3e:null 3f:["$","title",null,{"children":"Clustering Final Project | Table 3 | Observable"}] 45:T418,While artificial intelligence (AI) is becoming ever more pervasive in our daily lives, its adaptation and efficacy are often hindered by its interpretability. The Black Box problem refers to models that are used to solve complex problems but remain opaque to the human operator. Such an issue may lead to regulatory concerns, hidden bias, or distrust in the model's proposed solution (Zednik, 2021). Explainable AI (XAI) seeks to alleviate this emergent problem by “enabling human users to understand, appropriately trust, and effectively manage” artificial intelligence (Turek, 2018). This notebook contributes to the work of XAI through building an interactive visualization to explain the iterative process of various clustering algorithms. Users of this notebook can explore a novel dataset and apply centroid-based, density-based, and hierarchical clustering algorithms. Visualizations present the clustering of data points across various interactions to allow users to understand the computational decisions behind cluster assignment. 46:T4fc,The data source for this project is [Kaggle’s Data Science Job Postings and Salaries](https://www.kaggle.com/datasets/michaelbryantds/california-salaries-in-data-science?select=qualifications.csv). This dataset contains job postings for data science-related jobs in California taken from SimplyHired. The raw dataset of version 2 contains 1342 records and 8 attributes. Our final dataset will consist of cleaned one-hot encoded data, resulting in 1286 records and 423 attributes. The Data Science Job Postings and Salaries dataset consists of three separate data sources of the table dataset type: *benefits, qualifications, title_location_company_salary*. These tables are a simple combination of the information contained in the raw dataset, representing a binary encoding of the information contained in the categorical benefits column, a binary encoding of the categorical qualifications column, and the remainder of the table, respectively. We further derived data such as average commute time, years of school required for position, company rating via *Simply Hired*, and latitude and longitude and combined it with the original data to create a novel dataset. These additional derived parameters allowed for richer interaction with various clustering algorithms. 47:Tb31, chart1 = { let svg = d3 .create("svg") .attr("width", width-100) .attr("height", 680) .style("border", "0.1px solid lightgrey") let text1=svg .append("text") .attr("x", 100) .attr("y",30) .style("font-size","20px") .style("fill","darkorange") .html("Popularity of Data Science degrees: Hover a bar to find out or filter using the slider above!"); let text2=svg .append("text") .attr("x", 470) .attr("y",650) .style("font-size","15px") .style("fill","brown") .html("Degree type"); var y_axis = d3.axisLeft().scale(yScale_New).ticks(25); var x_axis=d3.axisBottom().scale(xScale_New); svg.append("g") .attr("transform", `translate(${40 + 110}, 0)`) .call(y_axis) .append("text") .style("fill", "brown") .style("font-size","14px") .attr("transform", `translate(-40,250) rotate(-90)`) .html("Frequency"); svg.append("g") .attr("transform", "translate(0," + 555 + ")") .call(x_axis) .selectAll("text") .style("text-anchor", "end") .attr("dx", "-.3em") .attr("dy", ".12em") .attr("transform", "rotate(-25)"); //// Define details-on-demand tag let bars = svg // MARKS: maps data rows to rects .selectAll("rect") .data(degreeSheet1) .join("rect") // creates new rects for each data row // CHANNELS: maps data columns to rect attributes .attr("x", (r) => xScale_New(r["Skill Name"])) .attr("width", xScale_New.bandwidth()-20) .attr("y", (r) => yScale_New(r["Occurance Count"])) .attr("height", (r) => yScale_New(0) - yScale_New(r["Occurance Count"])) .style("fill", (r) => cScale(r["Average Salary With Skill"])) //// Interaction events .on("mouseover", (event, r) => { bg = d3.select(event.target).style("fill"); // save the current color of the rect for mouseout d3.select(event.target).style("fill", "darkorange"); // Modify SVG DOM text.html(r["Skill Name"]+ " " + "(" + r["Occurance Count"] + ")") text.attr("x", xScale_New(r["Skill Name"])); // .attr("y", yScale(r.PercentCollegeGrad)); mutable hover1 = r; // Export hover selection }) .on("mouseout", (event, r) => { text.html(" "); d3.select(event.target).style("fill", bg); }); let bg = null; //if hover text is needed let text = svg .append("text") .attr("x", 100) .attr("y", 70) .style("font", "14px