Spatio-temporal variation of WQI, scaling and corrosion indices, and principal component analysis in rural areas of Marand, Iran

2020 ◽  
Vol 11 ◽  
pp. 100480
Author(s):  
Samira sheikhi ◽  
Hossein Shahbazi ◽  
Mohammad Mosaferi ◽  
Parisa Firuzi ◽  
Hassan Aslani
Keyword(s):  
Principal Component Analysis ◽  
Temporal Variation ◽  
Rural Areas ◽  
Principal Component ◽  
Component Analysis ◽  
Spatio Temporal

A principal component analysis of the determinants of spatial disparity between rural and urban localities of Ghana

2017 ◽  
Vol 44 (6) ◽  
pp. 715-731 ◽  
Author(s):  
Ivy Drafor
Keyword(s):  
Principal Component Analysis ◽  
Rural Areas ◽  
Urban Areas ◽  
Principal Component ◽  
Component Analysis ◽  
Spatial Inequality ◽  
Necessary Condition ◽  
Spatial Disparity ◽  
Content Type ◽  
Rural And Urban

Purpose The purpose of this paper is to analyse the spatial disparity between rural and urban areas in Ghana using the Ghana Living Standards Survey’s (GLSS) rounds 5 and 6 data to advance the assertion that an endowed rural sector is necessary to promote agricultural development in Ghana. This analysis helps us to know the factors that contribute to the depravity of the rural sectors to inform policy towards development targeting. Design/methodology/approach A multivariate principal component analysis (PCA) and hierarchical cluster analysis were applied to data from the GLSS-5 and GLSS-6 to determine the characteristics of the rural-urban divide in Ghana. Findings The findings reveal that the rural poor also spend 60.3 per cent of their income on food, while the urban dwellers spend 49 per cent, which is an indication of food production capacity. They have low access to information technology facilities, have larger household sizes and lower levels of education. Rural areas depend a lot on firewood for cooking and use solar/dry cell energies and kerosene for lighting which have implications for conserving the environment. Practical implications Developing the rural areas to strengthen agricultural growth and productivity is a necessary condition for eliminating spatial disparities and promoting overall economic development in Ghana. Addressing rural deprivation is important for conserving the environment due to its increased use of fuelwood for cooking. Absence of alternatives to the use of fuelwood weakens the efforts to reduce deforestation. Originality/value The application of PCA to show the factors that contribute to spatial inequality in Ghana using the GLSS-5 and GLSS-6 data is unique. The study provides insights into redefining the framework for national poverty reduction efforts.

Modeling Spatio-Temporal Characteristics of Metocean Conditions During Hurricanes Using Deep Neural Networks

2020 ◽  
Author(s):  
Chi Qiao ◽  
Andrew T. Myers
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Deep Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Offshore Structures ◽  
Complex Nature ◽  
Temporal Characteristics ◽  
Spatio Temporal

Abstract Surrogate modeling of the variability of metocean conditions in space and in time during hurricanes is a crucial task for risk analysis on offshore structures such as offshore wind turbines, which are deployed over a large area. This task is challenging because of the complex nature of the meteorology-metocean interaction in addition to the time-dependence and high-dimensionality of the output. In this paper, spatio-temporal characteristics of surrogate models, such as Deep Neural Networks, are analyzed based on an offshore multi-hazard database created by the authors. The focus of this paper is two-fold: first, the effectiveness of dimension reduction techniques for representing high-dimensional output distributed in space is investigated and, second, an overall approach to estimate spatio-temporal characteristics of hurricane hazards using Deep Neural Networks is presented. The popular dimension reduction technique, Principal Component Analysis, is shown to perform similarly compared to a simpler dimension reduction approach and to not perform as well as a surrogate model implemented without dimension reduction. Discussions are provided to explain why the performance of Principal Component Analysis is only mediocre in this implementation and why dimension reduction might not be necessary.

scholarly journals Principal Component Analysis of the LEADER Approach (2007–2013) in South Western Europe (Extremadura and Alentejo)

2019 ◽  
Vol 11 (15) ◽  
pp. 4034 ◽  
Author(s):  
Nieto Masot ◽  
Alonso ◽  
Moreno
Keyword(s):  
Principal Component Analysis ◽  
Rural Development ◽  
Rural Areas ◽  
Western Europe ◽  
Principal Component ◽  
Component Analysis ◽  
Demographic Variables ◽  
Development Aid ◽  
Border Regions ◽  
Political Differences

Since the end of the last century, the Rural Development Policy and the associated Rural Development Aid have been implemented (according to the LEADER Approach) in European rural areas as a model of endogenous, integrated, and innovative development. Its objective is to reduce the differences of development in these areas. The objective of this paper is to analyze statistically (using Principal Component Analysis) the investments and projects carried out during the period of 2007–2013 in the regions of Extremadura and Alentejo. These two border regions have many territorial similarities but also historical, cultural, and political differences. These variations may contribute to a different implementation of the LEADER Approach. As determined by the results from the statistical analysis of economic aids and demographic variables, it is evident that there are differences in the management of the Rural Development Aid in both territories but resemblances in the results.

DYNAMICS AND SPATIO-TEMPORAL VARIABILITY OF ENVIRONMENTAL FACTORS IN EASTERN AUSTRALIA USING FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS

2010 ◽  
Vol 18 (04) ◽  
pp. 763-785 ◽  
Author(s):  
JUDIT K. SZABO ◽  
EUGENIO M. FEDRIANI ◽  
M. MANUELA SEGOVIA-GONZÁLEZ ◽  
LEE B. ASTHEIMER ◽  
MIKE J. HOOPER
Keyword(s):  
Principal Component Analysis ◽  
Temporal Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Geographical Information ◽  
Spatial And Temporal Variability ◽  
Functional Principal Component ◽  
Eastern Australia ◽  
Spatio Temporal

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 1998–2004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types.

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