Toward the growing desire for a better living of the public in China, the spatial matching contradiction between supply and demand of consumption is increasingly sharp. Do the geographical characteristics of individual consumer preferences exert a significant impact on the spatial pattern of the segmented consumer market? This question lies at the very core of understanding and resolving the supply-demand contradiction. Starting from this fundamental query, this in-depth study put forward the innovative "Geo Wanderlust Index"(GWI), which took 290 administrative districts as the basic unit and integrated POI and regional statistical data to measure and identify the regional differences in residents' preferences for different types of leisure consumption. Based on controlling social and economic environment factors, this paper explores the relationship between residents' leisure consumption preference and natural geographical environment factors through a multiple linear regression model. The findings of this study are both fascinating and significant. It firmly confirmed the existence of geo-wanderlust. That is to say, residents' leisure consumption preferences are substantially influenced by the natural and social environments. For instance, residents in the Sichuan and Chongqing regions, as well as those in first-tier cities like Beijing, demonstrate a stronger inclination towards engaging in leisure activities. Interestingly, it was also discovered that regional characteristics do not conform to the traditional distribution demarcated by the classic geographical dividing line - the Hu Huanyong Line. Secondly, by subdividing different types of consumption, it is found that there are significant differences in the impact of natural and social environmental factors; and the worse the climatic conditions, the more people enjoy leisure consumption. For development consumption, the climate impact is not significant, but the social environment factor of housing prices has a significant positive impact, that is, people in areas with greater pressure prefer development leisure consumption. This study preliminarily confirms the explanatory power of neo-environmental determinism on regional differences in leisure consumption preferences, provides a new research perspective for exploring the spatial structure of residents' leisure consumption, as well as suggestions and references for optimizing the spatial layout of leisure industry.

Based on the fieldwork data of Macheng County in Hubei Province, this paper constructs an evaluation index system for the effect of collaborative governance of private medical institutions in underdeveloped counties of central China and analyses the effect and the cause of the collaborative governance of private medical institutions in underdeveloped counties of central China, from the four aspects of collaborative governance’s subject participation degree, object development degree, mechanism perfection degree, and object satisfaction degree. The results show that: 1) the subject participation of collaborative governance in underdeveloped counties of central China is in the low level, the treatment efficiency of medical disputes remains to be improved; 2) private medical institutions in underdeveloped counties of central China which are at an immature stage and lack of professional and standardized training, can’t compete with public hospitals from the perspectives of soft power and hard power; 3) the underdeveloped counties of central China pay no enough attention to the third-party mechanism, and do not play the role of third-party mechanism in the process of collaborative governance;4) It should improve the public’s medical literacy level and reduce the gap of medical information asymmetry in underdeveloped counties of central China. It’s found that it should optimize the collaborative governance path of private medical institutions, focus on improving the system of basic health services and accelerate the modernization of the service capacity in the underdeveloped counties of central China.

Based on the "flow space" perspective and with the help of GIS spatial analysis, this article builds an interactive spatial contact network. By means of the spatial approach simulation of intercity traffic contact, we deeply analyze the spatial distribution and agglomeration pattern of China's railway transport contacts. Selecting and successively connecting intercity neighbor contact path in the in-use railway lines, we completely simulate the operation route of each train by the method of spatial approach simulation and describe the actual operation path of railway traffic. Based on the railway traffic data of actual operation, this paper constructs an interactive contact network of intercity railway transportation in prefecture level, and analyses the spatial distribution and regional agglomeration of the national railway passenger transport contacts. Research shows that China's railway transport contacts are developed along the main railway lines, take the national and regional central cities as the contact core and the urban agglomerations as the main direction, railway traffic contact network presents the spatial pattern of tripartite confrontation that is Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration and Pearl River Delta urban agglomeration, and the high-speed railways are mainly distributed in the east of Beijing-Guangzhou railway line. In terms of the train type, the China's railway transport contacts are generally based on the normal speed trains, fast trains and high-speed trains, high-speed and multiple unit trains complement each other and develop in a synergetic way.

