{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:25:52Z","timestamp":1772252752670,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key S&amp;T Special Projects of China","award":["2017YFB0503704"],"award-info":[{"award-number":["2017YFB0503704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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 \u00b0C and 8.17 \u00b0C, 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.<\/jats:p>","DOI":"10.3390\/ijgi11010067","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:44:00Z","timestamp":1642365840000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["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"],"prefix":"10.3390","volume":"11","author":[{"given":"Meijie","family":"Chen","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5969-0729","authenticated-orcid":false,"given":"John P.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089-0374, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huangyuan","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyou","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"141447","DOI":"10.1016\/j.scitotenv.2020.141447","article-title":"Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran","volume":"747","author":"He","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_2","first-page":"157","article-title":"WHO Declares COVID-19 a Pandemic","volume":"91","author":"Cucinotta","year":"2020","journal-title":"Acta Biomed."},{"key":"ref_3","unstructured":"World Health Organization (WHO) (Emergency Situational Updates, 2021). Weekly Operational Update on COVID-19, Emergency Situational Updates."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100121","DOI":"10.1016\/j.puhip.2021.100121","article-title":"Modelling the effectiveness of intervention strategies to control COVID-19 outbreaks and estimating healthcare demand in Germany","volume":"2","author":"Chadsuthi","year":"2021","journal-title":"Public Health Pract."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109761","DOI":"10.1016\/j.chaos.2020.109761","article-title":"Analysis and forecast of COVID-19 spreading in China, Italy and France","volume":"134","author":"Fanelli","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1038\/s41591-020-1132-9","article-title":"Modeling COVID-19 scenarios for the United States","volume":"27","author":"Reiner","year":"2021","journal-title":"Nat. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107161","DOI":"10.1016\/j.asoc.2021.107161","article-title":"Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Averag","volume":"103","author":"ArunKumar","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.ijid.2021.04.021","article-title":"Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China","volume":"110","author":"Hu","year":"2021","journal-title":"Int. J. Infect. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1016\/j.aej.2021.02.058","article-title":"A multi-stage SEIR model to predict the potential of a new COVID-19 wave in KSA after lifting all travel restrictions","volume":"60","author":"Kolsi","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.psep.2021.03.032","article-title":"Forecasting outbreak of COVID-19 in Turkey; Comparison of Box\u2013Jenkins, Brown\u2019s exponential smoothing and long short-term memory models","volume":"149","author":"Guleryuz","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115104","DOI":"10.1016\/j.eswa.2021.115104","article-title":"News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston","volume":"180","author":"Desai","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"74501","DOI":"10.1289\/EHP4868","article-title":"Top 10 Research Priorities in Spatial Lifecourse Epidemiology","volume":"127","author":"Jia","year":"2019","journal-title":"Environ. Health Perspect."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.puhe.2020.09.016","article-title":"Socio-economic status and COVID-19\u2013related cases and fatalities","volume":"189","author":"Hawkins","year":"2020","journal-title":"Public Health"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110184","DOI":"10.1016\/j.envres.2020.110184","article-title":"How socio-economic and atmospheric variables impact COVID-19 and influenza outbreaks in tropical and subtropical regions of Brazil","volume":"191","author":"Martins","year":"2020","journal-title":"Environ. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"138226","DOI":"10.1016\/j.scitotenv.2020.138226","article-title":"Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China","volume":"724","author":"Ma","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102887","DOI":"10.1016\/j.scs.2021.102887","article-title":"Does airborne pollen influence COVID-19 outbreak?","volume":"70","author":"Ravindra","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2019034118","DOI":"10.1073\/pnas.2019034118","article-title":"Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe","volume":"118","author":"Damialis","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Messner, W. (2020). The Institutional and Cultural Context of Cross-National Variation in COVID-19 Outbreaks. medRxiv.","DOI":"10.1101\/2020.03.30.20047589"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9352","DOI":"10.1007\/s10668-020-01028-x","article-title":"A global analysis on the effect of temperature, socio-economic and environmental factors on the spread and mortality rate of the COVID-19 pandemic","volume":"23","author":"Rahman","year":"2021","journal-title":"Environ. Dev. Sustain."