Reducing MAUP bias of correlation statistics between water quality and GI illness

Swift, A.D.; Liu, Lin; Uber, James G.

doi:10.1016/j.compenvurbsys.2008.01.002

Cited by 56 publications

(44 citation statements)

References 45 publications

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“…While many studies have used synthetic data to demonstrate MAUP effects under different contexts (e.g. Amrhein, 1991;Saura-Martinez, 2001;Swift et al, 2008), the unique contribution of this study is the demonstration of spatial extent induced MAUP on environmental risk factors associ- Table 4. Results of multivariate logistic models fit within incremental distances from dogs' residences for Kansas GAP (Gap Analysis Program) land cover features associated with leptospirosis status in the study region (n = 68 cases, 156 controls).…”

Section: Discussionmentioning

confidence: 99%

“…Given that MAUP is difficult to overcome and a problem that appears to be here to stay, considerable research has focused on finding ways to mitigate its effects; although, many of the recommended approaches/work-arounds are very context specific and have mostly concentrated on zonal effects (Amhrein, 1995;Wu et al, 1997;Swift et al, 2008). The simplest way out of MAUP is for researchers to use individual level data (often referred to as basic units in the literature) since MAUP is a product of aggregation and scale dependence (Openshaw, 1984;Fotheringham, 1989).…”

Section: Discussionmentioning

confidence: 99%

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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%

Section: Discussionmentioning

confidence: 99%

Spatial scale effects in environmental risk-factor modelling for diseases

et al. 2013

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“…Bien que de nombreuses études fassent état de ce problème, peu de solutions efficaces ont été appliquées et aucune solution globale ne fait consensus (Swift et al, 2008 ;Arsenault et al, 2013). Nous définissons trois approches dans les solutions proposées.…”

Section: Figure 2 Les Différentes Formes De Problèmes Liées à L'échaunclassified

“…Par ailleurs, ces données possèdent un faible pouvoir de communication en comparaison avec leur équivalent cartographié selon des limites spatiales bien connues. Elles peuvent également souffrir d'un problème d'atomisme ou de la tentation d'ignorer la dimension spatiale des données pour produire des résultats (Swift et al, 2008 ;Arsenault et al, 2013).…”

Section: Figure 2 Les Différentes Formes De Problèmes Liées à L'échaunclassified

“…Robinson, 1956). Une seconde solution adaptative est fondée sur le fait de considérer le MAUP comme un outil plutôt que comme un problème, ce qui se justifie par le fait qu'il est directement lié à la structure spatiale de la variance (Swift et al, 2008). À partir de là, il est possible de préconiser le choix d'un découpage pertinent à partir de l'optimisation du résultat recherché (Openshaw, 1984).…”

Section: Figure 2 Les Différentes Formes De Problèmes Liées à L'échaunclassified

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Impact des niveaux d’échelle sur l’étude des feux de forêts du sud-est de la France

et al. 2016

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Area-to-point parameter estimation with geographically weighted regression

Murakami

Tsutsumi

2015

J Geogr Syst

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The modifiable areal unit problems (MAUP) is a problem by which aggregated units of data influence the results of spatial data analysis. Standard GWR, which ignores aggregation mechanisms, cannot be considered to serve as an efficient countermeasure of MAUP. Accordingly, this study proposes a type of GWR with aggregation mechanisms, termed area-to-point (ATP) GWR herein. ATP GWR, which is closely related to geostatistical approaches, estimates the disaggregate-level local trend parameters by using aggregated variables. We examine the effectiveness of ATP GWR for mitigating MAUP through a simulation study and an empirical study. The simulation study indicates that the method proposed herein is robust to the MAUP when the spatial scales of aggregation are not too global compared with the scale of the underlying spatial variations. The empirical studies demonstrate that the method provides intuitively consistent estimates.

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Reducing MAUP bias of correlation statistics between water quality and GI illness

Cited by 56 publications

References 45 publications

Spatial scale effects in environmental risk-factor modelling for diseases

Spatial scale effects in environmental risk-factor modelling for diseases

Impact des niveaux d’échelle sur l’étude des feux de forêts du sud-est de la France

Area-to-point parameter estimation with geographically weighted regression

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