Review on Geographically Weighted Regression (GWR) approach in spatial analysis

In spatial analysis, it is important to identify the nature of the relationship that exists between variables. Normally, it is done by estimating parameters with observations which taken from different spatial units that across a study area where parameters are assumed to be constant across space. However, this is not so as the spatial non-stationarity is a condition in which a simple model cannot explain the relationship between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. Non-stationarity means that the relationship between variables under study varies from one location to another depending on physical factors of the environment that are spatially autocorrelated. Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space. A robust algorithm has been successfully used in spatial analysis. GWR can theoretically integrate geographical location, altitude, and other factors for spatial analysis estimations, and reflects the non-stationary spatial relationship between these variables. The main goal of this study is to review the potential of the GWR in modelling the spatial relationship between variables either dependent or independent and its used as the spatial prediction models. Based on the application of GWR such as house property indicates that GWR is the best model in estimating the parameters. Hence, from the GWR model, the significance of the variation can also be tested

A systematic review of the statistical methodology used in establishing the link between climate factors and HFMD incidence

Hand, foot, and mouth disease (HFMD) is a common infectious disease caused by two main viruses, namely Coxsackievirus A16 and Human Enterovirus 71. It has been a significant public health disease and a substantial burden all over the world since 1969. Prior studies have shown that climate factors are significantly associated with HFMD cases by using various statistical methods. Therefore, this study aims to review the scientific studies related to climate and HFMD and hence, address the analytical techniques used. This study only includes quantitative studies from peer-reviewed and original papers published in international and national journals from the years 1957 to 2020. In total, there were 522 articles identified; however, there were only 29 studies that linked climate change and HFMD. Based on the articles reviewed, the modelling analysis technique, which includes the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), and the Generalized Additive Mixed Model (GAMM), represents the most popular analysis in identifying the association between HFMD and climate factors. The temperature and humidity showed the greatest impact on the occurrence of HFMD, and the suitable incubation period for all climatic factors was not more than three weeks

Exponential growth model and stochastic population models: a comparison via goat population data

A population dynamic model explains the changes of a population in the near future, given its current status and the environmental conditions that the population is exposed to. In modelling a population dynamic, deterministic model and stochastic models are used to describe and predict the observed population. For modelling population size, deterministic model may provide sufficient biological understanding about the system, but if the population numbers become small, then a stochastic model is necessary with certain conditions. In this study, both types of models such as exponential, discrete-time Markov chain (DTMC), continuous-time Markov chain (CTMC) and stochastic differential equation (SDE) are applied to goat population data of small size. Results from the simulations of stochastic realizations as well as deterministic counterparts are shown and tested by root mean square error (RMSE). The SDE model gives the smallest RMSE value which indicate the best model in fitting the data

Fitting the statistical distribution for daily rainfall in Peninsular Malaysia based on AIC criterion

This paper presents several types of exponential distributions to describe rainfall distribution in Peninsular Malaysia over a multi-year period. The exponential, gamma, mixed exponential and mixed gamma distributions are compared to identify the optimal model for daily rainfall amount based on data recorded at rain gauges stations in Peninsular Malaysia. The models are evaluated based on the Akaike Information criterion (AIC). The log likelihood ratio test has been employed to determine whether the differences in AIC between tested models are statistically significant. However, this test is restricted by the need of the models to be nested. Since the gamma is not nested in the mixed exponential model so comparison has been done indirectly using the mixed gamma as the nested model. Overall, this study has shown that the mixture of two distributions is better than single distributions for describing the daily rainfall amount in Peninsular Malaysia based on the AIC criterion and their differences in AIC are statistically significan

Spatial analysis of daily rainfall intensity and concentration index in Peninsular Malaysia

