44:["$","title",null,{"children":"Analysis and Recommendation for restaurants in Boston | socrano | Observable"}] 4c:T1324,//The network vis of the restaurant recommended to you. chart = { const nodes_og_data = data.nodes.map(d => Object.create(d)); const links = linkfilterer(data).map(d => Object.create(d)); const nodes = nodefilterer(data).map(d => Object.create(d)); const simulation = d3 .forceSimulation(nodes) .force( "link", d3 .forceLink(links) .id(d => d.ID) .strength(0.001) ) .force("charge", d3.forceManyBody()) .force("x", d3.forceX(d => x(d.Label)).strength(0.3)) .force("y", d3.forceY()); const svg = d3 .create("svg") .attr("viewBox", [-width / 1.5, -height / 1.8, width * 1.5, height * 1.5]); const link = svg .append("g") .attr("stroke", "#999") .attr("stroke-opacity", 0.3) .selectAll("line") .data(links) .join("line") .attr("stroke-width", d => Math.sqrt(d.value)); const tooltipx = 250; const tooltipy = -270; const node = svg .append("g") .attr("stroke", "#fff") .attr("stroke-width", 1.5) .selectAll(".node") .data(nodes) .join("circle") .attr("r", function(d) { return radius(d.radius) || 1; }) .attr("fill", color) .on("click", onMouseClick) // .on("mouseenter", onMouseEnter) // .on("mouseleave", onMouseLeave) .call(drag(simulation)); const tooltip = svg .append("text") .attr("fill", "black") // .attr("x", tooltipx) // .attr("y", tooltipy) .attr("font-size", 22) .text(""); const tooltip2 = svg .append("text") .attr("fill", "black") // .attr("x", tooltipx) // .attr("y", tooltipy + 22) .attr("font-size", 22) .text(""); const tooltip_address = svg .append("text") .attr("fill", "black") // .attr("x", tooltipx) // .attr("y", tooltipy + 44) .attr("font-size", 22) .text(""); const tooltip_count = svg .append("text") .attr("fill", "black") .attr("font-size", 22) .text(""); if (reset) { releasenode(simulation); } var isTooltipHidden = true; var isClicked = false; var previous_id = ""; function onMouseClick(evt, d) { var xpos = d.x + 20; var ypos = d.y; var link_count = links.filter( a => a.source.ID === d.ID || a.target.ID === d.ID ).length; if (isClicked == false) { tooltip .text("Item: " + d.name) .attr("x", xpos) .attr("y", ypos); tooltip2 .text("Type: " + d.Label) .attr("x", xpos) .attr("y", ypos + 22); tooltip_count .text("Count: " + link_count) .attr("x", xpos) .attr("y", ypos + 44); if (d.Label === "restaurant") { tooltip_address .text("Address: " + d.address) .attr("x", xpos) .attr("y", ypos + 66); } previous_id = d.ID; link .attr("display", "none") .filter(l => { return l.source.ID === d.ID || l.target.ID === d.ID; }) .attr("display", "block"); isClicked = true; } else if (isClicked == true && d.ID != previous_id) { tooltip .text("Item: " + d.name) .attr("x", xpos) .attr("y", ypos); tooltip2 .text("Type: " + d.Label) .attr("x", xpos) .attr("y", ypos + 22); if (d.Label != "city" || d.Label != "state") { tooltip_count .text("Count: " + link_count) .attr("x", xpos) .attr("y", ypos + 44); } if (d.Label === "restaurant") { tooltip_address .text("Address: " + d.address) .attr("x", xpos) .attr("y", ypos + 66); } else { tooltip_address.text(""); } previous_id = d.ID; link .attr("display", "none") .filter(l => { return l.source.ID === d.ID || l.target.ID === d.ID; }) .attr("display", "block"); isClicked = true; } else { tooltip.text(""); tooltip2.text(""); tooltip_count.text(""); tooltip_address.text(""); link.attr("display", "block"); isClicked = false; } } //Simple tooltip (Maybe I can do a better one) node.append("title").text(d => d.Label + ": " + d.name); simulation.on("tick", () => { link .attr("x1", d => d.source.x) .attr("y1", d => d.source.y) .attr("x2", d => d.target.x) .attr("y2", d => d.target.y); node.attr("cx", d => d.x).attr("cy", d => d.y); }); invalidation.then(() => simulation.stop()); let transform = d3.zoomIdentity; // // Zoom functionality (Geometrical Zoom, Experimental Functionality) // svg.call( // d3 // .zoom() // .extent([[0, 0], [width, height]]) // .scaleExtent([1, 8]) // .on("zoom", zoomed) // ); // function zoomed({ transform }) { // svg.attr("transform", transform); // } return svg.node(); }4d:T13d3,//The network vis of the restaurant recommended to you. thirdTypeSearchNetWork = { const nodes_og_data = thirdTypeFinalArray.nodes.map(d => Object.create(d)); const links = thirdTypeFinalArray.links.map(d => Object.create(d)); const nodes = thirdTypeFinalArray.nodes.map(d => Object.create(d)); const simulation = d3 .forceSimulation(nodes) .force( "link", d3 .forceLink(links) .id(d => d.ID) .strength(0.001) ) .force("charge", d3.forceManyBody()) .force("x", d3.forceX(d => x(d.Label)).strength(0.3)) .force("y", d3.forceY()); // .force("x", d3.forceCenter(d => x(d.group)).strength(0.3)) // .force("y", d3.forceCenter(d => y(d.group)).strength(0.3)); const svg = d3 .create("svg") .attr("viewBox", [-width / 1.5, -height / 1.8, width * 1.5, height * 1]); const link = svg .append("g") .attr("stroke", "#999") .attr("stroke-opacity", 0.3) .selectAll("line") .data(links) .join("line") .attr("stroke-width", d => Math.sqrt(d.value)); const tooltipx = 250; const tooltipy = -270; const node = svg .append("g") .attr("stroke", "#fff") .attr("stroke-width", 1.5) .selectAll(".node") .data(nodes) .join("circle") .attr("r", function(d) { return radius(d.radius)*2 || 1; }) .attr("fill", color) .on("click", onMouseClick) // .on("mouseenter", onMouseEnter) // .on("mouseleave", onMouseLeave) .call(drag(simulation)); const tooltip = svg .append("text") .attr("fill", "black") // .attr("x", tooltipx) // .attr("y", tooltipy) .attr("font-size", 22) .text(""); const