I was interested to see that a data set had been posted and that a competition had been started to visualize data collected from the Hubway bike system. For the uninitiated, the Hubway is a bike rental system with racks scattered across boston. Users pay with a credit card or have a subscription to use the bikes. When I was working in downtown Boston I would see these bikes all over the place, especially along the Esplanade, and going down Boylston Street.
The data set itself is a set of two Excel files — stations, trips — totaling about 10 MB zipped. While quite simple, the data by itself represents an opportunity to do some interesting analysis based on the lat/long pairs of associated with the start and end points of the bike rental system. The date pairs also represent lend themselves to time-series analysis. With date as a hinge, other data can be incorporated, and in my example I added a comprehensive Date Dim table that extends the data into a time hierarchy (weeks, months, years), and I pulled weather data from noaa.gov to give myself an opportunity to do some basic correlations.
Some of the challenges that I faced in working with this data in MicroStrategy included:
- Modeling the same table (stations) for the start and end points
- Calculating distance from a lat/long pair
- Using a web service to automate the elevation of the stations
- Plotting the lat/long coordinates on a map
I have yet to overcome items 3 & 4, but the first two were interesting problems. The ultimate goal of this exercise is to produce a meaningful visualization, and since MicroStrategy 9.3 was just released, this data set provides an opportunity to test some of the network diagrams, mapping widgets, and Visual Insight capabilities.
For problem #1, the solution in MicroStrategy is to use table aliases. Basically, from a modeling standpoint aliases mean that architects do not need to create views to replicate a table.
The table alias within MicroStrategy tells the SQL generation engine that the same table can be used twice.
To create a table alias, go to the schema → tables folder, and right click on a table that has already been modeled in. Select “Create Table Alias” and a new copy of the table will appear. For my purposes I created 2 stations tables, one that referenced the start, and one for the end. Within the attributes that reference the table, make sure that the mapping is set to manual, otherwise the automatic mapping will try to point to both the old and aliased table.
The resulting SQL for a report that wants to join Start Station and End Station would look something like this:
select a11.end_station_id id,
count(distinct a11.id) WJXBFS1
from trips a11
join stations a12
on (a11.end_station_id = a12.id)
join stations a13
on (a11.start_station_id = a13.id)
group by a11.end_station_id,
By aliasing the stations table twice, the engine is forced to join against itself, but the overhead from the database side is minimal. From this we can start to glean some basic information from the data. The South Station / North Station (TD Garden) ride is the most commonly used, and this is explained by the fact that there is no good way to get to South Station from North Station or vice versa! Taking a bike probably constitutes a ~ seven minute ride. I would speculate that these rides happen during rush hour, but I’ll table that speculation for future analysis.
The next challenge was to calculate distances between stations. I found a good site that showed how to do this in Excel, and fortunately transposing Excel syntax into MicroStrategy is straightforward since the functions are named exactly the same. Here is what the calculation looks like in Excel:
and here is what it looks like in MicroStrategy:
With this calculation in place, the previous report could be enhanced to include distance, and then by combining the distance with the trips you could derive a total mileage value.
The downside of this is that unless the start and end stations are different, then the total distance will be 0, as is the case with the Boston Public Library bike rack.
So, this is how I started the data analysis, and I have continued to build out other attributes to fully form the data and make it more interesting. The next steps are to start to visualize the data. I started to play with this, and with the availability of Cloud Personal, I threw up some data slices and created a first pass of a visualization.
In the coming weeks there should start to be some submissions coming online. I have been more focused on pulling outside data together to add flavor and color to the raw data set, and a colleague suggested I analyze other events like Red Sox games or holidays into my analysis. Any other suggestions?