Computer Algorithm Can Draw Congressional District Lines Without BiasSeptember 22, 2017
The issue of congressional redistricting has been the source of much debate. In fact, earlier this year, a federal court ruled that three Texas Congressional Districts were illegally drawn. In the official opinion, the court stated that “the political motive does not excuse or negate that use of race; rather, the use of race is ultimately problematic for precisely that reason — because of their political motive, they intentionally drew a district based on race in a location where such use of race was not justified by a compelling state interest.” Politicians have surreptitiously found ways to draw district maps in a way that hurts minorities, despite it being ruled as illegal by the Supreme Court. To combat the overwhelming bias that has been shown by congressional leaders, a team of University of Illinois computer scientists and engineers have crafted a computer algorithm that could make it much simpler to draw congressional district lines. These congressional district maps are redrawn based on data from the National Census Bureau, and this redrawing is conducted by the state legislature. This “can lead to oddly shaped and dispersed districts that favor one political agenda over another, according to Sheldon H. Jacobson, a computer scientist and professor at University of Illinois. The research was performed with Douglas M. King, who is a professor of industrial and enterprise systems engineering. The project is a geographically based and data-heavy algorithm that would allow an individual to input the specific parameters for redrawing congressional district maps. The program would then draw the districts computationally while automatically inputting other requirements, like requiring that each district be in a contiguous area of land. Jacobson stated that “as data scientists who study and analyze algorithms, we bring a nonpartisan approach to this problem.” He then went on to say that “It’s just data. It happens to have significant political ramifications, but it is still just data.” The study, which was written about in the Computational Optimization and Applications journal, utilizes public data, such as from the U.S. Census Bureau. King stated that “one thing we are very keen on is making sure that we are using publicly available data so that everything we are doing is very transparent, with the same data that would be available to other districting stakeholders.” Jacobson supplemented this by stating that “[the researchers] are not political scientists, [they] are data scientists, and [they] view the data, the census blocks, as pixels,” and that “[they] have to group these pixels in a manner such that you define districts, each of which will meet a particular property, such as roughly equivalent populations.” The impetus for this project is representing a balance of political affiliation among the population. “Ultimately, what we are offering is a process to explore redistricting options in an efficient, computational manner,” explained King.
Jacobson eloquently stated that “any legislator who is truly committed to their citizens must consider algorithmic redistricting as an available, and viable, option during the next redistricting period that will take place after the U.S. census in 2020.”
The study was bolstered by The National Science Foundation and the Air Force Office of Scientific Research. Whether this innovative and objective method for redrawing district lines will be adopted by Congress remains to be seen.