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K. Clarke (1997)
A self-modifying cellular automaton model of historical urbanization in the San Francisco Bayarea, Environment and Planning B, 24
E. Casetti, J. Jones (1991)
Applications of the Expansion Method
F. Liao, Y. Wei (2014)
Modeling determinants of urban growth in Dongguan, China: a spatial logistic approachStochastic Environmental Research and Risk Assessment, 28
Ying Long, Yizhen Gu, Haoying Han (2012)
Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of BeijingLandscape and Urban Planning, 108
Jun Luoa, Y. Weib (2009)
Landscape and Urban Planning
B. Pijanowski, Daniel Brown, B. Shellito, G. Manik (2002)
Using neural networks and GIS to forecast land use changes: a Land Transformation ModelComputers, Environment and Urban Systems, 26
E. Irwin, N. Bockstael (2007)
From the Cover: Land Change Science Special Feature: The evolution of urban sprawl: Evidence of spatial heterogeneity and increasing land fragmentationProceedings of the National Academy of Sciences of the United States of America
S. Pickett, M. Cadenasso, J. Grove, C. Nilon, R. Pouyat, W. Zipperer, R. Costanza (2001)
Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areasAnnual Review of Ecology, Evolution, and Systematics, 32
K. Seto, M. Fragkias, Burak Güneralp, Michael Reilly (2011)
A Meta-Analysis of Global Urban Land ExpansionPLoS ONE, 6
P. Verburg, Jan Eck, Ton Nijs, M. Dijst, P. Schot (2004)
Determinants of Land-Use Change Patterns in the NetherlandsEnvironment and Planning B: Planning and Design, 31
Jianguo Wu, G. Jenerette, A. Buyantuyev, Charles Redman (2011)
Quantifying spatiotemporal patterns of urbanization: The case of the two fastest growing metropolitan regions in the United StatesEcological Complexity, 8
(1976)
A land use and land cover classification system for use with remote sensor data (28 PP). Washington DC: United States Government Printing Office
Hossein Shafizadeh-Moghadam, M. Helbich (2015)
Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of MumbaiInt. J. Appl. Earth Obs. Geoinformation, 35
E Irwin, N Bockstael (2007)
The evolution of urban sprawl: Evidence of spatial heterogeneity and increasing land fragmentationPNAS, 104
M. Bürgi, A. Hersperger, Nina Schneeberger (2004)
Driving forces of landscape change - current and new directionsLandscape Ecology, 19
Zhiyong Hu, Chor-Pang Lo (2007)
Modeling urban growth in Atlanta using logistic regressionComput. Environ. Urban Syst., 31
A. Sorensen, P. Marcotullio, J. Grant (2017)
Towards Sustainable Cities : East Asian, North American and European Perspectives on Managing Urban Regions
Qian Zhang, J. Wallace, Xiangzheng Deng, K. Seto (2014)
Central versus local states: Which matters more in affecting China's urban growth?Land Use Policy, 38
Robert O’Brien (2007)
A Caution Regarding Rules of Thumb for Variance Inflation FactorsQuality & Quantity, 41
Chi Zhang, Jianguo Wu, N. Grimm, M. McHale, A. Buyantuyev (2013)
A hierarchical patch mosaic ecosystem model for urban landscapes: Model development and evaluationEcological Modelling, 250
Chi Xu, Maosong Liu, Chao Hong, Ting Chi, S. An, Xuejiao Yang (2012)
Temporal variation of characteristic scales in urban landscapes: an insight into the evolving internal structures of China’s two largest citiesLandscape Ecology, 27
Xia Li, Xiaoping Liu (2007)
Defining agents' behaviors to simulate complex residential development using multicriteria evaluation.Journal of environmental management, 85 4
K. Dahal, T. Chow (2014)
An agent-integrated irregular automata model of urban land-use dynamicsInternational Journal of Geographical Information Science, 28
O. Dubovyk, R. Sliuzas, J. Flacke (2011)
Spatio-temporal modelling of informal settlement development in Sancaktepe district, Istanbul, TurkeyIsprs Journal of Photogrammetry and Remote Sensing, 66
(2002)
Geographically weighted regression: The analysis of spatially varying relationships
A. Dewan, Y. Yamaguchi (2009)
Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanizationApplied Geography, 29
(2014)
America’s fastest growing cities
Seong‐Hoon Cho, D. Lambert, Zhuo Chen (2010)
Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture dataApplied Economics Letters, 17
GWLR user manual: Windows application for geographically weighted regression modeling
Guangjin Tian, Ouyang Yun, Q. Quan, Jianguo Wu (2011)
Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USAEcological Modelling, 222
N. Grimm, S. Faeth, N. Golubiewski, C. Redman, Jianguo Wu, X. Bai, J. Briggs (2008)
Global Change and the Ecology of CitiesScience, 319
J Luo, Y Wei (2009)
Modeling spatial variations of urban growth patterns in Chinese cities: The case of NanjingLandscape and Urban Planning, 91
Jianguo Wu, O. Loucks (1995)
From Balance of Nature to Hierarchical Patch Dynamics: A Paradigm Shift in EcologyThe Quarterly Review of Biology, 70
Kate Derickson (2015)
Urban geography IProgress in Human Geography, 39
Zhonghao Zhang, Shiliang Su, Rui Xiao, Diwei Jiang, Jiaping Wu (2013)
Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, ChinaApplied Geography, 45
J. Portugali (2009)
Self-Organization and the City
M. Gottdiener (1993)
The New Urban SociologyThe New Urban Sociology
P. Atkinson, S. German, D. Sear, M. Clark (2002)
Exploring the Relations Between Riverbank Erosion and Geomorphological Controls Using Geographically Weighted Logistic RegressionGeographical Analysis, 35
