AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence

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Allocation of Pre-Kindergarten Seats in New York City
Ravi Shroff, Richard Dunks, Jeongki Lim, Haozhe Wang, Miguel Castro

Last modified: 2014-06-18


We consider the problem of identifying locations in New York City that are currently underserved with respect to access to pre-Kindergarten programs.  We use two public datasets; the spatial distribution of four-year-olds, and the distribution and seating capacities of pre-Kindergarten programs in public schools and community based organizations.  We implement a random allocation algorithm to identify and map underserved locations, then see how these locations change as capacity is added in a random fashion.  Our model incorporates travel distance, and we measure the sensitivity of our results to variations in this parameter.  We provide evidence that as the pre-Kindergarten capacity in our model increases, the effectiveness of this capacity - as measured by the number of unused seats - decreases, to the extent that when the total capacity in the city equals the number of children, almost 20,000 seats remain unused.


education; monte carlo methods; algorithms; optimization; urban data

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