Optimization of land use decisions using binary integer programming: The case of Hillsborough County, Florida, USA

• Published in: Journal of environmental management 2019-04, Vol.235, p.240-249
• Main Authors: Türk, Ersin, Zwick, Paul D.
• Format: Article
• Language: English
• Subjects: Decision support systems; GIS; Land use allocation; Optimum; Sustainable urban development
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2019.01.031

The contradiction between urban growth and environmental sustainability is a fundamental question regarding the welfare of human beings and the future of the earth. Human labor transforms the earth into factors of production by consuming its natural resources. The rational way to minimize the negative effects of such transformative intervention is advanced land use planning. Nevertheless, it is not possible to say that traditional modes of land use planning have fulfilled this task. The main reason for this failure is the habit of using traditional decision making tools which, to a significant level, are technically incompatible to deal with such a task. Hopefully, recent improvements in planning support tools have potential to process and evaluate complex geographical data. This paper benefits from these tools and moves one step further by performing a land assignment model, which operates binary integer programming and geographical information systems in a way that it can deal with different land use scenarios. This new methodology, which named as inquired land assignment model (ILAM), is applied for three different land use scenarios at upper level planning scale for a case area – Hillsborough County in Florida, USA. Outputs represent reasonable and consistent land use assignment and allocation results, and the model is potentially applicable to various fields and lower scales of planning practices by simple modifications. •Land use assignment decisions were optimized.•The method developed was used to determine capacity in urban development.•Land use assignment decisions were assessed and quantitatively tested.•Opportunity costs of different land use assignment scenarios were computed.