You can view a more interactive version of the map with all of the layers here. The full accompanying essay can be found here.
Disclaimer: This map was not made by the Lehigh and Northampton Transportation Authority (LANta) and is not affiliated with them in any way. The map was originally created for a class project at Lehigh University.
By looking at the real-time LANta bus tracker, I used the line feature on ArcGIS.com to trace the routes as accurately as possible into my map. Each route had a different color on the LANta map, but I decided to choose my own colors to differentiate my map from theirs. The colors I used were blue (Route #101), green (Route #102), yellow (Route #103), red (Route #105), pink (Route #108), purple (Route #215), sea foam green (Route #217) and orange (Route #220). I tried to pick colors that were as different as possible so that they could be seen easily on the map and didn’t blend together. The display was a little cluttered, but I found it important to keep all the different colors anyway to show the high volume of routes that go through that area. Users of the map can click on a route to see its number, but there is also a legend on the menu of the web app, which is linked above.
After I laid out all the routes, I went back and put in all the bus stops for each individual route. Based on the sections of routes that I used, there were a total of 53 stops in South Bethlehem and 249 stops in North Bethlehem. In general, North Bethlehem is much larger than South Bethlehem, so this disparity in stops is expected. In the whole Lehigh Valley, there are thousands of stops that the LANta buses make. My map is by no means a perfect representation of every LANta stop in each region of Bethlehem, but instead just the stops that are on the routes that are primarily in Bethlehem.
Uncertainty
The areas of uncertainty in this digital project are also important to point out to remain transparent about possible shortcomings in the visualization. LANta has 28 routes in all and goes through other major areas such as Allentown and Easton, but I have decided to just focus on eight routes. I also didn’t use the exact latitude and longitude coordinates for each bus stop. Instead, I just made an educated guess as to where they were by comparing it to the master map that LANta provides. Providing the exact coordinates of each stop wasn’t of utmost importance here because the project deals more with making sure each point is mapped and that they were at least in the correct intersection.
All data has inherent biases, whether purposeful or not, and that can affect how the final data is created and displayed. Of the three base layers that I used in this project, all of the data was gathered and submitted by Esri. This is different from census data, which may or may not be more accurate. However, when I compared the Esri and census data, I found they were very similar, allowing for a reasonable level of interchangeability between the two sources. One reason why I chose the Esri data was because it was more recent. Their data was from 2012 while the census data was from 2010. But, it’s important to realize that both sets of data are outdated by a few years. The Esri data, while most updated, is still three years old, which should be taken into context when viewing this project and looking at the provided data. Either way, though, all of this data is ultimately gathered, recorded, digitized, coded and shared by people, meaning that neither set will be 100 percent fail proof. It would be nearly impossible to gather data that was 100 percent accurate, even for the census. So, while this isn’t projected data, it should be understood that it by no means is going to be fully accurate to the exact demographics of Bethlehem. My map didn’t take into account other factors such as ethnicity, Bethlehem’s cost of living and many other relevant elements of demographic data. Still, Esri’s demographic data is useful because it isolates one specific variable—race—in order to make the visualization more clear. For many of the sub-layers that I use, I include the census population as well as the Esri population in the legend to be transparent to the viewer about the differences in each estimate of the total population.
