Modelling urban growth over time using grid-digitized method with variance inflation factors applied to spatial correlat
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ORIGINAL PAPER
Modelling urban growth over time using grid-digitized method with variance inflation factors applied to spatial correlation Potjamas Chuangchang 1 & Orawit Thinnukool 2 & Phattrawan Tongkumchum 1
Received: 15 March 2015 / Accepted: 8 February 2016 # Saudi Society for Geosciences 2016
Abstract Analysis of land use change over time is useful information to support urban planning and management policies. Most land use modelling studies have used polygonal data structure. The main limitation of polygonal data structure is that it is difficult to measure changes in land use. This study proposes another method for predicting land use change. This method is based on an analog-to-digital conversion which replaces polygonal shapes by coded grid points. The method is applied to data from a survey of Phuket province from 1967 to 2009 where land use was classified broadly as forest, agriculture, urban, water bodies and miscellaneous land. Logistic regression was used to predict a binary land use outcome (urban/other), and location combined with land use at a previous survey was a determinant. To account for correlation in land use amongst nearby plots of land, variance inflation factors were used to compute standard errors of proportions of urban growth. The result of the present study discloses that greater urbanization was observed in the southern parts of Phuket during the period of study, and surprisingly, that reforestation occurred in 1985–2000. This study shows that analog-to-digital conversion methods are useful approaches to develop appropriate statistical models for land use change.
* Phattrawan Tongkumchum [email protected]
1
Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani, Thailand
2
Department of Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand
Keywords Urban growth . Digitization . Logistic regression . Land use . Phuket province
Introduction Land use data contain rich and valuable information about historical and cultural developments. Such data provide essential information relating to land development, a subject of intensive research in remote sensing and geographic information systems (GIS) as well as in economics (see for example, Bach et al. 2006; Lewis 2010; Stehman and Wickham 2011; Guo et al. 2013). It is important to have methods that make appropriate use of land-use data. Improved knowledge of historical land use not only improves our ability to manage the legacy of this history but also provides an insight into, or confirmation of, the likely impacts of current and future urbanization on land use, environment and ecosystem (Sheffield and Morse-McNabb 2012). The quantity and the location of land-use changes are the main issues to be addressed by city planners and decision makers, especially in a rapidly changing environment. Thus, the main objective of the modelling process is to understand and to predict future urban growth
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