Quantitative Geography III: Future Challenges &amp;amp; Challenging Futures

Wolf, Levi John; Fox, Sean; Harris, Richard; Johnston, Ron; Jones, Karen L.; Manley, David; Tranos, Emmanouil; Wang, Wenfei Winnie

doi:10.31235/osf.io/sxrk8

Cited by 3 publications

(2 citation statements)

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“…This is a separate methodological sensitivity of the statistic that should be considered in conjunction with existing concepts like the false discovery rate or the influence of spatial scale on the analysis (Escamilla et al, 2016). These challenges are not meant to discourage the use of local Moran's I i or a particular inferential method; rather, they should help advance the theoretical justification and methodological choices made by researchers when using local Moran's I i in increasingly popular and public facing (geographic) data science workflows (Singleton and Arribas-Bel, 2019;Wolf et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

On Null Hypotheses and Heteroskedasticity

Sauer¹,

Oshan²,

Rey³

et al. 2021

Preprint

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Bivand and Wong (2018), a recent review on spatial statistical software, noted important differences in the results of the local Moran’s Ii statistic depending on the method of inference. That review speculated the differences may be due to the presence of local spatial heterogeneity. In this paper we design an experiment to assess the impact of local heterogeneity on hypothesis testing for local statistics. In this experiment, we analyze the relationship between measures of local variance, such as the local spatial heteroskedasticity (LOSH) statistic, and components of the local Moran’s Ii statistic. We consider this experiment with controlled synthetic heteroskedastic data and with uncontrolled real world data. We show that in both situations the variance components of the local Moran’s Ii statistic demonstrate a varying correlation with alternative measures of local variance like LOSH. In addition, we resituate the available inferential methods and suggest an alternative explanation for the differences observed in Bivand and Wong 2018. Ultimately, this paper demonstrates that there are important conceptual and computational differences as to what constituents a null hypothesis in local testing frameworks. Therefore, researchers must be aware as to how their choices may shape the observed spatial patterns.

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Section: Discussionmentioning

confidence: 99%

On Null Hypotheses and Heteroskedasticity

Sauer¹,

Oshan²,

Rey³

et al. 2021

Preprint

Self Cite

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show abstract

“…Another closely related but different problem is the Uncertain Point Observation Problem (Robertson and Feick, 2018), which states the uncertainty of the assignation of a point observation to a given contextual area. As Wolf et al (2021) points out, there have been important analytic developments to tackle the issues associated with MAUP, but this developments represent empirical answers to a problem that, as has been evident in the work of Robertson and Feick (2018) and Kwan (2012), is in reality theoretically oriented: solving the MAUP through the development of Optimal Zoning Schemes (Bradley et al, 2017) does not automatically relate those zones to any geographically significant process or structure.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Detection of hierarchical crowd activity structures in geographic point data (v0.2)"

2022

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Emphasising spatial structure in geosocial media data using spatial amplifier filtering

Westerholt

2021

Environment and Planning B: Urban Analytics and City Science

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In this article, a new method called spatial amplifier filtering is proposed. The presented method is related to Moran eigenvector filtering and allows the accentuation of spatial structures in heterogeneous data sets. The spatial amplifier filtering technique is based on the inclusion of certain eigenvectors of a spatial weights matrix into a regression model. The application of this method can be seen as a pre-processing step prior to subsequent analyses, and to separate different types of spatially correlated components in a data set. For this purpose, three different types of the so-called spatial amplifiers are proposed, each consisting of different subsets of eigenvectors of the weights matrix. These amplifiers can either emphasise the positive or negative spatial autocorrelation, or spatial structuring in general. In this way, it is possible to make desired spatial structures more visible, especially in spatially highly mixed data sets, whereby the focus here is on geosocial media data. In the empirical part of the article, it is first shown why georeferenced social media data are difficult to handle from a spatial analysis perspective, motivating the need for the method proposed. Subsequently, the technique of amplifier filtering is applied to two data sets: a census data set from Brazil and Twitter data from London. The results obtained show that the method is capable of strengthening existing spatial structures and mitigating potentially disturbing spatial randomness patterns and other nuisances. This facilitates the interpretation especially of the Twitter data used. While the analysis of the unfiltered Twitter data with established methods reveals little information about possible spatial structures in the tweets, the filtered data offer a much clearer picture with distinguishable clusters. In addition, the method also provides insights into the internal irregularity of spatial clusters and thus complements the toolbox for investigating spatial heterogeneity.

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Quantitative Geography III: Future Challenges & Challenging Futures

Cited by 3 publications

References 43 publications

On Null Hypotheses and Heteroskedasticity

On Null Hypotheses and Heteroskedasticity

Peer Review #1 of "Detection of hierarchical crowd activity structures in geographic point data (v0.2)"

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

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