Arial") .style("fill","darkgreen"); bars.append("title").text((r, i) => r["Skill Name"]+ " " + "(" + r["Occurance Count"] + ")"); svg.node().filterBy = function (filterMin) { bars.style("fill", (r) => r["Average Salary With Skill"] >= filterMin ? cScale(r["Average Salary With Skill"]) : "grey" ); }; return svg.node(); }48:T953, chart2 = { let svg1 = d3 .create("svg") .attr("width", width-100) .attr("height", 720) .style("border", "0.1px solid lightgrey") let text1=svg1 .append("text") .attr("x", 90) .attr("y",30) .style("font-size","19px") .style("fill","brown") .html("Popularity of Job Titles in Data Science : Brush the top left corner of a bar to find out!"); let text2=svg1 .append("text") .attr("x", 470) .attr("y",680) .style("font-size","16px") .style("fill","brown") .html("Job title"); var y_axis = d3.axisLeft().scale(yScale_2).ticks(25); svg1.append("g") .attr("transform", `translate(${150}, 0)`) .call(y_axis) .append("text") .style("fill", "brown") .style("font-size","16px") .attr("transform", `translate(-40,250) rotate(-90)`) .html("Frequency"); svg1.append("g") .attr("transform", "translate(0," + 555 + ")") .call(d3.axisBottom(xScale_2)) .selectAll("text") .style("text-anchor", "end") .attr("dx", "-.3em") .attr("dy", ".12em") .attr("transform", "rotate(-35)"); //// Define details-on-demand tag let bars = svg1 // MARKS: maps data rows to rects .selectAll("rect") .data(dataSheet2) .join("rect") // creates new rects for each data row // CHANNELS: maps data columns to rect attributes .attr("x", (r) => xScale_2(r["Job_title"])+5) .attr("width", xScale_2.bandwidth()-12) .attr("y", (r) => yScale_2(r["Frequency"])) .attr("height", (r) => yScale_2(0) - yScale_2(r["Frequency"])) .style("fill", (r,i) => cScale_2(i)) //// Interaction events .on("mouseover", (event, r) => { bg = d3.select(event.target).style("fill"); // save the current color of the rect for mouseout d3.select(event.target).style("fill", "pink"); // Modify SVG DOM mutable hover2 = r; // Export hover selection }) .on("mouseout", (event, r) => { d3.select(event.target).style("fill", bg); }); let bg = null; bars.append("title").text((r, i) => r["Job_title"]+ " " + "(" + r["Frequency"] + ")"); svg1.node().filterBy = function (filterMin) { bars.style("fill", (r) => r["Frequency"] >= filterMin ? cScale_2(r["Frequency"]) : "grey" ); }; setupBrush(svg1, bars); return svg1.node(); }49:T416,Clustering is a form of unsupervised machine learning that seeks to segregate groups of points with similar traits and assign them into clusters, or groups, of varying number and size (Kaushik, 2020). The word similar alludes to the subjective nature of clustering, as there are numerous methods of determining likeness between 2 points. For example, in centroid based clustering, points are assigned to a set number of clusters based on their closest centroid. Further, the distance metric used in centroid clustering can further shift cluster assignment. Due to this subjectivity and nuance between algorithms and hyperparameters, clustering can be a difficult topic to learn for many aspiring data scientists. Our visualizations below seek to clarify some of the most popular clustering algorithms through concise description, accurate visual representation, and meaningful interactivity. After exploring this notebook, users should have a better understanding of the dataset presented as well as the processes behind 3 clustering algorithms.4a:T4e6,// scatterplot code based on https://observablehq.com/@d3/scatterplot chart = { kmeans(data, clusters) const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]) .attr("max-width", myWidth); xScale.domain(d3.extent(data, d => d.x)) yScale.domain(d3.extent(data, d => d.y)) svg.append("g") .call(xAxis) .append("text") .attr("x", myWidth) .attr("y", margin.bottom - 4) .attr("fill", "currentColor") .attr("text-anchor", "end") .text(x) svg.append("g") .call(yAxis) .append("text") .attr("x", -margin.left) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") svg.append("g") .attr("stroke-width", 3) .attr("fill", "none") .selectAll("circle") .data(data) .join("circle") .attr("cx", d => xScale(d.x)) .attr("cy", d => yScale(d.y)) .attr("fill", d => color(d.cluster)) .attr("r", 5) .append('title') .text((d) => `Title: ${d.JobTitle} \n Company: ${d.Company} \n City: ${d.City} \n Salary: ${d.Salary}`) // .on("mouseover", mouseover ) // .on("mousemove", mousemove ) // .on("mouseleave", mouseleave ) return svg.node(); }4b:Tdbd,tidyTree = { let tree = data_tree => d35.tree() .nodeSize([width/9,width/10]) .separation((a, b) => 2/a.depth) (d35.hierarchy(tree_data)); const