Practical Proposals for Specifying<i>k</i>-Nearest Neighbours Weights Matrices

Gerkman, Linda; Ahlgren, Niklas

doi:10.1080/17421772.2014.930167

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…10 LeSage and Pace (2009, p. 175-178) provide theoretical details regarding Bayesian comparison of models based on alternative weight matrices, as well as applied examples. One can also use J−tests proposed by Kelejian and Piras [15] as illustrated in Gerkman and Ahlgren [16].…”

Section: A Re-examination Of Kostov (2010)mentioning

confidence: 99%

The Biggest Myth in Spatial Econometrics

2014

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Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incorrectly interpreted the model coefficients as if they were partial derivatives, or from use of misspecified models.

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Section: A Re-examination Of Kostov (2010)mentioning

confidence: 99%

The Biggest Myth in Spatial Econometrics

2014

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“…A home's own price is excluded from the calculation by setting all values in the matrix diagonal to zero. The optimal specification of spatial weight matrices is an active area of research (Bhattacharjee and Jensen-Butler, 2013;Seya, Yamagata, and Tsutsumi, 2013;Gerkman and Ahlgren, 2014;Qu and Lee, 2015). Stakhovych and Bijmolt (2009) found that in simulated data, where the true data-generating process is known, selecting W using the Akaike information criterion (AIC) could reliably lead to models that accurately estimated regression coefficients.…”

Section: Empirical Methodsmentioning

confidence: 99%

Land Bank 2.0: An Empirical Evaluation

Whitaker

Fitzpatrick

2015

Journal of Regional Science

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ABSTRACT. In 2009, Cuyahoga County, Ohio (Cleveland and 58 suburbs), established a land bank to acquire low-value properties, mitigate blighted housing, and slow the decline of property values. This empirical study evaluates the effectiveness of the land bank by estimating spatially corrected hedonic price models using sales near the land-bank homes. The land bank reduces the negative externalities of the properties it acquires. Its largest impact is the preservation of equity in unsold homes. We also estimate the recovered value for homes sold during the study period and the property tax revenue that may have been forgone, absent the land bank.

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“…The spatial weights matrix W we use in the study is a K nearest neighbors' contiguity matrix, which was a distance-based matrix and could deal with the problem of "isolation" units (Gerkman and Ahlgren, 2014;Lesage and Fischer, 2008;Schneider et al, 2021).…”

Section: Spatial Weights Matrix Constructionmentioning

confidence: 99%

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

Chen

Wilson

et al. 2022

IJGI

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The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

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Practical Proposals for Specifyingk-Nearest Neighbours Weights Matrices

Cited by 19 publications

References 15 publications

The Biggest Myth in Spatial Econometrics

The Biggest Myth in Spatial Econometrics

Land Bank 2.0: An Empirical Evaluation

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

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