Abstract

We build upon much of the accumulated knowledge of the widely used SLEUTH urban land change model and offer advances. First, we use SLEUTH’s exclusion/attraction layer to identify and test different urban land cover change drivers; second, we leverage SLEUTH’s self-modification capability to incorporate a demographic model; and third, we develop a validation procedure to quantify the influence of land cover change drivers and assess uncertainty. We found that, contrary to our a priori expectations, new development is not attracted to areas serviced by existing or planned water and sewer infrastructure. However, information about where population and employment growth is likely to occur did improve model performance. These findings point to the dominant role of centrifugal forces in post-industrial cities like Baltimore, MD. We successfully developed a demographic model that allowed us to constrain the SLEUTH model forecasts and address uncertainty related to the dynamic relationship between changes in population and employment and urban land use. Finally, we emphasize the importance of model validation. In this work the validation procedure played a key role in rigorously assessing the impacts of different exclusion/attraction layers and in assessing uncertainty related to population and employment forecasts.

Highlights

  • More than a decade ago, Gardner and Urban [1] pointed out that despite an extensive literature on model validation it was not widely practiced

  • SLEUTH’s exclusion/attraction layer to identify and test different urban land cover change drivers; second, we utilize SLEUTH’s self-modification capability to couple the urban land cover change model with a demographic model; and third, we include the procedure of validation to both quantify the influence of land cover change drivers and assess uncertainty

  • We identified the exclusion/attraction layer that resulted in the best model performance and performed an additional validation where the amount of urban land cover change was constrained based on expected population and employment changes

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Summary

Introduction

More than a decade ago, Gardner and Urban [1] pointed out that despite an extensive literature on model validation it was not widely practiced. It was “common to find that the problems and pitfalls of validation and testing are poorly understood, inadequately executed, or entirely ignored”. While SLEUTH’s calibration process requires the use of historic land cover data, to our knowledge there is only one example [13] of a calibrated SLEUTH model being used to generate simulations of land cover change that are compared to an independent data set, data that were not used to calibrate the model.

The SLEUTH Model
Study Area
Data and Methods
SLEUTH Calibration
SLEUTH Validation
Forecasting to 2030
Results and Discussion
SLEUTH Forecasts
Conclusions

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