Collecting big data the slow way

One of the interesting side effects of the era of big data is finding out how much information is not actually automatically collected (or is at least not available to the general public or researchers without paying money). A quick example from the work of sociologist Matthew Desmond:

The new data, assembled from about 83 million court records going back to 2000, suggest that the most pervasive problems aren’t necessarily in the most expensive regions. Evictions are accumulating across Michigan and Indiana. And several factors build on one another in Richmond: It’s in the Southeast, where the poverty rates are high and the minimum wage is low; it’s in Virginia, which lacks some tenant rights available in other states; and it’s a city where many poor African-Americans live in low-quality housing with limited means of escaping it.

According to the Eviction Lab, here is how they collected the data:

First, we requested a bulk report of cases directly from courts. These reports included all recorded information related to eviction-related cases. Second, we conducted automated record collection from online portals, via web scraping and text parsing protocols. Third, we partnered with companies that carry out manual collection of records, going directly into the courts and extracting the relevant case information by hand.

In other words, it took a lot of work to put together such a database: various courts, websites, and companies had different pieces of information but a researcher to access all of that data and put them together.

Without a researcher or a company or government body explicitly starting to record or collect certain information, a big dataset on that particular topic will not happen. Someone or some institution, typically with resources at its disposal, needs to set a process into motion. And simply having the data is not enough; it needs to be cleaned up so it all works with the other pieces. Again, from the Eviction Lab:

To create the best estimates, all data we obtained underwent a rigorous cleaning protocol. This included formatting the data so that each observation represented a household; cleaning and standardizing the names and addresses; and dropping duplicate cases. The details of this process can be found in the Methodology Report (PDF).

This all can lead to a fascinating dataset of over 83 million records on an important topic.

We are probably still a ways off from a scenario where this information would automatically become part of a dataset. This data had a definite start and required much work. There are many other areas of social life that require similar efforts before researchers and the public have big data to examine and learn from.

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