These are my on-going course projects at NYU. Though the related details are not introduced at this time, you may find the code on handling these data sets useful to quickly start your own project.
Citibike trip history seasonal pattern
Citibike trip history data contains a wide range of information. We will extract and aggregated them in this example. Code is in Python 3.
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| Fig. How ridership splited among subscribers and customers |
Geo-distribution of NYC real estate sales records
NYC Department of Finance provides comprehensive data sets on real estate sales. They are extremely helpful for us to understand the housing market of this world financial center.
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| Fig. Real estate sale records of Brooklyn, NY, from 10/01/2018 to 09/30/2019 |
Statistics of MTA ridership
MTA provides data based on each turnstile. Proper handle of this data can unveil information on the aggregated demand and temperal dynamics of subway usage.
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| Fig. Ranking of entries - Stations on A line |
I also recorded some of my past projects at https://zuzhaoye.weebly.com, when I was a mechanical engineer.