a3-dbabbs-debricassart

Team Members

1. Philippe Vaillant (vaillant)
2. Dylan Babbs (dbabbs)

Visualizing Seattle Trips

For Assignment 3, we decided to visualize trips throughout the Seattle area from the 2014 Puget Sound Regional Travel Study. The dataset, compiled from household responses throughout King, Kitsap, Pierce, and Snohomish counties, was created during the Spring of 2014 by the Puget Sound Regional Council. The dataset contains 47,919 records of trips compiled from a combination of nearly 6,000 households. We decided to focus on records within the city of Seattle (instead of the entire Puget Sound region) for simplicity. There are 12,398 in this subset of records.

The visualization highlights the different travel paths of residents throughout US Cencus tracts of the city. Each path's weight between different tracts is determined based on the number of records. For example, a popular route such as U-District to Downtown will have a fairly thick line weight. In the second column of the page, a bar graph is present showing the distribution of trip times throughout the day by hour. Both the paths on the map and the bar graph can be filtered by mode of transportation and purpose of trip using checkboxes below the title and description.

As these trips correspond to a couple thousands of households, the visualization does not aim at any generalization. Instead, it provides a crucial exploratory tool for researchers and planners. Travel surveys are standard datasets in cities, but there is no easy way to reveal the results and flag interesting patterns. We believe that our tool could be refined and published so that planners in any city can make more sense from their travel survey dataset.

Running Instructions

Access our visualization at http://cse512-16s.github.io/a3-dbabbs-debricassart or download this repository and run python -m SimpleHTTPServer 8888 and access this from http://localhost:8888/.

Story Board

Changes between Storyboard and the Final Implementation

First storyboard design. All elements remain intact in the final visualization except for the donut chart visualizing the purpose of trip.

Final storyboard design. Donut chart was removed as well as some repositioning of elements. Map was repositioned to take over the entire left half of the frame, while the bar graph was repositioned on the bottom half of the right half of the frame. This repositioning was performed to place a greater emphasis on the main element of the visualization: the map.

Development Process

1. Deciding which dataset to use. We were both interested in visualizing either urban, travel, or transportation data. We considered using Seattle Metro GTFS data, Pronto bike share usage data, and Seattle parking data. We chose to visualize Puget Sound Regional Travel Survey because we were curious about the most popular travel destinations throughout Seattle and due to the large number of records within the dataset. We ended up using the 2014 survey instead of the more recent 2015 survey because the 2014 version had more records in the dataset. (1.5 hours)

2. Creating the storyboard and wireframe. After choosing the dataset, we decided to create a storyboard that answered some of our questions we had about the survey results. What are the most popular trip destinations throughout the city? What hours throughout the day are most people traveling? What are the most popular modes of transportations. What are the most common purposes for transportation: home, work, shopping? We chose to incorporate a map of Seattle visualizing routes between Cencus tracts via mouse hover, a bar graph of the distribution of trips by hour of the day, and filters to explore different modes of transportation and purpose of trip. (1.5 hours)

3. Preparing and transforming the data. In order to visualize the data in D3.js using paths between land tracts, we had to transform our data into the proper form. Using an R script, we aggregated all similar rows of destination tract, origin tract, mode, purpose, and time by count. The R script also removed nearly 80 other unnecessary columns from the dataset. Within the dataset, the purpose and mode columns values were integers which corresponded to string values in a lookup file. We created a separate Java script to change the integer values to their string full names. The reason for using two different files is that we did these scripts at different times. In addition, we had to download the Puget Sound Census tracts shapefile from the US Census Bureau (https://www.census.gov/geo/maps-data/data/tiger-data.html). The Puget Sound census tract shapefile has been clipped with a Seattle city boundary shapefile to get the area we were interested in. We used the shp2json package to convert our shapefile to a topojson file (https://github.com/substack/shp2json). (3 hours)

4. Creating HTML frame and simple visualizations. We chose to code our HTML frame, map tile and trips by hour bar graphs initially before creating our more advanced features (filters and paths). Our first frame was a weak HTML skeleton, but after a few passes, we changed our frame to bootstrap for pre-styled items and a mobile-friendly site in case our visualization consumers were on their mobile device. We chose the HERE Map Tile API overlay within Leaflet.js. Dylan chose HERE Maps over OpenStreetMaps for a personal bias (employer!). We then coded a bar graph with the number of trips per hour. (4 hours)

5. Creating paths between Cencus tracts. We spent a lot of time understanding how to combine Leaflet.js and D3. We used the overlay pane and reset and projectPoint functions to make sure that the basemap and our features from the shapefile would match. We first allowed zooming and dragging, but we realized that our map fits exactly the div and makes dragging and zooming almost irrelevant. Our idea was to visualize the outgoing trips from a tract when you hover on it. To create the path we adapted the code of Mike Bostock for the visualization of the American airports and flights (http://mbostock.github.io/d3/talk/20111116/airports.html). We added a weight property to the links so that we could set the thickness of the links based on the number of trips. There is no legend for the thickness, and adding one would have been our next step, had we had more time. Another difference with the airports visualization is that we use the centroid of the tracts, and therefore draw lines rather than paths since the centroid coordinates are already given in screen coordinates. The advantage of the method of Mike Bostock is that it only relies on CSS properties (hover). The data attached to the links is filtered by mode, purpose and hours. A tooltip has been added to give information about the total of outgoing trips made from a tract. (12 hours)

6. Filtering by purpose and mode. Using d3 events, we assigned actions to checkboxes in order to filter by mode or purpose. The d3 script would call on a method to update the data of the bar chart and paths with the parameters checked by the user. (10 hours)

7. Writing writeup and finalizing project. Yes, this was a step in the project. (2 hours)

Distribution of work: *Philippe: data exploration, data prep, geojson prep, mapping, paths, filtering, information pane/tooltip. *Dylan: data exploration, data prep, HTML front end, mapping tile, bar graph, write up.