The two concepts of "liu dong ren kou (the floating population)" and "ren kou liu dong (the mobility of the floating population)", and relevant data based on these two concepts, have long been used extensively in China's relevant research and policy making, playing a dominant role in the fields. Different from the concepts of "migrant" and "migration" in the international literature, which are focused on people's spatial mobility, "liu dong ren kou" and "ren kou liu dong" are identified and measured by the separation of one's place of household registration (hukou) from the place of residence, and inconsistent with relevant international practices. By analysing various census data and data from China Migrant Dynamic Survey (CMDS), this article examines the validity and reliability of these two concepts and data based on them in the international context, and reveals that they have become increasingly invalid and not reliable in measuring migration events since the reform and opening up. The results further demonstrate that these two concepts and the data based on them have been increasingly detached from the real migration events and processes; they may become invalid because of overestimating the volume of the mobile population, or ineffective due to systematic omitting of certain group of the mobile population (such as urban-urban migrants), and can even seriously mislead people's judgment on the changing direction of migration flows. In addition, data on the floating population cannot be used to calculate migration rate and are not comparable in the international context. Based on the above analysis, the article argues that while the concepts of "liu dong ren kou" and "ren kou liu dong" and relevant data based on these two concepts still need to be used for a long period of time due to the continuing existence of the hukou system and its roles in the provision of public services, social welfare and social security, relevant concepts, measurements and ways of data collection in China's migration research should be gradually shifted to and focused on the nature of migration as spatial events; transition data based on the usual residence five years and one year ago should be gradually used as the main data sources and included in the short form of future censuses, and migration event data based on population registration and administrative records should be more fully used, so that China's migration research can be conducted on the solid basis of valid and reliable data sources.

Urbanization is the outcome of population dynamics in both urban and rural areas, which has a profound influence on urban-rural development. This paper puts forward a method to identify regional types of urbanization from the perspective of urban-rural population change. Using this method and based on the census data, this paper analyzes the spatial pattern and influencing factors of China's urbanization. The results show that China has entered an era of rapid urbanization since 1990. Urbanization rates have risen in almost all regions, but with varied pathways over time and across regions. With the decline of natural population growth and the expansion of rural-urban migration, the process of urbanization depends more and more on rural population loss, leading to the transformation of urbanization regional type from "urban-rural growth" to "urban growth" in the country. Although the "urban growth" is the most common regional type of urbanization at both provincial and prefecture levels, the transformation of urbanization regional type from "urban growth" to "rural loss" has been witnessed in a large number of regions in the past decade. Besides, regions with the "urban-rural growth" type have shrunk to places such as Tibet and Shanghai while regions with the "urban-rural loss" type have expanded in non-provincial capital cities in Northeast China. At present, China's urbanization regional type is still driven by economic development: rural population declines faster in areas with rapid economic growth, leading to the "rural loss" urbanization; rapidly industrialized regions have undergone faster urban population growth and slower rural population loss than their counterparts; while the effect of social factors such as public services on urbanization regional type is limited. In addition, the impact of new urbanization strategy and rural revitalization strategy is not yet clear, given the limited attractiveness of urban agglomerations of the western region as well as small and medium-sized cities and the prevalence of rural population loss.

The "Hu Line" is a geographical line connecting Heihe City and Tengchong City in China. It is densely populated on the southeast side of the "Hu Line" but sparsely populated on the northwest side. Since the implementation of New-type Urbanization strategy in 2014, the spatial pattern of population distribution on both sides of the "Hu Line" has changed a lot. We put forward the "Quasi-Hu Line" based on the boundaries of prefecture-level administrative divisions. According to the seventh population census data in 2020, this study focuses on the new characteristics of population spatial patterns and population growth patterns on both sides of the "Quasi Hu Line" from 2010 to 2020. We analyze the driving factors combined with the development of New-type Urbanization. The main conclusions are as follows: (1) The human-land relationship reflected by the "Hu Line" was still stable, and the overall distributions of densely populated and sparsely populated areas were also steady. In 2020, the population ratio of the southeast side to the northwest side was 93.5∶6.5. (2) The population share of the northwest side still increased slightly, but the increment was significantly lower than that before 2010. (3) The centralization index of the population on the northwest side turned from decline to increase after 2010, which indicated the concentration of spatial population distribution in the northwest half was similar to that in the southeast half. (4) The spatial differentiation of population growth on the southeast side has been strengthened. The negative population growth area accounted for 54.22% of the southeast side from 2010 to 2020, exceeding the positive population growth area. The population growth on the northwest side had also changed from the "relative balance" model to the "Matthew effect" model, with the negative population growth area accounting for 26.14% of the northwest side. (5) The relative population growth advantage of provincial capitals was becoming much more prominent. The spatial polarization process of population growth slowing down or negative growth was formed in the surrounding areas of provincial capitals. In addition, such a process on the northwest side was more significant than on the southeast side. (6) The driving factors, including the level of economic development, the scale of urban population size, and the level of administrative divisions, significantly impacted population growth. For the northwest side, the level of administrative divisions had a particularly significant impact on population growth, while the impact of urban agglomerations was not noticeable. In general, the northwest side had begun to present similar population spatial distribution patterns and driving factors with the southeast side. In the context of population migration and urbanization, the novel patterns of spatial population distribution reflected a kind of "breakthrough" of the "Hu Line". With China's total population turning to decline and urbanization entering the mid-late stages, more attention should be paid to the regional differences and coordination of demographical structures on both sides of the "Hu Line".

