Part 1.
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Interacting with this data in the developer’s corner was a mix of good/bad, easy/hard. Following the step by step from the assignment sheet made it rather easy, but without those instructions I would have been rather lost. I imagine the information needed to access and make sense of this data was relatively easy to find, but who knows how much trial and error went into creating these instructions. Finding the appropriate strings within the data/code was also easy, but again, without the instructions it would have been rather difficult to sift through the code to find what was needed (the URI string). Like manipulated/interpreting any code like this, it takes some practice and intuition as to what you are looking for and what various strings might be.
Part 2.
Part 3.
The path, distance, and timestamp elements all track different things but contribute to the final product/dataset. Distance is simply the overall distance of the collect path (I haven’t crunched the numbers to try to figure out what the units are). Timestamp is present with the distance data as well as the path data, and it would seem like it is used as a common denominator or sorts, like an index number that both the distance and path elements can use in order for the two elements to be in synch. Path is a set of decimal degrees (and altitude) that I would think is used to calculate the distance.
Part 4.
The data collection is quite simple. It involved downloading an app that was pretty easy to use and logging some walks/commutes. I have used a few other apps like this (Map My Run and Strava specifically) to track my runs and bike rides, so this type of app and platform was rather familiar to me. Mapping the data was a little more involved, but as stated before, with step by step instructions it was pretty easy. It involved accessing the raw data through an API, and then copying and pasting that data into an open source platform that is able to read it and map it. The platform allows for a lot of editing and customization but is also a bit tedious to use.
Part 5.
The GPS sensor recorded latitude and longitude. This data was then integrated with other data (such as the time from the system clock) and also used to calculate new data (distance). This trip data has many implications and potential uses for addressing transportation issues. Knowing how people move around is vital when it comes to expanding or improving infrastructure. This data could be used to find popular routes that people use between various locations and might lead to adding lanes, streamlining traffic control devices, or eliminating roads all together. As with any data like this there are two overarching issues: getting everyday people to contribute the data, and having tools and algorithms in place to sort through and pinpoint the most useful data for a given purpose. I’ve always been amazed by how seemingly “stupid” many traffic lights are, and while this data wouldn’t be the go to data to increase traffic light efficiency, there are many areas in transportation that could be dramatically improved through smarter and increased use of censors (which is why this class exists!).
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