svg = d35.select(DOM.svg(width, width)) .style("width", "100%") .style("height", "auto") .style("padding", "10px") .style("box-sizing", "border-box") .style("font", "15px sans-serif"); const g = svg.append("g"); const linkgroup = g.append("g") .attr("fill", "none") .attr("stroke", "#555") .attr("stroke-opacity", 0.4) .attr("stroke-width", 1.5); const nodegroup = g.append("g") .attr("stroke-linejoin", "round") .attr("stroke-width", 3); function newdata (animate = true) { let root = tree(tree_data); let links_data = root.links(); let links = linkgroup .selectAll("path") .data(links_data, d => d.source.data.name+"_"+d.target.data.name) .attr("stroke","blue") links.exit().remove(); let newlinks = links .enter() .append("path"); let t = d3.transition() .duration(animate ? 400 : 0) .ease(d3.easeLinear) .on("end", function() { const box = g.node().getBBox(); svg.transition().duration(1000).attr("viewBox", `${box.x} ${box.y} ${box.width} ${box.height}`); }); let alllinks = linkgroup.selectAll("path") alllinks .transition(t) .attr("d", d3.linkHorizontal() .x(d => d.y) .y(d => d.x)); let nodes_data = root.descendants().reverse(); let nodes = nodegroup .selectAll("g") .data(nodes_data, function (d) { if (d.parent) { return d.parent.data.name+d.data.name; } return splitt(d.data.name)}); nodes.exit().remove(); let newnodes = nodes .enter().append("g"); let allnodes = animate ? nodegroup.selectAll("g").transition(t) : nodegroup.selectAll("g"); allnodes .attr("transform", d => ` translate(${d.y},${d.x}) `); newnodes.append("circle") .attr("r", 4.5) .on ("click", function (d) { let altChildren = d.data.altChildren || []; let children = d.data.children; d.data.children = altChildren; d.data.altChildren = children; newdata (); }); nodegroup.selectAll("g circle").attr("fill", function (d) { let altChildren = d.data.altChildren || []; let children = d.data.children; return d.children || (children && (children.length > 0 || altChildren.length > 0)) ? "#555" : "#999" } ); newnodes.append("text") .attr("dy", "0.31em") .text(d => d.data.name.includes("Predicted") ? (d.data.name ) : splitt(d.data.name)) .clone(true).lower() .attr("stroke", d => d.data.name.includes("Yes,") ? "lightgreen": d.data.name.includes("Predicted")||d.data.name.includes("root") ? "white":"pink" ); nodegroup.selectAll("g text") .attr("x", d => !d.children ? 6 : -6) .attr("text-anchor", d => !d.children ? "start" : "end") //.attr("transform", d => d.x >= Math.PI ? "rotate(180)" : null); } newdata (false); document.body.appendChild(svg.node()); const box = g.node().getBBox(); //box.width = box.height = Math.max(box.width, box.height)*1.2; svg.remove() .attr("width", box.width) .attr("height", box.height) .attr("viewBox", `${box.x} ${box.y} ${box.width} ${box.height}`); return svg.node(); }4c:T464,1. Importing modules tutorial: https://observablehq.com/@observablehq/how-to-require-stubborn-modules 2. Density Based Clustering in JavaScript: https://github.com/uhho/density-clustering 3. K-Means Clustering by Sergei Pond: https://observablehq.com/@spond/k-means-clustering-algorithm 4. K-Means Clustering by Michelle Weigle: https://observablehq.com/@weiglemc/clustering-example-with-d3 5. DBSCAN by Fati Chen: https://observablehq.com/@stardisblue/hello-turf-clustersdbscan 6. Hierarchical Clustering by Mike Bostock: https://observablehq.com/@d3/cluster 7. Kaushik, S. An Introduction to Clustering and different methods of clustering (2020). Analytics Vidhya. https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ 8. Turek, M. Explainable Artificial Intelligence (XAI). DARPA. (2018). https://www.darpa.mil/program/explainable-artificial-intelligence 9. Zednik, C. Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence. Philos. 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Project","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":11,"value":"## Project Overview","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"$45","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":650,"value":"## Dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":652,"value":"$46","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"## Exploratory Data Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"Exploratory Data Analysis(EDA) is used to analyze and investigate datasets and summarize their main characteristics using different types of visualization methods. It helps the data scientists to manipulate the data in different manner by finding patterns, anomalies, test a hypothesis etc. EDA provides a better understanding of the dataset variables and the relationship between them. We use EDA to ensure that the results produced are valid and are applicable to our business goals. Once EDA is complete, its features can be used for more sophisticated data analysis that includes the use of machine learning algorithms. \n\nWe have included two visualizations as part of our exploratory data analysis. We have added different interaction components to these charts to make it more interesting and appealing to the user. The other visualizations that we had in Tableau would also come under the exploratory data analysis category.