Understanding the geographical pattern and driving factors of China's population migration in the first 20 years of the 21st century has become a pivotal issue in promoting the construction of the new-type urbanization and interaction development between regions during the 14th Five-Year Plan period (2021-2025). Supported by the data of the sixth and seventh national population censuses and 2005 and 2015 national 1% population sample surveys, this paper aims to investigate the spatiotemporal evolution characteristics and influencing factors of interprovincial migration in China from 2000 to 2020. Relevant population geospatial analysis and the eigenvector spatial filtering Poisson pseudo-maximum likelihood estimation (ESF PPML) gravity model are employed. The following conclusions are drawn: (1) China's population migration patterns were generally stable from 2000 to 2020, where notable imbalance and network spillover effects were also detected. A large number of migrants from central, western and northeast parts of China tended to move to the eastern region. (2) The overall migration patterns between 2000 and 2020 revealed several changing characteristics. The degree of imbalance and spatial agglomeration of migration patterns gradually weakened. Yet, a north-south differentiation emerged. It is also found that the out-migration trend of the northeast region increased with the elaspe of time. (3) The ESF PPML model indicated that gravity factors (population scale, geographical distance), regional socio-economic factors (wage disparity, proportion of investment in science and technology and education, medical care), social network factors, environmental comfort (PM_2.5 concentration) and living costs (proportion of housing price) drove the interprovincial migration pattern between 2000 and 2020. (4) The influence of regional economic differences on interprovincial migration reflected a weakening trend. The pattern of population migration gradually turned into the decision-making of diversified demands for the economy, public services and urban amenities, while the living cost factor represented by housing price had played a gradually significant part. The conclusion of this paper provides policy references for the construction of the new-type urbanization and coordinated development between regions during the 14th Five-Year Plan period in China.

Based on the data of the sixth and seventh national censuses (Jiangsu province), this paper seeks to explore the spatiotemporal differentiation and influencing mechanism of the distribution of floating population in cities (districts), towns, and rural areas from 2010 to 2020. The findings show that: (1) The floating-permanent population ratio in cities and towns in Jiangsu generally increased from 2010 to 2020, but the rate of increase in towns was significantly higher than that in cities; the floating-permanent population ratio decreased in nearly half of the units in rural areas. (2) The gradient pattern (south Jiangsu - central Jiangsu - north Jiangsu) of the distribution of the floating population remained stable. South Jiangsu is still a highland for attracting the floating population, but towns in central and north Jiangsu have become attractive to the floating population in the province. There are also differences in the distribution of the intra- and inter-floating population in cities, towns, and rural areas. (3) The share of the floating population in parts of rural areas in south Jiangsu and some towns in central and north Jiangsu increased significantly, reflecting the agglomeration capacity of the towns and rural areas for the floating population. (4) The results of the models show that the increase in government social investments is important to enhancing the attractiveness of cities, towns, and rural areas to the floating population; the Engel coefficient of urban residents has different effects on intra- and inter-floating population in cities and towns; the impacts of industry in cities, towns and rural areas are positive; the upgrading of the economic structure only has a positive effect on the ratio of intra-floating population in cities and towns; the effect of the farming industry is negative in rural areas.

