For example, Aberg & Loftsgordan (EB) and Aberg & Loftsgordan (WB) should be merged into Aberg & Loftsgordan.
Doing so, the stop removal feature to implement in #8 could be slightly easier.
Also, the rendered map will not have too many clustered stops which are essentially the same.
Possible workarounds
Utilize the function of finding the stops within a certain range, and create a new stop at the center of these stops.
Better to create a new model and controller for these stops.
Try to plot a map once this is completed.
Utilize primary_street and cross_location at C14 ([13]) and C16 ([15]) to group.
This could be faster because less calculation needed.
Need to do a sanity check to the data to ensure that there are no weird glitches in these fields.
Things to consider
Need to create API and fields for accessing and storing the stops under the merged stops, because the ridership data correspond to these stops, not the merged one.
Ridership data could be summed for future access like approximating the time for heading to the stops.
Note
Some stops were not grouped but essentially the same. For example, E Gorham St. and E Johnson St. is the same, but because they are single-way streets, bus stops/routes were being forced to separate, so they are counted as 2 stops instead of 1.
For example,
Aberg & Loftsgordan (EB)
andAberg & Loftsgordan (WB)
should be merged intoAberg & Loftsgordan
.Doing so, the stop removal feature to implement in #8 could be slightly easier.
Also, the rendered map will not have too many clustered stops which are essentially the same.
Possible workarounds
primary_street
andcross_location
at C14 ([13]
) and C16 ([15]
) to group.Things to consider
Note