Sandra Oliveira, J. Pereira, J. San-Miguel-Ayanz, L. Lourenço (2014)
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted RegressionApplied Geography, 51
Michael Reilly, Margaret O'Mara, K. Seto (2009)
From Bangalore to the Bay Area: Comparing transportation and activity accessibility as drivers of urban growthLandscape and Urban Planning, 92
R. Thapa, Y. Murayama (2010)
Drivers of urban growth in the Kathmandu valley, Nepal: Examining the efficacy of the analytic hierarchy processApplied Geography, 30
Chunhong Zhao, Jennifer Jensen, Benjamin Zhan (2017)
A comparison of urban growth and their influencing factors of two border cities: Laredo in the US and Nuevo Laredo in MexicoApplied Geography, 79
K. Dahal, S. Benner, E. Lindquist (2017)
Urban Hypotheses and Spatiotemporal Characterization of Urban Growth in the Treasure Valley of Idaho, USAApplied Geography, 79
M. Batty (2008)
The Size, Scale, and Shape of CitiesScience, 319
M. Herold, H. Couclelis, K. Clarke (2005)
The role of spatial metrics in the analysis and modeling of urban land use changeComput. Environ. Urban Syst., 29
B. Bhatta (2010)
Analysis of Urban Growth and Sprawl from Remote Sensing Data
M. Aguayo, T. Wiegand, G. Azócar, K. Wiegand, Claudia Vega (2007)
Revealing the Driving Forces of Mid-Cities Urban Growth Patterns Using Spatial Modeling: a Case Study of Los Ángeles, ChileEcology and Society, 12
J. Foley, R. DeFries, G. Asner, C. Barford, G. Bonan, S. Carpenter, F. Chapin, M. Coe, M. Coe, G. Daily, H. Gibbs, J. Helkowski, T. Holloway, E. Howard, C. Kucharik, C. Monfreda, J. Patz, I. Prentice, N. Ramankutty, P. Snyder (2005)
Global Consequences of Land UseScience, 309
Xiaoma Li, Weiqi Zhou, Z. Ouyang (2013)
Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors?Applied Geography, 38
K. Dahal, S. Benner, E. Lindquist (2018)
Analyzing Spatiotemporal Patterns of Urbanization in Treasure Valley, Idaho, USAApplied Spatial Analysis and Policy, 11
K. Clarke, Stacy Hoppen, L. Gaydos (1997)
A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay AreaEnvironment and Planning B: Planning and Design, 24
Jianguo Wu, J. David (2002)
A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applicationsEcological Modelling, 153
State and County Quick Facts
J Anderson, E Hardy, J Roach, R Witmer (1976)
A land use and land cover classification system for use with remote sensor data
Chi Xu, Maosong Liu, Cheng Zhang, S. An, Wen Yu, J. Chen (2007)
The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of ChinaLandscape Ecology, 22
M Pacione (2009)
Urban Geography: A global perspective
Kalin Müller, C. Steinmeier, M. Küchler (2010)
Urban growth along motorways in SwitzerlandLandscape and Urban Planning, 98
J. Riera, P. Voss, S. Carpenter, T. Kratz, T. Lillesand, Jill Schnaiberg, M. Turner, Mark Wegener (2001)
Nature, society and history in two contrasting landscapes in Wisconsin, USA: Interactions between lakes and humans during the twentieth centuryLand Use Policy, 18
Jianquan Cheng, I. Masser (2003)
Urban growth pattern modeling: a case study of Wuhan city, PR ChinaLandscape and Urban Planning, 62
(2011)
Planning area policy
Cheng Li, Junxiang Li, Jianguo Wu (2013)
Quantifying the speed, growth modes, and landscape pattern changes of urbanization: a hierarchical patch dynamics approachLandscape Ecology, 28
P. Silva, M. Berg, A. Serrano, F. Dubs, J. Sousa (2012)
Environmental factors at different spatial scales governing soil fauna community patterns in fragmented forestsLandscape Ecology, 27
N. Batisani, B. Yarnal (2009)
Urban expansion in Centre County, Pennsylvania: spatial dynamics and landscape transformations.Applied Geography, 29
A. Marshall (2000)
How Cities Work: Suburbs, Sprawl, and the Roads Not Taken
Urban landscape is a system of hierarchically nested spatial structures or land patches with their distinct dynamics and causal factors. The lower level structures evolve together to give out different forms, patterns and extents during the process of urbanization. An understanding of driving factors of these hierarchical structures has important implications for future land use planning and urban management. Adopting an innovative framework of urban patch hierarchy, this study investigates the drivers and spatiotemporal variability of their explanatory power at urban and intra-urban patch levels, with Treasure Valley of Idaho, USA as a case of study. We calibrated global and local logistic regression models against a set of 18 spatialized variables. Results show that growth drivers and their impact vary both across the study site and along the cycles of urbanization at different levels of urban landscape. Urban agglomeration factors including the proximity to urbanized area had the highest impact on new development, urban growth forms and land uses. Factors were less stable at the lower level because of their subtle interconnectedness with local structures. The study further revealed an existence of distinct patterns of association among the growth forms, land use types, and the included factors. Specific growth forms and land use classes demonstrated identical temporal patterns with certain driving factors.
Applied Spatial Analysis and Policy – Springer Journals
Published: Jun 15, 2017
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