Does zoning matter? A comparative analysis of landscape change in Redland, Florida using cellular automata

• Zoning impact is measured using cellular automata.
• The most accurate measurements are made in high pressure growth areas.
• Land use zoning is a strong predictor of urban growth in our study site.
• Evidence indicates that urban growth models should use zoning information.

Landscape change is a key feature of social–ecological change, and is especially marked in, urbanizing regions. Planning institutions use land use zoning to control and direct such changes. Urban growth models are commonly used to better understand past landscape changes as well as, forecast and plan for future landscape changes. Many of these models, however, do not utilize zoning, information in their deployment because many model designers do not believe zoning to be a relevant, criterion for the prediction of urban growth. This research offers a novel methodology for integrating, zoning information into a cellular automaton urban growth model, SLEUTH. It additionally tests the, utility of such information by comparing metrics of fit with past data under different zoning inclusion, conditions in a community of Miami-Dade County, Florida. These conditions include one scenario, where zoning is ignored and three others where it is included. The latter three test different methods, of including zoning data for three generalized zoning categories – arbitrarily guessing, measuring urban, growth in each zoning category for the entire study area, and measuring urban growth in each zoning, category only in those areas more likely to experience growth. Results indicate that this final condition, generates the highest model performance metric and creates a more fair comparison since remote, areas in the study area, less likely to experience growth, exaggerate differences in urban growth rates, across the different zoning categories. We conclude that zoning information, when utilized, appropriately, improves model performance and is therefore relevant for landscape change.