Spatio-temporal Distribution Characteristics of Water Quality in Miju River and Erhai Lake

2019 ◽  
Vol 93 (sp1) ◽  
pp. 31
Author(s):  
Junlong Liu ◽  
Jijun Xu ◽  
Jin Chen ◽  
Xiaofeng Hong ◽  
Mingyuan Zhou
Keyword(s):  
Water Quality ◽  
Temporal Distribution ◽  
Erhai Lake ◽  
Distribution Characteristics ◽  
Spatio Temporal

Spatio-temporal distribution characteristics of cropland water use efficiency and its relationship with climate in Anhui Province

2018 ◽  
Vol 38 (17) ◽  
Author(s):  
王芳 WANG Fang ◽  
汪左 WANG Zuo ◽  
黄静 HUANG Jing ◽  
杨淑杰 YANG Shujie ◽  
贺广均 HE Guangjun ◽  
...  
Keyword(s):  
Water Use Efficiency ◽  
Water Use ◽  
Temporal Distribution ◽  
Anhui Province ◽  
Distribution Characteristics ◽  
Use Efficiency ◽  
Spatio Temporal

scholarly journals Spatio-temporal distribution characteristics of cysticercosis from 2000 to 2014 in Dali, Yunnan province, China.

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Huanzhang Li ◽  
Xinzhong Zang ◽  
Xiaokang Hu ◽  
Eniola Michael Abe ◽  
Menbao Qian ◽  
...  
Keyword(s):  
Public Health ◽  
Yunnan Province ◽  
Disease Incidence ◽  
Temporal Distribution ◽  
Public Health Problem ◽  
Effective Control ◽  
Distribution Characteristics ◽  
Hotspot Analysis ◽  
Spatio Temporal ◽  
And Control

Cysticercosis remains a public health problem in China, with disease prevalence attributed to poor socio-economic and public health conditions. This parasitic food-borne disease was prioritized for effective control following implementation of the national surveys on parasitic diseases carried out in China. We predicted the cysticercosis distribution in Dali, Yunnan Province by assessing spatio-temporal distribution characteristics between 2000 and 2014 to better understand the trend of the disease incidence. A database of cysticercosis cases was provided by the clinical department at the Dali Prefectural Institute of Research and Control of Schistosomiasis. Describing the epidemiological features of cysticercosis and analyzing its spatiotemporal distribution of cases using mapping, scanning and spatial autocorrelation analysis, our findings found a total of 3,347 patients with cysticercosis infection, neurocysticercosis in particular. Cysticercosis prevalence was the highest among young and middle-aged male farmers, and also predominant among the Bai nationality. Three aggregation areas were identified during the period 2000-2014. Hotspot analysis implicated Dali City, Eryuan County and Yangbi County between 2000 and 2007, with areas gradually shifting towards the western and northern parts of the province. The hotspot map indicated that Eryuan County was a constant problem with respect to cysticercosis. The results indicated three cysticercosis clusters in Dali that could be attributed to environmental factors and unhealthy lifestyles. Multi-sectoral control initiatives are, therefore, recommended in these areas to effectively control and prevent cysticercosis among the population.

scholarly journals Spatio-temporal distribution characteristics of COVID-19 in China: a city-level modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianqian Ma ◽  
Jinghong Gao ◽  
Wenjie Zhang ◽  
Linlin Wang ◽  
Mingyuan Li ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Southern China ◽  
Early Stage ◽  
Hot Spot ◽  
Statistical Significance ◽  
Mainland China ◽  
Temporal Distribution ◽  
Distribution Characteristics ◽  
Hotspot Analysis ◽  
Spatio Temporal

Abstract Background The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China. Objective To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China. Methods A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space–time scan statistic were conducted. Results The high incidence stage of China’s COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran’s I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China. Conclusions Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.

scholarly journals Spatio-temporal distribution and classification of utilization of urban bare lots in low-slope hilly regions

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246746
Author(s):  
Qi Cao ◽  
Manjiang Shi
Keyword(s):  
Remote Sensing ◽  
Temporal Distribution ◽  
Urban Landscapes ◽  
Distribution Characteristics ◽  
Remote Sensing Images ◽  
Landscape Index ◽  
Activity Intensity ◽  
Transportation Service ◽  
Spatio Temporal

Urban bare lots are persistent phenomena in urban landscapes in the course of urbanization. In the present study, we examined the spatio-temporal distribution of urban bare lots in low-slope hilly areas, and to assess the major pathways by which they are generated and later re-transformed for exploitation. We extracted land use and land cover (LULC) change information and analyzed spatio-temporal distribution characteristics of urban bare lots using Landsat TM/OLI series remote sensing images. Subsequently, we proposed an index system for their evaluation and classification, and identified five types of urban bare lots. Urban bare lot quantity and distribution are closely correlated with human activity intensity. Stakeholders should consider the multiple effects of location, topography, landscape index, transportation, service facilities, and urban planning in urban bare lot classification activities for renovation and re-transformation.

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