GIS-Based Analysis of Spatial Distribution Characteristics of Geographical Indication Products in Zhejiang Province

2020 ◽  
Vol 09 (03) ◽  
pp. 148-155
Author(s):  
杭秀 谢
2021 ◽  
Vol 15 (1) ◽  
pp. e0008956
Author(s):  
Limei Wu ◽  
Yunliang Shen ◽  
Qiang Yao ◽  
Xudong Sang ◽  
Lijuan Fei ◽  
...  

Background After the elimination of leprosy in 1995, there were 10–30 newly detected leprosy cases every year in Zhejiang Province, and the epidemiological characteristics of the newly detected leprosy cases have changed. While most of the newly detected cases came from other provinces in China, not Zhejiang, it brought a new challenge for leprosy prevention and control in post- elimination era in Zhejiang, China. This study was aimed to understand the temporal-spatial distribution characteristics of newly detected leprosy cases, and provide the scientific rationales for the development of leprosy control strategy. Methods Data on the demographic of Zhejiang Province from 2011 to 2019 were obtained from the China Information System for Disease Control and Prevention, and the epidemiological data on leprosy cases newly detected in Zhejiang Province from 2011 to 2019 were obtained from the LEPROSY MANAGEMANT INFORMATION SYSTEM IN CHINA (LEPMIS), and temporal-spatial distributions were described. The geographic information system software—ArcGIS 10.4 was used to draw the statistical maps, and Geoda 1.14.0 was used for local spatial autocorrelation analysis (local Getis coefficient method). Ridley-Jopling classification was used to classify the clinical types into I, TT, BT, BB, BL or LL. Two-group classification system developed by the World Health Organization (WHO) was used and cases were classified into multibacillary (MB) type or paucibacillary (PB) type. Results A total of 167 leprosy cases were reported in Zhejiang Province during 2011–2019, including 107 cases in males and 60 in females. The mean age at diagnosis was 37.99±14.81 years, and 94.01% of the cases were detected through the examination at skin-clinics. The number of workers, MB cases, G2D cases were 81 (48.50%), 159 (94.01%), 24 (14.37%) respectively, and the rate of early detection increased from 45.16% in 2011 to 90.91% in 2019. Leprosy cases were reported in all the prefectures of Zhejiang except Zhoushan City. The cases in local population accounted for 23.35% (39 cases), and the cases in floating population (especially coming from high epidemic provinces in China) accounted for 76.65% (128 cases). The annual number of newly detected cases showed a decreasing trend, from 31 cases in 2011 to 11 in 2019. Time of the floating population living in Zhejiang Province ranged from several months to more than 10 years. The annual proportion of new cases with G2D declined from 22.58% in 2011 to 9.09% in 2019. The results of local indicators of autocorrelation (LISA) analysis showed that the high-high areas were mainly concentrated in the middle and northeast of Zhejiang Province, while the low-low areas were in the east and southwest. Conclusion A few scattered cases still can be seen in post-elimination era, and there was a spatial clustering of the newly detected leprosy cases in Zhejiang Province. Most of the cases in Zhejiang Province were from other high epidemic provinces in China, which brought a new challenge for leprosy control and prevention in post- elimination era in Zhejiang, and it is also necessary to strengthen the early detection and standard management of the leprosy cases in floating population in Zhejiang.


CONVERTER ◽  
2021 ◽  
pp. 280-287
Author(s):  
Di Lv, Yue Qin

The agglomeration of cultural and creative industries has developed into a remarkable trend under economic internationalization. In this paper, literature review and research status of cultural and creative industries are firstly conducted. The spatial distribution characteristics of cultural and creative industries in Zhejiang province are analyzed by using location entropy and data from 2012 to 2017. The conclusion shows that the cultural and creative industries in Zhejiang province present a spatial clustering trend, but different cities have different development levels. This paper constructs the indicator system of influencing factors and analyzes its influencing factors by means of grey correlation. It shows that economic factors have the greatest influence on the agglomeration of cultural and creative industries, followed by technological, resource and market factors. The development and agglomeration of the cultural and creative industry in Zhejiang province depends on the development of industry, theapplicationandmanagementofinformationtechnology and the lack of creative talents is the obstacle to its development. Therefore, education should be developed, mass innovation should be encouraged, and a good market environment should be created for the cultural and creative industry.


2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.


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