{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:39:53Z","timestamp":1779291593858,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["17ZDA055"],"award-info":[{"award-number":["17ZDA055"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>COVID-19 has seriously threatened people\u2019s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran\u2019s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China\u2019s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.<\/jats:p>","DOI":"10.3390\/ijgi10080510","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T11:12:52Z","timestamp":1627470772000},"page":"510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3246-8586","authenticated-orcid":false,"given":"Hanchen","family":"Yu","sequence":"first","affiliation":[{"name":"Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China"},{"name":"School of Architecture, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Bardin","sequence":"additional","affiliation":[{"name":"Spatial Analysis Research Center, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1174-4940","authenticated-orcid":false,"given":"Hengyu","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Government, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenjing","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021, March 17). WHO Statement on Cases of COVID-19 Surpassing 100,000. Available online: https:\/\/www.who.int\/news\/item\/07-03-2020-who-statement-on-cases-of-covid-19-surpassing-100-000."},{"key":"ref_2","unstructured":"National Health Commission of the PRC (2021, March 17). NHC Statement on China\u2019s Wuhan Reports Zero Increase in Novel Coronavirus Infections, Available online: http:\/\/en.nhc.gov.cn\/2020-03\/19\/c_77952.htm."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109652","DOI":"10.1016\/j.envres.2020.109652","article-title":"Correlation between environmental pollution indicators and COVID-19 pandemic: A brief study in Californian context","volume":"187","author":"Bashir","year":"2020","journal-title":"Environ. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103712","DOI":"10.1016\/j.rinp.2020.103712","article-title":"A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan","volume":"20","author":"Liu","year":"2021","journal-title":"Results Phys."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Wang, Y., Chen, F., and Zhu, M. (2020). Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17113903"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"138201","DOI":"10.1016\/j.scitotenv.2020.138201","article-title":"Association between ambient temperature and COVID-19 infection in 122 cities from China","volume":"724","author":"Xie","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"138513","DOI":"10.1016\/j.scitotenv.2020.138513","article-title":"Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China","volume":"726","author":"Liu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110421","DOI":"10.1016\/j.envres.2020.110421","article-title":"Exploring the linkage between PM2.5 levels and COVID-19 spread and its implications for socio-economic circles","volume":"193","author":"Ali","year":"2021","journal-title":"Environ. Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Setti, L., Passarini, F., De Gennaro, G., Barbieri, P., Perrone, M.G., Borelli, M., Palmisani, J., Di Gilio, A., Piscitelli, P., and Miani, A. (2020). Airborne Transmission Route of COVID-19: Why 2 Meters\/6 Feet of Inter-Personal Distance Could Not Be Enough. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17082932"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Mukherji, N. (2020). The social and economic factors underlying the incidence of COVID-19 cases and deaths in US counties. MedRxiv.","DOI":"10.1101\/2020.05.04.20091041"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Whittle, R.S., and Diaz-Artiles, A. (2020). An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City. BMC Med., 18.","DOI":"10.1186\/s12916-020-01731-6"},{"key":"ref_13","unstructured":"Almagro, M., and Orane-Hutchinson, A. (2020). JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time. J. Urban Econ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Diao, Y., Kodera, S., Anzai, D., Gomez-Tames, J., Rashed, E.A., and Hirata, A. (2021). Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: A comparative study of scenarios in China, England, Germany, and Japan. One Health, 12.","DOI":"10.1016\/j.onehlt.2020.100203"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.amepre.2020.04.003","article-title":"Disparities in the Population at Risk of Severe Illness From COVID-19 by Race\/Ethnicity and Income","volume":"59","author":"Raifman","year":"2020","journal-title":"Am. J. Prev. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, B., Liu, J., Li, Y., Fu, S., Xu, X., Li, L., Zhou, J., Liu, X., He, X., and Yan, J. (2020). Airborne particulate matter, population mobility and COVID-19: A multi-city study in China. BMC Public Health, 20.","DOI":"10.1186\/s12889-020-09669-3"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"140515","DOI":"10.1016\/j.scitotenv.2020.140515","article-title":"A vulnerability-based approach to human-mobility reduction for countering COVID-19 transmission in London while considering local air quality","volume":"741","author":"Sasidharan","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_18","first-page":"389","article-title":"Population flow drives spatio-temporal distribution of COVID-19 in China","volume":"582","author":"Jia","year":"2020","journal-title":"Nat. Cell Biol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fan, C., Cai, T., Gai, Z., and Wu, Y. (2020). The Relationship between the Migrant Population\u2019s Migration Network and the Risk of COVID-19 Transmission in China\u2014Empirical Analysis and Prediction in Prefecture-Level Cities. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17082630"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.tranpol.2020.05.012","article-title":"Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China","volume":"94","author":"Zhang","year":"2020","journal-title":"Transp. Policy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"144325","DOI":"10.1016\/j.scitotenv.2020.144325","article-title":"The impact of non-pharmaceutical interventions, demographic, social, and climatic factors on the initial growth rate of COVID-19: A cross-country study","volume":"760","author":"Duhon","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"142523","DOI":"10.1016\/j.scitotenv.2020.142523","article-title":"The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors","volume":"755","author":"Perone","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.annepidem.2020.08.012","article-title":"Black\/African American Communities are at highest risk of COVID-19: Spatial modeling of New York City ZIP Code\u2013level testing