Department of Politics, Princeton University, Princeton, NJ, 08544, USA.
Sci Rep. 2024 Sep 27;14(1):22097. doi: 10.1038/s41598-024-71578-x.
Schools in the United States remain heavily segregated by race and income. Previous work demonstrates districts can promote group diversity within their schools with policies like redrawing attendance zones. Yet, the promise of such policies in many areas is limited by the fact that most school segregation occurs between school districts, and not between schools in the same district. I adapt Markov Chain Monte Carlo algorithms from legislative redistricting to redraw school district boundaries that decrease segregation while maintaining desirable criteria like distance to school and using only existing school facilities. Focusing on New Jersey, where the segregation of Black and Hispanic students from White and Asian students is among the worst in the country, I demonstrate that redrawing school districts could reduce more than 40% of existing segregation in the median New Jersey county, compared to less than 5% for redrawing attendance zones alone. Finally, I show how my proposed methodology can be applied to as few as two districts to reduce segregation in proposed consolidations, when small districts are merged into a larger district.
美国的学校仍然严重按照种族和收入进行隔离。先前的研究表明，学区可以通过重新划分招生区域等政策来促进学校内部的群体多样性。然而，在许多地区，这些政策的前景受到限制，因为大多数学校隔离发生在学区之间，而不是同一学区内的学校之间。我从立法重新划分选区中采用马尔可夫链蒙特卡罗算法来重新划分学区边界，以减少隔离，同时保持距离学校和使用现有学校设施等理想标准。以新泽西州为例，该州的黑人和西班牙裔学生与白人和亚裔学生之间的隔离程度是全国最严重的，我证明，与单独重新划分招生区域相比，重新划分学区可以减少中位数新泽西州 40%以上的现有隔离，而单独重新划分招生区域的比例不到 5%。最后，我展示了如何仅使用两个学区就可以减少拟议合并中的隔离，当小学区合并为一个更大的学区时，如何应用我提出的方法。
