Spatial Analysis of Emigrant Loans in NYC

 Kernel Densities and Mean Centers of Emigrant Loans before/after 1900 in New York City





In all honesty, I have more questions about the data set now than I did before the project, but I guess that is what history is about, so maybe I have gone in the right direction. I would first like to note that I also generated and looked at the Near Table but decided not to add much about it here since I there is already a lot from this series of maps I have here now.

I originally tried to think about some grand way to designate time differences, but I decided it would be most valuable to choose a time bin that everyone could be familiar with. So, I chose the turn of the century to show the differences in loan densities for emigrant individuals in New York City. I first looked at the overall kernel density for loans, which we also looked at in class. This showed the most expensive and concentrated loans in the East Village area. Notice the mean centers for all three of the time delineations, and how all three fall just below the area that is most concentrated for the density.

Next, I looked at the loan density for before 1900 and after 1900. Here, I paid close attention to making sure I imported the symbology from the other to make sure they were being analyzed similarly. I used the default symbology for the density for all of the years since we were familiar with it, and also because the imported symbology did not have enough classes to show differentiation in the data.

After 1900, the loan density becomes more heavily weighted at more expensive loans. It also stretches up closer to the East Village. This can be seen reflected in the mean centers, the mean center for after 1900 is, in fact, further North than the mean center for before 1900.

I can think of a lot of questions I have, and that could be answered, from this data set. For instance, is the loan difference being higher after 1900 due to inflation? What other historical factors happen around the turn of the century that could impact this data set? How can this data set apply to the financial state of emigrants and what can it reveal in the loan situations of those same areas of NYC today? There is so much to be exposed in this data set, which can be interpreted in so many different historical narratives.

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