Data



Scraping for Eviction Decisions





During contextual inquiry, the question of legal evictions via a full legal process was brought to the table as a potential dataset to study. We quickly discovered that the form that the data came in was largely text based with little structure data available. To overcome this, the team wrote a Python script to scrape the database for keywords. The documentation is available here.


This graphs below were generated by scraping SAFLII's database for the term "Prevent of Illegal Evictions," which points to all cases that were decided consideration the PIE Act. The data shows that number of eviction decisions largely corresponds with total court case decisions, so there is no emphasis on hearing evictions over other cases. However, as shown in Figures 2 and 3, there is a spike in 2015-2018 that represents a deviation from the norm, perhaps suggesting evictions are seeing an uptick in legal attention. One could also argue that this is due to a rise of evictions that are contestably illegal as well. Other interesting trends: the graphs suggest that overall, eviction decisions have been on the rise over the course of the past twenty or so years. There also is an enormous spike between 2010 and 2012. It is unclear whether this represents the complete dataset as it impossible to know whether all of the court decisions are digitized and made available online.


The findings from this correspond with Heatmaps in the 'Maps' section of the site.



Figure 1





Figure 2.





Figure 3.





Figure 4.