results","volume":"51","author":"DiMaggio","year":"2020","journal-title":"Ann. Epidemiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"140929","DOI":"10.1016\/j.scitotenv.2020.140929","article-title":"Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors","volume":"744","author":"Xie","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Amin, R., Hall, T., Church, J., Schlierf, D., and Kulldorff, M. (2020). Geographical surveillance of COVID-19: Diagnosed cases and death in the United States. Medrxiv.","DOI":"10.1101\/2020.05.22.20110155"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"110057","DOI":"10.1016\/j.chaos.2020.110057","article-title":"A SIR model assumption for the spread of COVID-19 in different communities","volume":"139","author":"Cooper","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_28","first-page":"410","article-title":"Effect of non-pharmaceutical interventions to contain COVID-19 in China","volume":"585","author":"Lai","year":"2020","journal-title":"Nat. Cell Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/S0140-6736(20)30421-9","article-title":"COVID-19 control in China during mass population movements at New Year","volume":"395","author":"Chen","year":"2020","journal-title":"Lancet"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104272","DOI":"10.1016\/j.jpubeco.2020.104272","article-title":"Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China","volume":"191","author":"Fang","year":"2020","journal-title":"J. Public Econ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s12061-019-09322-6","article-title":"China\u2019s Highly Educated Talents in 2015: Patterns, Determinants and Spatial Spillover Effects","volume":"13","author":"Gu","year":"2020","journal-title":"Appl. Spat. Anal. Policy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127222","DOI":"10.1016\/j.ufug.2021.127222","article-title":"An Implementation Evaluation Framework of Ecological Spatial Planning at the Municipal Level Based on Multi-dimensional Data: A Case Study in China","volume":"63","author":"Fan","year":"2021","journal-title":"Urban For. Urban Green."},{"key":"ref_33","first-page":"184","article-title":"Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates","volume":"236","author":"Harko","year":"2014","journal-title":"Appl. Math. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1017\/S0266466609100099","article-title":"A spatial dynamic panel data model with both time and individual fixed effects","volume":"26","author":"Lee","year":"2010","journal-title":"Econom. Theory"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103010","DOI":"10.1016\/j.cities.2020.103010","article-title":"Spread of COVID-19 in China: Analysis from a city-based epidemic and mobility model","volume":"110","author":"Wei","year":"2021","journal-title":"Cities"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, S., Zhang, G., and Wu, S. (2015). Internal migration and the health of the returned population: A nationally representative study of China. BMC Public Health, 15.","DOI":"10.1186\/s12889-015-2074-x"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1126\/science.1245200","article-title":"The hidden geometry of complex, network-driven contagion phenomena","volume":"342","author":"Brockmann","year":"2013","journal-title":"Science"},{"key":"ref_38","unstructured":"China Health Commission (2021, March 17). China-World Health Organization Joint Investigation Report on New Coronavirus Pneumonia (COVID-19), Available online: http:\/\/www.nhc.gov.cn\/jkj\/s3578\/202002\/87fd92510d094e4b9bad597608f5cc2c.shtml."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fielding-Miller, R.K., Sundaram, M.E., and Brouwer, K. (2020). Social determinants of COVID-19 mortality at the county level. PLoS ONE, 15.","DOI":"10.1101\/2020.05.03.20089698"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.strueco.2021.01.001","article-title":"Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?","volume":"56","author":"Bloise","year":"2021","journal-title":"Struct. Chang. Econ. Dyn."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104235","DOI":"10.1016\/j.jpubeco.2020.104235","article-title":"How many jobs can be done at home?","volume":"189","author":"Dingel","year":"2020","journal-title":"J. Public Econ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1007\/s00148-020-00778-2","article-title":"Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China","volume":"33","author":"Qiu","year":"2020","journal-title":"J. Popul. Econ."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1111\/gean.12189","article-title":"Inference in multiscale geographically weighted regression","volume":"52","author":"Yu","year":"2020","journal-title":"Geogr. Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100453","DOI":"10.1016\/j.spasta.2020.100453","article-title":"On the measurement of bias in geographically weighted regression models","volume":"38","author":"Yu","year":"2020","journal-title":"Spat. Stat."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1080\/13658816.2019.1572895","article-title":"A comment on geographically weighted regression with parameter-specific distance metrics","volume":"33","author":"Oshan","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1111\/grow.12453","article-title":"Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression","volume":"52","author":"Gu","year":"2021","journal-title":"Growth Chang."},{"key":"ref_48","first-page":"1","article-title":"Exploring the Spatially-Varying Effects of Human Capital on Urban Innovation in China","volume":"4","author":"Lao","year":"2021","journal-title":"Appl. Spat. Anal. Policy"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"De Cos, O., Castillo, V., and Cantarero, D. (2021). Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space\u2013Time 3D Bins of Geocoded Daily Cases. ISPRS Int. J. Geo Inf., 10.","DOI":"10.3390\/ijgi10040261"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/S0140-6736(20)30154-9","article-title":"A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster","volume":"395","author":"Chan","year":"2020","journal-title":"Lancet"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"140430","DOI":"10.1016\/j.scitotenv.2020.140430","article-title":"Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries","volume":"742","author":"Thu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41562-020-0887-9","article-title":"Applying principles of behaviour change to reduce SARS-CoV-2 transmission","volume":"4","author":"West","year":"2020","journal-title":"Nat. Hum. Behav."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/510\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:36:06Z","timestamp":1760164566000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,28]]},"references-count":52,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["ijgi10080510"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10080510","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,28]]}}}