Table of Content

    20 October 2012, Volume 67 Issue 10 Previous Issue    Next Issue
    Spatial Pattern and Industrial Sector Structure Analysis on the Coupling and Coordinating Degree of Regional Economic Development and Environmental Pollution in China
    MA Li, JIN Fengjun, LIU Yi
    2012, 67 (10):  1299-1307.  doi: 10.11821/xb201210001
    Abstract ( )   PDF (46958KB) ( )   Save
    There is coupling relationship between regional economy and environmental system. On the one hand, the economy system explored and processed resources from natural system, and discharged pollutants back to the natural system. On the other hand, natural ecological environment system provides resources and space for social economy activities, and feeds back with the natural disaster or environmental pollution. It is important to explore the relationship between economic development and environmental pollution. Given more existing researches focusing on the time scale, it is necessary to carry out the quantitative assessment research on the interaction between economic sub-system with environmental sub-system on the spatial scale. In this paper, the comprehensive assessment indicators for regional social economy system and environment system were constructed firstly. Then, the coupling degree and coordinating degree between regional economic development and environmental pollution was calculated. Based on the research on spatial patterns of coupling and coordinating degree of 350 prefectural units, it can be found that the economic and environment development in most prefectural units are still at a lower level of coupling and coordination. There are significant disparities in coupling and coordinating degree between eastern coastal areas and western inner areas. According to the coupling and coordinating value, Chinese territory could be divided into four types, i.e., economy-environment harmonious area, economy-environment gearing area, economy-environment rivaling area and low coupling degree of economy-environment area. Based on the industrial sector structure analysis of four types of area, it can be found that there is a spatial corresponding relationship between the regional industrial sector structure and the coupling-coordinating level. In the economy-environment harmonious area, high-level manufacturing industries of electrical machinery, electronic equipments account for major proportion in total industrial output value. As for economy-environment gearing area, its industrial value is concentrated on manufacture of machinery and equipment and a few polluting departments such as metallurgy, chemical industry and production of electric power. Economy-environment rivaling area is the agglomerating area of polluting industrial sectors, such as iron and steel industry, petrifaction industry, mining of coals, building material industry and production electric power. So, it is also the high environment risk area of China in future. In low coupling degree of economy-environment area, its industry is concentrated on producing and processing of primary products.
    References | Related Articles | Metrics
    Manufacture Restructuring and Main Determinants in Beijing Metropolitan Area
    ZHANG Xiaoping, SUN Lei
    2012, 67 (10):  1308-1316.  doi: 10.11821/xb201210002
    Abstract ( )   PDF (1812KB) ( )   Save
    At the transformation stage to post-industrialization process, the spatial distribution of economic activities in metropolitan areas tends to change greatly. As one of the key driving forces for urban restructuring, the agglomeration of manufacturing industry has important influence on urban functional optimization. Based on the manufacturing enterprises databases, this paper discussed the relationship between spatial shift of manufacturing agglomeration and the urban function restructuring in Beijing. By contrasting spatial shift of manufacturing density in Beijing metropolitan area in 1996, 2001 and 2010 respectively, the coupling relationship between the manufacturing diffusion and urban function optimization is explored. With the negative binomial models, factors affecting the location of manufacturing enterprises as well as its influence variation among sectors in Beijing are examined. The results indicated that factors such as locational accessibility, agglomeration economy, and development zones are the key determinants for manufacturing enterprises diffusing and re-agglomeration in Beijing metropolitan area. It was also explored that during the process of suburbanization, firms in different sectors can lead to different locational decisions. The high-tech manufacturing enterprises are more likely to agglomerate in central urban area compared to the resource-intensive manufacturing firms. It was concluded that both the market-oriented factors and the government planning factors have great significance on the spatial restructuring and urban function improvement of Beijing.
