The Central Plains Economic Zone is an important engine of the central plains rising strategy, and it is also the strategy fulcrum of the regional coordinated development. Building a scientific and reasonable regional economic relation network is an important part of the construction of the Central Plains Economic Zone. This paper takes the CentralPlainseconomiczone as astudy area and its counties as the network nodes. The economic relation intensity among 231 nodes in 1996 and 2010 are calculated based on the gravity model. The regional quantity in the model is represented by the geometrical mean of GDP and population of the nodes. The GDP of 2010 have been deflated to the price level of 1996. The distance coefficient is represented by the shortest journey time by highway between the cities. Economic linkage network has been built in terms of the first, top5 and top10 in connecting intensity of each nodes, using the tools of GIS and method of Social Network Analysis to analyze the spatial pattern and evolutional characteristics of the network, and the analysis is mainly based on the concept of degree, degree distribution, clustering coefficient and betweenness centrality, etc. The results show that: (1) The CentralPlainseconomiczone has an unbalanced economic linkage network, and the distribution of the node degree exhibit an obvious rightward "inclined long-tailed distributions". The number of nodes with degree less than or equal to the median accounts for more than 85% of the total number of nodes, and a small number of nodes hold the absolute network power. Zhengzhou is the center of the network, while Luoyang and Handan are the sub-centers. The connectivity of the central and northern nodes is relatively strong, while measures should be taken to increase the connectivity of the eastern and southern nodes. (2) The network power gap between the central and marginal nodes is further widening, and the Gini coefficient and concentration ratios (CR1,CR5,CR10) of the node degree have increased significantly from 1996 to 2010.Thenetworkpowerofthecorenodesfurther increases, whilethe connectingabilityofthemarginalnodesdeclinesgradually. Theunbalancednetworkdevelopment is more pronounced,and this will restrain the interactional development between the CentralPlainsEconomicZone and its adjacent regions.(3) The network has a multiple hub-and-spoke structure. The correlation between the node degree and clustering coefficient is significantly negative. This proves that the nodes with high degree are the main connection objects of the adjacent nodes, while there are fewer mutual connections between the peripheral nodes. With the high degree nodes as the core, the network formed several sub-networks with hub-and-spoke structure of different levels. Zhengzhou is the primary hub, and plays as the core leading role for the entire economic zone, while Luoyang, Handan and Luohe are the secondary hubs, mainly playing the roles of leading and radiating for the surrounding cities and counties. The construction of the cross-shape development axis and "米" glyph development zone will strengthen the economic linkage between the primary hub and the secondary hub, it will also help the entire region to realize the advantageous complementarities and linkage development. (4) The broker nodes play an important intermediary role in the network connectivity, and the betweenness centrality is not only closely related to the node degree, but also influenced by its location in the network. The broker nodes in the economic linkage network of the Central Plains Economic Zone are mainly located in the Central Plains City Group, so the development of the Central Plains City Group will have significant impact on improving the whole connectivity of the network.