The article based on the big data of traffic flow, this paper explores the impact of traffic congestion on the spatial and temporal accessibility of pre-hospital emergency medical services. The research shows that :(1) The impact of traffic congestion on the accessibility of pre-hospital emergency medical services is particularly obvious during the peak period. Compared with the average peak period, the road congestion index increases by 30.6%, the shortest accessibility time of pre-hospital emergency medical service increases by 68.4%, and the average accessibility time increases by 18.9%. Traffic congestion reduces the driving speed of ambulances by 22.8%.The accessibility of pre-hospital emergency medical services was significantly improved during the off-peak period, 54.7% and 96.9% of residents could receive timely treatment within 15 and 30 minutes.(2) In the study period, the interpolation results of pre-hospital emergency accessible time were all distributed in concentric rings, and the accessible time gradually increased from the central urban area to the peripheral area.The traffic congestion during peak hours has a significant impact on the coverage area of 15min, 20min and 30min.Under the -off-peak period, the coverage area of the 25min increased significantly, and the first-aid effect in the peripheral areas was significantly improved.Compared with the off-peak period, the coverage area of 15min was significantly increased in the evening peak period. Therefore, the public transportation special road plays a certain role in promoting the accessibility of pre-hospital emergency medical services.(3) Compared on traffic congestion factors, it can be seen that the impact of traffic congestion on accessibility is directly proportional to the transport network density and population density, that is, traffic congestion has a significant impact on areas with population density and transport network density. Ignoring factors of traffic congestion, the number of streets in C1 increased by 40%, and the total population covered by C1 increased by 39.8%.Therefore, solving the traffic congestion problem will directly improve the degree of convenience for residents in central urban areas to obtain pre-hospital emergency medical services.

By integrating and exploring the big data of network and social survey data, this article takes Luoyang City as research area and analyzes its spatial network structure characteristics of the tourist flows applying the social network analysis, mathematical statistics analysis and spatial analysis. Conclusions are shown as follows: 1) The network structure of tourist flow shows a monocentric and reticular distribution feature, it presents dense spatial distribution in urban area. Instead, there show relatively sparse in Funiu Mountain scenic area and Daimei Mountain scenic area, and other scenic areas are extremely weak. 2) Overall, tourist flow network has a low density and its structure is in a very loose state and unevenly distributed with a clear centralized trend and rather high dependency on the core scenic areas. It is a typical core-periphery network in Luoyang, the trickle-down effect of the core scenic spots is limited on the periphery ones. 3) The tourist flow has a relatively strong "Gang" character, forming an inter-connected and outer-loosened independent structure. There are 24 factions in the tourism flow network of Luoyang urban area, which presents the structural characteristics with "Longmen Grottoes, Baima Temple and Lijingmen" as the center. 4) According to the characteristics of Luoyang's tourism flow network structure, Luoyang can be refined as the core tourism area of Luoyang City, the sub-core tourism areas( consisting of Daimei Mountain scenic area and Funiu Mountain scenic area), and the marginal tourism areas, which forms a "one core, two wings" tourism spatial structure.

Based on the big data of tourism routes,the spatial analysis method of near field O-D contact is used to accurately simulate and deeply analyze the spatial agglomeration and distribution characteristics of tourism flows in China. On this basis,the national tourism geographic division scheme is discussed,and the spatial structure map of different travel types are elaborated. The study believes that the spatial concentration of tourism flows is much higher than the population and economic agglomeration in the general sense. It is a type of first-class economic activity and is subject to the "two-eighth law". China's tourism flow generally presents a spatial distribution pattern of "large dispersion,small aggregation,namely,dispersion and concentration". The tourism flow on the national scale is decentralized and balanced,indicating that the development of tourism in the west has largely offset the regional development differences between East and West. The regional agglomeration characteristics of provincial-scale tourist routes are obvious,and a few popular scenic spots are over concentrated. The tourism industry in the resource-exhausted areas in Northeast China is relatively inactive,and the tourism industry in the central region,especially in the "Yellow River flooding Area",has experienced a considerable degree of "partial collapse". Studies have shown that the spatial distribution pattern of tourist routes clearly indicates the spatial extent of China's tourism geographic division. According to the principles of spatial agglomeration,reception volume,spatial similarity and cultural homogeneity of tourism routes,14 national tourism geographical divisions such as Northeast Forest Sea and Snow Plain Tourist Area,Beijing-Tianjin-Hebei Historical and Cultural Tourism Area and Grassland Tourist Area Outside The Great Wall are preliminarily divided. The study also shows that there are large differences in the spatial agglomeration characteristics of different modes of travel. This study has important theoretical and empirical reference value for comprehensively identifying the spatial agglomeration characteristics of China's tourism routes,the regional spatial distribution of tourism flows,the geographical division of tourism,the location of regional tourism functions and the choice of development paths in the era of big data.