    References | Related Articles | Metrics
    MAUP Effects on the Detection of Spatial Hot Spots in Socio-economic Statistical Data
    QI Lili, BO Yanchen
    2012, 67 (10):  1317-1326.  doi: 10.11821/xb201210003
    Abstract ( )   PDF (6318KB) ( )   Save
    The study of spatial distribution of population and economic situations is important for government policy making. County-level agriculture statistical data in 2000 and Beijing's second economic census data in 2008 were collected in order to explore the hot spots' scale effects. First, China's county-level agriculture statistical data and Beijing's second economic census data were aggregated to different scales based on certain aggregation rules. Second, hot spots detection was implemented based on G value at each scale respectively. Third, the changes of hot spots at different scales were analyzed. Fourth, factors affecting the changes were identified by employing Logistic Regression Model and a prediction model was built. Results show that, space hot spots explored by G value have significant MAUP effects. The higher the aggregation level, the greater the spatial scale, the less the number of hot spots. The number of units in a hot spot on the confidence level of 99.9% has a significant effect on the changes of hot spots. The mean G value of a hot spot on the confidence level of 98% has a significant effect on the changes of hot spots. Hot spots will become less susceptible to MAUP when they have more units and a larger G value. When the hot spot distribution is already known in the fine scale, changes of a hot spot can be predicted based on the model we built, which depends on unit number that the hot spot contains and mean G value of the hot spot. The prediction accuracy of China's county-level agriculture statistical data can reach 93.8% and that of Beijing's second economic census data can reach 94.2%. The consistent conclusion of the two datasets shows that scale effects on the detection of spatial hot spots have nothing to do with variables and study areas.
    References | Related Articles | Metrics
    Regional Carbon Footprint and Interregional Transfer of Carbon Emissions in China
    SHI Minjun, WANG Yan, ZHANG Zhuoying, ZHOU Xin
    2012, 67 (10):  1327-1338.  doi: 10.11821/xb201210004
    Abstract ( )   PDF (1819KB) ( )   Save
    Obligation assignment of carbon emission reduction needs to evaluate carbon emission charge by taking into account interregional transfer of carbon emissions. Carbon footprint, as a concept of carbon emission measurement, can evaluate life cycle carbon emissions of production and service to meet final demand. It should include direct carbon emissions caused by fossil energy as well as indirect carbon emissions induced by intermediate products production. This paper aims to estimate carbon footprint of each province and inter-provincial transfer of carbon emissions in China based on an input-output approach and China IRIO 2002 database. The results indicate there are significant differences of carbon footprint and per capita carbon footprint among provinces in China. The provinces with higher carbon footprint, mainly located in northern China, have large economic scale. The provinces with high per capita carbon footprint include developed metropolitan regions and energy-rich regions with a high proportion of energy intensive sectors. Interregional transfer of carbon emissions has emerged from energy-rich regions with a high proportion of energy intensive sectors to developed coastal regions and developing regions with incomplete industrial systems. The results imply developed coastal regions should bear more obligation of carbon emission reduction. As a significant amount of carbon emissions of energy-rich regions with a high proportion of energy intensive sectors is induced by provision of energy intensive products for developed coastal regions and developing regions with incomplete industrial systems, interregional transfer of carbon emissions should be taken into account for regional obligation assignment of carbon emission reduction. It can be considered to reduce obligation of carbon emission reduction for those energy-rich regions with a high proportion of energy intensive sectors. Otherwise, a compensation mechanism should be considered for developed coastal provinces to provide financial and technological aid to energy-rich regions with a high proportion of energy intensive sectors for improvement of energy use efficiency and reduction of carbon emissions.
    References | Related Articles | Metrics
    Identifying Commuting Pattern of Beijing Using Bus Smart Card Data
    LONG Ying, ZHANG Yu, CUI Chengyin
    2012, 67 (10):  1339-1352.  doi: 10.11821/xb201210005
    Abstract ( )   PDF (5318KB) ( )   Save
    This paper combines the one-week bus smart card data (SCD) and one-day household travel survey as well as the parcel-level land use map for identifying jobs-housing places and commuting trips in the Beijing Metropolitan Area with an area of 16,410 square kilometers. The identification result is aggregated in the bus stop and traffic analysis zone (TAZ) levels, respectively. In particular, commuting trips with commuting time and distance attached from three typical residence communities and those to five typical business zones are mapped and compared with each other to analyze commuting patterns of Beijing. The identified commuting trips are compared with those in the household travel survey in terms of commuting time and distance, indicating that our results are coincident with the survey significantly. Our approach is proved to have its potential in identifying more solid identification result based on rules extracted from existing surveys or censuses.
