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Browsing by Author "Biswas, Subhodip"

Now showing 1 - 4 of 4
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    Memetic algorithms for Spatial Partitioning problems
    Biswas, Subhodip; Chen, Fanglan; Chen, Zhiqian; Lu, Chang-Tien; Ramakrishnan, Naren (ACM)
    Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially-aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm called SPATIAL and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL is helpful in the real-life planning process, its applicability to different scenarios, and motivate future research directions.
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    Redistrict: Designing a Self-Serve Interactive Boundary Optimization System
    Sistrunk, Andreea; Self, Nathan; Biswas, Subhodip; Luther, Kurt; Verdezoto, Nervo; Ramakrishnan, Naren (ACM, 2023-07-10)
    The assignment of parcels of land afects many communal activities, from voting to public school assignments. This process creates unease and often has a strong impact on communities. We propose Redistrict, an interactive web-based system designed to support redistricting deliberations for public school zoning. Redistrict helps community members explore and experiment with the possible consequences of various zoning scenarios. This point-and-click digital discovery activity allows the user to understand long-term implications of proposed zonings and to provide feedback in an easy, intuitive way. By providing the opportunity for more people, individually or collectively, to look at the problem from diferent points of view, Redistrict promotes transparency, shared understanding, and cooperation. We designed Restrict to serve as a common information space to help cultivate trust and enable communities to grow stronger, smarter, and more resilient.
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    Redistrict: Online Public Deliberation Support that Connects and Rebuilds Inclusive Communities
    Sistrunk, Andreea; Self, Nathan; Biswas, Subhodip; Luther, Kurt; Diaz Verdezoto, Nervo; Ramakrishnan, Naren (ACM, 2024-04-23)
    Public deliberations are often a staple ingredient in community decision-making. However, traditional, time-constrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for public school zoning. Seeking alternative ways of support, we evaluated the potential introduction of an online platform that combines multiple streams of data, visualizes school attendance boundaries, and enables the manipulation of representations of land parcels. To capture multiple stakeholders’ values about the potential to enhance public engagement in school rezoning decision-making through an online platform, we conducted interviews with 12 participants with previous experiences in traditional, in-person deliberations. Insights from the interviews highlight the several roles an online platform could take, especially as it provides alternative means of participation (online, synchronous, and asynchronous). Additionally, we discuss the potential for technology to increase the visibility and participation of multiple community actors in public deliberations and present implications for the design of future tools to support public decision-making.
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    Spatial Optimization Techniques for School Redistricting
    Biswas, Subhodip (Virginia Tech, 2022-06-03)
    In countries like the US, public school systems function through school districts, which are geographical areas where schools share the same administrative structure and are often coterminous with the boundary of a city or a county. School districts play an important role in the functioning of society. In a well-run school district with safe and well-functioning schools, graduating enough students can enhance the quality of life in its area. Conversely, a poorly run district may cause growth in the area to be far less than surrounding areas, or even a decline in population over time. To promote the efficient functioning of the school district, the boundaries of public schools are redrawn from time to time by the school board/planning officials. In the majority of the cases, this process of redrawing the school boundaries, also called school redistricting or school boundary formation, is done manually by the planners and involves hand-drawn maps. Given the rapid advancements in GIS made in the last decade and the availability of high-quality geospatial data, we opine that an objective treatment of the school redistricting problem by a data-driven model can assist the school board/ decision-makers by providing them with automated plans. These automated plans may serve as possible suggestions to the planners, who can adapt them to prepare their own plans in the way they see fit based on their subjective knowledge and expertise. In this dissertation, we propose algorithmic techniques for solving the problem of (school) redistricting, which is an NP-hard problem. We primarily investigate optimization-based algorithms for solving the problem. Our approaches include (i) clustering, (ii) local search, and (iii) memetic algorithms. We also propose ways of solving the problem using exact methods and fair redistricting techniques based on ethical considerations. The techniques developed here are generic enough to be applied to other redistricting problems with some degree of modification in the objective function and constraint-handling techniques. The source code and corresponding datasets are available at https://github.com/subhodipbiswas/schoolredistricting.