Tourism development meets the problem of the regional difference and unbalanced development,which causes low development potential of urban tourism and low supply allocation efficiency. Taking China's prefecture-level administrative regions,municipalities and counties directly administrated by the provincial government as study area,this paper comprehensively combines the data of 12135 tourist resources,tourism economy,population scale and traffic environment,and analyzes spatial mismatch of tourism resources and economy in Chinese cities and their spatial distribution characteristics by means of regression analysis and spatial analysis. In resource-economic mismatch area,there are 34 cities with scale advantage (SA area) and 48 cities with traffic advantage (TA area),which respectively presents the advantage of "south" and "provincial marginalization" characteristics. Resource-economic disadvantage in mismatch area has 46 cities with scale advantages (SI area) and 75 cities with traffic advantages (TI area),which presents the features of "centralization in the south and central" and marginalization in the northwest border of China. This paper discusses the opportunities and challenges that each region may face in the process of tourism development,and makes a quantitative comparison of the tourism development gap between cities,which is conducive to fully stretching the potential of urban tourism development,so as to maximize the revenue of tourism development under the background of regional tourism.

Based on the exhibition big data mining in Dianping.com network platform, with GIS spatial analysis, complex network analysis and multiple linear regression model, this paper analyzed the spatial pattern and influential factors of exhibition passenger flow in China. The study shows that, firstly, exhibition source market in China is mainly concentrated in a few large cities such as Shanghai, Beijing, Tianjin and Shenzhen, shows the spatial distribution characteristics of "more in the east and less in the west", "large dispersion and small concentration". The Yangtze River delta, Beijing-Tianjin-Hebei and Pearl River delta are the three major clusters of exhibition source market in China. Secondly, exhibition passenger flow in China forms a diamond-shaped space structure centered on Shanghai, Beijing, Guangzhou, Shenzhen, Chengdu and Chongqing. The exhibition passenger flow generally shows obvious distance attenuation characteristics, about 82.05% of the passenger flow is mainly concentrated in the range of 0-50 km. Thirdly, exhibition passenger flow network in China obvious hierarchical structure, taking Shanghai as the core and Beijing, Hangzhou and Shenzhen as the sub-core, showing obvious small-world effect. Fourthly, the level and scale of exhibition development, business development, economy development level are the main influential factors of spatial pattern of exhibition passenger flow in China. Among those, the level of exhibition development is the primary influential factor.

With the rapid development of Internet and MICE tourism, the MICE city as the carrier of MICE activities has been receiving more and more attention from tourists in recent years. Based on a large quantity of the daily tourism microblogs data of China's MICE cities collected via Sina Microblog platform during 2011-2018, this paper tries to explore the spatial-temporal characteristics and influencing factors of tourists' tourism microblogs to MICE cities(TMMC) by means of nuclear density analysis, grey correlation analysis and so on. The study found that: 1) As for temporal characteristics of TMMC, it shows an fluctuating Increment trend during 2011-2018 and "double peaks and one valley" seasonal fluctuaiton with "Short off season and long peak season" each year. In short time, the holiday feature showed that the pre-holiday attention decreased significantly, reached the lowest point on the first day of the holiday, then began to rise slowly, and increased significantly on the first day after the holiday, and rose to the stable interval on the second day after the holiday. The overall performance was "U" type distribution. 2) As for spatial characteristics of TMMC, the spatial structure is gradually changed from "multi-core" to "arched" clustered distribution. The data distribution is concentrated in some regions, and tends to gather in cities with high economic development level, high development level of exhibition industry, and high Internet development level. 3) Influencing factors such as the development level of the MICE industry, the level of economic development are proved to be important factors influencing the TMMC. Influencing factors indicators such as the number of exhibitions, the exhibition area are proved to be important indicators that affect the TMMC.

