Analysing street heritage trees surface temperature for UHI mitigation using remote sensing and GIS application / Nor Suhaida Yusof, Nur Huzeima Mohd Hussain and Noradila Rusli

Urban Trees are important in reducing the heat by providing the shade and cooling effect to the urban environment. Every tree species provides different cooling effect depending on their tree characteristics. Evergreen species such as heritage tree are significant in reducing the surface temperature. In particular, heritage trees do have environmental implications which provide lots of benefits for the environment and human health. The aim of this paper is to analyse the heritage trees surface temperature in mitigating urban heat island (UHI) at Taiping Old Town. The research has conducted utilizing Landsat 8 OLI data and on-site data collection. This research integrated Geographic Information System (GIS) and remote sensing in data processing and SPSS for analysis. The result shows the low significant relationship of tree characteristics and Land Surface Temperature (LST) with (R²=0.17) indicates that external factors can reduce the cooling effect from heritage trees in reducing the surface temperature in the urban area. Moreover, there is also an analysis on the LST of land cover features together with the frequency of heritage trees. The findings revealed that the higher frequency of heritage trees planted at the hard surface; the higher the ability to reduce the LST (about 5.3°C) in urban areas

The relationship of heritage trees in urban heat island mitigation effect at Taiping, Perak, Malaysia / Nor Suhaida Yusof, Nur Huzeima Mohd Hussain and Noradila Rusli

Every tree species provide different cooling effect depending on their tree characteristics. Evergreen species such as heritage trees are significant in reducing the surface temperature. The aim of this paper is to determine the relationship of heritage treesin mitigating urban heat island at Taiping Old Town. The research had been conducted through Landsat 8 OLI and field data collection. Thisresearch integrated the Geographic Information System (GIS) and remote sensing in data processing and analysis. The results show the low significant relationship of tree characteristics and Land Surface Temperature (LST) with (R²=0.17) which indicate that external factors may also influence the changes in temperature

Seeking higher order construction of cognitive abilities in a psychosocial learning environment

In this study, we created a psychosocial learning environment consisting of five types of interaction, namely: student collaboration; specific learning objectives and curriculum coherence; learning facilities; independent learning; and constructivist instruction. This research aimed to determine the scope in which the five modes of contact improved students’ learning outcomes in higher order cognitive abilities. This quantitative study involved form four accounting students (N=352) in Malaysia who completed a self-administered questionnaire that included the higher order cognitive abilities (HOCA) test, the instruments of students’ perceived learning environment, and zone-specific demographic data. The results showed two of the five inventory of students’ perceived learning environment (ISPLE) scores. Specific learning objectives and curriculum coherence, were the most significant predictors and strongly correlated with higher order cognitive abilities. Even the components of the psychosocial learning environment impact HOCA in most subjects. However, researchers have obtained new findings that explain other factors that need to be studied to evaluate or encourage HOCA in accounting subjects. Thus, the researcher suggests further research using self-learning methods through modules to assess and promote HOCA in accounting

Robust Correlation Procedure via Sn Estimator

Pearson correlation coefficient is the most widely used statistical technique when measuring a relationship between the bivariate normal distribution when the assumptions are fulfilled. However, this classical correlation coefficient performs poor in the presence of an outlier. Therefore, this study aims to propose a new version of robust correlation coefficient based on robust scale estimator Sn. The performance of the proposed robust correlation coefficient is assessed based on correlation value, average bias and standard error. The performance of the proposed coefficient is compared with the classical correlation together with the existing robust correlation coefficient. Classical correlation coefficient performs well under the condition of perfect data. However, its performance becomes worst when data is contaminated. Under the condition of data contamination, robust correlation coefficient performed better compared to classical correlatio

Measuring the relationship of bivariate data using Hodges-Lehman estimator

The relationship of bivariate data ordinarily measured using correlation coefficient. The most commonly used correlation coefficient is the Pearson correlation coefficient. This coefficient is well-known as the best coefficient for interval or ratio bivariate data with a linear relationship. Even though this coefficient is good under the mentioned condition, it also becomes very sensitive to a small departure from linearity.Usually, this is because of the existence of an outlier. For that reason, this paper provides new robust correlation coefficients which combine the elements of nonparametric technique from the Hodges Lehmann estimator and the parametric technique based on the Pearson correlation coefficient. This paper also introduces different scale estimators such as median and median absolute deviation (MADn) and denoted by rHL(med) and rHL(MADn) respectively. The performance of the proposed correlation coefficients is measured by the coefficient values and these values are also being compared to the Pearson correlation coefficient and several existing robust correlation coefficients. The results show that the Pearson correlation coefficient (r) with no doubt is very good under perfect data condition, but with only 10% outliers, it not only give poor correlation value but turns the direction of the relationship to negative. While the rHL(med) and rHL(MADn) offer the highest coefficient values and these values are robust to the existence of outliers by up to 30%. With very good performance under all data conditions yet simple in the calculation, the rHL(med) and rHL(MADn) is considered a good alternative to the r when need to deal with outlier