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"142396","DOI":"10.1016\/j.scitotenv.2020.142396","article-title":"Analyzing the spatial determinants of local COVID-19 transmission in the United States","volume":"754","author":"Andersen","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"144455","DOI":"10.1016\/j.scitotenv.2020.144455","article-title":"Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong","volume":"764","author":"Kwok","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"379","DOI":"10.33182\/ml.v17i2.935","article-title":"Coronavirus and migration: Analysis of human mobility and the spread of COVID-19","volume":"17","author":"Sirkeci","year":"2020","journal-title":"Migr. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"830","DOI":"10.2471\/BLT.20.258152","article-title":"Fangcang shelter hospitals during the COVID-19 epidemic, Wuhan, China","volume":"98","author":"Li","year":"2020","journal-title":"Bull. World Health Organ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"taaa038","DOI":"10.1093\/jtm\/taaa038","article-title":"High population densities catalyse the spread of COVID-19","volume":"27","year":"2020","journal-title":"J. Travel Med."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Luo, M., Qin, S., Tan, B., Cai, M., Yue, Y., and Xiong, Q. (2021). Population Mobility and the Transmission Risk of the COVID-19 in Wuhan, China. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10060395"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"143343","DOI":"10.1016\/j.scitotenv.2020.143343","article-title":"Natural and human environment interactively drive spread pattern of COVID-19: A city-level modeling study in China","volume":"756","author":"Wu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5977","DOI":"10.1038\/s41598-021-85493-y","article-title":"The impact of environmental variables on the spread of COVID-19 in the Republic of Korea","volume":"11","author":"Lim","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1007\/s11869-020-00894-8","article-title":"A brief review of socio-economic and environmental impact of COVID-19","volume":"13","author":"Bashir","year":"2020","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"138778","DOI":"10.1016\/j.scitotenv.2020.138778","article-title":"COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis","volume":"728","author":"Qi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"138704","DOI":"10.1016\/j.scitotenv.2020.138704","article-title":"Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China","volume":"727","author":"Zhu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"138860","DOI":"10.1016\/j.scitotenv.2020.138860","article-title":"Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020","volume":"728","author":"Gupta","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"138810","DOI":"10.1016\/j.scitotenv.2020.138810","article-title":"Impact of weather on COVID-19 pandemic in Turkey","volume":"728","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.ijid.2021.03.014","article-title":"Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors","volume":"105","author":"Wang","year":"2021","journal-title":"Int. J. Infect. Dis."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1289\/ehp.6735","article-title":"Spatial epidemiology: Current approaches and future challenges","volume":"112","author":"Elliott","year":"2004","journal-title":"Environ. Health Perspect."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.pt.2019.12.012","article-title":"Spatial Lifecourse Epidemiology and Infectious Disease Research","volume":"36","author":"Jia","year":"2020","journal-title":"Trends Parasitol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, Z. (2021). Spatiotemporal Evolution Patterns of the COVID-19 Pandemic Using Space-Time Aggregation and Spatial Statistics: A Global Perspective. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080519"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102418","DOI":"10.1016\/j.scs.2020.102418","article-title":"Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach","volume":"62","author":"Sannigrahi","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, H., Li, J., Bardin, S., Gu, H., and Fan, C. (2021). Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080510"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1289\/ehp.10816","article-title":"Methodologic Issues and Approaches to Spatial Epidemiology","volume":"116","author":"Beale","year":"2008","journal-title":"Environ. Health Perspect."},{"key":"ref_41","first-page":"1247","article-title":"Multiscale Geographically Weighted Regression (MGWR)","volume":"107","author":"Fotheringham","year":"2017","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1538-4632.1996.tb00936.x","article-title":"Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity","volume":"28","author":"Brunsdon","year":"1996","journal-title":"Geogr. Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"144257","DOI":"10.1016\/j.scitotenv.2020.144257","article-title":"Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors","volume":"761","author":"Han","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"138884","DOI":"10.1016\/j.scitotenv.2020.138884","article-title":"GIS-based spatial modeling of COVID-19 incidence rate in the continental United States","volume":"728","author":"Mollalo","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.amepre.2020.06.006","article-title":"The Impact of Social Vulnerability on COVID-19 in the U.S.: An Analysis of Spatially Varying Relationships","volume":"59","author":"Karaye","year":"2020","journal-title":"Am. J. Prev. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102471","DOI":"10.1016\/j.healthplace.2020.102471","article-title":"Spatial variation in socio-ecological vulnerability to COVID-19 in the contiguous United States","volume":"66","author":"Snyder","year":"2020","journal-title":"Health Place"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"102627","DOI":"10.1016\/j.scs.2020.102627","article-title":"Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR)","volume":"65","author":"Mansour","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102784","DOI":"10.1016\/j.scs.2021.102784","article-title":"Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States","volume":"68","author":"Maiti","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10109-005-0155-6","article-title":"Multicollinearity and correlation among local regression coefficients in geographically weighted regression","volume":"7","author":"Wheeler","year":"2005","journal-title":"J. Geogr. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1068\/a38218","article-title":"Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR)","volume":"40","author":"Griffith","year":"2008","journal-title":"Environ. Plan. A"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.spasta.2016.12.001","article-title":"A Moran coefficient-based mixed effects approach to investigate spatially varying relationships","volume":"19","author":"Murakami","year":"2017","journal-title":"Spat. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"117205","DOI":"10.1016\/j.atmosenv.2019.117205","article-title":"An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China","volume":"223","author":"Tan","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_53","unstructured":"Murakami, D. (2017). Spatial regression modeling using the spmoran package: Boston housing price data examples. arXiv."},{"key":"ref_54","unstructured":"(2020, June 21). COVID-19 Epidemic Data in China, Available online: http:\/\/www.nhc.gov.cn\/xcs\/yqtb\/list_gzbd.shtml."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1175\/2011BAMS3015.1","article-title":"The Integrated Surface Database: Recent Developments and Partnerships","volume":"92","author":"Smith","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_56","unstructured":"(2020, June 16). The Integrated Surface Database, Available online: https:\/\/www.ncei.noaa.gov\/products\/land-based-station\/integrated-surface-database."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1080\/13658810601169899","article-title":"An evaluation of void-filling interpolation methods for SRTM data","volume":"21","author":"Reuter","year":"2007","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_58","unstructured":"Jarvis, A., Reuter, H.I., Nelson, A.E., and Guevara, E. (2020, June 20). Hole-Filled Seamless SRTM Data V4, International Centre for Tropical Agriculture (CIAT). Available online: https:\/\/srtm.csi.cgiar.org."},{"key":"ref_59","unstructured":"(2020, June 21). Statical Year Book of 2018. Available online: https:\/\/data.cnki.net\/Yearbook."},{"key":"ref_60","unstructured":"(2020, June 21). Baidu Qianxi Platform. Available online: https:\/\/qianxi.baidu.com\/."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","article-title":"An Analysis of Transformations","volume":"26","author":"Box","year":"1964","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1111\/j.1541-0064.1996.tb00462.x","article-title":"Spatial Autocorrelation And Eigenfunctions Of The Geographic Weights Matrix Accompanying Geo-Referenced Data","volume":"40","author":"Griffith","year":"1996","journal-title":"Can. Geogr.\/Le G\u00e9ogr. Can."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"140093","DOI":"10.1016\/j.scitotenv.2020.140093","article-title":"Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities","volume":"740","author":"Runkle","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"45087","DOI":"10.1007\/s11356-021-13834-5","article-title":"Association between environmental factors and COVID-19 in Shanghai, China","volume":"28","author":"Ma","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1111\/tgis.12333","article-title":"Mapping the spatial disparities in urban health care services using taxi trajectories data","volume":"22","author":"Chen","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_66","unstructured":"Casella, G., Fienberg, S., and Olkin, I. (2006). Springer Texts in Statistics, Springer International Publishing."