\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1082,"value":"### [A] Popularity of Data Science degrees and their average salary","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1779,"value":"The first visualization below compares the popularity of different data Science-related degrees in the state of California. To add another dimension to this bar chart, users can also compare the average salary that data scientists earn with these degrees while taking into account its relationship with popularity. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1049,"value":" Filter Interaction ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":744,"value":"Filter by salary using the slider below. The average salary for a Data Scientist in California ranges between 90k and 165k! ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":748,"value":"viewof filter = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1059,"value":"Using filter, you've selected an average salary greater than ${filter}. Those degrees with salaries lesser than ${filter} are greyed out.","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1047,"value":" Hover Interaction ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":849,"value":"Using hover, you've selected ${hover1[\"Skill Name\"]} which has an average salary of ${hover1[\"Average Salary With Skill\"]} and occurs ${hover1[\"Occurance Count\"]} time(s) in the dataset.","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":807,"value":"legend({\n color: cScale,\n title: \"Averge Salary ($) based on degree type \"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":756,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":776,"value":"### [B] Popularity of Data Science job titles in California","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1781,"value":"The second visualization below focuses on the popularity of the different data science job titles in California since there exists different titles for a data scientist these days like data analyst, data engineer, ML engineer etc.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1101,"value":" Brush Interaction ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1103,"value":"Using brush, select different job titles of interest to you to see their popularity in California. Note that in order to select a bar, you must brush the top left corner in your rectangular selection.","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":843,"value":"The following job titles frequencies in the dataset are: ${brushed.map((key,element)=>{return key.Job_title + \" -> \" + key.Frequency + \" times, \\n\"\n })}","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":791,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"## Clustering","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":658,"value":"$49","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":660,"value":"### K- Means Clustering","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":668,"value":" K-Means allows the user to partition data points into K clusters based on the nearest mean center, or centroid. Our design allows the user to select 2 dimensions on which to cluster. Upon selection, the data points are projected into the 2D space below according to their respective values. Users can select any number of K clusters via the sliding bar or via direct numerical entry. The algorithm partitions the data points into K clusters, identified by K colors. The user can iteratively explore these 3 interactions to better understand both the k-means clustering algorithm and the natural clusters that emerge across the data. Our K-Means implementation was inspired by [Sergei Pond](https://observablehq.com/@spond/k-means-clustering-algorithm) and [Michelle Weigle](https://observablehq.com/@weiglemc/clustering-example-with-d3).