Extending the automated zoning procedure to reconcile incompatible zoning systems

Martin, David

doi:10.1080/713811750

Cited by 77 publications

(69 citation statements)

References 17 publications

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“…This approach has later been expounded by others using automated zoning procedures (Martin, 2001;Cockings and Martin, 2005;Haynes et al, 2007;Parenteau and Sawada, 2011). The problem with this approach however is that the determination of an optimal spatial extent even if they are computationally derived for studying disease systems could be very challenging and incomplete, considering the potential for multiple transmission pathways operating at different spatial scales and complex host and pet owner movement behaviour.…”

Section: Discussionmentioning

confidence: 99%

Spatial scale effects in environmental risk-factor modelling for diseases

et al. 2013

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Abstract. Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents) on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR) test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD) and Kansas Gap Analysis Program (KS GAP) around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

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Section: Discussionmentioning

confidence: 99%

Spatial scale effects in environmental risk-factor modelling for diseases

et al. 2013

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“…(Recent examples of methods that could be used for such tests are those developed by Östh et al 2015. ) In the context of the Chicago data analysed here a simulation exercise whereby an algorithm (such as AZP : Martin 2003;Cockings et al 2011) is used to generate a sufficient number of different aggregations of, for example, Chicago's 2,459 EDs in 1930 to produce a frequency distribution of MORs -at both scales -could be used to determine whether the observed values for the given configuration is either an outlier or typical of all possible ways of grouping the EDs into 50 larger units. Such an exercise is outwith the consideration of the current paper, and the subject for further research.…”

Section: The Modifiable Areal Unit Problemmentioning

confidence: 99%

Spatial scale and measuring segregation: illustrated by the formation of Chicago’s ghetto

2016

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms 1 Spatial Scale and Measuring Segregation: Illustrated by the Formation of Chicago's Ghetto Abstract. Few studies of residential segregation in cities have directly addressed the issue of spatial scale, apart from noting that the traditional indices of segregation tend to be larger when calculated for small rather than large spatial units. That observation however ignores Duncan et al.'s (1961) explication that any measure of segregation at a fine-grained scale necessarily incorporates, to an unknown extent, segregation at a larger scale within which the finer-grained units are nested. To avoid that problem, a multi-level modelling perspective is introduced that identifies the intensity of segregation at each scale net of its intensity at any larger scale included in the analysis. It is applied to an analysis of the emergence of Chicago's Black ghetto over the twentieth century's first three decades, using data at the ward and ED scales. It shows that across Chicago as a whole segregation was equally as intense at the two scales, with statistically significant increases in that intensity at both scales across the three decades. At the finer scale, however, segregation was much more intense across the EDs within those wards that formed the core of the emerging ghetto than it was in the reminder of the city.

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“…This process is carried out by clustering the units into a smaller number M of zones in order to create a new spatial structure that reflects a specific set of criteria. The issue of spatial partitioning is present in a wide spectrum of applications, as evinced by the literature on demarcating political-electoral districts (Horn 1995, Ricca and Simeone 1997e, Mehrotra et al 1998; delimitating socio-economic zones under a particular jurisdiction or administrative control (Openshaw and Alvanides 2001, Alvanides et al 2002, Martin 2003; delimitating market areas or sales 'territories' (Zoltners and Sinha 1983, Rios-Mercado and Fernandez 2009; delimitating school districts (Caro et al 2004, Ahmadi 2006; demarcating zones for delivering mobile domiciliary services, such as planning salt apportionment operations on roads (Muyldermans et al 2002) and demarcating police districts (D'Amico et al 2002); delimitating zones that receive energy resources (electrical power) (Bergey et al 2003, Tiede andStrobl 2006); and finally, delimitating zones for land-use allocation and/or land acquisition and apportionment (Cova and Church 2000, Aerts and Heuvelink 2002, Williams 2002.…”

Section: Introductionmentioning

confidence: 99%