Riding With Strangers

Team Members: Aaron Oh, Andrew Hoang, Eric Pan, Saba Noorassa

Instructions: Select a starting point to see how long it takes to travel to other points on the map from that point. Select a destination point to see the in depth data on travel between the two points. You can also do address lookups using the form below the map. Dark grey regions are not covered by the dataset.

Source: None Selected
Dest: None Selected
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Design Rationale

We chose our interactions based off the limitations of our dataset. Since our dataset focused on measurements between two points, our visual encodings and interaction techniques also focused primarily on supplementing that fact in our data. We tried to make interacting with our visualization as intuitive as possible, so clicking on a point on a map makes it your source destination, while clicking a second time on the map lets you choose your target destination, which we thought users would find most intuitive. Continuing to click on the map changes the target destination, while clicking on the source destination resets the map, which we found the most intuitive out of the options we were considering (having a clear button, using double click to select source, having a button to switch source and destination). We enabled basic map interactions such as zooming in, zooming out, and panning, with the standard interactions that we assumed people would try (scroll to zoom, and mouse drag to pan). We also have a way to search between specific addresses, although they also map to our region data. That interface mimics Google Maps search features, with source and destination fields.

After selecting a source and target region, we display the names of the regions at the top for the user to see. They can also select a quarter that they are interested in to see data pertaining solely to that quarter. The information we display comes in the form of a bar chart and a line chart, depicting average travel time by day and by hour of day. We chose a line graph to capture the temporal nature of our day for the hour of day, but we recognize that some users may be led to believe that there is a relationship that doesn't exist. However, most users will be looking for general estimations, so we felt that the decision to use a line graph helped highlight the differences between travel times by hour best. After selecting the source destination, we also visualize the average time it takes to get to any other region on the map using a blue green color map like the one used in Uber Movement. We chose bright to dark to depict distance because of how people tend to focus on brighter regions first.
We wanted to show travel time based off specific hours for a day of the week or for holidays, but were unable to due to the nature of our dataset.

Development Process

We had a fairly structured development process, complete with a Trello board. After doing preliminary dataset analysis as a group (which took about 3 hours), we created tasks and assigned them to one another (1 hour). Some of us brainstormed ideas on visualization and data aggregation (5 hours), while others focused on visualizing a map of Seattle and creating regions based off a Geo-Map in D3 so that we could detect and display our visualizations appropriately. We allocated three team members to visualize the map, which took a surprising amount of time (about 5 hours). The other member implemented functions for calling the Google API for address to longitude latitude coordinates before mapping that into one of the regions on the map (5 hours). We then had a member work on creating interactions with the map (pan, zoom) while another worked on selecting and deselecting regions of the map. In the meantime, another member worked on creating bar and line charts given a start and end destination, while the final member worked on preprocessing the data and created json objects for everyone else to work with. In total, each of these tasks took about 3 hours. We also allocated a member to creating tooltips, but eventually gave up on that venture because of the deadline (3 hours). Afterwards, we had one member working on the writeup while another member worked on creating the time to travel color map, and another focused on fixing bugs in our visualization, with the final person continuing to work on creating graphs (6 hours). Overall, I would estimate we spent about 80 people hours on this project.
Deliverables:

CSE 442 Data Visualization - University of Washington