Temporal and spatial distribution characteristics of water resources in Guangdong Province based on a cloud model

Zhou, Qi; Wang, Wei; Pang, Yong; Zhou, Zhi-yong; Luo, Hui

doi:10.1016/j.wse.2015.09.001

Cited by 20 publications

(8 citation statements)

References 15 publications

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“…The sound intensity is chosen as a representative acoustic signal index to evaluate the crispness of the samples. The one‐dimensional cloud model based on sound intensity (Figure ) demonstrates the three numerical characteristics of each sample were well matched with the corresponding cloud models, and the same result was obtained by Zhou, Wang, Pang, Zhou, and Luo (). As acoustic signals released when compressing is proportional to the crispness of the foods (Iliassafov & Shimoni, ), the three time‐domain acoustic signal characteristics can represent the crispness of the samples.…”

Section: Resultsmentioning

confidence: 99%

A comparison of food crispness based on the cloud model

Wang

Sun

Hou

et al. 2017

Journal of Texture Studies

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Section: Resultsmentioning

confidence: 99%

A comparison of food crispness based on the cloud model

Wang

Sun

Hou

et al. 2017

Journal of Texture Studies

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“…It includes suppliers, warehouses, distribution centers, retail outlets, and flowing commodities between various agencies. Activities of production, storage, and sales in logistics network will produce a large amount of data concerning customer and goods source (Pearce et al 2016;Zhou et al 2015). They will all manifest traits of data points under conditions of geographical phenomenon.…”

Section: Research Approachesmentioning

confidence: 99%

Spatial–temporal evolution of the distribution pattern of customer sources in tea trade of Fujian enterprise supply chain

Zhang

Chang

2018

Microsyst Technol

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The temporal and spatial operation of commodity business activities is neither random nor accidental. It is characterized by inner laws and features. Based on spatio-temporal analysis in GIS, the objective of this study was to use spatial autocorrelation method and kernel density estimation to study the temporal and spatial distribution pattern of customer sources in tea trade extracted from enterprise supply chains in Fujian Province, China. Using data of Fujian tea business as an example, customer sources showed a typical clustered pattern overall that could be classified into several hot areas. The distribution of customer sources is dynamic along with time. These hot areas spread from coastal cities to inland cities, ranging from urban to suburban. Meanwhile, it showed a relatively irregular distribution in suburban areas with aggregation distribution near urban areas. This study applied GIS spatio-temporal analysis technology to the analysis of an enterprise supply chain, synthesizing both spatial and temporal information and successfully integrating business with geography.

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“…Results of a few studies conducted in different settings of Ethiopia revealed that an unimproved source of drinking water was significantly clustered spatially [ 4 , 21 , 22 ]. However, the temporal pattern of unimproved drinking water sources was not incorporated in these studies and they used only one cross-sectional data; it may be unable to analyze the data in time and space domains [ 23 ]. In addition, taking drinking water source data aggregately may ignore urban-rural disparities.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of the Proportion of Unimproved Drinking Water Sources in Rural Ethiopia: Evidence from Ethiopian Socioeconomic Surveys (2011 to 2019)

Kassie

Mengistu

2022

Journal of Environmental and Public Health

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Currently, around 36% of the rural Ethiopian population is accessing drinking water from unimproved sources and it is unevenly distributed through time and geographic regions. Therefore, this study aimed to analyze the spatiotemporal patterns of unimproved drinking water sources and identify hotspot areas in rural Ethiopia. Ethiopian Socioeconomic Survey (ESS) data obtained from the Central Statistical Agency were used. It was conducted in four waves from 2011 to 2019. A two-stage probability sampling design was applied. The sample of enumeration areas and households were taken as the first and second stages of sampling, respectively. A total of 3912, 3775, 3698, and 3115 sample households with complete information on drinking water sources were taken in each wave of ESS data, respectively. Weighted proportions, autocorrelation (Moran’s “I”) statistic, and hotspot analyses were applied to estimate the prevalence, test the presence of clustering, and identify vulnerable areas with unimproved drinking water sources. The STATA version 14, Excel, and ArcGIS 10.6 were used to manage and analyze data. The proportions of households with unimproved drinking water sources were 0.497, 0.385, 0.298, and 0.363 in consecutive waves of ESS data. The results also revealed the existence of geographical and temporal variations of access to drinking water from unimproved sources, and the most recent vulnerable (hotspot) areas in the borders of the West and East Gojjam zones in the western Amhara region, Zone one in southern Afar region, and Liben, Afder, Shebelle, Korahe, and Nobob zones in Somali region were identified. In conclusion, this study reveals significant geographic inequalities in the use of improved drinking water sources. This may be necessary for policies and coverage targeting the most vulnerable regions. The presented map and analytical approaches can provide a mechanism to monitor future reductions in inequality within countries by reflecting resource allocation priorities.

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Temporal and spatial distribution characteristics of water resources in Guangdong Province based on a cloud model

Cited by 20 publications

References 15 publications

A comparison of food crispness based on the cloud model

A comparison of food crispness based on the cloud model

Spatial–temporal evolution of the distribution pattern of customer sources in tea trade of Fujian enterprise supply chain

Spatiotemporal Analysis of the Proportion of Unimproved Drinking Water Sources in Rural Ethiopia: Evidence from Ethiopian Socioeconomic Surveys (2011 to 2019)

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