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1080\/17421772.2014.930167","article-title":"Practical Proposals for Specifying k-Nearest Neighbours Weights Matrices","volume":"9","author":"Gerkman","year":"2014","journal-title":"Spat. Econ. Anal."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1080\/17421770802353758","article-title":"Spatial Growth Regressions: Model Specification, Estimation and Interpretation","volume":"3","author":"Lesage","year":"2008","journal-title":"Spat. Econ. Anal."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Rogerson, P. (2001). Statistical Methods for Geography, SAGE Publications.","DOI":"10.4135\/9781849209953"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1080\/01621459.1984.10478083","article-title":"Cross-Validation of Regression Models","volume":"79","author":"Picard","year":"1984","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_71","unstructured":"Ghojogh, B., and Crowley, M. (2019). The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Liu, L., and \u00d6zsu, M.T. (2009). Cross-Validation BT. Encyclopedia of Database Systems, Springer.","DOI":"10.1007\/978-0-387-39940-9"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Brenning, A. (2012, January 22\u201327). Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352393"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2980","DOI":"10.1016\/j.jspi.2010.03.045","article-title":"The Moran coefficient for non-normal data","volume":"140","author":"Griffith","year":"2010","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2015.12.002","article-title":"Spatially varying coefficient models in real estate: Eigenvector spatial filtering and alternative approaches","volume":"57","author":"Helbich","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"141663","DOI":"10.1016\/j.scitotenv.2020.141663","article-title":"The spread of COVID-19 virus through population density and wind in Turkey cities","volume":"751","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.apr.2020.10.002","article-title":"How do low wind speeds and high levels of air pollution support the spread of COVID-19?","volume":"12","author":"Coccia","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Abdel-Aal, M.A.M., Eltoukhy, A.E.E., Nabhan, M.A., and AlDurgam, M.M. (2021). Impact of climate indicators on the COVID-19 pandemic in Saudi Arabia. Environ. Sci. Pollut. Res.","DOI":"10.1007\/s11356-021-17305-9"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.psep.2020.05.029","article-title":"Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks","volume":"141","author":"Saba","year":"2020","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/s42003-021-01677-2","article-title":"An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China","volume":"4","author":"Shi","year":"2021","journal-title":"Commun. Biol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"taaa037","DOI":"10.1093\/jtm\/taaa037","article-title":"The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China","volume":"27","author":"Lau","year":"2020","journal-title":"J. Travel Med."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"141347","DOI":"10.1016\/j.scitotenv.2020.141347","article-title":"Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China","volume":"746","author":"Sun","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"143783","DOI":"10.1016\/j.scitotenv.2020.143783","article-title":"Meteorological factors and COVID-19 incidence in 190 countries: An observational study","volume":"757","author":"Guo","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.ijid.2020.04.068","article-title":"Effect of temperature on the infectivity of COVID-19","volume":"95","author":"Ujiie","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.pcad.2020.04.012","article-title":"Towards precision management of cardiovascular patients with COVID-19 to reduce mortality","volume":"63","author":"Zhou","year":"2020","journal-title":"Prog. Cardiovasc. Dis."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"40424","DOI":"10.1007\/s11356-021-12364-4","article-title":"Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic","volume":"28","author":"Yin","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"103153","DOI":"10.1016\/j.jtrangeo.2021.103153","article-title":"Exploring the dynamic impacts of COVID-19 on intercity travel in China","volume":"95","author":"Li","year":"2021","journal-title":"J. Transp. Geogr."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/1\/67\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:30Z","timestamp":1760362050000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/1\/67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,15]]},"references-count":87,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["ijgi11010067"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11010067","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-910492\/v1","asserted-by":"object"}]},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,15]]}}}