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":88,"value":"viewof x = Select(Object.keys(kmeans_formatted[0]).slice(9), {label: \"x\", value: \"RetirementPackage Normalized\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"viewof y = Select(Object.keys(kmeans_formatted[0]).slice(9), {label: \"y\", value: \"CompanyRating Normalized\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"viewof clusters = Range([2,10], {step: 1, value: 2, label: \"Clusters\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"### DBSCAN","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":689,"value":"Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm that combines together the data points into clusters that are closely packed together. The points that do not fall anywhere near the data points are generally called as outliers in this case. In this design, we can give two inputs to the algorithm namely number of points per cluster and the epsilon value. The epsilon value returns an estimate of neighboring clustering threshold. Based on these hyperparameters, the data points will be clustered together. The user can explore these inputs interactively by modifying all these inputs and see how the model changes via the interactive visualization below. Our DBSCAN implementation was inspired by [Fati Chen](https://observablehq.com/@stardisblue/hello-turf-clustersdbscan). You can zoom/pan with your mouse and can hover over each job marker to see extra data about the job.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":295,"value":"viewof epsilon = Range([0.2, 4], { step: 0.05, value: 0.2, label: \"epsilon\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"viewof numPoints = Range([1, 10], { step: 1, value: 1, label: \"numPoints\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":331,"value":"
Current number of clusters = ${numClusters}
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":370,"value":" viewof map = createMap(L,[36.746841,-119.772591], 6)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":708,"value":"### Decision Tree Clustering - Hierarchical, Nonexclusive","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":710,"value":"Decision trees are a machine learning technique which use a tree model of decisions to display the plausible of outcomes when applying different sets of attributes. Each internal node represents a 'test', on a different attribute, and each branch represents the outcome of the test. Each leaf node represents a class label, and these class labels are associated with a different predictive outcome. In this design, the user can explore a visualization of a decision tree for various features in the dataset. Our work was inspired by [Mike Bostock](https://observablehq.com/@d3/cluster)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3575,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"svg_tree_legend","database":"$undefined"},{"id":1906,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":845,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":847,"value":"## Variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1238,"value":"### Exploratory Data Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1248,"value":"#### [A] Popularity of Data Science degrees and their average salary","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":738,"value":"xScale_New = d3\n .scaleBand()\n .domain(degreeSheet1.map((d) => d[\"Skill Name\"]))\n .range([150, width-250])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":740,"value":"yScale_New = d3.scaleLinear().domain([1, 1200]).range([555, 55])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":742,"value":"cScale = d3.scaleSequential(d3.interpolateGreens).domain([90000, 165000])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":760,"value":"chart1.filterBy(filter)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":762,"value":"mutable hover1 = {\n var initial = {\n \"Skill Name\": \"Bachelor of Science\",\n \"Occurance Count\": \"240\",\n \"Average Salary With Skill\": \"146794.1176\"\n }\n return initial\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1251,"value":"#### [B] Popularity of Data Science job titles in California","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":783,"value":"xScale_2 = d3\n .scaleBand()\n .domain(dataSheet2.map((d) => d[\"Job_title\"]))\n .range([150, width-250])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":785,"value":"yScale_2\n= d3.scaleLinear().domain([0, 640]).range([555, 45])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":787,"value":"cScale_2 = d3.scaleSequential(d3.interpolateRainbow).domain([14, 0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":793,"value":"mutable hover2=null;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":795,"value":"mutable brushed = []","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":799,"value":"function setupBrush(svg, rects) {\n // D3 Brush API\n const brush = d3.brush().on(\"start brush end\", brushing);\n svg.append(\"g\").call(brush); // Create brush and register events\n\n // Brush event callback function\n function brushing(event) {\n if (event.selection) {\n //console.log(event.selection);\n const [[left, top], [right, bottom]] = event.selection; // The brush pixel coordinates\n \n //// do brush stuff here...