As the speed of population ageing increases in China, understanding the spatial pattern evolution of the older adults' health level is of great significance to the implementation of the healthy aging strategy. Based on the self-rated health data of the older adults from the sixth and seventh national censuses, this paper tried to depict the dynamic evolution of the older adults' health level in urban and rural areas across different provincial-level regions in China from 2010 to 2020 by using exploratory spatial data analysis method. Then this paper built Gravity-GTWR models to explore the temporal and spatial variations in the factors influencing the older adults' health, such as socioeconomic development, population migration, environmental pollution and protection. The main findings were as follows: (1) From 2010 to 2020, the health level of China's older adults increased overall, but the gap between urban and rural areas further expanded. In terms of the health level of the urban older adults, the pattern of "decreasing along east-middle-west" tended to be strengthened. As for the health level of the rural older adults, the spatial pattern has transferred from "decreasing along east-middle-west" to "high in the south and low in the north". (2) Although the role of socioeconomic development was weakened, it was still the primary factor that shaped and altered the spatial pattern. The rural-urban population migration widened the urban-rural gap in the health of older adults. (3) The role of natural environmental background tended to be weakened, while the role of environmental pollution was enhanced. The strengthened environmental protection measures started to exert positive effects on the health level of older adults in urban areas.

Understanding the characteristics of transnational migration in Asia is beneficial for China to formulate a reasonable international migration policy in the new era and promote the high-quality development of the Belt and Road Initiative. Based on the data of bilateral international migration flows from 1990 to 2015, this study tries to clarify the temporal and spatial patterns and influencing factors of Asian transnational migration by using social network analysis and spatial filtering panel negative binomial gravity model (ESF). The results are listed as follows: First, the main migration flows in Asia from 1990 to 2000 were concentrated in West Asia, South Asia and Southeast Asia, and most of them occurred between the adjacent countries and regions. From 2000 to 2015, there was a number of large-scale migration flows across the above three subregions. Second, the scale of the migration network in Asia is relatively low, and the degree of closeness between the Asian countries has increased first and then weakened over time. Third, political instability and war conflicts of the origin were important driving forces for transnational migration in Asia. Besides, the differences of economic development and national income between countries were the crucial driving forces, while multi-dimensional proximity factors played an important role in promoting transnational migration. Fourth, from 1990 to 2015, the effect of economic differences on transnational migration increased first and then weakened. In addition, political instability has always been the main influencing factors for migration. Besides, the development of import trade has positively promoted transnational migration, and the attractiveness of studying abroad has a fluctuating effect on transnational migration. Finally, non-economic and structural impacts were the most important factors influencing the transnational migration in Asia.

The household registration (hukou) system divides China's internal migration into two types: hukou and non-hukou migration. Hukou migration is also known as permanent migration, while non-hukou migration is temporary migration. Permanent migrants are those whose registered place is the same as their regular residence place, and temporary migrants otherwise. Most temporary rural-urban migrants without local hukou cannot obtain the same public services as urban inhabitants, which affects the improvement of the quality of urbanization. Hukou migration is the last step of population urbanization. However, existing studies mostly focus on non-hukou migration, but ignore hukou migration. Also, to connect the behaviors and intentions of hukou transfer, it is necessary to explore spatial patterns and determinants of hukou migration. The comparison and integration of hukou and non-hukou migration contributes to constructing an integrated conceptual framework of China's internal migration, which is an effective way to theorize and internationalize China's internal migration research. This paper estimates the scale of hukou and non-hukou net migration at prefecture-level cities across the country from 2011 to 2017. We analyze and compare the basic spatial patterns of the two, and explore their influencing factors. The results showed that hukou and non-hukou migration have overall similar spatial positive autocorrelation and different local clustering characteristics in inland regions. Although two types of migrants are primarily concentrated in three coastal regions, there are significant internal variations due to local urban system. It is easy to change residential location but difficult to obtain local hukou in Beijing-Tianjin-Hebei region, but totally contrast in the other two regions. The scale distribution of non-hukou migration is more polarized than that of hukou migration. We also found that the growth of the two is positively correlated with the existing floating migrants. The cities with more floating migrants will attract more hukou and non-hukou migrants to flow in. Hukou migration mainly responds to differences in economic opportunities, whereas non-hukou migration mainly responds to differences in public services such as education and health care. Non-hukou migration has gradually shifted from economic-driven migration to amenity migration, while hukou migration showed stronger market-oriented characteristics than before. But behind that, there is two-way selectivity between the big city and the talent group. Finally, the paper discusses the transformation characteristics of China's internal migration and the reform direction of the hukou system from the perspective of theory and policy. We suggest the amenity-based urban growth strategy against the context of changing residents' demands.