Leisure and residence are two important functions of cities. Combined with the macro-background of urban transformation in the leisure era and using POI (Points of Interest) data, this article studies the coordination and matching relationship between the two urban functions, which has practical significance for the rational allocation and construction of urban leisure places and residences. Based on the POI data of 2 524 leisure places and 703 residential places in 10 districts of Qingdao, it explores the spatial distribution pattern of leisure and residence places in Qingdao and their coordinated matching relationship by the means of ArcGIS spatial analysis. The results show that: 1) There has deviations in the spatial distribution center and orientation of leisure and residence places in Qingdao. Leisure places present a development pole along the eastern coast of Jiaozhou Bay and multiple agglomeration centers, while residence places present patchy distribution(such as Licang District, Chengyang District and Jimo District) and cluster distribution(such as Laixi District and Pingdu District). 2) The order of agglomeration degree and convenience level of different types of leisure places from high to low is catering, art gallery, tourist attractions, library, shopping mall, museum, park, cinema, gymnasium, cultural station, science and technology museum. 3) According to the changing trend of the quantity distribution of leisure places in the 3 000 m range of residence places, it can be divided into three sections: 0~500 m walking leisure area, 500~1 500 m RBD leisure area and 1 500~3 000 m characteristic leisure area. 4) It is only Laoshan District which realizes the coordination and matching of leisure and residential layout in Qingdao, it is Shinan, Huangdao, Shibei and Jiaozhou whose leisure function is ahead of residence, the leisure function of Licang, Pingdu, Laixi, Jimo and Chengyang is relatively lagging behind. Based on the results of the matching relationship between different types of leisure and residence places in different districts, this paper puts forward development suggestions.

This article uses the improved potential model to calculate the spatial accessibility of medical resources in Shenzhen,and measures their differences under different modes of travel and living conditions based on the actual road network analysis and the Mann-Whitney U test. It also focuses on the spatial deprivation phenomenon caused by the spatial differentiation of living. The results show that: 1) From the perspective of space,the accessibility of medical resources generally shows decreasing characteristic from hospital clusters in the core areas to the periphery,the accessibility of community health centers is better than the accessibility of general hospitals. 2) It has the phenomenon of uneven distribution in better medical resources. 3) The accessibility of medical resources by the mean of public transport is much lower than that by the mean of private vehicles,low- and middle-income groups who rely on public transport are at a greater disadvantage in terms of access to healthcare services. 4) The overall living environment is worse in communities with high value for medical accessibility,and the phenomenon of spatial disparity in living is related to spatial scarcity in core areas and the distribution of urban villages. 5) It exists the spatial deprivation to the low-income groups living in the periphery of Shenzhen, which is caused by the unequal distribution of resources and opportunities and mobility barriers.

Creative industry is a powerful approach to develop economy and optimize cities. Spatial-temporal perspective is an effective logic to understand the development of creative industry. This paper analyzes and combs the concept,characteristics and scope of creative industry,uses big data technology to obtain the data of creative industries in Xi'an,and selects 2005,2010,2015 and 2020 as nodes to establish time series. By using the methods of spatial autocorrelation,kernel density analysis and standard deviation ellipse,this paper explores the spatial pattern and evolution process of creative industry in Xi'an from three aspects: spatial distribution pattern,development direction and influencing factors. The results show that: 1) The creative industry of Xi'an steady distributes in the urban area. 2) The creative industry as a whole has a spatial-temporal pattern of both accumulation and diffusion. The evolution process has the trend of multi-core diffusion distribution. The clusters gather as a tight group,then develop along the north-south axis of the city,and form a continuous patchy pattern covering the urban area from the northeast to the southwest. 3) There are significant differences among spatial-temporal patterns of the creative industry sectors and the influence factors which form them. 4) The spatial-temporal pattern of creative industry changes under the influence of comprehensive factors. Non-interfering factors work at beginning but less impact after the interfering factors appear. 5) Market factor and infrastructure factor effect creative industry sectors generally and steady. 6) The appearance of difference of influence factors is related to the difference of the production mode and product characteristics between industries.

