed(binGuruObj.visualize(pb, binguru.PRETTY_BREAKS, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":599,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":600,"value":"### Manual Interval\nThis method organizes data into bins based on predetermined criteria or when automated methods are inappropriate or unavailable. This method can be less explainable, time-consuming, and error-prone to execute, especially when the dataset is large or complex.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":678,"value":"mi = binGuruObj.manualInterval([0.5, 0.75, 1.25, 1.5, 2])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":682,"value":"embed(binGuruObj.visualize(mi, binguru.MANUAL_INTERVAL, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":603,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":604,"value":"## Other Methods\nThese methods do not fall into the above categories but are still important and commonly found across maps.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":611,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":614,"value":"### Unclassed / Continuous\nThis method divides data into a continuous range of values, rather than into discrete bins; it uses a color ramp, typically progressing from low to high values, to represent the data, with each color pixel in the ramp representing a different data value.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":618,"value":"unc = binGuruObj.unclassed()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":622,"value":"// Note: This visualization is hard-coded to the viridis color scale as there is no API available that transforms a Vega-Lite color scheme into its d3-scale-chromatic equivalent to generate the desired \"stops\" for the desired linear gradient here.\nembed(binGuruObj.visualize(unc, binguru.UNCLASSED, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":626,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":627,"value":"### Unique Values\nThis method treats each continuous data value as categorical and maps each unique bin of equal values to a distinct color.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":628,"value":"uniq = binGuruObj.unique()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":629,"value":"embed(binGuruObj.visualize(uniq, null, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":646,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":647,"value":"### RESILIENCY\nThis is a new ensemble data binning method that assigns areal units to their most agreed-upon, consensus bin as it persists across multiple chosen binning methods.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":671,"value":"binningMethods = [\"equalInterval\", \"percentile\", \"quantile\", \"geometricInterval\", \"boxPlot\", \"standardDeviation\", \"maximumBreaks\", \"fisherJenks\"]\n// binningMethods = []","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1115,"value":"// binningMethodObjs = {\n// let obj = {};\n// obj[binguru.EQUAL_INTERVAL] = ei;\n// obj[binguru.PERCENTILE] = pe;\n// obj[binguru.QUANTILE] = qa;\n// obj[binguru.GEOMETRIC_INTERVAL] = gi;\n// obj[binguru.BOXPLOT] = bp;\n// obj[binguru.STANDARD_DEVIATION] = sd1;\n// obj[binguru.MAXIMUM_BREAKS] = mb;\n// obj[binguru.FISHER_JENKS] = fj;\n// return obj;\n// }\nbinningMethodObjs = {}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":648,"value":"// One of the two parameters must be passed. Priority is given to `binningMethods`\nres = binGuruObj.resiliency(binningMethods, binningMethodObjs)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":649,"value":"embed(binGuruObj.visualize(res, binguru.RESILIENCY, inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":737,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":840,"value":"### Visualize on a Choropleth Map","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":837,"value":"// viewof geoDataFile = Inputs.file({label: \"Upload a TOPOJSON file\", accept: \".json\", required: true});\n// geoData = geoDataFile.json({typed: true})\ngeoData = d3.json(\"https://vega.github.io/vega-datasets/data/us-10m.json\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":876,"value":"// viewof inputDataFile = Inputs.file({label: \"Upload the input data file\", accept: \".csv\", required: true});\n// inputData = inputDataFile.csv({typed: true})\ninputData = d3.tsv(\"https://vega.github.io/vega-datasets/data/unemployment.tsv\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":981,"value":"inputDataFeature = \"rate\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":927,"value":"binGuruObj2 = new binguru.BinGuru(inputData.map(d => parseFloat(d[inputDataFeature])), inputs.binCount);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":966,"value":"fj2 = binGuruObj2.fisherJenks()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":968,"value":"/* binguruRes: any, inputData: number[], geoData: any[], inputDataFeature: string, geoDataFeature: string, geoDataLookup:string = \"id\", inputDataKey:string = \"id\", colorSchemeCode = \"viridis\" */\nembed(binGuruObj.map(fj2, inputData, geoData, inputDataFeature, \"counties\", \"id\", \"id\", inputs.colorScheme));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":845,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":739,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 30:"$2f:body"