tooltip2 = svg .append("text") .attr("fill", "black") // .attr("x", tooltipx) // .attr("y", tooltipy + 22) .attr("font-size", 22) .text(""); const tooltip_address = svg .append("text") .attr("fill", "black") // .attr("x", tooltipx) // .attr("y", tooltipy + 44) .attr("font-size", 22) .text(""); const tooltip_count = svg .append("text") .attr("fill", "black") .attr("font-size", 22) .text(""); if (reset) { releasenode(simulation); } var isTooltipHidden = true; var isClicked = false; var previous_id = ""; function onMouseClick(evt, d) { var xpos = d.x + 20; var ypos = d.y; var link_count = links.filter( a => a.source.ID === d.ID || a.target.ID === d.ID ).length; if (isClicked == false) { tooltip .text("Item: " + d.name) .attr("x", xpos) .attr("y", ypos); tooltip2 .text("Type: " + d.Label) .attr("x", xpos) .attr("y", ypos + 22); tooltip_count .text("Count: " + link_count) .attr("x", xpos) .attr("y", ypos + 44); if (d.Label === "restaurant") { tooltip_address .text("Address: " + d.address) .attr("x", xpos) .attr("y", ypos + 66); } previous_id = d.ID; link .attr("display", "none") .filter(l => { return l.source.ID === d.ID || l.target.ID === d.ID; }) .attr("display", "block"); isClicked = true; } else if (isClicked == true && d.ID != previous_id) { tooltip .text("Item: " + d.name) .attr("x", xpos) .attr("y", ypos); tooltip2 .text("Type: " + d.Label) .attr("x", xpos) .attr("y", ypos + 22); if (d.Label != "city" || d.Label != "state") { tooltip_count .text("Count: " + link_count) .attr("x", xpos) .attr("y", ypos + 44); } if (d.Label === "restaurant") { tooltip_address .text("Address: " + d.address) .attr("x", xpos) .attr("y", ypos + 66); } else { tooltip_address.text(""); } previous_id = d.ID; link .attr("display", "none") .filter(l => { return l.source.ID === d.ID || l.target.ID === d.ID; }) .attr("display", "block"); isClicked = true; } else { tooltip.text(""); tooltip2.text(""); tooltip_count.text(""); tooltip_address.text(""); link.attr("display", "block"); isClicked = false; } } //Simple tooltip (Maybe I can do a better one) node.append("title").text(d => d.Label + ": " + d.name); simulation.on("tick", () => { link .attr("x1", d => d.source.x) .attr("y1", d => d.source.y) .attr("x2", d => d.target.x) .attr("y2", d => d.target.y); node.attr("cx", d => d.x).attr("cy", d => d.y); }); invalidation.then(() => simulation.stop()); let transform = d3.zoomIdentity; // // Zoom functionality (Geometrical Zoom, Experimental Functionality) // svg.call( // d3 // .zoom() // .extent([[0, 0], [width, height]]) // .scaleExtent([1, 8]) // .on("zoom", zoomed) // ); // function zoomed({ transform }) { // svg.attr("transform", transform); // } return svg.node(); }4e:T84b,data_abstraction = md`## Data Abstraction: The data is from YELP, but we download it from Kaggle: https://www.kaggle.com/yelp-dataset/yelp-dataset?select=yelp_academic_dataset_business.json The original dataset is a JSON file. It includes most businesses in great cities of Canada and the US, and their information like the position and their attributes. We clean the data and select the data we want. The final dataset only includes the restaurants, and their stars, position, zip code, city, states, and food type. We finish this step by Jupyter Notebook: https://colab.research.google.com/drive/1XeoOHY9GAYoVfmaOxdNDz0wuhqLCYfKg?usp=sharing Then we import the data to Neo4j and use neo4j as a server. The original dataset type: Table The final dataset type: Network Items (Nodes): * City - The city restaurants located. * Food - The food type restaurants provide. * Hours - The day restaurant is open. * Restaurant - Certain restaurant. * State - The state cities belong to. * stars - The average evaluation of restaurants from the customers. Links (Relationships): * Assoc - Our network is not complicated, so we do not specify the relationships. Attributes: * Stars - The attribute of node stars, shows the number of stars a restaurant gets. (Quantitative) * Time - Shows when will a restaurant be open on a certain day (like Monday). (Temporal) * ZIPCode - The zip code of a certain restaurant. (Categorical) * address - The address of a certain restaurant. (Categorical) * categories - The food type shown by Food node. (Categorical) * city - The city name of City node. (Categorical) * latitude - The latitude of a certain restaurant. (Quantitative) * longitude - The longitude of a certain restaurant. (Quantitative) * name - The name of a certain restaurant. (Categorical) * state - The name of State node. (Categorical) We processing the data to present the nodes and links in different tables at the beginning of this page. It can help you understand the data more easily. #### Ordering Direction: Longitude, latitude, and Time are *cyclic*, Stars is *Sequential* #### Data Availability: *Dynamic* `4f:T9eb,insights =md`## Insight: #### Example Insight1: Considering the positive relationship between customers number and rating, the review count median of 5 stars restaurants will be 120% of the review count median of 4 stars restaurants. #### Example Insight2: There will be some difference between the distribution of categories in different areas. For example, one area may contains a lot of NightLife Club, while another one is mainly for lunch. #### Example Insight3: The most popular restaurant category is American (Tradition). #### Example Insight4: Adding Sandwiches to the menu can help the restaurants with Breakfast&Brunch category to attract more customers. #### Insight 1: (Ruizhe) The review count mean of 5 stars restaurants in Boston is only 15, which is 15% of the review count mean of 4 stars restaurants. The review count usually has positive relationship with customers number. In this case, this insight can lead to two possible thinking: 1. This insight mean the customers number of 5 stars restaurants is much less than 4 stars restaurants. 