\n rects.style(\"fill\", (r,i) =>\n pointInRect(\n [xScale_2(r.Job_title), yScale_2(r.Frequency)],\n event.selection\n )\n ? \"brown\"\n : cScale_2(i) \n );\n mutable brushed = dataSheet2.filter((r) =>\n pointInRect(\n [xScale_2(r.Job_title), yScale_2(r.Frequency)],\n event.selection\n )\n );\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":797,"value":"function pointInRect(p, r) { // p=[x,y], r=[[l,t],[r,b]]\n const [x, y] = p;\n const [[left, top], [right, bottom]] = r;\n return left <= x && x <= right && top <= y && y <= bottom;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1243,"value":"### K-Means Clustering","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":127,"value":"data = formatData(kmeans_formatted)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3616,"value":"data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"myWidth = Math.min(height, width);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"height = 600","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"margin = ({top: 25, right: 20, bottom: 35, left: 50})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"xScale = d3.scaleLinear()\n .range([margin.left, width - margin.right])\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"yScale = d3.scaleLinear()\n .range([height - margin.bottom, margin.top]).nice()\n //.tickFormat(d3.format(\"d\"));\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(d3.axisBottom(xScale).ticks(width / 80))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(yScale))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"color = d3.scaleOrdinal(d3.schemeTableau10); ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"function formatData (local_data) {\n let new_data = JSON.parse(JSON.stringify(local_data))\n new_data.forEach(d => {\n d.x = d[x]\n d.y = d[y]\n })\n return new_data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1246,"value":"### DBSCAN","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1186,"value":"colorArrayNew = [\"rgb(175, 240, 91)\",\"rgb(30, 184, 208)\",\"rgb(255, 140, 56)\",\"rgb(74, 245, 108)\",\"rgb(110, 64, 170)\",\"rgb(255, 83, 117)\",\"rgb(155, 61, 179)\",\"rgb(121, 246, 89)\",\"rgb(200, 61, 172)\",\"rgb(71, 117, 222)\",\"rgb(201, 211, 58)\",\"rgb(155, 61, 179)\",\"rgb(110, 64, 170)\",\"rgb(26, 213, 177)\",\"rgb(255, 107, 83)\",\"rgb(47, 150, 224)\",\"rgb(238, 67, 149)\",\"rgb(40, 234, 141)\",\"rgb(232, 176, 46)\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":565,"value":"//markerg.clearLayers()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":562,"value":"mutable markerg = L.layerGroup().addTo(map)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":383,"value":" apply = {\n //const markers = L.layerGroup([dbscanParsed_short.map(v => createMarker(v))]);\n //const markers = L.layerGroup(dbscan.clusters.map(v => createMarker(v)));\n//const markerGroup = L.layerGroup().addTo(map);\n markerg.clearLayers();\n\n\n\n // get points information\n let dataArr = [];\n locationData_with_Info_New.forEach((obj) => {\n dataArr.push([obj.Latitude, obj.Longitude]);\n });\n\n // run dbscan algorithm\n let clusters = dbscan.run(dataArr, epsilon, numPoints);\n mutable numClusters = clusters.length;\n\n let clusterIdx = 0;\n\n let pointClusters = [];\n\n clusters.forEach((c) => {\n let clusterArr = [];\n c.forEach((pointIdx) => {\n clusterArr.push(dataArr[pointIdx]);\n const ref = createMarker(pointIdx, dataArr, clusterIdx)\n ref.addTo(markerg);\n });\n pointClusters.push(clusterArr);\n clusterIdx += 1;\n });\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":385,"value":"function createMarker(pointIdx, dataArr,clusterIdx) {\n const ref = L.circleMarker([dataArr[pointIdx][1],dataArr[pointIdx][0]], {\n fillColor: colorArrayNew[clusterIdx],\n color: 'none',\n weight: 1,\n radius: 10,\n fillOpacity: 0.75,\n })//.bindTooltip(\"Title: \"+ \"\\n\" + locationData_with_Info[pointIdx].Title + \" \")\n\n\n.bindTooltip(`Title: ${locationData_with_Info_New[pointIdx].Title}
\n Company: ${locationData_with_Info_New[pointIdx].Company}
