Incorporating sprawl and adjacency measures in land‐use forecasting model: A case study of Collin County, TX

Abstract

AbstractSprawl measures have largely been neglected in land‐use forecasting models. The current approach for land‐use allocation using optimization mostly utilizes objective functions and constraints that are non‐spatial in nature. Application of spatial constraints could take care of the contiguity and compactness of land uses and can be utilized to address urban sprawl. Because a land‐use model is used as an input to transportation modeling, a better spatial allocation strategy for more compact land‐use projections will promote better transportation planning and sustainable development. This study formulates a scenario‐based approach to normative modeling of urban sprawl. In doing so, it seeks to improve the land‐use projections by employing a spatial optimization model with contiguity and compactness consideration. This study incorporates urban sprawl measures based on smart growth principles together with a mixed‐use factor, and adjacency consideration of nearby land uses. The objective function used in the study maximizes net suitability based on imposed constraints. These constraints are based on smart growth principles that enhance walkability in neighborhoods, promote better health for residents, and encourage mixed‐use development. The formulated model has been applied to Collin County, TX, a fast‐developing suburban county located to the north of the Dallas–Fort Worth metroplex. The suitability of land cells indicates the probability of conversion, which is calculated using spatial discrete choice analysis with Moran eigenvector spatial filtering for vacant cells at a resolution of 150 × 150 m employing factors of the built environment, and socioeconomic and demographic characteristics. This study demonstrates how spatial proximity between land uses, which has been ignored to date, can be used to control sprawl, resulting in better mixing of different land uses based on constraints imposed in a spatial optimization problem.