For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distri...For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distributions of node strength and edge weight, and the exponent can be adjusted by not only the parameter δ but also the probability q. Furthermore, we investigate the weighted average shortest distance, clustering coefficient, and the correlation of our network. In addition, the weighted assortativity coefficient which characterizes important information of weighted topological networks has been discussed, but the variation of coefficients is much smaller than the former researches.展开更多
The capacity of livestock breeding in China has increased rapidly since 1949, and the total output of meat, poultry and eggs maintains the world's top first in recent 20 years. Livestock emissions and pollution is...The capacity of livestock breeding in China has increased rapidly since 1949, and the total output of meat, poultry and eggs maintains the world's top first in recent 20 years. Livestock emissions and pollution is closely associated with its population and spatial distribution. This paper aims to investigate the spatial patterns of livestock and poultry breeding in China. Using statistical yearbook and agricultural survey in 2007, the county-level populations of livestock and poultry are estimated as equivalent standardized pig index (ESP), per cultivated land pig index (PCLP) and per capita pig index (PCP). With the help of spatial data analysis (ESDA) 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, PCLP is clustering locally but not significant globally. Furthermore, the thematic map series (TMS) and related gravity centers curve (GCC) are introduced to explore the spatial patterns of livestock and poultry in China. The indicators are classified into 16 levels, and the GCCs for the three indicators from level 1 to 16 are discussed in detail. For districting purpose, each interval between gravity centers of near levels for all the three indicators is calculated, and the districting types of each indicator are obtained by merging adjacent levels. The districting analysis for the three indicators shows that there exists a potential uniform districting scheme for China's livestock and poultry breeding. As a result, the China's livestock and poultry breeding would be classified into eight types: extremely sparse region, sparse region, relatively sparse region, normally sparse region, normal region, relatively concentrated region, concentrated region and highly concentrated region. It is also found that there exists a clear demarcation line between the concentrated and the sparse regions. 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.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.10675060
文摘For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distributions of node strength and edge weight, and the exponent can be adjusted by not only the parameter δ but also the probability q. Furthermore, we investigate the weighted average shortest distance, clustering coefficient, and the correlation of our network. In addition, the weighted assortativity coefficient which characterizes important information of weighted topological networks has been discussed, but the variation of coefficients is much smaller than the former researches.
基金Key Program of Special Science Research in Environmental Protection Public Welfare Industry, No.201009017Research Plan of LREIS, No.088RA900KAKey Project for the Strategic Plan in IGSNRR, CAS, No.2012ZD010
文摘The capacity of livestock breeding in China has increased rapidly since 1949, and the total output of meat, poultry and eggs maintains the world's top first in recent 20 years. Livestock emissions and pollution is closely associated with its population and spatial distribution. This paper aims to investigate the spatial patterns of livestock and poultry breeding in China. Using statistical yearbook and agricultural survey in 2007, the county-level populations of livestock and poultry are estimated as equivalent standardized pig index (ESP), per cultivated land pig index (PCLP) and per capita pig index (PCP). With the help of spatial data analysis (ESDA) 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, PCLP is clustering locally but not significant globally. Furthermore, the thematic map series (TMS) and related gravity centers curve (GCC) are introduced to explore the spatial patterns of livestock and poultry in China. The indicators are classified into 16 levels, and the GCCs for the three indicators from level 1 to 16 are discussed in detail. For districting purpose, each interval between gravity centers of near levels for all the three indicators is calculated, and the districting types of each indicator are obtained by merging adjacent levels. The districting analysis for the three indicators shows that there exists a potential uniform districting scheme for China's livestock and poultry breeding. As a result, the China's livestock and poultry breeding would be classified into eight types: extremely sparse region, sparse region, relatively sparse region, normally sparse region, normal region, relatively concentrated region, concentrated region and highly concentrated region. It is also found that there exists a clear demarcation line between the concentrated and the sparse regions. 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.