This study provides a new way to analyze the urban network structure based on the actual schooling flow of primary school students. Using the mobile signaling data of Shenyang in 2018,this paper measures the spatial structure characteristics of Shenyang's residence-school network using social network analysis and dominant flow analysis. The results indicate that: 1) Shenyang's residence-school network has a polycentric hierarchical structure which is consist of the dominant area (Taiyuan Neighborhood),the subdominant areas (Fengyutan neighborhood,Guihe Neighborhood),the control nodes (Xita Neighborhood,Huangcheng Neighborhood) and other Neighborhoods as subordinate. 2) Qilu Neighborhood-Guihe Neighborhood-Taiyuan Neighborhood-Fengyutan Neighborhood is the primary axis of the residence-school network structure in Shenyang,while Liaohe Neighborhood-Huanghe Neighborhood-Xita Neighborhood-Taiyuan Neighborhood as the secondary axis. 3) The basic structure of Shenyang residence-school indicates "one main group,three subgroups and multi groups" characteristics. 4) It shows spatial dependent on the schooling behavior of primary school students,and the administrative division plays a vital role in schooling behavior.

Integrating data from statistical yearbook and geographic spatiotemporal data, this paper utilises multi-dimensional urban factor flows to comprehensively analyse functions of different cities within Wuhan urban agglomeration and their urban connections based on each factor flow, and further takes assignment method to score the four urban factor flows. According to above, this paper puts forward suggestions for the functional layout and development of Wuhan urban agglomeration. The results are as follows, 1)Wuhan is the core of the agglomeration development and the situation that one city alone is dominant is still significant. However, the overall polarization and radiation capacity of the urban agglomeration is relatively weak and more cities are in medium or low level of flow intensity; 2)The inter-city space within the agglomeration is not closely related and the differences in these levels of spatial association are relatively large; 3)The suggestion on spatial layout of the Wuhan urban agglomeration is put forward from the perspective of points, lines and areas in this paper according to the result of valuation process. Points: The spatial layout is in "1323" mode. Specifically, Wuhan is the central city with the sub-central cities of Huangshi, Ezhou and Xiaogan, core areas of Xianning and Huanggang and Xiantao and affected zones of Xiantao, Qianjiang and Tianmen. Lines: The spatial layout is in the "small" Chinese character with three development axes, Xiaogan-Wuhan-Ezhou-Huangshi, Wuhan-Xianning and Wuhan-Huanggang; Areas: The Wuhan urban cluster is further divided into three development areas, namely Wuhan-Ezhou-Huanggang-Huangshi, Wuhan-Xianning and Wuhan-Xiaogan-Xiantao-Qianjiang-Tianmen.

Under the background of increasingly aging population,it is very important to speed up the construction of urban facilities for the elders and quantitatively do research on their site selection. Taking Wuhan City as the research object and based on POI data and the sixth national census, this paper quantitatively simulates the location and layout of facilities for the elders on a 500m grid scale by the means of machine learning algorithms,which combines with the distribution characteristics of current urban facilities for the elders. Firstly, it simulates 4 878 suitable sites from the 31 390 grids in Wuhan. Secondly,according to the degree of aging,it screens out 1366 sites have priority over others. This study initially achieves quantitative site selection of facilities for the elders at a refined scale. Research conclusions are as follows:1) Compared with the current facilities for the elders,the prediction results have an accuracy of 95.7%, the method is reliable. 2) Forecast results show that the newly-added facilities for the elders in Wuhan are mainly planned within third ring road where the aging population gathered. At the same time, it should plan the facilities in some streets of remote urban areas where the aging population is serious. 3) This research attempts to use machine learning algorithms to plan and select the location of facilities for the elders,which can optimize the overall layout and try to avoid the subjectivity of site selection. This method also has certain reference value in the site selection of other public facilities.