    References | Related Articles | Metrics
    The Measurement, Spatial Differentiation and Driving Forces of Social Deprivation in Low-income Neighborhoods in Chinese Large Cities
    YUAN Yuan, LI Shan
    2012, 67 (10):  1353-1361.  doi: 10.11821/xb201210006
    Abstract ( )   PDF (862KB) ( )   Save
    Social deprivation is a measurement of social security and welfare, social participation and integration of residents. Based on the 1809 individual questionnaire survey data in six large cities and three kinds of neighborhoods of China's three regions, this paper systematically analyzes the measurement, spatial differentiation and driving forces of social deprivation. Firstly, at the individual level, this paper measures the individuals' condition of social deprivation based on the chosen indicators. The methods of principal component analysis and cluster analysis are adopted to deal with 13 selected indicators related to social deprivation. The paper identifies whether an individual is suffering from social deprivation. Secondly, according to the clustering results of social deprivation at the individual level, this paper calculates the location quotient of deprived families in six large cities and three kinds of neighborhoods, and analyzes the spatial difference. Thirdly, the logistic regression model is used to identify the significant attributes affecting social deprivation in the individual and spatial dimensions respectively. In the spatial dimension, the research indicates that social deprivation is most significant in urban villages, followed by the old urban neighborhoods, and the last is workers villages at the neighborhood level. At the city level, eastern cities have the highest concentration degree of social deprivation, followed by central cities and western cities. The logistic model results indicate that social deprivation is a combined outcome of institutional driving force (individual's Hukou type) and family attributes (including individual's education level, age). Hukou type, as an institutional driving force, is the common factor for three types of cities, and family attributes also have a certain effect in the eastern and central cities. Institutional force has significant impact on social deprivation in urban villages, while family attributes play greater roles in old urban neighborhoods and workers villages.
    References | Related Articles | Metrics
    The Organizational System of Urban Administration in Jinshi· Dilizhi (the Chapter on Geography from the History of the Jurchen Jin Dynasty)
    HAN Guanghui, WEI Dan, HE Wenlin
    2012, 67 (10):  1362-1374.  doi: 10.11821/xb201210007
    Abstract ( )   PDF (638KB) ( )   Save
    Since the end of the Tang and the Five Dynasties, great changes have taken place in urban administrative organizations, as verified by urban administration organizations which had independent administrative functions at different levels and scales, including the Jingxunyuan (警巡院), the Lushisi (录事司) and the Sihousi (司候司). As urban administration organizations of county level in the Jin dynasty, their official systems were clearly recorded in the Chapter on Officials from the History of the Jurchen Jin Dynaty, whereas the Organizational System of Urban Administration at county level was not found in the Chapter on Geography from the History of the Jin Dynasty. It is worthwhile to research and revise this point.
    References | Related Articles | Metrics
    The Study of Size-Grade of Settlements around the Songshan Mountain in 9000-3000 aBP Based on SOFM Networks
    LU Peng, TIAN Yan, YANG Ruixia
    2012, 67 (10):  1375-1382.  doi: 10.11821/xb201210008
    Abstract ( )   PDF (1594KB) ( )   Save
    Choosing site area, culture layer thickness, important relics and important remains as the variables, we used cluster analysis of the ancient settlements of four cultural periods respectively, which were Peiligang, Yangshao, Longshan and Xiashang in 9000-3000 aB.P. around the Songshan Mountain through the SOFM networks method, and classified each type of ancient settlements into different size-grades. By this means, the Peiligang settlements were divided into two grades, Yangshao and Longshan settlements were divided into three grades respectively, and Xiashang settlements were divided into four grades. The result suggested that the size-grade diversity of ancient settlements was not significant during Peiligang period in this area. The size-grade diversity of ancient settlements began at about the mid-late stage of Yangshao period (5000 aB.P.), continued during Longshan period and finally formed in Xiashang period. Moreover, the result also reflected the regional difference of cultural characteristic in a certain period, which was mainly represented in the three Peiligang cultural systems distributed in different areas. There were also different spatial characteristics between Xia and Shang cultures. Based on the size-grade study on ancient settlements around the Songshan Mountain, we found that the SOFM networks method was very suitable for size-grade classification of ancient settlements, as using this method, adjacent cells would compete and learn from each other, which could reduce the effect on classification result by the inaccuracy of site acreages.