2. Maybe, when the review count is low, the customers are mainly the people who like this restaurants. But when the customers increase, the rating will tend to show the evaluation of most people. And it may lead to the decrease of stars. #### Insight 2: (Ruizhe) Though "Bars" and "NightLife" are the most popular categores, the most popular categories in 02128 area is "Breakfast&Brunch" and "Sandwiches". So 02128 area may be the living area. (The areas in Boston which is prosperity in food field are: 02116, 02139, 02169, 02215. For thouse vistors want to play and enjoy food, these areas might be good choice) #### Insight 3: (Tianhao） Bars/Nightlife is the most popular category among restaurants in greater Boston area (Boston and Cambridge). #### Insight 4: (Tianhao) For those Breakfast and Brunch restaurants interested in attracting more customers, adding Traditional American food or Cafe food to their menu could possibly help base on rating distribution. #### Something we need to mention： \nBesides insights that we developed, the user will also be able to discover their own insight via specifying zip code, food type(category) as well as other attributes. For instance: In the area with zip code 02115, the food type closest to Pizza is sandwich. In Boston, restaurants that are similar to Taco Bell and are still open are The Lower Depths, Boloco, Chipotle, Currito, and Restaurante Montecri. And their user ratings are ranging from 3.5 to 4.5. `50:T698,m_c = md`## Marks and Channels: #### Geometry: Marks: points (each restaurant is a point), map (shows the geometry information). Channels: The position of point (shows the actual position of the restaurants). #### Network: Marks: points (each node is a point), lines (each link is a line). Channels: The color of points shows their type (color cannot be change). The original positions of nodes also shows their type (it can be changed by user). The size of nodes shows how many times they appear in the dataset. In another words, the size shows the preference of customers. #### Boxplot Chart (For insight 1): Marks: area (box shows the general information of certain stars level) and points (each point is a restaurant) Channels: The color/y-coordinate of box and points shows their type. The x-coordinate position shows their review count number. The range/size of box shows the Q1, mean, and Q3 of the review counts for all restaurants in this star level. #### Point Chart (For insight 2): Marks: area (each circle is the number of certain category in certain area) Channels: The channels are mainly location and size. The x coordinate shows the category of the point. The y coordinate shows the area of the point. The size of point shows how many restaurants in this area (y coordinate) have this category (x corrdinate). #### Bar Chart (For insight 3/4): Marks: area (each area is a bar, shows the number of review count of certain category in certain stars level) Channels: The channels are mainly the length of bar (which shows the number of review count), the x-coordinate of the bar (which shows the stars level), and the color of the bars (which shows the categories). `51:T5ba,//Function to deal with data return several node and relationship at same time function dealWithDataWithParallel(queryArray) { var NodeIdArray = []; var RelationshipIdArray = []; var nodeArray = []; var relArray = [] for (let index = 0; index < queryArray.length; index++) { var sampleArray = queryArray[index]._fields for (let i = 0; i < sampleArray.length; i++) { var id = sampleArray[i].identity.low if (sampleArray[i].constructor.name === "Node") { if (!(NodeIdArray.includes(id))) { NodeIdArray.push(id) if (sampleArray[i].labels[0] === "Restaurant") { var res = sampleArray[i].properties res["ID"] = id res["Label"] = sampleArray[i].labels[0] nodeArray.push(sampleArray[i].properties) } else { var otherNode = sampleArray[i].properties; nodeArray.push({name:otherNode[Object.keys(otherNode)[0]], ID: id, Label:sampleArray[i].labels[0]}) } } } else { if (!(RelationshipIdArray.includes(id))) { RelationshipIdArray.push(id) var link = sampleArray[i].properties link.ID = sampleArray[i].identity.low link.source = sampleArray[i].start.low link.target = sampleArray[i].end.low relArray.push(sampleArray[i].properties) } } } } var finalArray = buildDataArray(nodeArray, relArray); return finalArray }52:T5a8,//Function to deal with data get one node or relationship one time function dealWithData() { var NodeIdArray = []; var RelationshipIdArray = []; var nodeArray = []; var relArray = [] for (let index = 0; index < all.length; index++) { var sample = all[index]._fields[0] var id = sample.identity.low if (sample.constructor.name === "Node") { if (!(NodeIdArray.includes(id))) { NodeIdArray.push(id) if (sample.labels[0] === "Restaurant") { var res = sample.properties res["ID"] = id res["Label"] = sample.labels[0] nodeArray.push(sample.properties) } else { var otherNode = sample.properties; nodeArray.push({name:otherNode[Object.keys(otherNode)[0]], ID: id, Label:sample.labels[0]}) } } } else { if (!