Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simulation study

S, Lee; Lee, Monghyeon; Chun, Yongwan; Griffith, Daniel A.

doi:10.1080/13658816.2018.1542699

Cited by 21 publications

(12 citation statements)

References 26 publications

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“…The results show that the main reason for the positive relationship between spatial autocorrelation and the number of areal units, as reported in Table , is that the variance of the economic indicators within delineated regions decreases by the number of the total areal units (Fotheringham & Wong, ; Lee et al, ). Furthermore, regional delineations that capture commuting well, most notably LMR, LLM and NUTS 2—as measured by the indicators presented in Table —, do not cover areal units with very high variations (columns ‘max.'…”

Section: Resultsmentioning

confidence: 85%

“…broader regions seem to be less able to capture homogeneous basic units, which is in line with previous research on MAUP effects (Cliff & Ord, ; Griffith et al, ). These results might reflect the scale or zoning effects of the MAUP (Lee et al, ). Although several studies have investigated the relationship between spatial autocorrelation and the MAUP, there is still uncertainty concerning the precise effects of the MAUP (Amrhein & Reynolds, ; Fotheringham, Densham, & Curtis, ; Fotheringham & Wong, ; Lee et al, ).…”

Section: Resultsmentioning

confidence: 97%

“…This means that social interrelations in terms of labour exchange, for instance, which explain the emergence of spatial clustering of observations, are more likely to occur between those areal units that are close to each other. Results of previous research indicate that lower level aggregations are indeed more homogeneous than higher level aggregations (Cliff & Ord, 1981;Griffith et al, 2003;, Lee, Lee, Chun, & Griffith, 2018). However, we will address this question here again for the German case and using a new methodological approach:…”

Section: Research Question 2 Does Modularity Positively Correlate Wimentioning

confidence: 82%

“…Although several studies have investigated the relationship between spatial autocorrelation and the MAUP, there is still uncertainty concerning the precise effects of the MAUP (Amrhein & Reynolds, 1996;Fotheringham, Densham, & Curtis, 1995;Fotheringham & Wong, 1991;Lee et al, 2018). The results show that the main reason for the positive relationship between spatial autocorrelation and the number of areal units, as reported in Table 2, is that the variance of the economic indicators within delineated regions decreases by the number of the total areal units (Fotheringham & Wong, 1991;Lee et al, 2018). Furthermore, regional delineations that capture commuting well, most notably LMR, LLM and NUTS 2-as measured by the indicators presented in Table 1-, do not cover areal units with very high variations (columns 'max.'…”

Section: Explorative Resultsmentioning

confidence: 99%

“…Following Tobler (, p. 236), spatial interrelations are much more likely in the case of areal units which are spatially close to each other: “[…] I invoke the first law of geography: everything is related to everything else, but near things are more related than distant things.” This means that social interrelations in terms of labour exchange, for instance, which explain the emergence of spatial clustering of observations, are more likely to occur between those areal units that are close to each other. Results of previous research indicate that lower level aggregations are indeed more homogeneous than higher level aggregations (Cliff & Ord, ; Griffith et al, ;, Lee, Lee, Chun, & Griffith, ). However, we will address this question here again for the German case and using a new methodological approach:

Research question 3 .…”

Section: Spatial Interdependencies and Spatial Clusteringmentioning

confidence: 86%

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Are functional regions more homogeneous than administrative regions? A test using hierarchical linear models

Wicht

Kropp

Schwengler

2020

Papers in Regional Science

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We investigate whether economic functional regions capture spatial clustering of core economic indicators better than administrative regions. For this purpose, we use hierarchical linear models to measure the degree of homogenization of different regional delineations. Our results for Germany show that economic functional regions tend to capture spatial clustering better than administrative ones. However, a considerable amount of clustering at a lower aggregation level cannot be accounted for by economic functional regions, especially around metropolitan centres. Furthermore, economic functional regions, which depict commuting interrelations well, are less able to capture spatial homogenization than other economic functional delineations.

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 97%

Section: Research Question 2 Does Modularity Positively Correlate Wimentioning

confidence: 82%

Section: Explorative Resultsmentioning

confidence: 99%

Research question 3 .…”

Section: Spatial Interdependencies and Spatial Clusteringmentioning

confidence: 86%

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Are functional regions more homogeneous than administrative regions? A test using hierarchical linear models

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The spatiotemporal socio-demography of the Tokyo capital region: a data-driven explorative approach

Tateishi

2023

Rev Reg Res

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In the coming decades, most of Asia’s population will reside in megacities, vast urban regions accommodating 10–30 million people. However, Asian megacities will be at the same time situated in the countries whose national population is projected to decline rapidly in the coming decades. Hence, for scholars and policymakers of Asian countries, understanding how the socio-demography of mature, post-growth, megacities will evolve within space and time is crucial to envision long-term and effective spatial governance. Prior studies have shown that varied migration patterns among socio-demographic groups lead to synchronized re-urbanization, post-suburbanization, and urban shrinkage in mature city regions. However, existing studies have limitations: they often exclude large Asian megacities, lack micro-scale analyses, and use predefined spatial typologies/divisions that obscure detailed patterns. To address these research gaps, this study investigated sub-municipal spatiotemporal patterns in Tokyo, the largest Asian megacity, using micro-scale job-household data and unsupervised machine learning clustering. The study revealed that Tokyo, like Euro-American cities, has experienced regional synchronization of (re)urbanization and (post)suburbanization within a complex landscape of shrinkage. However, the synchronized sub/urban growth is not uniform across localities within Tokyo. Complex migration flows seem to create disparities in demographic growth and decline, emphasizing the need for collaborative governance among localities within a megacity. The study contributes to a wider audience who are interested not only in the evolution of cities but also in an emerging application of machine learning to quantitative urban analyses.

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Scale implications and evolution of a social vulnerability index in Atlanta, Georgia, USA

Karanja

Kiage

2022

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Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simulation study

Cited by 21 publications

References 26 publications

Are functional regions more homogeneous than administrative regions? A test using hierarchical linear models

Are functional regions more homogeneous than administrative regions? A test using hierarchical linear models

The spatiotemporal socio-demography of the Tokyo capital region: a data-driven explorative approach

Scale implications and evolution of a social vulnerability index in Atlanta, Georgia, USA

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