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Part 1 New York Taxi Trip Date, search for histories behind mistakes
Cleansing information is a heavy but important job for the real life science team. Here is a talk about my privacy practices for NYC Taxi Trip datas. I have seen a lot of contributions/blogs from researchers who have complained that much of their research has been spent on cleansing them.
Based on personal experiences in several learning/volunteering programs, this stage requires a lot of patience and care for detail. However, I often had the feeling that the unusual or incorrect information was actually more interesting. Whenever I have screened some erroneous information, I can better understand the overall image and assess the information value of the record.
A good example is the NYC Taxi Trip Date. Likewise, I would like to examine my own area of all domestic information in order to get more insight from the information. In addition it turned out that one does not need even a basis card level for the card of the taxi collecting points with sufficient quantity of dates.
Pick-up points themselves influenced all routes and paths! First, I have the two datafiles, travel dates and fares prepare and united. And then I found a lot of apparent mistakes in the information. Obviously some column have incorrect numbers, e.g. zero numbers of people. A further interesting phenomena is the super-short trip: A possible statement that I can think of is that maybe some people get into a taxi and then get off immediately so that the amount of travel is close to zero and they have payed the minimum price of $2.5.
Lots of series have zero for recording or output or almost the same place for recording and output. How is then the longer driving route possible? Particularly when most pick-up and drop-off co-ordinates are either zero or the same place. If the taxi is caught in transit so that there is no relocation and the route is captured by the meter, the driving takes less than 10 seconds and still cannot be accounted for.
And there are also some very cheap fare for very shorter journeys. The majority of them have pick-up and drop-off co-ordinates at zero or at the same positions. There are no good reasons for this and I don't want to make too many hypotheses because I'm not really used to NYC taxi rides.
My guesswork is that a NYC locale can probably give some insight into them, and we can validate them with dates. It is possible to further check the travel times and distances by verifying the mean travel speeds. Approaching zero either in terms of timing or in terms of range may lead to an excessively large deviation in the computed forward velocity. Given the possible entry errors in terms of timing and range, we can round up the clock in seconds to the nearest minute before we calculate the mileage.
First, review the recordings that have a very brief period of travel and a non-trivial route: When the pickup and drop-off co-ordinates are not empty, we can compute the great circles between them. Make sure the real driving distance is the same or greater than this. When both the great circling distances and the driving distances are not trivial, it is more likely that the less than 10 seconds driving times are inaccurate.
Something doesn't have to be right if the great circling range is much greater than the driving route. Notice that the information here is restricted to the subsets of short-haul travel times, but this kind of failure can occur in all datasets. It was either that the meter had some mistakes when it reported the route, or that the co-ordinates of the satellites were incorrect.
Since the whole travel period is very brief, I think that it is rather the issue with global positioning system co-ordinates. In addition, measuring times and distances should be much easier and more dependable than measuring your position with your satellite. By agreeing with the NYC limit, we can further verify the precision of the co-ordinates.
A more elaborate way is to use a shape file, but it will be much more slow when verifying dots. As the taxi ride can actually have at least one end outside the New York neighborhood, I don't think we have to be too harsh at the border of the New York neighborhood. When I checked the journeys taken from the JFK airfield, I found another check of the coordinates of the satellites.
Also I found some interesting datasets while reviewing the receipts of taxi drivers. And who are these super-man taxi drivers who have deserved much more? Further we can examine whether there is a temporal intersection between drop-off and next pick-up, or whether the pick-up was too far away from the last drop-off but I think there is no need to do that before I have a better theorem.
I haven't dug too much yet in this case because I'm not really acquainted with the New York taxi, but there are already many interesting phenomena. From these mistakes we can know a great deal about the qualities of certain field types. My other projekt, Datenreinigung, is not just about finding interesting histories.
In fact, it did help a great deal with the processing of your personal information.