Studying the spatio-temporal pattern of county economic development is extremely important for revealing the evolutionary mechanism of regional economy and achieves the sustainable development of China's county economy. Although considerable research attention has examined the regional economic pattern of China at the provincial and national levels in a certain time, we currently know the least about the profiles of county economic development across different periods. There is general agreement in the regional geography community that understanding the dynamic pattern of county economic development can provide a scientific base for the policy making and the implementation of regional development planning. Thus, the spatio-temporal pattern of county economic development in China merit further investigation. Based on the 2352 counties' per capita GDP in 1982, 1990, 2000 and 2010, the spatial autocorrelation analysis and variogram were used to investigate the evolutionary characteristics of spatiotemporal patterns of county economic development and to explore the possible mechanism behind the changes in the spatial pattern. Results showed that the county economic development and growth exhibit a positive spatial autocorrelation, which indicates that some counties with similar economic development levels clustered. But the spatial autocorrelation of the economic growth over the past three decades was not obvious. Since 1982, the spatial concentration of the county economy increased gradually and the spatial dependence enhanced over time. The difference of county economic development in eastern area of China is greater than that in the middle and western regions. In all four studied years, there are nine county units with a "high-high" pattern of economic development, i.e., Wujin, Kunshan, Dantu, Taicang, Changshu and Wuxi. Meanwhile, there are about forty-three county units maintaining their locations of "low-low" pattern of economic development for all studied years. Furthermore investigations revealed that the proportion of the county unites with homogeneity economic level increased from 19.56% in 1982 to 27.26% in 2010, whereas unites with heterogeneity level decreased from 3.06% in 1982 to 2.55% in 2010. This result demonstrated that there was an obvious heterogeneity for China's county economic development and the polarization effect in county economic development was enhancing over time. Overall, the hotspot areas of China's economic development mainly clustered in the eastern and northern regions of China, while the hotspot ones were concentrated in its central, southern and southwestern areas. Furthermore, the continuity and self-organization of Chinese county patterns was enhancing, whereas the random components of spatial disparity patterns was decreasing over time, which means that the structural differentiation caused by spatial autocorrelation was becoming more apparent. There was a relatively good homogeneousness in the economic development in the direction of northeast-northwest. The possible drivers behind the county economic development patterns could be partly attributed to its economic location, regional development policy, special resources and background of historical development. Among these possible causes, the regional development strategies may be the external contributors to the evolution in regional economic patterns. These findings have important theoretical and practical significance on narrowing the gap between urban and rural development.

Since the late 20th Century, the hotspots of international direct investment have transferred from manufacturing industries to service industries. The development of service international direct investment became the motor of global direct investment. As a result, the locational choice of service international direct investment has acted as a popular topic of economical geography. After thirty years' reforming and opening up, Chinese is now a major destination of international direct investment. The structure of international direct investment in China has also changed because of more critical policies and variable environments. Recently, the tertiary industries are attracting more and more foreign direct investment. However, there are little literatures about the location choice theories of foreign direct investment in the tertiary industries. The empirical literatures are even less. Real estate is one of the major industries that foreign direct investment focuses on. We hold the real estate industry as an example to study the location choice of foreign direct investment in Chinese tertiary industries. Special intention is paid to the interaction of foreign direct investment and domestic investment, especially the exclusion impact of domestic investment. Empirical study of the location choices of real estate foreign direct investment during 2006-2011 shows that although foreign real estate investment concentrates in the metropolises along the eastern seashore, it shows a trend to shift to the big cities in the inner land, especially to those cities locates along the Yangtze River. Compared to foreign direct investment from other regions and countries, foreign direct investment from Hong Kong, Macao and Taiwan spreads more widely in the inner land. The study uses a panel Tobit model to declare that intensive competitions from domestic real estate firms and disadvantages of outsiders make foreign firms to give up the optimized locations, where the profit rate is highest. Compared to foreign real estate forms, domestic real estate firms are much more familiar with the real estate market so that they deal with little uncertainty and risk. What's more, the regulations of domestic real estate firms are looser, and they may enjoy more flexible company frameworks and investment destinations. As a consequence, the domestic real estate firms have more market power than foreign real estate firms. The domestic real estate firms could occupy the optimal locations, known as "the first line cities" including Bejing, Shanghai and Guangzhou. Foreign firms move to suboptimal locations, where the market scale is large and the profit rate isn't highest, ensuring that they can increase the total sum of profit. Such cities are called "the secondary line cities", including cities located along the Yangtze River and capitals of eastern and central provinces. Because the levels of marketization of land in different cities increased rapidly in the past six years, foreign firms can chose locations without the restriction of land institutions. Additional analyses about the newly built-up foreign and domestic real estate firms indicate that the trend to transfer from "the first line cities" to "the secondary line cities" exists in both foreign and domestic firms.

