A new method for selecting sites for soil sampling, coupling global weighted principal component analysis and a cost-constrained conditioned Latin hypercube algorithm

Nketia, Kwabena Abrefa; Asabere, Stephen Boahen; Erasmi, Stefan; Sauer, Daniela

doi:10.1016/j.mex.2019.02.005

Cited by 8 publications

(5 citation statements)

References 16 publications

(37 reference statements)

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“…(2014), Saib et al ., (2015), Mishra (2018), Nketia et al . (2019), Benita et al . (2020) and Bai et al ., (2022) was applied in the present paper for combining indicators to measure the composite reality of the UHWI scenario for residents of hilly Darjeeling Town.…”

Section: Methodsmentioning

confidence: 99%

“…Selected variables and their respective domains with justification for assessing UHWI.Index construction methodsA cutting-edge spatially explicit method for dimensionality reduction followed byHarris et al (2014),Saib et al, (2015),Mishra (2018),Nketia et al (2019),Benita et al (2020) andBai et al, (2022) was applied in the present paper for combining indicators to measure the composite reality of the UHWI scenario for residents of hilly Darjeeling Town. Implementing the Geographically Weighted Principal Components Analysis (GWPCA) method has proven to be highly effective in assessing the spatiality of urban housing quality in hilly Darjeeling towns.…”

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confidence: 99%

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Hilly terrain and housing wellness: Geo‐visualizing spatial dynamics of urban household quality in the Himalayan town of Darjeeling, India

Roy,

Majumder,

Bose

et al. 2024

Singap J Trop Geogr

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Darjeeling, renowned as the ‘Queen of the Himalayas’, is one of the high‐altitude towns in India, distinguished by its exceptional topography and picturesque landscape. Given the challenges posed by limited land availability, susceptibility to natural hazards, and the need for context‐specific housing interventions in such hilly terrains, understanding housing conditions becomes paramount. Thus, this research explores the spatial patterns and heterogeneity of urban housing wellness in Darjeeling's hilly urban centre. A comprehensive assessment was conducted utilizing 15 key indicators. Three principal indices were formulated: Residence Quality, Residence Essential Services, and Residence Asset & Possession, culminating in the Urban Housing Wellness Index (UHWI). The index construction employed the Geographically Weighted Principle Component Analysis (GWPCA) technique. In further analysis, Univariate Local Indicators of Spatial Association (LISA) were used to determine clustering and spatial dependence, while Moran's I was utilized to gauge the spatial autocorrelation of housing conditions. A notable clustering pattern and spatial autocorrelation was observed in the urban housing wellness of the study area. The present study offers novel insights into the intricate dynamics of housing conditions in unique hilly terrains.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Hilly terrain and housing wellness: Geo‐visualizing spatial dynamics of urban household quality in the Himalayan town of Darjeeling, India

Roy,

Majumder,

Bose

et al. 2024

Singap J Trop Geogr

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“…The 36 measurement locations were stratified following an unbiased approach that coupled the global weighted principal component algorithm with a cost-constrained conditioned Latin hypercube algorithm [3] . With this approach, it was possible to account for the maximum local spatial structures of the study area, while selecting optimized locations that highly influenced SM variability.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on spatial and temporal modelling of soil water storage in the Guinea savannah zone of Northern Ghana

2022

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“…Since precision agriculture usage and environmental management saw a rapid growth in recent years, various soil sampling approaches have been proposed [2,3,4,5,6,7,8,9,10,11,12,13,14,15]. Different modifications of Latin Hypercube sampling, simple random sampling, and grid sampling are commonly investigated ways to achieve an optimal sampling scheme.…”

Section: Introductionmentioning

confidence: 99%

Optimal soil sampling design based on the maxvol algorithm

Petrovskaia¹,

Ryzhakov²,

Oseledets³

2021

Preprint

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Spatial soil sampling is an integral part of a soil survey aimed at creating a soil map. We propose considering the soil sampling procedure as a task of optimal design. In practical terms, optimal experiments can reduce experimentation costs, as they allow the researcher to obtain one optimal set of points. We present a sampling design, based on the fundamental idea of selecting sample locations by performing an optimal design method called the maxvol algorithm.It is shown that the maxvol-base algorithm has a high potential for practical usage. Our method outperforms popular sampling methods in soil taxa prediction based on topographical features of the site and deals with massive agricultural datasets in a reasonable time.

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A new method for selecting sites for soil sampling, coupling global weighted principal component analysis and a cost-constrained conditioned Latin hypercube algorithm

Abstract: Graphical abstract

Cited by 8 publications

References 16 publications

Hilly terrain and housing wellness: Geo‐visualizing spatial dynamics of urban household quality in the Himalayan town of Darjeeling, India

Hilly terrain and housing wellness: Geo‐visualizing spatial dynamics of urban household quality in the Himalayan town of Darjeeling, India

Data on spatial and temporal modelling of soil water storage in the Guinea savannah zone of Northern Ghana

Optimal soil sampling design based on the maxvol algorithm

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