Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and...Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and time with high relative risk locations for tuberculosis incidence in Beijing area. A retrospective space-time clustering analysis was conducted at the districts level in Beijing area based on reported cases of sputum smear-positive pulmonary tuberculosis (TB) from 2005 to 2014. Global and local Moran’s I, autocorrelation analysis along with Ord (Gi*) statistics was applied to detect spatial patterns and the hotspot of TB incidence. Furthermore, the Kuldorff’s scan statistics were used to analyze space-time clusters. A total of 40,878 TB cases were reported in Beijing from 2005 to 2014. The annual average incidence rate was 22.11 per 100,000 populations (ranged from 16.55 to 25.71). The seasonal incidence occurred from March to July until late autumn. A higher relative risk area for TB incidence was mainly detected in urban and some rural districts of Beijing. The significant most likely space-time clusters and secondary clusters of TB incidence were scattered diversely in Beijing districts in each study year. The risk population was mainly scattered in urban and dense populated districts, including in few rural districts.展开更多
With the incorporation of spatial statistic method, this paper constructs a state-space model of housing market bubbles, discussing the spatial pattern of housing market bubbles in China,and identifying the dynamic ev...With the incorporation of spatial statistic method, this paper constructs a state-space model of housing market bubbles, discussing the spatial pattern of housing market bubbles in China,and identifying the dynamic evolution process. The results show that: The bubbles of housing market walked along a path from low level to high level and then downsized to a low level during the period of 2009 and 2014, and the highest level stayed at 2011. From overall, the level of housing market bubbles had shown significant spatial autocorrelation and spatial agglomeration. In detail, the direction of North-South in China showed the inverted U shape, i.e., Central region was with high bubbles, and two ends contained low bubbles; from East-West direction, the East had high bubbles and the West contained comparatively low bubbles. Local spatial test indicates that there were some approximate spatial features in housing market bubbles among the adjacent regions. Observed from the level of housing market bubbles, China contained 3 plates: The first was the plate with low bubble level,including 3 provinces in North-East China(provinces of Jilin, Heilongjiang and Liaoning were included,but Dalian in Liaoning province was excluded; the second was the Central and West plate(the provinces of Yunnan, Guizhou, Sichuan, Guangdong, Guangxi, Hunan, Hubei, Gansu, Fujian, Jiangxi and Hainan were included in this plate), which was also featured with low bubble; and the third was Central East plate(provinces or provincial regions of Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai, Shandong,Anhui, Shanxi, Shaanxi and Inner Mongolia were included), which was characterized as high bubble region.展开更多
文摘Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and time with high relative risk locations for tuberculosis incidence in Beijing area. A retrospective space-time clustering analysis was conducted at the districts level in Beijing area based on reported cases of sputum smear-positive pulmonary tuberculosis (TB) from 2005 to 2014. Global and local Moran’s I, autocorrelation analysis along with Ord (Gi*) statistics was applied to detect spatial patterns and the hotspot of TB incidence. Furthermore, the Kuldorff’s scan statistics were used to analyze space-time clusters. A total of 40,878 TB cases were reported in Beijing from 2005 to 2014. The annual average incidence rate was 22.11 per 100,000 populations (ranged from 16.55 to 25.71). The seasonal incidence occurred from March to July until late autumn. A higher relative risk area for TB incidence was mainly detected in urban and some rural districts of Beijing. The significant most likely space-time clusters and secondary clusters of TB incidence were scattered diversely in Beijing districts in each study year. The risk population was mainly scattered in urban and dense populated districts, including in few rural districts.
基金Supported by the China Scholarship Council,the Natural Science Foundation of Hunan(2017JJ3010)the Science Foundation for the Excellent Youth Scholars of Department of Education of Hunan(13B008)
文摘With the incorporation of spatial statistic method, this paper constructs a state-space model of housing market bubbles, discussing the spatial pattern of housing market bubbles in China,and identifying the dynamic evolution process. The results show that: The bubbles of housing market walked along a path from low level to high level and then downsized to a low level during the period of 2009 and 2014, and the highest level stayed at 2011. From overall, the level of housing market bubbles had shown significant spatial autocorrelation and spatial agglomeration. In detail, the direction of North-South in China showed the inverted U shape, i.e., Central region was with high bubbles, and two ends contained low bubbles; from East-West direction, the East had high bubbles and the West contained comparatively low bubbles. Local spatial test indicates that there were some approximate spatial features in housing market bubbles among the adjacent regions. Observed from the level of housing market bubbles, China contained 3 plates: The first was the plate with low bubble level,including 3 provinces in North-East China(provinces of Jilin, Heilongjiang and Liaoning were included,but Dalian in Liaoning province was excluded; the second was the Central and West plate(the provinces of Yunnan, Guizhou, Sichuan, Guangdong, Guangxi, Hunan, Hubei, Gansu, Fujian, Jiangxi and Hainan were included in this plate), which was also featured with low bubble; and the third was Central East plate(provinces or provincial regions of Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai, Shandong,Anhui, Shanxi, Shaanxi and Inner Mongolia were included), which was characterized as high bubble region.