(RelationshipIdArray.includes(id))) { RelationshipIdArray.push(id) var link = sample.properties link.ID = sample.identity.low link.source = sample.start.low link.target = sample.end.low link.value = 1 relArray.push(sample.properties) } } } function addRadius(d) { d["radius"] = relArray.filter(a => a.source === d.ID || a.target === d.ID).length; return d; } nodeArray.map(addRadius); var finalArray = buildDataArray(nodeArray, relArray); return finalArray }53:T44c,//Build bar chart and pie chart for given field. function barAndPiechart(data, field) { var array = data.nodes.filter(d => d.Label === field); array.map(d => d.count = countRelations(d, data)).filter(d => d.count !== 0); const bar = vl.markBar() .data(array) .encode( vl.x().fieldN("name"), vl.y().fieldQ("count"), vl.color().fieldN("name"), vl.tooltip([ { field: "name", type: "Categorical"}, { field: "count", type: "quantitative" } ]) ) const color = d3.scaleSequential(d3.interpolateViridis).domain([0, array.length-1]); const colors = { domain: array.map(d=>d.group), range: array.map( (i,j) => color(j) ) }; const pie = vl.markArc({outerRadius: 150}) .data(array) .encode( vl.theta().fieldQ('count'), vl.color().fieldN('name'), vl.tooltip([ { field: "name", type: "nominal" }, { field: "count", type: "quantitative" } ]) ) .config({view: {stroke: null}}) .width(300) .height(300) return vl.hconcat( vl.layer(bar), vl.layer(pie) ).render(); 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for a recommendation Engine based on yelp dataset and we use cloud-based neo4j server to store and retrieve data.\n\nCreated by Ruizhe Qiu, Tianhao Qu`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3291,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":876,"value":"title0 = md`## Data Overview`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1198,"value":"overviewmd = md`Everything below is the overview for all restaurants stored in our cloud server. We reconstructed our data retrieved from neo4j server so that it is Javascript friendly.\n\n**Please patiently wait for 10-30 seconds, depending on your computer performance, for everything to load from the cloud server. There is a lot of data. Refresh the page if nothing is displayed.**\n\n**If you want to see the code for each part, hover your mouse to the left side of the cell and you will then see three dots, click on it and then click \"edit\" (Or simply click on the left side of the cell).**`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2896,"value":"title1 = md` ### Relationship between Stars and Review Count`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2995,"value":"title2 = md`#### Intro to Box Plot\n\nQuartiles divide a rank-ordered data set into four equal parts. The values that divide each part are called the first, second, and third quartiles; and they are denoted by Q1, Q2, and Q3, respectively.\n\n* Q1 is the \"middle\" value in the first half of the rank-ordered data set.\n* Q2 is the median value in the set.\n* Q3 is the \"middle\" value in the second half of the rank-ordered data set\n\n

Photo and more information: [Box Plot/Wikimedia](https://en.wikipedia.org/wiki/Box_plot#Elements_of_a_box_plot)

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2197,"value":"review_boxplot = {\n const a = vl.markBoxplot()\n .data(allResWithTheirInfo)\n .encode(\n vl.x().fieldQ(\"review_count\").scale({type: \"sqrt\"}),\n vl.y().fieldN(\"stars\"),\n vl.color().fieldN(\"stars\"),\n vl.tooltip([\n { field: \"name\", type: \"Categorical\"},\n { field: \"address\", type: \"Categorical\"},\n { field: \"review_count\", type: \"quantitative\" }\n ])\n );\n \n return a.render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3034,"value":"viewof stackedBar = vl.markBar()\n .data(allResWithTheirInfo)\n .encode(\n vl.x().fieldQ(\"review_count\").scale({type: \"sqrt\"}).bin({maxbins: 100}),\n vl.y().count(),\n vl.color().fieldN(\"stars\").sort(vl.fieldQ(\"stars\").order(\"descending\")),\n vl.tooltip([\n { field: \"name\", type: \"Categorical\"},\n { field: \"address\", type: \"Categorical\"},\n { field: \"review_count\", type: \"quantitative\" }\n ])\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2899,"value":"dist1 = md` ### Distribution of Categories in Different Area`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2706,"value":"zip_food = {\n const zipAndFood = vl.markCircle()\n .data(zipcode_food)\n .encode(\n vl.y().fieldO(\"zip\"), \n vl.x().fieldO(\"food\"),\n vl.size().sum(\"count\")\n );\n \n return zipAndFood.render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":921,"value":"location = md` ### Location Overview`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":949,"value":"//The location situation of all data.\ngeo_1 = geometry(data.nodes)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":953,"value":"gmap1 = md` #### Google Map\n\nWe integrated Google Map API to our front end. This visualization is very useful to locate precise location for these restaurants.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":985,"value":"//The location situation of all data on Google Map.\nallDataGoogle = {\n let div = html`