Based on registered population data, this paper examined a variety of population density models to explore the spatio-temporal pattern of the registered population of Nanjing during the past century. The results showed that: (1) The number of registered residents in Nanjing from 1928 to 2017 showed a flat "S"-shaped growth trend, forming an extensive dispersion with localized concentration types. The population density was the greatest in the Qinhuai River area, which has been the main registered population center. (2) The spatial structure of the registered population included: point-single center, axis-double center, enclave compound-multi center, and radiation-circle polycentric center. (3) The polycentric model, which is based on the central place theory, provided the best explanation for the features of the spatial structure of the registered population. The registered population distribution pattern in Nanjing was attributed to the joint action of four spatial location rules: the distance-decay law, allometric growth effect, spatial polarization characteristic, and regional-edge structure. (4) The transitions in the city during the past century were not a de-urbanization but a temporary fluctuation because of social transformation. Notable differences remained between traditional-Chinese residential center and the central business district.

The migration of skilled individuals has become an important issue in promoting new-type urbanization in China and a key factor affecting China's regional innovation output and high-quality development. Considering the issues of zero inflation and network autocorrelation in skilled migration data, this paper combines the eigenvector space filtering (ESF) technique and the "two-stage" hurdle model into a comprehensive united framework to construct a longitudinal spatial hurdle gravity model. It then has been employed in the case study exploring the spatiotemporal patterns and influencing factors of interprovincial skilled migration in China during 2000-2015. The results are listed as follows: First, from 2000 to 2015, the mobility proportion of the skilled migration increased first and then decreased. The agglomeration pattern of skilled migration maintains the imbalance of its spatial distribution. With the elapse of time, the migration of skilled presents a dispersing trend and drives the decline of its spatial distribution and agglomeration. Talent migration presents a persistent and significant network autocorrelation, and its distribution presents a persistent and significant spatial autocorrelation. Second, China's interprovincial skilled migration during 2000-2015 was driven by gravity factors (population scales at origin and destination, distance), regional economic and scientific and technological development (average wage, spending on science & technology and education), natural amenities (average temperature difference, air quality), urban amenities (public health and education services, urban greening), and other factors (social networks, the cost of living, and population density). Third, the migration of skilled people can be regarded as a "two-stage" process, where factors affecting its migration probability and migration scale are different. Such differences are mostly reflected in factors of amenities versus economy. Fourth, the impact of economic growth, investment in science and education, natural amenities, and basic public services on skilled migration has increased over time, while the impact of wages and urban greening has weakened over time. The conclusion of this paper provides policy references for regional talent management and the balance of regional development.

The social integration of urban floating population is the key task of the new-type urbanization in China. In view of the deficiencies of existing studies on the spatial characteristics of social integration and the role of destination city (place of inflow) factors, based on the China Migrants Dynamic Survey in 2017, this paper employs spatial analysis and hierarchical linear model to reveal the spatial pattern and influencing mechanism of social integration of migrants in cities above prefecture level in China. The results show that: (1) the overall social integration index of China's urban migrants is 0.43, which has the dimensional characteristics of behavioral integration (0.71) > psychological integration (0.61) > economic integration (0.33). (2) The social integration of migrants has spatial autocorrelation and agglomeration. The high-level social integration areas are mainly distributed in Northeast China, Shandong Peninsula, and Sichuan and Chongqing, and the low-level social integration areas are mainly found in the southeast coastal areas, Guanzhong Plain and North China Plain. These regions have the basic characteristics that the social integration level of migrants in small cities with a population of less than 0.5 million is higher than that in large cities. (3) Both the destination city and migrant individual factors jointly affect and shape the level and spatial pattern of migrants' social integration. Moreover, it is found that the population size, house price level and public services in the destination city regulate the impact of individual factors by four regulatory mechanisms: positive polarization, negative polarization, positive homogenization and negative homogenization. This paper calls for the research on the social integration of migrants from the human-land interaction perspective highlighting the relation of destination-individual factors to reveal the effect of destination factors on individual factors. This approach can provide a place-based policy implication for promoting the construction of new-type urbanization in China.