Type I error of the modified Wilcoxon signed rank test under leptokurtic distribution

Group comparisons are at the heart of many research questions addressed by researchers.Making inferences and drawing conclusions through statistical hypothesis testing on the differences between groups is actively adopted by researchers in many disciplines.When the groups are dependent, and violation of normality assumption occurred, the most commonly used method like paired t-test, usually produced doubtful result which will lead to misleading conclusions.As alternative, researchers tend to choose non parametric Wilcoxon signed rank test for the purpose.The computation of this statistic involves ranking the absolute difference of each pair of observations and any pair with 0 differences will be discarded.In this study, the statistic was modified by includimg the 0 differences in the ranking. The empirical Type I error rates of the modified statistical test was measured via Monte Carlo simulation. These rates were obtained under the combination of leptokurtic distributional shapes with various sample sizes and number of replications.The modified Wilcoxon signed rank test was found to be more robust under symmetric lepto!antic with conservative values as compared to the skewed leptokurtic distribution. The finding also indicated that different number of replications had no effect on Type I error

Modified Wilcoxon procedure for dependent group

Nonparametric methods require no or very limited assumptions to be made about the format of the data, and they may therefore be preferable when the assumptions required for parametric methods are not valid.The Wilcoxon signed rank test applies to matched pairs studies.For two tail test, it tests the null hypothesis that there is no systematic difference within pairs against alternatives that assert a systematic difference. The test is based on the Wilcoxon signed rank statistic W, which is the smaller of the two ranks sums. The step to compute the statistic W considered positive and negative differences and omit all the zero differences. In this study, we modify the Wilcoxon signed rank test using the indicator function of positive, zero and negative differences to compute the Wilcoxon statistic, W. The empirical Type I error rates of the modified statistical test was measured via Monte Carlo simulation.These rates were obtained under different distributional shapes, sample sizes, and number of replications.The modified Wilcoxon signed rank test was found to be robust under symmetric distributions.The result shows that this test produced liberal Type I error rates under skewed distribution.The use of the indicator positive, zero and negative differences influence the result of the Wilcoxon statistic.These finding was demonstrated using an example data

The performance of robust correlation coefficient under contaminated bivariate data

Classical correlation coefficient is a powerful statistical analysis when measuring a relationship between the bivariate normal distribution when the assumptions are fulfill.However, this classical correlation coefficient performs poor in the presence of outlier. Thus, this study aims to propose new version of robust correlation coefficient based on MAD n and S n .The performance of this proposed robust correlation coefficient will be evaluated based on three indicators which were the value of the correlation coefficient, average bias and standard error. The proposed procedure is expected to produce MAD n correlation coefficient and S n correlation coefficient.Both coefficients are expected to perform better than classical correlation coefficient and resistance to the outlier

Performance of the Modified Wilcoxon Signed Rank Test

Nonparametric methods require only few assumptions to be made about the format of the data, and they may therefore be preferable when the assumptions required for parametric methods are not valid. The Wilcoxon signed rank test applies to matched pairs studies. For two tail test, it tests the null hypothesis that there is no systematic difference within pairs against alternatives that assert a systematic difference. The test is based on the Wilcoxon signed rank statistic W, which is the smaller of the two ranks sums. The steps to compute W consider the positive and negative differences and omit all the zero differences. In this study, we modify the Wilcoxon signed rank test using the indicator function of positive, zero and negative differences to compute the Wilcoxon statistic, W. The empirical Type I error rates of the modified statistical test was measured via Monte Carlo simulation. These rates were obtained under different distributional shapes, sample sizes, and number of replications. The modified Wilcoxon signed rank test was found to be robust under symmetric distributions even though the values are quite conservative. The finding also demonstrated that different number of replication does not influence the result because there is not much difference in the value of the Type I error rates obtaine