`;\n\n const mapHolder = [0];\n\n // Like Leaflet and Mapbox GL JS, Google Maps is very picky about\n // needing to be initialized on a div that's already included\n // in the page. So I yield the div first, to establish it in\n // the page.\n yield div;\n let map = new google.maps.Map(div, {\n center: { lng: -71, lat: 42.5 },\n zoom: 10\n });\n mapHolder[0] = map;\n \n const marker = thirdTypeFinalArray.nodes.map(\n p =>\n new google.maps.Marker({\n position: { lng: p.longitude, lat: p.latitude },\n map: mapHolder[0],\n title: p.name,\n })\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1631,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1637,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3111,"value":"data_used_insight = md`## Data Used for Each Insight:\n* Insight 1: Review Count (Quantitative), Stars(Quantitative).\n* Insight 2: Categories (Categorical), Zip Code (Categorical).\n* Insight 3: Categories (Categorical), City (Categorical), Stars(Quantitative).\n* Insight 4: Stars(Quantitative), Categories (Categorical), City(Categorical).\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1638,"value":"task_abstraction = md`## Task Abstraction:\n\n#### End User(Target Audience):\nThe customers who want to find some new restaurants. The restaurants who want to identify the average level of restaurants in Boston.\n\n#### Task 1:\nExplore the Trend. (Find the trend of the relationship betweenreview count and stars)\n\n#### Task 2:\nExplore the outliers. (Find the special place of the relationship between category popularity and area)\n\n#### Task 3:\nLocate the feature. (Find the most popular category in great Boston and Cambridge)\n\n#### Task 4:\nDiscover food options. (Dicover food options in addition to the selected food category base on the popularity and rating distribution.)\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1640,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1369,"value":"codes = md`## Code that supports all visualizations/UI above:\n\n*Click on the left side of the cell to see the source code. Do not try to modify anything as it might break the code.*`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2332,"value":"popular_categories = { var og_data = nodefilterer(data)\n var og_link = linkfilterer(data)\n var array = og_data.filter(d => d.Label === \"Food\");\n array.map(d => d.count = og_link.filter(a => d.ID === a.source || d.ID === a.target).length).filter(d => d.count !== 0);\n var topValues = array.map(d => d.count).sort((a,b) => b-a).slice(0,10);\n array = array.filter(d => topValues.includes(d.count)).filter(d => d.name !== \"Restaurants\" && d.name != \"Food\");\n var list_array_id = array.map(d => d.ID);\n \n var res_with_these_food = list_array_id.map(d => findNodeConnected(d))\n .map(d => d.map(obj => Object.assign({}, obj, {star: find_and_add_attr(obj)})));\n \n var update_list = [];\n \n for (let i = 0; i < array.length; i++) {\n var res_food = res_with_these_food[i].map(d => Object.assign({}, d, {category: array[i].name}));\n update_list.push(res_food);\n }\n\n return update_list;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2249,"value":"function find_and_add_attr(node) {\n var obj = allResWithTheirInfo.filter(a => a.name === node.name);\n return obj[0].stars\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2242,"value":"// Find all the nodes that are connected to the given node id\nfunction findNodeConnected(nodeid) {\n var links = linkfilterer(data).filter(d=> d.target === nodeid).map(d => d.source)\n var nodewithid = nodefilterer(data).filter(d => links.includes(d.ID))\n return nodewithid\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2839,"value":"income_data = income_data_raw.map(d => ({\nzipcode: d.zipcode,\nIncome: parseFloat(d.Income)\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2835,"value":"income_data_raw = FileAttachment(\"Income.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":859,"value":"firster_type_search_filter = md`### First Type Search Filter`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1915,"value":"filters_keyword = [cityfilter,categoryfilter, starfilter];","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":864,"value":"//Find the ID of restaurants with given requirements.\nfilter_keyword = {\n var city = cityfilter;\n var category = categoryfilter;\n var star = starfilter;\n \n// if (star.includes(\"No preference\")) {\n// star = starArray;\n// }\n \n// if (city.includes(\"No preference\")) {\n// city = cityArray;\n// }\n\n// if (category.includes(\"No preference\")) {\n// category = categoryArray;\n// }\n \n var filters_keyword = [cityfilter,categoryfilter, starfilter];\n var filters = [];\n \n for (let i = 0; i < 3; i++) {\n var newNode = data.nodes\n .filter(d => filters_keyword[i].includes(d.name))\n .map(d => d.ID);\n filters[i] = newNode;\n }\n\n \n let selection = new Set(data.links.map(d => d.source));\n\n for (let filter of filters) {\n \n selection = new Set(\n data.links\n .filter(d => filter.includes(d.target))\n .filter(d => selection.has(d.source))\n .map(d => d.source)\n )\n \n }\n\n return selection;\n //return filters;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":868,"value":"//Trans the ID to the whole node\nresult_filtered = {\n return result.filter(d => filter_keyword.has(d.ID));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1000,"value":"//All nodes and relationships to these selected restaurants.\nfilterData = buildDataArray(nodefilterer(data), linkfilterer(data))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"import_attribute = md`### Import Attribute`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"//The list of category node.\ncategoryArrayOriginal = buildDataArrayOriginal(\"match (n:Food) return distinct n.categories order by n.categories\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"//The list of category name.\ncategoryArray = categoryArrayOriginal.records.map(d => d._fields[0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"//The list of zipcode node.\nzipcodeArrayOriginal = buildDataArrayOriginal(\"MATCH ()-[r]-() WHERE EXISTS(r.ZIPCode) RETURN DISTINCT r.ZIPCode order by r.ZIPCode\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"//The list of zipcode name.