Sprawl develop objective measures of sprawl which would be

Sprawl is definitely indicated as a signifier of unrestricted development that consumes an excess of materials through low-density dispersion. Research has convincingly linked it to such diverse phenomena as automobile reliant travel behavior (Ewing & Cervero, 2010), reduced public transit ridership (Taylor, Miller, Iseki, & Fink, 2009), increased public infrastructure costs (Stephenson and Speir 2002), pronounced electrical power consumption (Ewing & Rong, 2008), elevated obesity rates (Kaestner and Zhao 2010), and the spatial mismatch between bad employment and populations opportunities (Covington 2009), among others. It is extremely difficult to define urban sprawl. The initial definition of sprawl was used for growth management in Florida in the early 90s (Ewing 1997). The definition which was eventually adopted by the State included four types of urban forms (figure 1) which are the following:

1.      Scattered or leapfrog development

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2.      Commercial strip development

3.      Low-density development

4.      Single-use developmentOne common characteristic of all four types of development pattern is poor accessibility. (Hamidi & Ewing, 2014) In scattered development, residents and service providers must pass the vacant land on their way from one developed use to another. In classic strip development, the consumer must pass other uses on the way from one store to the next; it is just opposite of multipurpose travel to an activity center. Obviously, in low-density and single-use development, everything is far apart because of large land holdings and isolation of land uses. In sprawl, poor accessibility generally leaves the residents with no choice other than using automobiles for kilometers and kilometers of travel.Measuring SprawlBeginning around 2000, there came a shift and researchers pursued to develop objective measures of sprawl which would be related to quantifiable outcomes with the purpose of changing the debate over sprawl from subjective to objective and quantitative.(Hamidi & Ewing, 2014)  Early attempts to quantify the amount of urban sprawl were quite rudimentary as most of the researchers created measures of urban sprawl that were only focused on population density (Pendall 1999; Fulton et al. 2001; Lang 2003). The probable reason for density to be the primary indicator of sprawl in the early studies was that it is anything but difficult to calculate and catches one critical dimension of sprawl. However, considering only density as a measure of sprawl undermines the complexity of sprawl and does not capture all the essential elements of sprawl. (Hamidi & Ewing, 2014)The same mistakes were made in initial research related to quantitative studies of sprawl using satellite imagery and GIS as they neglected the land use interactions and street patterns. (Besussi and Chin 2003; Burchfield et al. 2006). Most of these studies used land maps taken from satellite imagery to calculate form factors such as edge density and fractal dimension (Huang, Lu, and Sellers 2007; Martellozzo and Clarke 2011). Increase in availability and enhancement of quality of satellite imagery have made it relatively simpler in recent years to study landform maps (Batisani and Yarnal 2009). As these methods did not take into account the land use and street connectivity patterns they are not good enough to differentiate between the development patterns leading to high accessibility and development patterns leading to low accessibility. (Hamidi & Ewing, 2014)A notable thing about urban form measurement in these experiments was the various sprawl ratings offered to various metros by numerous analysts. With the exception of Atlanta, which often ranks as among the worst, the various variables utilized to operationalize sprawl lead to different outcomes. In a study, Portland was ranked among the very least sprawling along with Los Angeles which was ranked among the most sprawling (Glaeser, Kahn, and Chu 2001). Whereas in another different study, the rankings of two cities were reversed (Nasser and Overberg 2001). Another prominent deficiency was the failure to corroborate sprawl metrics against logical outcomes such as travel characteristics of the population. (Hamidi & Ewing, 2014)Sprawl has a consistently recognized outcome with respect to automobile dependence, with increase in sprawl automobile dependence increases. Contemporary research as shown that after controlling for other relevant influences, sprawling cities have reasonably high auto ownership, low transit commute mode share, low walkability, as well as long drive times to the workplace. (Hamidi, Ewing, Preuss, & Dodds, 2015)Most researchers now agree that sprawl is a multidimensional phenomenon that is best measured by a combination of factors (Cutsinger, Galster, Wolman, Hanson, & Towns, 2005; Ewing, Pendall, & Chen, 2003) (Galster et al. 2001). This multi-dimensionality of sprawl has led to a lot of questions such as: What are the various dimensions of sprawl? How to measure them? Should these dimensions be combined into a single sprawl index and, if so, how? (Hamidi & Ewing, 2014)Galster et al. (2001) helped pioneer this multifaceted measures of sprawl, initially proposing eight dimensions of sprawl: density (residential units per square mile of developable land), focus (whether housing is sent out evenly over the urbanized location), clustering (the level to which growth is distributed equally within subareas), centrality (how good advancement would be in relation to the CBD), continuity, heterogeneity (mixing), nuclearity (whether the urban area is polycentric or mono), and also proximity (the degree to which predominantly noncommercial and nonresidential square mile grids are geographically near to one another). Sprawl then was defined as any pattern of land use development that had low levels in one or more of these dimensions. The researchers defined each dimension and quantified six of the eight measures for multiple urbanized areas. (Cutsinger et al., 2005) updated the index by using twelve conceptually distinct dimensions of land use patterns as well as enhanced the analytical procedure by including a mixed-use development metric – connected but distinct from proximity, and also examined the sprawl profiles for fifty towns and cities in the United States.