Quantifying spatiotemporal patterns of urbanization: The case of the two fastest growing metropolitan regions in the United States

Urbanization is the most drastic form of land use change affecting biodiversity and ecosystem functioning and services far beyond the limits of cities. To understand the process of urbanization itself as well as its ecological consequences, it is important to quantify the spatiotemporal patterns of urbanization. Based on historical land use data, we characterize the temporal patterns of Phoenix and Las Vegas – the two fastest growing metropolitan regions in the United States – using landscape pattern metrics at multiple spatial resolutions. Our results showed that the two urban landscapes exhibited strikingly similar temporal patterns of urbanization. During the past several decades, urbanization in the two desert cities resulted in an increasingly faster increase in the patch density, edge density, and structural complexity at both levels of urban land use and the entire landscape. That is, as urbanization continued to unfold, both landscapes became increasingly more diverse in land use, more fragmented in structure, and more complex in shape. The high degree of similarity between the two metropolitan regions may be attributable to their resemblance in the natural environment, the form of population growth, and the stage of urban development. While our results corroborated some theoretical predictions in the literature, they also showed spatiotemporal signatures of urbanization that were different from other cities. Resolving these differences can certainly further our understanding of urban dynamics. Finally, this study suggests that a small set of landscape metrics is able to capture the main spatiotemporal signatures of urbanization, and that the general patterns of urbanization do not seem to be significantly affected by changing grain sizes of land use maps when the spatial extent is fixed. This landscape pattern analysis approach is not only effective for quantifying urbanization patterns, but also for evaluating spatial urban models and investigating ecological effects of urbanization.