\n Location: ${locationData_with_Info_New[pointIdx].Location}
\n Salary: ${locationData_with_Info_New[pointIdx].Salary}
\n`)\n \n \n return ref\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":388,"value":"function getCenter(data) {\n const xavg = (d3.sum(data.map(v=>v.Latitude))/data.length)\n const yavg = (d3.sum(data.map(v=>v.Longitude))/data.length)\n return {Latitude: xavg, Longitude: yavg};\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":310,"value":"mutable numClusters = 0","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":324,"value":"// https://github.com/uhho/density-clustering\nclustering = require(\"https://bundle.run/density-clustering@1.3.0\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":326,"value":"dbscan = new clustering.DBSCAN()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2603,"value":"### Decision Tree Clusters","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3655,"value":"function splitt(name) {\n \n let str=name.substring(0,name.indexOf(\",\"))\n let str1=name.substring(name.lastIndexOf(\":\")+1)\n return str+\",\" + \"#\" + str1;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1918,"value":"tree = data => d3.tree()\n .size([2 * Math.PI, radius])\n .separation((a, b) => (a.parent == b.parent ? 1 : 3) / a.depth)\n (d3.hierarchy(data))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1925,"value":"width_tree = 932","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1920,"value":"radius = width / 1.8","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1770,"value":"hierarchyData = d3.group(masterDataCopy, d => d.AI, d => d.AWS, d => d.Agile)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1761,"value":"masterDataCopy.forEach(element => removeNull(element))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1713,"value":"function removeNull(data) {\n for (const key in data) {\n if(data[key] === null) {\n data[key] = 0\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"## Data Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1908,"value":"tree_data = FileAttachment(\"tree_data@4.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3625,"value":"tree_data[\"name\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"masterData = await FileAttachment(\"cleaned_custom_parsed_V3_Master_Sheet.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1764,"value":"masterDataCopy = structuredClone(masterData)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"degreeSheet1 = FileAttachment(\"Degree - Sheet1-1@3.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":778,"value":"dataSheet2 = FileAttachment(\"Job titles@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"skillData = await FileAttachment(\"cleaned_custom_parsed_V3_Skills_Sheet.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"benefitData = await FileAttachment(\"cleaned_custom_parsed_V3_Benefit_Sheet.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"avgSkillSalaryData = await FileAttachment(\"cleaned_custom_parsed_V3_AvgSkillSalary_Sheet.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":349,"value":"kmeans_formatted = await FileAttachment(\"kmeans_norm@5.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"locationData = FileAttachment(\"DBSCAN Parsed@2.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":624,"value":"locationData_with_Info = FileAttachment(\"DBSCAN Parsed - data_with_meta.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":673,"value":"locationData_with_Info_New = FileAttachment(\"DBSCAN Parsed - data_with_meta_use.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":996,"value":"//locationData_with_Info_Master = FileAttachment(\"DBSCAN Parsed - data_with_meta_Master.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":809,"value":"## Package Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1935,"value":"d35 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"import {Range, Select} from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"import {kmeans} from \"@spond/k-means-clustering-algorithm\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":767,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"import { L } from '@mbostock/hello-leaflet'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":366,"value":"import { createURL, createMap, inputs } from '@stardisblue/noag-utils'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":703,"value":"## References","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":299,"value":"$4c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W4d","stdlib":"1"}} 44:"$43:body"