The management of urban sprawl is fundamental to achieving sustainable urban development. Monitoring urban sprawl is, however, challenging. This study proposes the use of two spatial statistics, namely global Moran and local Moran to indentify statistically significant urban sprawl hot and cold spots. The findings reveal that the Moran indexes are sensitive to the distance band spatial weight matrices employed and that multiple bands should be used when these indexes are used. The authors demonstrate how the indexes can be used in combination with various visualisation methods to support planning decisions.
TopIntroduction
Urban sprawl is a fundamental theme in the sustainability debate (Zhao et al., 2011) and literature often equates sprawl with unsustainability (Le Néchet, 2012). However, what is unsustainable about urban sprawl are its wasteful forms, so that future generations can be deprived as a result of dwindling resources used to make way for urban expansion (Gerundo & Grimaldi, 2011). If urban growth is to continue it better be sustainable rather than wasteful (Ewing, 1997; Veneri, 2010). Planners have accordingly adopted the use of geographic information systems (GIS) and earth observation (EO) data to identify wasteful forms of urban sprawl commonly referred to as hot spots (Levine, 1996; Liu, Dong & Chi, 2010). Most GIS packages are able to do this by providing summary statistics such as means, sums and medians. However, such statistics cannot be generalised over various locations making it difficult to compare various urban centres. Moreover, visual representations of such results are also known to be misinterpreted (Zhang et al., 2008). What is required are new software and exploratory data analysis techniques that focus on the spatial aspects of the data such as dispersion, concentration and spatial autocorrelation which can be used to capture the impacts of urban sprawl on urban sustainability (Anselin, 1995; Le Néchet, 2012). This provides a scientific basis for improved sustainable land use planning. Similar analyses have been successfully carried out for pollution (Zhang et al., 2008), conflict (Anselin, 1995), disease (Ruiz et al, 2004; Zhang & Lin, 2006) and crime management (Anselin et al., 2000; Lin & Brown, 2006).
Concerning urban sprawl, a search on Science Direct, Scopus and Web of Science databases1indicate that Tsai (2005) was the first to apply the global Moran index (Moran I) to distinguish urban sprawl from compactness. Similar studies citing Tsai (2005) ensued, such as Gerundo & Grimaldi (2011), Le Néchet (2012), Sim & Mesev (2011), Zhao et al. (2011) which employed global Moran index (Moran I) and other metrics to distinguish between urban sprawl and compactness in European, American and Asian cities. Moran I has attracted interest from researchers and planners because it is apparently more robust than other metrics (Bhatta, Saraswati & Bandyopadhyay, 2010). To the best knowledge of the authors, no studies exist that have applied the global Moran and local Moran indexes with cluster and outlier analysis to cadastral data to determine sprawl hot spots and cold spots. This study investigates the use of these indexes with GIS software and cadastral data to identify urban sprawl hot spots. Moreover, the impact of various simulations (weights) of the indexes is explored and the practical implementation of this process in policy change and decision making toward sustainable urban development is investigated.