Influence of Different Spatial Aggregations on Variables Implemented in Macroscopic Road-Safety Modeling

Gomes, Marcos José Timbó Lima; Cunto, Flávio José Craveiro

doi:10.1061/jtepbs.0000334

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Therefore, methodologies that cannot fully cope with the MAUP, including the part of the problem related to the area of reference, should be avoided.” This argument entails a direct implication for any DDDM planning model, namely its requirement to be able to manage the MAUP. Because not just the areal units themselves are susceptible to the MAUP, but also the variability of the descriptive statistics, such as SD and correlation coefficients, are scale-dependent (Gomes and Cunto, 2020). So why even bother designing a DDDM planning model, if the MAUP will inevitably distort its predictions?…”

Section: Discussionmentioning

confidence: 99%

Scale-dependent complexity in administrative units and implications for data-driven decision-making models

Søder

2023

Planning Theory

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Through analysis and discussion of basic systemic properties of a rural municipality, this paper explores how aggregating data in planning and land use modeling can potentially obscure intricate real-world behavior. Complexity theory is applied as a theoretical framework for explaining this hypothesis. Thus, the aim of this study is to address the author’s desire to understand systemic complexity when designing a data-driven decision-making model for rural planning. The novelty of this approach is two-fold: one, most studies on scalability issues in planning addresses spatial complexity, more so than systemic complexity within the complex system that the very act of planning strives to dictate. Two, although delimited to the scope of the study, the accessibility to and use of complete and valid socio-demographic data enables a rarely demonstrated accurate representation of an entire population. It is ultimately observed that on the disaggregated municipal level, systemic dispersion increases parallelly with population size, a correlation that is significantly influenced by gender ratio in any given parish – a characteristic that was not visible at the aggregated municipal level. In addition to advancing the understanding and placement of complexity science within spatial data science, these insights will make it easier to assess the generalizability of any given administrative unit by quantifying basic complexity attributes; in this case based on the correlation dispersion caused by the fragmentation of a municipality into its comprising parishes.

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Section: Discussionmentioning

confidence: 99%

Scale-dependent complexity in administrative units and implications for data-driven decision-making models

Søder

2023

Planning Theory

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“…Spatial autocorrelation analysis is often used as a partition reference in spatial data research. Marcos et al [6] used the spatial autocorrelation of traffic accident data as the basis for dividing research units. In this study, spatial autocorrelation was also used to test the MAUP effect of variables.…”

Section: Analysis Of Maup Effect Onmentioning

confidence: 99%

Understanding the Modifiable Areal Unit Problem and Identifying Appropriate Spatial Units while Studying the Influence of the Built Environment on the Traffic System State

Zhang

Jiang

et al. 2022

Journal of Advanced Transportation

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Spatially aggregated data are prone to the effects of the modifiable areal unit problem (MAUP), which applies to built environments and traffic data. Although various studies have been carried out to explore the impact of built environment factors on traffic systems, few have considered MAUPs, which may result in statistical inconsistency. The purpose of this study is to assess the effects of MAUPs on statistical variables and geographically weighted regression results when evaluating the influence of the built environment on the traffic system state. Fifty sets of spatial configurations were created using the different aggregation criteria. The variance inflation factor and spatial autocorrelation of the variables, as well as the R2 and root mean squared error of the GWR model, were used to assess the MAUP effect. The results show that the index variation is more dependent on the scale of the spatial unit than on zoning type. In the case study presented, based on the available dataset, the optimal spatial unit size for analyzing the influence of the built environment on Jinan’s traffic system was 900 m × 900 m.

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Influence of Different Spatial Aggregations on Variables Implemented in Macroscopic Road-Safety Modeling

Cited by 2 publications

References 21 publications

Scale-dependent complexity in administrative units and implications for data-driven decision-making models

Scale-dependent complexity in administrative units and implications for data-driven decision-making models

Understanding the Modifiable Areal Unit Problem and Identifying Appropriate Spatial Units while Studying the Influence of the Built Environment on the Traffic System State

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