Assignment 3: Creating Interactive Visualization Software

Due: Monday Oct 25, 2021 by 11:30am

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In this assignment, you will explore the issues involved in implementing interactive visualization software. Specifically we would like you to implement the interactive technique of dynamic queries – first explored in the HomeFinder application. However, instead of housing prices you will build an interactive visualizations for a dataset containing information about South Bay Restaurant Inspection Scores. Note that this data is from around 2013 and earlier, but it does contain scores for various eating establishments on the Stanford campus.


The data includes lat/lon locations for each restaurant along with other descriptive fields as noted below. There are 2899 restaurants all together in the data set. Your goal is to show these restaurant data points on a map of Stanford/Palo Alto and provide the following dynamic query functionality:

  • You must allow users to specify two draggable locations A (e.g. point of interest 1) and B (e.g. point of interest 2), as well as an adjustable radius for each one and filter the restaurants to those that lie within the intersection of the circles around A and B. (Other restaurants should be grayed out.) The locations of the circles should be draggable, and the circles can already be on the page upon loading. You may use either sliders or a draggable edge on the circles to adjust their radii. If you want to be fancy you may try letting users adjust the radii using either sliders or a draggable edge, but be careful, implementing both so that they update properly (i.e. the sliders update when you drag the edge and vice versa) is somewhat tricky.

  • When the user hovers over a restaurant point, the name of the restaurant and its inspection score should be visible, and not visible otherwise.

  • You must provide at least 1 additional filtering controls that allow users to filter out specific aspects of the data (e.g. limit the inspection score to a range of values, limit to restaurants with names starting with a specific letter, limit the location to a certain street name, etc.)

The application should update at interactive rates (0.1s update rate) and part of this assignment is to write the code so that the filters operate quickly.

You can work by yourself or with a partner for this assignment. Groups of three or more are not allowed. Your group must write code for this assignment. You are free to write the code in any programming language/environment you prefer, including JavaScript, C++, Java, etc. In addition you may use any software toolkit to help you build the code. However, we strongly recommend using JavaScript and D3 for this assignment. The teaching staff will provide support for coding the assignment using these tools either in Observable or as a standalone D3 application. We cannot guarantee that we can help you in other coding environments. We expect you to write the code from scratch, but if you use any pre-existing resources (e.g. Stack Overflow, extensively peruse related code on GitHub, etc.) we expect you to list them as part of your submission.

No matter what language/libraries you use we would like you to submit either an Observable notebook or a final executable program that we can execute on our own on either a Mac OS X or a Windows machine. Ideally, you should submit the work as a link to a website (e.g. an Observable notebook or a standalone webpage, such as one hosted through GitHub pages) where we can run your code along with the source code. If this is a problem for you, please talk to us right away.


Your final submission should include:

  • A link to a published version of your Observable notebook or the bundled source code for your application uploaded as file (either a .zip or .tar.gz archive) to Canvas. If your code is designed to be run in a browser you must also provide a link to a live version on the web, Please ensure that the software submitted is in working order. If any special instructions are needed for building or running your software, please provide them.
  • For submissions by groups of two, please also include a breakdown of how the work was split among the group members.
  • We expect you to write the code from scratch, but if you use any pre-existing resources (e.g. Stack Overflow, extensively peruse related code on GitHub, etc.) we expect you to list them as part of your submission.
  • Finally, please include a commentary on the development process, including answers to the following questions: Roughly how much time did you spend developing your application? What aspects took the most time?

Upload a link to a published version of your Observable notebook with an embedded write-up, or upload the bundled code and your write-up as a PDF, to Canvas. If you’re working in a group of two, please just have one person submit to Canvas, but make sure to include both group members’ names in your write up.

Your assignment must be posted to Canvas by Monday Oct 25, 2021 by 11:30am.

Upload your work to Canvas.

Restaurant Scores Data

We have set up the South Bay Restaurant Inspection Scores dataset to contain 2899 rows with the following fields:

  • Name: A string describing the name of the restaurant.
  • Grade: A string describing the inspection grade (PASS, FAIL Not Available).
  • Score: An integer representing the inspection score when available.
  • Latitude: A float describing latitude of the restaurant.
  • Longitude: A float describing longitude of the restaurant.
  • Address: A string describing the street and city of the restaurant.