\nzipcodeArray = zipcodeArrayOriginal.records.map(d => d._fields[0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"//The list of star node.\nstarArrayOriginal = buildDataArrayOriginal(\"match (n:stars) return distinct n.Stars order by n.Stars\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"//The list of star name.\nstarArray = starArrayOriginal.records.map(d => d._fields[0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"//The list of city node.\ncityArrayOriginal = buildDataArrayOriginal(\"match (n:City) return distinct n.city order by n.city\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":101,"value":"//The list of city name.\ncityArray = cityArrayOriginal.records.map(d => d._fields[0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"//The list of time name.\ntimeArray = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\",\"Sunday\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":640,"value":"import_all_data = md`## Import All Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":641,"value":"//The original node list from Neo4j.\nallNodeOriginal = buildDataArrayOriginal(\"MATCH (n) RETURN n\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":643,"value":"//The original relationships list from Neo4j.\nallRelationshipOriginal = buildDataArrayOriginal(\"MATCH ()-[r:Assoc]->() RETURN r\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":645,"value":"//The node list from Neo4j.\nallNode = allNodeOriginal.records","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":647,"value":"//The relationships list from Neo4j.\nallRelationship = allRelationshipOriginal.records","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":649,"value":"//All data from neo4j, contains nodes and relationships.\nall = allNode.concat(allRelationship)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":651,"value":"//The object contains nodes and relationships in {node, link} structure.\ndata = dealWithData()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1809,"value":"filterCity = {\n data.nodes.filter(d => d.Label === \"City\")\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1822,"value":"function filterLinkType(type) {\n var filter = data.nodes.filter(d => d.Label === type);\n var filterID = filter.map(d => d.ID);\n var resultLink = data.links.filter(d => filterID.includes(d.target));\n return resultLink;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1860,"value":"cityLink = filterLinkType(\"City\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2105,"value":"foodLink = filterLinkType(\"Food\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1870,"value":"resNode = data.nodes.filter(d => d.Label === \"Restaurant\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2174,"value":"starNode = data.nodes.filter(d => d.Label === \"stars\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2154,"value":"starLink = filterLinkType(\"stars\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1875,"value":"cityNode = data.nodes.filter(d => d.Label === \"City\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1891,"value":"city = cityNode.filter(d => d.ID === cityLink[0].target)[0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2191,"value":"allResWithTheirInfo = FileAttachment(\"MA Dataset sample 2000.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2387,"value":"function divide(res, key, expected) {\n var yes = [];\n var no = [];\n \n for (let i = 0; i < res.length; i++) {\n if (res[i].attributes.hasOwnProperty(key)) {\n if (res[i].attributes[key] === expected) {\n yes.push(res[i]);\n } else {\n no.push(res[i]);\n }\n } else {\n no.push(res[i]);\n }\n }\n \n return {yes: yes, no: no};\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2391,"value":"selectedCity = cityNode.map(d => Object.assign({}, d, {resNumber: countRelations(d, data)-1})).filter(d => d.resNumber > 40)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2564,"value":"foodNode = data.nodes.filter(d => d.Label === \"Food\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2573,"value":"selectedFood = foodNode.map(d => Object.assign({}, d, {resNumber: countRelations(d, data)-1})).filter(d => d.resNumber > 150).filter(d => d.name != \"Restaurants\" && d.name !== \"Food\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2710,"value":"function addZipNode() {\n var zipcodeNode = [];\n for (let i = 0; i < zipcodeArray.length; i++) {\n var zip_code = {name: zipcodeArray[i]};\n zipcodeNode.push(zip_code);\n }\n return zipcodeNode;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3130,"value":"zipcodeNode = addZipNode();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2714,"value":"selectedZipcode = zipcodeNode.map(d => Object.assign({}, d, {resNumber: countRelationsForZip(d, data)-1})).filter(d => d.resNumber > 10).filter(d => d.resNumber > 35)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2618,"value":"function getZipFood() {\n var zipcode_food = []\n for (let i1 = 0; i1 < selectedZipcode.length; i1++) {\n for (let i2 = 0; i2 < selectedFood.length; i2++) {\n var zip_name = selectedZipcode[i1].name;\n var food_name = selectedFood[i2].name;\n var count_list = allResWithTheirInfo.filter(d => d.postal_code === zip_name && d.categories.includes(food_name));\n zipcode_food.push({zip: zip_name, food: food_name, count: count_list.length})\n }\n }\n return zipcode_food;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3138,"value":"zipcode_food = getZipFood();","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3105,"value":"sorted_percentage = {\n var og_df = popular_categories.flat();\n var categories = og_df.map(d => d.category)\n \n const distinct = (value, index, self) => {\n return self.indexOf(value) === index;\n }\n \n var distinct_cat = categories.filter(distinct);\n var resultset = [];\n \n for (let i = 0; i < distinct_cat.length; i++) {\n var target_cat = distinct_cat[i];\n var primary_filter = og_df.filter(d => d.category === target_cat);\n var total_num = primary_filter.length;\n var star_list = [1,1.5,2,2.5,3,3.5,4,4.5,5]\n \n for (let j = 0; j < star_list.length; j++) {\n var star = primary_filter.filter(d => d.star === star_list[j]).length / total_num * 100;\n resultset.push({\"category\": target_cat, \"star\" : star_list[j], \"percentage\": star})\n }\n \n }\n return resultset\n\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"md`## Import Filter Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":731,"value":"// //Use the query to do the recommendation.\n// givenQuery = \n// \"call {match (f:Food {categories:\"+addApostrophe(foodtype)+\"})<-[:Assoc]-(r:Restaurant)-[:Assoc]-> (f2:Food) where f2.categories <> 'Restaurants' and f2.categories <> 'Food' with f2, count(*) as nAssoc order by count(*) desc limit 5 return collect(f2.categories) as topcat} match (c:City)<-[rel1:Assoc {ZIPCode:\"+addApostrophe(zipcode)+\"}]-(r2:Restaurant)-[rel2:Assoc]->(f3:Food) with r2, c, f3, rel1, rel2 where f3.categories in topcat or f3.categories = \"+addApostrophe(foodtype)+\" match (h:Hours)<-[m:Assoc]-(r2)-[rel3:Assoc]->(s:stars) where h.name = \"+addApostrophe(time)+\" and time(head(split(m.Time, '-'))) <= time() + duration({hours:-4}) return r2, c, s, f3, m, rel1, rel2,rel3 order by s.Stars desc limit 10\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"// //Get the data according to query.\n// queryArrayOriginal = buildDataArrayOriginal(givenQuery);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":744,"value":"// //Deal with the original data.\n// queryArray = queryArrayOriginal.records","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":746,"value":"// //Final array that include the data in {node, link} structure.\n// finalArray = dealWithDataWithParallel(queryArray);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1493,"value":"third_type_data = md` ## Import Third Type Search Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1496,"value":"//Query for third type search\nthirdQuery = \n\"call {match (f:Food)<-[:Assoc]-(r:Restaurant {name:\"+addApostrophe(Restaurant)+\"})-[:Assoc]->(c:City {city:\"+addApostrophe(cityForThird)+\"}) where f.categories <> 'Restaurants' and f.categories <> 'Food' return collect(f.categories) as cats} match (f2:Food)<-[:Assoc]-(r2:Restaurant)-[:Assoc]->(c2:City {city:\"+addApostrophe(cityForThird)+\"}) with r2, f2, cats where f2.categories in cats with id(r2) as rid, count(f2) as outgoing, cats order by outgoing desc limit 10 match (s:stars)<-[rel1:Assoc]-(r3:Restaurant)-[rel2:Assoc]->(c3:City {city:\"+addApostrophe(cityForThird)+\"}) where rid = id(r3) with rel1, s, r3, rel2, c3, cats order by s.Stars desc limit 10 match (r3)-[rel3:Assoc]->(h3:Hours) where h3.name = \"+addApostrophe(timeForThird)+\" and time(head(split(rel3.Time, '-'))) <= time() + duration({hours:-4}) UNWIND cats as food match (f:Food {categories:food}) match (r3:Restaurant) - [rel4:Assoc] -> (f:Food) return r3, rel1, s, rel2, c3, h3, f, rel3, rel4\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1502,"value":"//Read from neo4j by third query.\nthirdQueryArrayOriginal = buildDataArrayOriginal(thirdQuery);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1510,"value":"//Deal with the original data.\nthirdQueryArray = thirdQueryArrayOriginal.records","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1520,"value":"//Final array for third type search that include the data in {node, link} structure.\nthirdTypeFinalArray = dealWithDataWithParallel(thirdQueryArray);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3279,"value":"mapValue = {\n thirdTypeFinalArray.links.map(d => d.value = 1)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3280,"value":"mapRadius = {\n thirdTypeFinalArray.nodes.map(d => d.radius = thirdTypeFinalArray.links.filter(a => a.source === d.ID || a.target === d.ID).length)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"func = md`## Function`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":765,"value":"data_func = md`### Data Function`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"//Read from neo4j according to given query.\nfunction buildDataArrayOriginal(q) {\n const session3 = driver.session();\n const dataArrayOriginal = session3.run(q);\n return dataArrayOriginal;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":739,"value":"//Add apostrophe for string.\nfunction addApostrophe(d) {\n return \"'\" + d + \"'\"\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":653,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"//Combine node and relationship to one array\nfunction buildDataArray(node, link) {\n const dataArray = {nodes: node, links: link}\n return dataArray;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"//Count relationship related to node\nfunction countRelations(obj, array) {\n return array.links.filter(d => obj.ID === d.source || obj.ID === d.target).length\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2718,"value":"//Count relationship related to node\nfunction countRelationsForZip(obj, array) {\n return array.links.filter(d => obj.name === d.ZIPCode).length\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":132,"value":"//Build relationship array with given filter.