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PLoS One. 2019 Mar 6;14(3):e0211964. doi: 10.1371/journal.pone.0211964. eCollection 2019.

Analysis on urban densification dynamics and future modes in southeastern Wisconsin, USA.

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College of Earth Sciences, Jilin University, Changchun, Jilin, China.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, United States of America.
Urban Development and Mobility Department, Luxembourg Institute of Socio-Economic Research (LISER), Esch-sur-Alzette, Luxembourg.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Key Laboratory of Songliao Aquatic Environment, Ministry of Education, Jilin Jianzhu University, Changchun, Jilin, China.


Urban change (urbanization) has dominated land change science for several decades. However, few studies have focused on what many scholars call the urban densification process (i.e., urban intensity expansion) despite its importance to both planning and subsequent impacts to the environment and local economies. This paper documents past urban densification patterns and uses this information to predict future densification trends in southeastern Wisconsin (SEWI) by using a rich dataset from the United States and by adapting the well-known Land Transformation Model (LTM) for this purpose. Urban densification is a significant and progressive process that often accompanies urbanization more generally. The increasing proportion of lower density areas, rather than higher density areas, was the main characteristic of the urban densification in SEWI from 2001 to 2011. We believe that improving urban land use efficiency to maintain rational densification are effective means toward a sustainable urban landscape. Multiple goodness-of-fit metrics demonstrated that the reconfigured LTM performed relatively well to simulate urban densification patterns in 2006 and 2011, enabling us to forecast densification to 2016 and 2021. The predicted future urban densification patterns are likely to be characterized by higher densities continue to increase at the expense of lower densities. We argue that detailed categories of urban density and specific relevant predictor variables are indispensable for densification prediction. Our study provides researchers working in land change science with important insights into urban densification process modeling. The outcome of this model can help planners to identify the current trajectory of urban development, enabling them to take informed action to promote planning objectives, which could benefit sustainable urbanization definitely.

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