The Yellow River Basin is a hub region in the south and north of China. With the gradual implementation of the "Yellow River Basin Ecological Protection and High Quality Development Strategy",the flow of factors will inevitably be accelerated and the formation of city networks will be promoted. Based on Tencent's big data of population migration,this paper uses a complex network analysis method to systematically analyze the regional differences and spatial directivity of city network connections in the Yellow River Basin. The purpose is to identify the spatial and temporal patterns of internal and external connections in the Yellow River Basin. Provide decision-making reference for high-quality development of the basin. The results show that: 1) The city network density of the basin is low,and the average path length is long,which does not match its regional status of "contact north and south". 2) Inside the basin,a backbone network is formed with the "Longhai Railway-the main stream of the lower Yellow River" and the Baohai High-speed Railway as the axis,and the core-periphery structure is prominent. 3) The internal and external network of the basin forms two major (Zhengzhou and Xi'an) and one small core cities(Jinan),and the high-level network uses these as the core nodes to radiate out of the domain. The city nodes with strong distributed capacity are concentrated on the "Longhai Railway-the main stream of the lower Yellow River". The main contact directions outside the region are the Pearl River Delta and Yangtze River Delta. 4) Compared with the internal and external networks of the basin,the advantages of the cities along the "Longhai Railway-the main stream of the lower Yellow River" and the east of the Baohai High Speed Railway are more prominent. 5) The spatial orientation of network connections in different regions of the basin has obvious differences. The internal connection networks of various provinces point to the provincial capital,and the spatial orientation of the internal-external connection networks has obvious upper,middle,and downstream differences.

Based on the big data analysis of high-speed railway passenger transportation ,this paper makes a preliminary study on the spatial pattern, the whole and the local connection and the node attribute of Chinese city network by using GIS visualization, social network analysis and statistical analysis. The results show that: 1) The links between cities is based on the high-speed railway lines to form the “four vertical and four horizontal” eight passenger flow lines and the multi-scale urban network, but the influence of the location conditions shows the characteristics of the eastward and the westward decline. 2) The network of Chinese cities has low density and a certain core edge structure in terms of overall connection. The connectivity and agglomeration intensity coefficients are similarity but reflect the different regional association model. 3) In the local connection, the network is roughly formed with eight subgroups of geographically adjacent cities and has a certain coupling with the spatial distribution of urban agglomeration.4) In the individual network, the distribution of node degree shows a certain power-law distribution and in addition to economic and population scale has a strong correlation, the city administrative scale also plays a more important role.

The primary task in targeted poverty alleviation is the precision identification of poverty-striken households. Through our indoor investigations into the five administrative villages in the western, southern and central areas of Hunan Province, combined with previous research results, we find that there exists the conspicuous problem of deviation in the identification of poverty-striken households in targeted poverty alleviation. According to the current work patterns in poverty alleviation, this paper sets up the model for the precision identification of poverty-striken households, and, based on typical cases in the above-mentioned investigations, offers a deep analysis of the reasons for deviation in the identification of poverty-striken households. To solve the problem of deviation in the identification of poverty-striken households once for all, the following things should be done: the first is the construction of a big data platform for poverty alleviation development, and the solution to the problem of inaccuracy in the information about rural households’ property, income and expenses, through such techniques as relevant data mining and cloud computing; the second is the precision classification of the poverty-striken population, to improve accuracy in the identification and therefore avoid randomness in the assistance; the third is the adjustment and perfection of the regulation for democratic appraisal, as well as the multidimensional evaluations of the degree of a household’s poverty, namely, the villager representatives’ quantitative evaluations from different aspects and then the village cadres’ exertion of their decision right according to the results of the quantitative evaluations and the actual conditions of the poverty-striken households, by which means the responsibilities of the village cadres are strengthened and deviation in the identification of poverty-striken households are effectively avoided.

It is of great significance to investigate spatial characters of the urban commercial facilities, given that the commercial activity is a key component of the coupled human-natural complex system. By employing the POI(Points of Interest)data, Shenyang's retail commercial distribution pattern was extracted by kernel density method, different retail layout characteristics were compared, and then the retail industry hot blocks were explored by local Getis-Ord G^* i index. The results show that the Shenyang's retail business centers have formed “block aggregation, multi center development” spatial distribution pattern; Different kind retail businesses have unequal distribution, shopping malls excessively focused on the core business district, the supermarkets' gathering regions distribute widely, the convenient stores' distribution is similar to that of the whole retailing industry in Shenyang; Significant hotspot regions are mainly distributed in Shenhe, Heping and Tiexi District, the rest business districts have not yet formed significant retail hotspot in statistical sense. This research, on the on hand, contributes to the application of POI analysis in investigating the human-natural relationship at a city scale; on the other hand, benefits for the knowledge body of policy making in urban planning.