    References | Related Articles | Metrics
    Spatial Patterns and Gravity Centers Curve of Livestock and Poultry Breeding in China
    FU Qiang, ZHU Yunqiang, SUN Jiulin, KONG Yunfeng
    2012, 67 (10):  1383-1398.  doi: 10.11821/xb201210009
    Abstract ( )   PDF (9057KB) ( )   Save
    This paper aims to examine the spatial distribution patterns of livestock and poultry breeding in China. Using statistical data from Chinese yearbooks and agricultural survey in 2007, the county-level populations of livestock and poultry are estimated in terms of equivalent standardized pig index, per cultivated land pig index and per capita pig index. With the help of spatial data analysis tools in Geoda and ArcGIS software, especially the Moran's I and LISA statistics, the nationwide global and local clustering trends of the three indicators are examined respectively. The Moran's I and LISA analysis shows that ESP and PCP are significantly clustering both globally and locally. However, the per cultivated land pig index is clustering locally but not significant globally. Furthermore, the thematic map series and related gravity centers curve are introduced to explore the spatial patterns of livestock and poultry in China. Based on 1-16 levels of the thematic map design, the centers curve for each indicator are discussed in detail. For districting purpose, each level of the three indicators is adjusted by the intervals between gravity centers of near levels, and the level is classified into one of district types. The districting analysis for three indicators shows that there exists a potential uniform districting scheme for China's livestock and poultry breeding (eight districts in China). As a result, the China's livestock and poultry breeding would be classified into eight districts: extremely sparse area, sparse area, relatively sparse area, normally sparse area, normal area, relatively concentrated area, concentrated area and highly concentrated area. It is also found that there exists a clear demarcation line between the concentrated and the sparse regions of livestock and poultry breeding in China. The line starts from the county boundary between Xin Barag Left Banner and Xin Barag Right Banner, Inner Mongolia Autonomous Region to the west coast of Dongfang County, Hainan Province.
    References | Related Articles | Metrics
    Rural Landscape Changes and Its Optimization Strategies: From the Perspective of Ideal Type Narratives
    FANG Yangang, LIU Jisheng
    2012, 67 (10):  1399-1410.  doi: 10.11821/xb201210010
    Abstract ( )   PDF (720KB) ( )   Save
    Rural landscape is the main part of a terrestrial ecosystem, and plays a vital role in the human-environmental interactions. It mainly consists of rural building, settlement, agricultural land, and natural space, and has both commercial functions such as production functions and consumption functions, and noncommercial functions such as ecosystem service functions and defending functions of private rights. It is a cross field of geography, ecology, and social science. The development of urbanization, transportation network and globalization since the second half of the 20th century has profoundly changed rural space and rural landscapes. Different disciplines and researchers have different descriptions, interpretations and assessments in terms of these changes. Consequently, the rational understanding of rural landscape changes is impeded, as well as the optimized utilization and efficient protection of rural landscape. Using the research results published at home and abroad, this article put forward four more inclusive and explanatory ideal type narratives of rural landscape changes: protection narrative, modernization narrative, fair subsistence narrative, and endogenous development narrative. In this way, this paper refined the diversified values, assumptions, theories, viewpoints and strategies of rural landscape changes more objectively and comprehensively. The shift of rural areas from an economy centered on agriculture to a more service-centered economy is inevitably an increasingly global process. Meanwhile the rural landscapes and their functions have a significant spatial heterogeneity and temporal variability. In the end, this paper uses the natural, landscape, social-economic and spatial indicators to identify seven future scenario types of rural landscape evolution in China: arable production landscape, tourism landscape, natural landscape, agriculture-nature balanced landscape, nature-tourism balanced landscape, agriculture-residence balanced landscape, and rights competed landscape. Each type of rural landscapes has different locations, compositions and functions. Their optimizing strategies are discussed respectively so as to provide a scientific reference for the construction of healthy, productive, attractive and harmonious rural landscape in the future.