This study presents the spatial analysis of daily rainfall intensity and concentration index over Peninsular Malaysia. Daily rainfall data from 50 rainfall stations are used in this study. Due to the limited number of stations, the geostatistical method of ordinary kriging is used to compute the values of daily rainfall concentration and intensity and to map their spatial distribution. The resultant analysis of rainfall concentration indicated that the distribution of daily rainfall is more regular over the west, northwest and southwest regions compared to the east. Large areas of the eastern Peninsula display an irregularity in distribution of daily rainfall. In terms of number of rainy days, analysis of daily rainfall confirms that a large number of rainy days across the Peninsula arise from low-intensity events but only contribute a small percentage of total rain. On the other hand, a low frequency of rainy days with high-intensity events contributes the largest percentage of total rain. The results indicated that the total rain in eastern areas is mainly contributed by the high-intensity events. This finding explains the occurrence of a large number of floods and soil erosions in these areas. Therefore, precautionary measures should be taken earlier to prevent any massive destruction of property and loss of life due to the hazards. These research findings are of considerable importance in providing enough information to water resource management, climatologists and agriculturists as well as hydrologists for planning their activities and modelling processes

Functional data analysis technique on daily rainfall data: a case study at North Region of Peninsular Malaysia

The study of rainfall features and patterns are very useful for water management systems, water resources engineering and also in agricultural planning. It can be beneficial in order to reduce the risks and losses. Functional data analysis technique is one of the method can be used to explore and display the pattern and variation of the rainfall data. This technique displays the pattern in the form of curves. The first and second derivatives of the curves represent the rate of change and the acceleration of the curves. The objective of the study is to model two rainfall features; rainfall amount and rainfall occurrence by using functional data analysis technique at eight rainfall stations from the north part of Peninsular Malaysia. Markov chain model has been used to model the rainfall occurrence and Fourier basis to smoothing the data. The results show that both of the rainfall features have similar bimodal pattern. Although the mean curves are slightly similar, the first peak of variance curve for rainfall occurrence is higher than the second peak which is difference with variance curve for rainfall amount. The relationship between rainfall amount and rainfall occurrence for both observed and estimated curve is also discusse

Trends in Peninsular Malaysia rainfall data during the Southwest monsoon and Northeast monsoon seasons: 1975-2004

This study investigated the spatial pattern and trends of the daily rainfall data in Peninsular Malaysia based on seasonal rainfall indices. Five rainfall indices which describe the main characteristics of rainfall, the total amount of rainfall, frequency of wet days, rainfall intensity, extreme frequency, and extreme intensity, were employed in this study. The statistics of rainfall indices were calculated in terms of their means for four regions in Peninsular Malaysia for the period 1975 to 2004. The findings indicate that the southwest monsoon had the greatest impact on the western part of the Peninsula, particularly in characterizing the rainfall pattern of the northwest region. During this season, the northwest region could be considered as the wettest region since all rainfall indices tested are higher than in other regions of the Peninsula. Otherwise, the northwest region is denoted as the driest part of the Peninsula during the northeast monsoon period. The northwest region is less influenced by the northeast monsoon because of the existence of the Titiwangsa Range, which blocks the region from receiving heavy rainfall. On the other hand, it is found that the lowlands areas such as the eastern part of the Peninsula are strongly characterized by the northeast monsoonal flow. Based on the results of the Mann-Kendall test, as the trend of the total amount of rainfall and the frequency of wet days during the southwest monsoon decrease at most of the stations, the rainfall intensity increases. In contrast, increasing trends in both the total amount of rainfall and the frequency of wet days were observed at several stations during the northeast monsoon, which give rise to the increasing trend of rainfall intensity. The results for both seasons indicate that there are significantly decreasing trends in the frequency of wet days during the extreme events for most of the stations on the peninsula. However, a smaller number of significant trends was found for extreme intensity

Trend analysis for drought event in Peninsular Malaysia

In this paper, the geostatistics application is employed for analysis of drought events in verifying the upward or increasing and downward or decreasing trend during the drought occurrence. About 33 years of daily precipitation data obtained from 69 stations during the period of November, 1975 to October, 2008 in Peninsular Malaysia are analyzed to characterize the trend of dry events. The amount of precipitation is classified based on the standardized precipitation index (SPI) to determine the drought periods and proceed with the Mann-Kendall test for trend identification. These results are further verified by applying the kriging method. The kriging results describe that the trend values for drought events in Peninsular Malaysia interprets an upward trend especially in eastern and western parts