<i>The Zone Definition Problem in Location‐Allocation Modeling</i>

Fotheringham, A. Stewart; Densham, Paul J.; Curtis, Andrew

doi:10.1111/j.1538-4632.1995.tb00336.x

Cited by 63 publications

(51 citation statements)

References 27 publications

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“…A recent paper by Fotheringham, Densham, and Curtis (1995) has been particularly critical of results obtained from the application of location models to aggregate data. Specifically, Fotheringham, Densham, and Curtis (1995) analyzed the locational results from the p-median problem applied to various levels of aggregated data.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, Fotheringham, Densham, and Curtis (1995) analyzed the locational results from the p-median problem applied to various levels of aggregated data. They generated solutions for these problems using an interchange heuristic (Densham and Rushton 1992).…”

Section: Introductionmentioning

confidence: 99%

“…They generated solutions for these problems using an interchange heuristic (Densham and Rushton 1992). Fotheringham, Densham, and Curtis (1995) addressed the issue of stability that has been the subject of previous research work (see Gould, Nordbeck, and Rystedt 1971;Goodchild 1979). The major criticism focused on the absence of distinct clusters among the solution locations resulting from the application of the p-median problem to aggregate data.…”

Section: Introductionmentioning

confidence: 99%

“…Daskin et al (1989) concluded that significant aggregation would not result in large errors. Fotheringham, Densham, and Curtis (1995) analyzed the question of stability based upon the desire to witness spatial structure. That is, they expected obvious spatial clusters to result that corresponded to the number of located facilities when aggregate solutions were displayed together.…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Data Aggregation on the Stability of p‐Median Location Model Solutions

Murray

Gottsegen

1997

Geographical Analysis

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INTRODUCTIONSpatially aggregated data are typically utilized in most location modeling studies. There is, however, a concern that the use of such data may produce unintended effects and perhaps lead to poor decision making. This makes the study of aggregation effects both a relevant and important research area.The necessity for aggregating data arises out of the need to represent and analyze complex spatial systems. Some form of aggregation is associated with most digital representations of spatial information. Location planning studies carried out using spatial optimization models often rely on aggregate data in order to reduce computational effort and facilitate understanding. However, such aggregation must be done without sacrificing the integrity of the spatial structure of the planning area or the needs and objectives of the study. To this end, it is important to assess the quality of optimization model solutions in the context of the original disaggregate data (see Daskin et al. 1989). Analysis on Alan T. Murray andJonathan M. Gottsegen / 201 aggregate data will be useless if it cannot reasonably be interpreted and implemented in the disaggregate form. In other words, analyses using aggregate data must evidence adequate spatial and/or objective function stability when their solutions are mapped back into the original disaggregate structure. Alan T. Murray is a postdoctoral research fellow at Queensland University of Technology in the AustralianNumerous researchers have investigated some of the inherent issues associated with the use of aggregate data in location planning models. The interest has generally been to identify any biasing influences as well as infer some form of spatial stability in the results of location model solutions. Although some previous research has concluded that the locational results obtained from spatial optimization models using aggregate data are not stable, the issue of stability has been analyzed from a limited perspective. This paper presents a broader analysis of locational results utilizing aggregate data and finds that a significant amount of stability does exist. This is counter to some of the more critical conclusions reached in previous studies, and it suggests that the condemnation of the use of location planning models using aggregate data may be inappropriate.We begin this paper with a review of previous research associated with aggregation analysis. This is followed by a discussion of the analysis approach and the location model utilized in this study. We then present and discuss location model results on data at various degrees of spatial aggregation. We conclude with general comments and suggestions for future research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Influence of Data Aggregation on the Stability of p‐Median Location Model Solutions

Murray

Gottsegen

1997

Geographical Analysis

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“…Additionally, we used average wage, per capita saving deposit balances, and the number of hospital and social welfare beds as the index of the level of living standard, medical treatment, and social welfare for each county. We opted to normalize the data by total population to avoid the modifiable areal unit problem (Fotheringham et al 1995). Besides the variables in Table 1, the total population data of 2005, 2007, 2008, and 2010 for each county were collected from the annual Sichuan Statistical Yearbooks (Sichuan Provincial Bureau 2006,2008,2009,2011) to calculate the change of population growth rate (Ddp) before and after the earthquake (Fig.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Geographical Analysis of Community Resilience to Seismic Hazard in Southwest China

Wang

Liu

2016

Int J Disaster Risk Sci

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This article presents an explorative analysis of community resilience to seismic hazard in the 2008 Wenchuan Earthquake area of Southwest China. We used a regression model to analyze the impact of 13 key socioeconomic and demographic variables on community resilience in 105 counties, based on data derived from population census and provincial statistical yearbooks of China. In this research, we argue that community resilience should be measured by the change of population growth rate (Ddp) instead of population growth rate (dp) when using socioeconomic data from a fast-growing country such as China. Using Ddp as the dependent variable resulted in a better regression model. To avoid the common multicollinearity problems among the independent variables, a principal component-based factor analysis was used to consolidate the socioeconomic variables into four comprehensive factors. The geographically weighted regression coefficient maps revealed the spatial pattern of the association of the variables with resilience. We also used the K-means cluster method to segment the study area into four subregions that exhibit localized characteristics defined by the regression coefficients. In this way, we could infer location-sensitive disaster management policies that help to enhance social resilience to seismic hazards.

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Quantative Methods and Geographic Information Systems

Charlton¹

2007

The Handbook of Geographic Information Science

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The Zone Definition Problem in Location‐Allocation Modeling

Cited by 63 publications

References 27 publications

The Influence of Data Aggregation on the Stability of p‐Median Location Model Solutions

The Influence of Data Aggregation on the Stability of p‐Median Location Model Solutions

Geographical Analysis of Community Resilience to Seismic Hazard in Southwest China

Quantative Methods and Geographic Information Systems

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