Refining landscape change models through outlier analysis in the Muskegon watershed of Michigan

Abstract

Balancing natural resource protection and urban development is of concern to researchers, planners and citizens who are aware of the environmental, social and economic impacts of urban land use. Land-use change models can assist in finding this balance. An objective of this research was to build a better model of land-use change by integrating quantitative and qualitative techniques. A modelling approach is presented that combines statistical logistic regression with field-based outlier analysis. To this end, a collaborative effort between researchers, who are adept at building models, and local experts, who better understand the dynamics of landscape change in their communities, was undertaken. The findings indicate that this modelling approach is successful in improving overall model performance, as measured by pseudo r-squared value, and identifying additional drivers of land-use change, namely zoning, soil suitability and distance to highway interchange. Most importantly, this collaborative modelling process, involving researchers and local planners, has practical utility for land-use decision making.

Modelling urban growth: Integrating both physical and human dimensions in developing world context

“…In explaining the emergence of an urban area, mathematical urban modelling largely employs a linear or logistic regression (Aspinall, 2004;Mertens and Lambin, 2000), Markov-chain change probability (Petit and Lambin, 2002), or a gravity model (Kong et al, 2010;Lowry, 1965). In this type of modelling, the relationship that links the factors and the possibility of urban changes becomes more explicit compared to the early descriptive urban models, rendering the explanatory power and predictive capability better than the early urban models (Machemer et al, 2006). The parameters in a linear equation, for example, represent the weight that measures the contribution scale of a factor on urban growth.…”