Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem

Tuson, Matthew; Yap, Matthew; Kok, Mei Ruu; Murray, Kevin; Turlach, Berwin A.; Whyatt, David

doi:10.1186/s12942-019-0170-3

Cited by 42 publications

(34 citation statements)

References 34 publications

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“…But, in the context of data disaggregation, the MAUP bias may be smaller if the spatial units are able to capture the spatial variability of the phenomenon at hand. Recently Tuson et al [51] proposed a theorethical and statistical framework to address the MAUP trying to detect a minimal geographical unit of analysis. Though promising, in our case the minimal geographical unit of analysis is determined by the minimal administrative level available, making the results dependent on the units used.…”

Section: Overall Maup Biasmentioning

confidence: 99%

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Gilbert

Chaiban

et al. 2020

PLoS ONE

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The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson's r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson's r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products. OPEN ACCESS Citation: Da Re D, Gilbert M, Chaiban C, Bourguignon P, Thanapongtharm W, Robinson TP, et al. (2020) Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem. PLoS ONE 15(1): e0221070. https://doi.org/10.

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Section: Overall Maup Biasmentioning

confidence: 99%

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Gilbert

Chaiban

et al. 2020

PLoS ONE

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“…While some association studies have examined the impact of the MAUP's zonation aspect (e.g. see [24,32,33]), to our knowledge, no similar study exists in disease mapping. Further, as far as we are aware, no previous study has investigated the impact of the MAUP in the context of efficiency.…”

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confidence: 99%

Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps

et al. 2020

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Background In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in ‘single-aggregation disease maps’ whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated. Results We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016. Conclusions The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

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“…Studies analyzing spatiotemporal data define temporally independent spatial analysis units as time passes and build and analyze spatiotemporal data based on the data used to solve the MAUP that changes over time. (9)(10)(11)(12) The MAUP affects results when point-based measures of spatial phenomena, such as the startup, closure, or operation of small businesses, are aggregated into districts. The results of data aggregation are dependent on the analyst's choice of which "modifiable area unit" to use in the analysis.…”

Section: Discussionmentioning

confidence: 99%

Development and Demonstration of Grid-based Spatiotemporal Data Model for Monitoring Small Businesses

Choi¹,

Shin²,

Ahn³

2019

Sensors and Materials

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The objective of this study is to propose a spatiotemporal data model (STDM) to systematically analyze the dynamic changes of market areas using the small business database provided by the Market Area Information System of Small Enterprise and Market Service and demonstrate the applicability of the model according to a series of analysis scenarios. In the literature review section, we reviewed studies related to indexes that summarize the changes in the small enterprise and market area and reflect their characteristics as well as studies on STDM and discussed the direction for application. In the methodology section, we presented methodologies to establish the STDM and relative diversity index (RDI) using the nationwide small business database. In the case study section, we established a nationwide STDM to monitor the changes in the RDI of the small business database based on a 1 km grid and demonstrated the applicability of the model by analyzing the RDI in the macroscale at the metropolitan/provincial level and in the 1 km grid-based mesoscale. Moreover, on the basis of the empirical results, we discussed that applying the grid-based STDM facilitates comparative analysis in terms of macroscopic, regional, and microscopic aspects by solving the modifiable areal unit problem (MAUP) that changes according to time, bringing convenience to the visualization analysis of spatiotemporal patterns, and establishing a grid-based RDI of small businesses with consistent spatial units.

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Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem

Cited by 42 publications

References 34 publications

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps

Development and Demonstration of Grid-based Spatiotemporal Data Model for Monitoring Small Businesses

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