From 2020 to 2030, accelerating the citizenization of the floating population is the key to promoting new urbanization and achieving common prosperity. The urbanization rate of registered population in China is roughly 18% lower than that of permanent residents. The aging pressure and lack of labor force make big cities introduce relevant policies to attract talents, and the citizenization process needs to be improved urgently, with the focus on megacities. The transformation number of registered people in megacities varies greatly, and the academic world lacks research on this, which makes the transformation number of registered residents in megacities become an important academic issue. This paper, from both natural and social perspectives, selects concise indicators, and combines the Possibility-Satisfiability model to estimate the urbanization transformation gap of annual household registration, and constructs a panel data model to empirically analyze the factors leading to the gap of household registration in megacities. The main factors affecting the transformation of registered population in megacities are medical service, as well as educational resources and urban water supply. It is urgent for urban and rural administrators to change the passive and rigid institutional mechanisms and realize the flexible and normal governance.

Using data of the latest three decennial censuses, this paper systematically analyzes the overall spatial pattern, changing trends, and influence mechanisms of China's population distribution since 2000. The study reveals that the general population distribution pattern of "dense east and sparse west" in China has not changed, which is determined by the differences in natural geographic environments on the two sides of Hu Line. In the first decade of the century, population concentration was mainly found in the southeastern area, but it has been observed in all regions in the recent decade. Provincial capitals have become the winners of population competition and the advantages of population growth of provincial capitals have been further strengthened in the recent decade compared with 2000-2010. However, the administrative power-led effects of the increasing dominance of provincial capitals will gradually weaken with the development of their provinces. The regional differentiation of population changes is also apparent: coastal region gradually forms a dense belt of population and towns with solid population attraction based on the development of urban agglomerations; the northeast region almost turns to a complete population shrinking status; central provinces still compete fiercely for population based on their capitals; the population of Sichuan, Chongqing, and Guizhou has stopped the decline and returned to growth; and the northwest region faces the risk of population loss. In addition, this paper finds that the driving factors of regional population growth have gradually changed from economic factors to both economic and amenity factors, and the difference of amenities among regions may become an important factor for future changes in China's population geography.

Access to homeownership profoundly affects floating population's social integration in the destination city and, in the long term, wealth accumulation. While housing differentiation within China's floating population has received increasing attention in the past two decades, the varied housing outcomes of the floating population experiencing different geographic mobility have been rarely investigated. Using data from the 2017 China Migrants Dynamic Survey, this study employs logistic regression models to examine the association between migrants' hierarchical migration patterns and their housing outcomes in the destination city. The results show that the migration patterns of China's floating population are mostly featured by moving up along the urban hierarchy. There are significant disparities in housing outcomes among floating population with different origins and destinations. Migrants originating from cities with higher gross domestic product (GDP) per capita and average housing prices are more capable to afford a home in the destination city. Moving to cities with higher average housing prices implies fewer opportunities to purchase local commodity housing. As GDP per capita in destination cities increases, the probability of floating population purchasing commercial housing increases first but then decreases, while the probability of purchasing housing with incomplete property rights exhibits the opposite phenomenon. Furthermore, migrants making larger upward movements are less likely to own a home in the destination city, but more likely to rent formal housing. This study highlights the role of geographical mobility between different origins and destinations in affecting floating population's housing outcomes in the destination city and furthermore provides insight into understanding housing inequality.

Based on the data of usual residence from 2000 and 2010 censuses and 1995 and 2015 micro-censuses and using the method proposed by Abel G. J., this paper estimates the O-D migration flows among prefectural-level administrative units in China for the period 1995-2015. By using the indicators of migration flows and migration rates, and the methods of GIS spatial analysis and community detection in social network analysis, it further reveals the characteristics of spatio-temporal evolution of migration in China since 1995. The results can be summarized as follows. (1) The scale and intensity of migration in China have been increasing, while their differences among regions have been gradually decreasing. This suggests that migration has experienced a gradual transformation from a "low-activity era" including a limited number of regions to a "high-activity era" involving the majority of regions. (2) Six regional types of migration among prefectural-level administrative units, namely, large-scale active net in-migration, small-scale net in-migration, large-scale active net out-migration, small-scale active net out-migration, active balanced migration and inactive migration, are identified. The spatio-temporal evolution characteristics of the regional types of migration in China indicate that each active migration regional type has been undergoing a change of continuous diffusion, while the inactive migration regional type has seen a significant reduction over time. (3) The spatial patterns of migration are quite different between regions on both sides of "Hu Line", manifested in the fact that the scale and intensity of migration flows on the southeastern side of the line is higher than that on the northwestern side, which means that the "Hu Line" is quite stable and robust in the last 20 years. (4) Patterns of migration flows indicate that while intra-provincial migration has continuously strengthened, inter-provincial migration has exhibited more complicated spatial patterns, mainly reflected in different changing trends of attractiveness to migrants among the three coastal city clusters, and the increasing migration flows within southwest China. As a result of combined effects of inter- and intra-provincial migration flows, the structure of migration flows in China's eastern, central and western regions has been undergoing changes, resulting in the internal differentiation of urban agglomerations and the declining spatial scope under their influence in the coastal areas, Hubei as the single independent "city community" in central China, as well as the relative stability in northwest China and continuous change in southwest China.

