Unsupervised on the Streets of New York. Taking a Deeper Look at Gentrifying Census Tracts with Cluster Classification
Born and raised in New York City, I have seen Harlem as a child in the mid to late 90’s go from an overwhelming minority neighborhood to the gentrified haven of brownstones it is today. There have been academic papers done on the changes in neighborhoods and one of the more recent Ellen & Ding (2016) was the benchmark with which I compared my model.
Taking a data science approach, I wanted to ask a very specific question:
The most important feature of this project to understand is that this is unsupervised learning. This means that the target variable was not provided for the model. So instead of predicting an already decided outcome, the model would be taking the data and coming to its own conclusions. In the end, I will compare this to the findings from the academic paper I referenced.
The best data set for this type of work was the U.S. Census Tract Data and through some research, I found a study on diversity done at Brown University (Longitudinal Tract Data Base — LTDB). The study collected and compiled census and American Community Survey (ACS) information from 2000, 2010, and 2012.
The types of data were naturally split into two different data sets. General demographic data was built into the census itself. This included age, family size, race, and ethnicity. The survey data included much more detailed information — immigration status, types of employment, and income. All of these features are included for every census tract in the four boroughs. A combination of these two datasets would be the most productive in terms of identifying gentrification. The project continued in the following way.
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In this post, I use an unsupervised learning approach to compare Houston Artists using the Spotify’s Web API and Tableau. We'll walk through the OSEMN framework for this machine learning example. The acronym, OSEMN, stands for Obtain, Scrub, Explore, Model, and iNterpret.