Does financial deepening drive spatial heterogeneity of PM2.5 concentrations in China? New evidence from an eigenvector spatial filtering approach

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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Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach

Ecological Indicators ◽

10.1016/j.ecolind.2021.108001 ◽

2021 ◽

Vol 129 ◽

pp. 108001

Author(s):

Wenming Shi ◽

Yuquan Du ◽

Chia-Hsun Chang ◽

Son Nguyen ◽

Jun Wu

Keyword(s):

Spatial Heterogeneity ◽

Spatial Filtering ◽

Driving Factors ◽

So2 Emissions ◽

Filtering Approach

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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

10.21203/rs.3.rs-910492/v1 ◽

2021 ◽

Author(s):

Meijie Chen ◽

Yumin Chen ◽

John P Wilson ◽

Huangyuan Tan ◽

Tianyou Chu

Keyword(s):

Risk Factors ◽

Health Risk ◽

Spatial Heterogeneity ◽

Mainland China ◽

Spatial Filtering ◽

Hubei Province ◽

Varying Coefficient Model ◽

Varying Coefficient ◽

Eigenvector Spatial Filtering ◽

Spatially Varying

Abstract Background: The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of health risk factors on COVID-19 rates in different places, but the problem of spatial heterogeneity has not been adequately addressed.Methods: In this paper, we developed an Eigenvector Spatial Filtering based spatially varying coefficient model (ESF-SVC) to reveal the spatially varying impact of certain health risk factors on the COVID-19 spread. The experiment was conducted during 7 weeks within two study extents (Hubei province and mainland China). Spatial varying coefficient maps were produced for spatial pattern discovery.Results: Results showed that the ESF-SVC model could take good control of over-fitting problems, with average adjusted R2 16.31% (in Hubei province) and 10.25% (in mainland China) higher than that of GWR. The cross validation RMSE of ESF-SVC model was also the lowest. In Hubei province, Population density and wind speed had a significant impact on COVID-19 infection rates and that their effect was constant across cities. While in mainland China, migration score, building density, temperature and altitude showed significant impact and their effect varies across space. The influence of migration score was less contributive and less significant in cities around Wuhan than cities farther away, while the altitude showed stronger contributions in high altitude cities.Conclusions: Our study hopes to provide not only a feasible path to solve the problem of spatial autocorrelation and spatial heterogeneity in COVID-19 characterization but also an intuitive way to discover spatial patterns in large study areas, which could help people and government awareness of the potential health risks and shed some light in COVID-19 control strategies.

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House price estimation using an eigenvector spatial filtering approach

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-09-2019-0097 ◽

2019 ◽

Vol 13 (5) ◽

pp. 845-867 ◽

Cited By ~ 2

Author(s):

Michael James McCord ◽

John McCord ◽

Peadar Thomas Davis ◽

Martin Haran ◽

Paul Bidanset

Keyword(s):

Spatial Autocorrelation ◽

House Prices ◽

Spatial Scales ◽

House Price ◽

Spatial Filtering ◽

Spatial Dependency ◽

Content Type ◽

Price Estimation ◽

Eigenvector Spatial Filtering ◽

Filtering Approach

Purpose Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity. Design/methodology/approach Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria. Findings The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error. Originality/value Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.

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Modeling Network Autocorrelation in Space–Time Migration Flow Data: An Eigenvector Spatial Filtering Approach

Annals of the Association of American Geographers ◽

10.1080/00045608.2011.561070 ◽

2011 ◽

Vol 101 (3) ◽

pp. 523-536 ◽

Cited By ~ 80

Author(s):

Yongwan Chun ◽

Daniel A. Griffith

Keyword(s):

Spatial Filtering ◽

Space Time ◽

Flow Data ◽

Migration Flow ◽

Network Autocorrelation ◽

Time Migration ◽

Eigenvector Spatial Filtering ◽

Filtering Approach

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A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8110508 ◽

2019 ◽

Vol 8 (11) ◽

pp. 508

Author(s):

Lan Hu ◽

Yongwan Chun ◽

Daniel A. Griffith

Keyword(s):

Multilevel Model ◽

House Prices ◽

House Price ◽

Spatial Filtering ◽

Neighborhood Environment ◽

Model Specification ◽

Spatial Effects ◽

Residential Areas ◽

Fairfax County ◽

Eigenvector Spatial Filtering

House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices.

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