The Chinese government has curbed the rapid transmission of COVID-19 through a population flow control rarely seen in history. What is the effect of population flow control on pandemic prevention and control? How does it affect China's population mobility and short-term population distribution? In this paper, an SEIR model of virus transmission dynamics is used to evaluate the effectiveness of the control measures, and mobile location data are employed to track the temporal and spatial changes of population mobility in China, in order to review the positive and negative effects of population flow control during the major outbreaks of COVID-19: (1) Population flow control has significantly stabilized the daily new infection, serving as an essential part of China's non-pharmacological intervention measures in response to major public emergencies of COVID-19. Population flow control postponed the arrival of the peak day of daily new infections in China by 1.9 times, and reduced the number of newly infected people on that day by 63.4%. In the selected 5 provinces, 5 cities in Hubei, and 6 cities outside Hubei, the peak days were postponed by 1.4-8 times, 5.6-16.7 times, and 2.3-7.2 times, respectively, and the number of newly infected people on that day was reduced by 56.9%-85.5%, 62.2%-89.2%, and 67.1%-86.2%, respectively. Therefore, population flow control bought valuable buffer time for the prevention and control of the pandemic, and greatly weakened the impact of concentrated transmissions on medical facilities. (2) Population flow control limited intercity population flow. From January to April 2020, the average daily population flow intensity in China decreased by 40.18% compared with the same period in 2019. In particular, the coming-back-to-work flow after the Spring Festival travel rush in 2020 (from January 25 to February 18) decreased by 66.4% on average. (3) Population flow control and people's fear of the pandemic greatly affected the Spring Festival travel rush in 2020, and the spatial and temporal and distribution of China's population was changed for a short period. This paper helps the understanding of the impact of the population flow control strategy introduced by the government on major public emergencies, as well as the influences of geographical characteristics upon on the population flow and distribution.

It is essential to unravel the spatial and temporal patterns of the spread of the epidemic in China during the backdrop of the global coronavirus disease 2019 (COVID-19) outbreak in 2020, as the underlying drivers are crucial for scientific formulation of epidemy-preventing strategies. A discriminant model for the spatio-temporal pattern of epidemic spread was developed for 317 prefecture-level cities using accumulated data on confirmed cases. The model was introduced for the real-time evolution of the outbreak starting from the rapid spread of COVID-19 on January 24, 2020, until the control on March 18, 2020. The model was used to analyze the basic characteristics of the spatio-temporal patterns of the epidemic spread by combining parameters such as peak position, full width at half maximum, kurtosis, and skewness. A multivariate logistic regression model was developed to unravel the key drivers of the spatio-temporal patterns based on traffic accessibility, urban connectivity, and population flow. The results of the study are as follows. (1) The straight-line distance of 588 km from Wuhan was used as the effective boundary to identify the four spatial patterns of epidemic spread, and 13 types of spatio-temporal patterns were obtained by combining the time-course categories of the same spatial pattern. (2) The spread of the epidemic was relatively severe in the leapfrogging model. Besides the short-distance leapfrogging model, significant differences emerged in the spatial patterns of the time course of epidemic spread. The peaks of the new confirmed cases in various spatio-temporal patterns were mostly observed on February 3, 2020. The average full widths at the half maximum of all ordinary cities were approximately 14 days, thus, resonating with the incubation period of the COVID-19 virus. (3) The degree of the population correlation with Wuhan city has mainly influenced the spreading and the short-distance leapfrogging spatial patterns. The existence of direct flight from Wuhan city exhibited a positive effect on the long-distance leapfrogging spatial pattern. The number of population outflows has significantly affected the leapfrogging spatial pattern. The integrated spatial pattern was influenced by both primary and secondary epidemic outbreak sites. Thus, cities should pay great attention to traffic control during the epidemic as analysis has shown that the spatio-temporal patterns of epidemic spread in the respective cities can curb the spread of the epidemic from key links.