To Build or Not To Build: Determining a Quantitative Metric for Land Planning and Allocation

Abstract

Land planning is crucial to ensure that urban development occurs with consideration to the economic, social, and environmental interests of a community. Many conflicting factors must often be considered to adhere to optimal land planning. In this paper, our team makes a quantitative decision metric that can analyze these factors and determine the “best” choice from a given set of development options and the allocation of those choices. First, linear programming is used to determine two “best” development options: one that maximizes both economic and social factors and one that minimizes negative environmental factors while maximizing social. The maxima and minima from linear programming are then applied to the Technique for Order of Preference by Similarity to the Ideal Solution to obtain a third real-world “overall best” option that balances economic and environmental factors with a desired weighting. A genetic algorithm is then used to determine the optimal positioning of the three established “bests” by analyzing opportunity costs based on an environmental degradation penalty index. Finally, the Cobb-Douglas Function is used to conduct a short- and long-term analysis of each result’s profit by solving differential equations about inflation. This model is then applied to the parcel of land in Victory, NY, using data obtained from research. The ideal option and positioning are found to be 267 acres of a sports complex in the northern half of the land, 129 acres of regenerative farm directly west of the sports complex, 344 acres of a solar array in the southernmost region of the land, and 1 acre of agritourism center on the eastern side of the land. Conducting a sensitivity analysis on our model reveals that the linear programming results are most affected by the area and societal benefit restrictions but that the TOPSIS results remain relatively stable regardless of the changing parameters. Our model is adjusted to account for Micron Technology, Inc. building a nearby fabrication facility. As this facility brings more jobs and thus more people, the profit of facilities that involve tourism will increase. However, nature-based facilities will suffer detriment due to pollution caused by the facility. With these adjustments, the model is re-run, and the results are compared to the previous results. In this scenario, there would be a greater area of the solar array and agritourist center, a smaller sports complex, no regenerative farm, and 128 acres of ranch. Finally, the generalizability of our model is discussed by first discussing its application in Shenzhen, China, and then widening the scope to any location in any country. Our model will provide the most implementable results in rural environments due to its quantitative nature that cannot consider complicated urban planning laws but that the model can be applied to nearly any scenario as long as data is provided.