\nfunction linkfilterer(data) {\n var ids = result_filtered.map(d => d.ID);\n var pre_result = data.links.filter(\n a => ids.includes(a.source) || ids.includes(a.target)\n );\n return data.links.filter(\n a => ids.includes(a.source) || ids.includes(a.target)\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"//Build nodes array with given filter.\nfunction nodefilterer(data) {\n var templist = [];\n var lin2 = linkfilterer(data);\n\n for (const i of lin2) {\n if (!templist.includes(i.source) || !templist.includes(i.target)) {\n templist.push(i.source);\n templist.push(i.target);\n }\n }\n var unique_temp = [];\n templist.forEach(c => {\n if (!unique_temp.includes(c)) {\n unique_temp.push(c);\n }\n });\n return data.nodes.filter(a => templist.includes(a.ID));\n // return templist;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1279,"value":"//Deal with the choice when users select no preference in first type search.\nfunction nullFilter(filter) {\n if (filter === \"No preference\") {\n return \"\";\n } else {\n return filter;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":768,"value":"vis_func = md`### Vis Function`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":783,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":922,"value":"//Build geometry vis (for the location of restaurants).\nfunction geometry(data) {\n const map = vl.markGeoshape({fill: '#ddd', stroke: '#fff', strokeWidth: 1})\n .data(vl.topojson(MA).feature('cb_2015_massachusetts_county_20m'));\n \n var points = null;\n \n points = vl.markCircle({size: 9})\n .data(data)\n .encode(\n vl.longitude().fieldQ('longitude'),\n vl.latitude().fieldQ('latitude'),\n vl.tooltip([\n { field: \"name\", type: \"categorical\" },\n ])\n );\n \n return vl.layer(map, points)\n .project(vl.projection('albersUSA').translate(-7300, 2600).scale(23000))\n .width(300).height(300)\n .config({view: {stroke: null}})\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":924,"value":"geo_file = md` #### Geometry File`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1037,"value":"//The json file for MA map.\nMA = FileAttachment(\"MA-25-massachusetts-counties.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":931,"value":"//import map from google\ngoogle = require('https://maps.googleapis.com/maps/api/js?key=AIzaSyB4JQkUKSUqHQmteJ5xdk4RdrFqHi04hdg').catch(\n () => window.google\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"neo4j_driver = md` ### Neo4j Driver`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":631,"value":"//Neo4j driver builder.\nneo4j = {\n const neo4j = await require('neo4j-driver');\n return neo4j;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":632,"value":"//Neo4j driver to connect to server.\ndriver = neo4j.driver(\n \"neo4j+s://df82a806.databases.neo4j.io:7687\",\n neo4j.auth.basic(\"Reader\", \"Reader\")\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"d3_js_set = md` ### D3.js Set`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":194,"value":"//Decide the radius of node in network vis.\nradius = d3\n .scaleLinear()\n .domain(d3.extent(data.nodes, d => d.radius))\n .range([4, 20])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":209,"value":"//Decide the x coordinate of node in network vis.\nx = d3\n .scalePoint()\n .domain(d3.group(data.nodes, d => d.Label).keys())\n .range([-width / 2 + 50, width / 2 - 50])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":210,"value":"//Decide the y coordinate of node in network vis.\ny = d3\n .scalePoint()\n .domain(d3.group(data.nodes, d => d.Label).keys())\n .range([-height / 2 + 50, height / 2 - 50])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"//The height of vis.\nheight = 680","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"//Decide the color of node in network vis.\ncolor = {\n const scale = d3.scaleOrdinal(d3.schemeCategory10);\n const res = d => scale(d.Label)\n res.scale = scale;\n return res;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":200,"value":"//Function to help the node move in network vis.\nreleasenode = simulation =>\n function(d) {\n d.fx = null;\n d.fy = null;\n simulation.alphaTarget(0.3).restart();\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":201,"value":"//Function to help the node move in network vis.\ndrag = simulation => {\n function dragstarted(event, d) {\n if (!event.active) simulation.alphaTarget(0.3).restart();\n d.fx = d.x;\n d.fy = d.y;\n }\n\n function dragged(event, d) {\n d.fx = event.x;\n d.fy = event.y;\n }\n\n function dragended(event, d) {\n if (!event.active) simulation.alphaTarget(0);\n\n d.fixed = false;\n simulation.resume();\n // d.fx = null;\n // d.fy = null;\n }\n\n return d3\n .drag()\n .on(\"start\", dragstarted)\n .on(\"drag\", dragged)\n .on(\"end\", dragended);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"import_file = md` ### Import`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":170,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"import { Search, Table, Checkbox, Select } from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"import { Button } from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"import {swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":776,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1540,"value":"import {text} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W54","stdlib":"1"}} 24:null