With the economic integration of the Beijing-Tianjin-Hebei Region, there exists a poverty belt around Beijing and Tianjin within Hebei Province, which has received attention from academia and the public, so as to put the setting standards of poor counties into the spotlight. Despite some applications of not merely economic indicators but also social and natural indexes to identify poverty in academic researches, the setting of poor counties is only based on economic indicators in China. This paper adds indicators in social dimension representing human poverty and in natural dimension representing natural poverty to build a multidimensional poverty index system, and utilizes SOFM (self-organizing feature mapping) nonlinear clustering tool to classify all of the 136 counties in Hebei Province into 5 categories in view of the county poverty degree by the economic dimension & economic-social-natural dimensions separately. Clustering maps are compared with the map of currently poor counties in Hebei Province afterwards. The results show that the counties with low grades are the ones with high poverty degree. Meanwhile, counties with low grades are the majority, suggesting a high poverty degree all over Hebei Province. About the clustering results, whether in the single economic dimension or economic-social-natural dimensions, they are both consistent with the current distribution pattern of poor counties in Hebei Province which helps the transformation of "relative poverty" to "absolute poverty". Besides, in comparison to the results based on the single (economic) dimension, multidimensional (economic-social-natural) evaluation is more comprehensive because it takes current situation and potential of poverty into overall consideration. Furthermore, potential poverty degree in the natural dimension influences the comprehensive poor degree, indicating that both the current and potential poverty degree of poverty areas around Beijing and Tianjin are high and the region should cooperate with Beijing and Tianjin actively. In contrast, the potential poverty degree of poor counties in the south and middle Hebei Province is relatively low and the region is more easily to be lifted out of poverty. Moreover, these regions covers many counties that are not considered to be poor ones but very likely to become poor ones. Therefore, these regions deserve more attention and being treated differently. To make advances in the poverty relief and development work, the government should not only deal with things distinctively but also combine prevention with treatment and link the areas together. In this way we can expect the achievement of Beijing-Tianjin-Hebei coordinated development and the harmoniously simultaneous growth of wealth of society.

Logistics company is an economic entity to specialize in organization and operation of logistics activities and has a strategic significance in supporting various economic activities of circulation field and effective operation of the whole social-economic system, even impacting the reorganization of regional spatial structures. However, review articles in the literature show that for a long time most scholars only pay attention to the distribution features or spatial modes of logistics companies at the city scale and organization mode of logistics operational network at the regional or national scale, due to the limit of data collection and methodology, but the distribution patterns of logistics companies and the dynamic mechanisms at the national scale are seldom investigated. Particularly, the number of logistics companies has been increasing explosively since the end of 1990s in Mainland China. For this purpose, in this paper, we choose 1855 A-level logistics companies, rated by the government authority by various criterions over the past seven years, as the study samples, from the spatial scales of region, province and city, to describe and analyze the spatial features of the logistics companies in China, including the overall pattern, spatial centralization, coverage and spatial differentiation. And we explore the dynamic mechanisms of spatial disparities of logistics companies' distribution from the multi aspects. The results show that there are obvious spatial distribution disparities among the logistics companies in China. The coastal region has more logistics companies than inland region and their numbers show a "2:1" ratio. Interestingly, the eastern region also has obvious larger number than central and western regions, with "4:2:1" ratios. Generally, the southern provinces have more logistics companies, and the northern, northeastern, northwestern and southwestern provinces have fewer. Furthermore, the southeast coastal provinces especially Jiangsu and Zhejiang have the most companies. The spatial disparity of logistics companies' distribution among the cities seems to be more significant, showing clear separation of aggregation regions and sparse regions. The logistics companies not only are concentrated in the provincial capitals and economic centers, but also cover a large number of prefectural-level cities, even extending into many counties. More interestingly, South Jiangsu, Shanghai, South Anhui and Zhejiang have a large number of logistics companies and large coverage of various cities. But in each province, logistics companies are mainly located at the capital city and important economic centers and port cities. Logically, this spatial pattern is determined by the various factors combined. The results also show that the economic scale and industrial structure (among eastern, central and western regions, or between northern and southern regions), opening up and international trade, location and transport condition become the important factors to influence the spatial differentiation of logistics companies' distribution. It`s noteworthy that the development of a large number of local and specialized industrial clusters and private economies promotes the emergence and centralized distribution of logistics companies in Zhejiang and Jiangsu. More strikingly, the logistics planning and support policies made and provided by local governments also profoundly influence the development and flourish of logistics market and companies, especially in the coastal region and large cities of central and western regions. Theoretically, this distribution disparity among logistics companies generates different supporting capabilities for each region to operate the socio-economic system effectively. This research can provide a guidance to optimize the distribution of logistics companies and organize the logistics activities.