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A spatial decision support system prototype for housing mobility program planning

Author

Listed:
  • Michael P. Johnson

    (Assistant Professor of Management Science and Urban Affairs, H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, PA, USA (e-mail: johnson2@andrew.cmu.edu))

Abstract

. This paper presents a GIS-based decision support system prototype intended for use by public housing authority (PHA) administrators and planners designing policy for housing mobility programs. Housing mobility programs enable low-income families, many of whom live in government-operated public housing, to move to more desirable private-market rentals via rent subsidies. Unfortunately, housing authority planners have limited ability to visualize alternative relocation schemes of cohorts of low-income families or the impacts associated with these relocation policies. Thus, they are often not able to give highest-quality advice to clients regarding places to search for private-market rental housing. Housing Location Planner assists PHA planners in three ways: it analyzes spatial, demographic and housing market characteristics of the study area, selects certain portions of the study area for input to an optimization model which generates alternative family allocations, and displays optimization model results in a way that links decision variable values and objective function values. Housing Location Planner is seen as a first step in the development of even more sophisticated multi-stakeholder spatial decision support systems for subsidized housing planning in which one or more alternative allocations of families across a study area is chosen as a basis for policy initiatives.

Suggested Citation

  • Michael P. Johnson, 2001. "A spatial decision support system prototype for housing mobility program planning," Journal of Geographical Systems, Springer, vol. 3(1), pages 49-67, May.
    • Handle: RePEc:kap:jgeosy:v:3:y:2001:i:1:d:10.1007_pl00011467
    DOI: 10.1007/PL00011467
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    Cited by:

    1. Rey, David & Almi’ani, Khaled & Nair, Divya J., 2018. "Exact and heuristic algorithms for finding envy-free allocations in food rescue pickup and delivery logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 112(C), pages 19-46.
    2. Mahmoudi, Monirehalsadat & Shirzad, Khadijeh & Verter, Vedat, 2022. "Decision support models for managing food aid supply chains: A systematic literature review," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    3. Johnson, Michael P., 2001. "Tenant-based subsidized housing location planning under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 35(3), pages 149-173, September.
    4. Nair, D.J. & Grzybowska, H. & Fu, Y. & Dixit, V.V., 2018. "Scheduling and routing models for food rescue and delivery operations," Socio-Economic Planning Sciences, Elsevier, vol. 63(C), pages 18-32.
    5. Kalcsics, Jörg & Nickel, Stefan & Pozo, Miguel A. & Puerto, Justo & Rodríguez-Chía, Antonio M., 2014. "The multicriteria p-facility median location problem on networks," European Journal of Operational Research, Elsevier, vol. 235(3), pages 484-493.
    6. Lin, Meiyan & Lin, Shaodan & Ma, Lijun & Zhang, Lianmin, 2022. "The value of the Physical Internet on the meals-on-wheels delivery system," International Journal of Production Economics, Elsevier, vol. 248(C).

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