This data is a subset of a more complete SCC DineOut dataset that you can query here to get inspection scores for restaurants throughout Santa Clara County. We have scraped and filtered the data from this site to cover the restaurants from Stanford and Palo Alto south through Sunnyvale.



You can use this SVG map of the region covered in the dataset as the base for your visualization. (You are also welcome to use a different strategy for mapping, but make sure we can see geographic detail comparable to the provided map.)

If you use the provided map, here’s a sample D3 snippet to set it up. You’ll need to adjust this depending on how you’re designing your visualization tool.

// Assumes you've included D3 version 7 somewhere above:
// e.g. <script src=""></script>

// Set up size
var mapWidth = 1000;
var mapHeight = 750;

// Set up projection that the map is using
var scale = 190000;
var projection = d3.geoMercator()
  .center([-122.061578, 37.385532]) 
  .translate([mapWidth / 2, mapHeight / 2]);

// This is the mapping between <longitude, latitude> position to <x, y> pixel position on the map
// projection is a function and it has an inverse:
// projection([lon, lat]) returns [x, y]
// projection.invert([x, y]) returns [lon, lat]

// Add an SVG element to the DOM
var svg ='body').append('svg')
  .attr('width', mapWidth)
  .attr('height', mapHeight);

// Add SVG map at correct size, assuming map is saved in a subdirectory called `data`
  .attr('width', mapWidth)
  .attr('height', mapHeight)
  .attr('xlink:href', 'data/map.svg');

Once you’ve run this code, projection is an instance of a D3 projection. If you pass it the longitude and latitude of a restaurant, it will return an array [x, y], which is the pixel on your SVG backdrop that matches to the longitude/latitude pair. E.g. if you had only one restaurant to draw with longitude business_longitude and latitude business_latitude, you might draw a point for it with something like:

var projectedLocation = projection([business_longitude, business_latitude]);
var circle = svg.append('circle')
  .attr('cx', projectedLocation[0])
  .attr('cy', projectedLocation[1])
  .attr('r', 1);


How to respond to DOM events e.g. clicks?

Similar to jQuery, D3 provides a simple interface to add even listeners: use the on method on any selection. For example, to listen to click events on circles and print out the associated data object:

  .on('click', function(d) { console.log(d); });

Why is my data undefined?

You are most likely trying to use your data before it is ready/loaded. In JavaScript, HTTP requests are handled asynchronously. When you call d3.csv, the browser starts makes an HTTP request to that resource, and it immediately continues to execute the following code:

// In D3 v7, the csv function uses Promises instead of asynchronous callbacks to load data
d3.csv("file.csv").then(function(data) {

// This code is going to run before data is loaded, and you cannot use the data here
console.log('We don't have the data yet.');

// This will print:
// => We don't have the data yet.
// => We have the data now!

How should I be doing my D3 development?

If you submit an Observable notebook link, we will be testing your visualizations by running the cells directly within the notebook itself, using the most recent stable version of Google Chrome.

If you submit bundled code, we’ll be testing your visualizations in the most recent stable version of Google Chrome (unless you come talk to us with a really good reason to do something differently for you), so use Chrome to develop. Chrome’s DevTools can be quite helpful as you work. Chrome also supports many ES6 and beyond features (const and let, arrow functions, async and await, etc.) so you’re welcome to use these if you’re familiar with them, but there’s definitely no need.

You might also need to run a local web server while you’re developing, because Chrome may fail to load data through d3.csv (or other XMLHttp Requests) for security reasons if you don’t. To do this, you can run python -m http.server from the directory where your code lives. (Your command line should give you a localhost link.) If that doesn’t work for you, come talk to us.

Other resources for learning D3 (and other web programming)

  • Review the slides for 10/11 and 10/13 (These have some links to other resources in them too), and other links on the homepage of the website.
  • There are lots of D3 code samples on Observable, a website run by the creator of D3, Mike Bostock. You can definitely take a look at examples here (and on Stack Overflow, etc.) for learning techniques, but please be very transparent by citing any external code snippets that you adapt, or even ones that simply inspire how you do something. We expect your design choices and your implementation to be original.

  • When in doubt, refer to the D3 API documentation. It is dense in places, but very thorough.