    References | Related Articles | Metrics
    Early-warning of Land Ecological Security in Hunan Province Based on RBF
    XU Mei, ZHU Xiang, LIU Chunla
    2012, 67 (10):  1411-1422.  doi: 10.11821/xb201210011
    Abstract ( )   PDF (1209KB) ( )   Save
    Based on the related data of land ecological security of Hunan Province from 1996 to 2010, this paper constructed a land ecological security early-warning index system for Hunan Province from three aspects which were pressure, state and response. And then it used the RBF model to make a prediction for land ecological security development trend of Hunan Province in 2011-2015, and at last made a warning analysis of the land ecological security of the province from 1996 to 2015 according to the early-warning index and warning degree standards. The results are shown as follows. (1) RBF model has a relatively high simulation accuracy, which can well fit the land ecological security system's development trends of Hunan Province in 2011-2015. (2) In terms of each subsystem's warning degree, the early-warning index of the pressure system showed an upward trend with fluctuations in 1996-2010, and the warning degree rose from light alarm to moderate alarm, the indicator light turned from blue lamp to yellow lamp gradually; the early-warning index will continue to rise from moderate alarm (yellow lamp) to heavy alarm (orange lamp) with fluctuations in 2011-2015. As for the state system, its early-warning index showed an upward trend with fluctuations in 1996-2010, although it remained in the moderate alarm (yellow lamp) state, alert should be maintained on the situation; in 2011-2015, the signs of serious deterioration will not occur because of the further effects and regulation of the response system, and it will remain at the level of 2008-2010. As for the response system, it showed a downward trend in 1996-2010, from which the state of huge alarm (red lamp) plummeted to no alarm (green lamp); its early-warning index will decline in 2011-2015 which will keep the state of green lamp and be increasingly close to the lower limit of the state of no alarm. (3) Overall, in 1996-2010, the land ecological security warning degree of Hunan Province showed a slight downward trend. In 2011-2015, its early-warning index will be about 0.42, and warning degree will keep in the state of moderate alarm (yellow lamp). (4) The main factors that influence the land ecological security of Hunan Province include the proportion of agricultural economy, the amount of farmland used for construction, the proportion of natural disaster-affected area, per capita area of construction land, the proportion of the tertiary industry, and the proportion of nature reserves. These are the focus of land ecological security regulation in the future.
    References | Related Articles | Metrics
    Spatial Pattern Evolution of Shanghai Tourist Lodging Industry during the World Expo Construction Period
    WANG Chaohui, LU Lin, FANG Ting, XIA Qiaoyun
    2012, 67 (10):  1423-1437.  doi: 10.11821/xb201210012
    Abstract ( )   PDF (5849KB) ( )   Save
    Taking the lodging industry as an example, the paper integrates qualitative and quantitative methods and the spatial analysis method of GIS to study the space pattern evolution characteristics of the lodging industry and explores the formation and the evolution mechanism of the lodging industry in the metropolitan city. The results are shown as follows. (1) During the early period of the World Expo construction, Shanghai's lodging industry presented an initial pattern of "two-center agglomeration, along the axis of the distribution to a gradient" while a general pattern of "multicenter agglomeration, many axes gradient distribution" when the Expo was being held. (2) During the Expo construction period, the overall space layout of the lodging industry spread gradually from the inner ring road to the outer ring road, and the main cluster center evolved from two centers to multiple centers with three prominent evolutionary directions in space, namely, the northwest of the city, Pudong direction and the development direction from the central area to the peripheral area along both sides of the Huangpu River. (3) During the Expo construction period, Shanghai's lodging industry developed quickly and presented an overall agglomeration layout, the star hotels are characterized by high concentration-high concentration development, and the economy hotels are featured by low concentration-high concentration development. (4) The 2010 World Expo exerted a distinct influence on Shanghai's lodging industry and formed an intensive industrial cluster center and concentrated distribution site centering on Expo garden. Comparatively, the influence of the Expo on economy hotels was stronger than on star hotels. (5) In general, the spatial pattern of formation and evolution of the lodging industry corresponded with the urban spatial development pattern and the urban function structure evolution. Various elements, such as the politics, economy, mega events, the development environment, and the business entity, promote the agglomeration and decentralization of the urban lodging industry effectively through mutually influenced comprehensive mechanism. (6) Together with the government policy, the comprehensive effect of the city development, the great economic effect, the post-Expo development, the overall development of the Expo garden, the 2010 Shanghai World Expo influences the direction and speed of the spatial pattern evolution of the urban lodging industry. The study provides scientific foundation and theoretical basis for the construction distribution of lodging industry system and metropolitan tourism industry planning in the context of the mega event.
    References | Related Articles | Metrics