scholarly journals Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties

2021 ◽  
Vol 18 (11) ◽  
pp. 5541
Author(s):  
Jinting Zhang ◽  
Xiu Wu ◽  
T. Edwin Chow
Keyword(s):  
Economic Condition ◽  
Principal Component ◽  
Weighted Regression ◽  
Sensitive Period ◽  
Time Data ◽  
Detection Analysis ◽  
Five Factors ◽  
Texas County ◽  
Weighted Regression Analysis ◽  
Geographically Targeted

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality, and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

scholarly journals Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality Impacts on Texas Counties

2021 ◽  
Author(s):  
Jinting Zhang ◽  
Xiu Wu ◽  
T. Edwin Chow
Keyword(s):  
Economic Condition ◽  
Principal Component ◽  
Sensitive Period ◽  
Time Data ◽  
Detection Analysis ◽  
Five Factors ◽  
Texas County ◽  
Weighted Regression Analysis ◽  
Geographically Targeted ◽  
Data Tracking

Abstract As COVID-19 run rampant in high-density housing sites, it is important to use real-time data tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research is appropriately analyzed the disparities between spatial-temporal clusters, expectation Maximization clustering (EM) and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to South-east Texas analysis in GWR modeling. The sensitive period took place in the last two quarters in 2020. We explored Postgre application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform Principal Component Analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility and satisfy for the need of Emergency Operations Plan (EOP).

Using Principal Component Scores to Enhance the Validity and Reliability of Big Five Personality Measures

2017 ◽  
Vol 38 (2) ◽  
pp. 83-93
Author(s):  
Jeffrey M. Cucina ◽  
Nicholas L. Vasilopoulos ◽  
Arwen H. DeCostanza
Keyword(s):  
Big Five ◽  
Discriminant Validity ◽  
Reliability And Validity ◽  
Principal Component ◽  
Big Five Personality ◽  
Validity And Reliability ◽  
Scoring Method ◽  
Five Factors ◽  
Big Five Factors ◽  
Component Scores

Abstract. Varimax rotated principal component scores (VRPCS) have previously been offered as a possible solution to the non-orthogonality of scores for the Big Five factors. However, few researchers have examined the reliability and validity of VRPCS. To address this gap, we use a lab study and a field study to investigate whether using VRPCS increase orthogonality, reliability, and criterion-related validity. Compared to the traditional unit-weighting scoring method, the use of VRPCS enhanced the reliability and discriminant validity of the Big Five factors, although there was little improvement in criterion-related validity. Results are discussed in terms of the benefit of using VRPCS instead of traditional unit-weighted sum scores.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

scholarly journals Metacognitive Rating Scale: A Study Applying a Korean Translation to Individuals with Schizophrenia

2021 ◽  
Vol 18 (13) ◽  
pp. 6853
Author(s):  
Mihwa Han ◽  
Kyunghee Lee ◽  
Mijung Kim ◽  
Youngjin Heo ◽  
Hyunseok Choi
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Mental Status ◽  
Rating Scale ◽  
Anger Expression ◽  
Principal Component ◽  
Negative Beliefs ◽  
Five Factors ◽  
Metacognitive Beliefs

Metacognition is a higher-level cognition of identifying one’s own mental status, beliefs, and intentions. This research comprised a survey of 184 people with schizophrenia to verify the reliability of the metacognitive rating scale (MCRS) with the revised and supplemented metacognitions questionnaire (MCQ) to measure the dysfunctional metacognitive beliefs of people with schizophrenia by adding the concepts of anger and anxiety. This study analyzed the data using principal component analysis and the varimax method for exploratory factor analysis. To examine the reliability of the extracted factors, Cronbach’s α was used. According to the results, reliability was ensured for five factors: positive beliefs about worry, negative beliefs about uncontrollability and danger of worry, cognitive confidence, need for control, and cognitive self-consciousness. The negative beliefs about uncontrollability and danger of worry and the need for control on anger expression, which were both added in this research, exhibited the highest correlation (r = 0.727). The results suggest that the MCRS is a reliable tool to measure the metacognition of people with schizophrenia.

Factors of values in the Dutch Language and their relationship to factors of personality

2008 ◽  
Vol 22 (2) ◽  
pp. 81-108 ◽  
Author(s):  
Boele De Raad ◽  
Jan Pieter Van Oudenhoven
Keyword(s):  
Big Five ◽  
Principal Component ◽  
Value Systems ◽  
Regression Analyses ◽  
Value System ◽  
Self And Other ◽  
Guiding Principle ◽  
Five Factors ◽  
Dutch Language ◽  
The One

Following the psycholexical approach, several thousands of potential value descriptors were selected from the Dutch lexicon. This set was subsequently reduced according to criteria of relevance to a list of 641 values. The value list was administered to 634 participants (self‐ and other‐raters), who had to indicate the extent to which each value was a guiding principle in the life of the target. Principal component analyses were performed yielding eight factors of values. In addition, ratings were collected on markers of three other systems of values, including the one described by Schwartz (1992). Finally, A Big Five questionnaire, the FFPI, was administered. Correlation and regression analyses were performed to describe the relations between the different value systems, and between the Dutch value system and the Big Five factors. Copyright © 2007 John Wiley & Sons, Ltd.

scholarly journals Detection and Microscopy of Alnus glutinosa Pollen Fluorescence Peculiarities

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 959
Author(s):  
Šaulienė ◽  
Šukienė ◽  
Daunys ◽  
Valiulis ◽  
Lankauskas ◽  
...  
Keyword(s):  
Image Analysis ◽  
Principal Component ◽  
Fluorescence Analysis ◽  
Alnus Glutinosa ◽  
Fluorescence Properties ◽  
Light Spectrum ◽  
Time Data ◽  
Process Data ◽  
Pollen Storage ◽  
Black Alder

Alnus glutinosa is an important woody plant in Lithuanian forest ecosystems. Knowledge of fluorescence properties of black alder pollen is necessary for scientific and practical purposes. By the results of the study, we aimed to evaluate possibilities of identifying Alnus glutinosa pollen fluorescence properties by modeling ozone effect and applying two different fluorescence-based devices. To implement the experiments, black alder pollen was collected in a typical habitat during the annual flowering period in 2018–2019. There were three groups of experimental variants, which differed in the duration of exposure to ozone, conditions of pollen storage before the start of the experiment, and the exposure time. Data for pollen fluorescence analysis were collected using two methods. The microscopy method was used in order to evaluate the possibility of employing image analysis systems for investigation of pollen fluorescence. The second data collection method is related to an automatic device identifying pollen in real time, which uses the fluorescence method in the pollen recognition process. Data were assessed employing image analysis and principal component analysis (PCA) methods. Digital images of ozone-exposed pollen observed under the fluorescence microscope showed the change of the dominant green colour toward the blue spectrum. Meanwhile, the automatic detector detects more pollen whose fluorescence is at the blue light spectrum. It must be noted that assessing pollen fluorescence several months after exposure to ozone, no effect of ozone on fluorescence remains.

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