The Malaysian government implemented The Movement Control Order (MCO) on 18 March 2020 to control the spread of the COVID-19 outbreak. However, the third wave that started in September 2020 during the Recovery Movement Control Order (RMCO) phase saw a continuous increase in the number of cases. In this study, the exploratory spatial data analysis (ESDA) was used to analyse the existence of COVID-19 spatial clusters. Moran's index was used to map the spatial autocorrelation (cluster) to showcase the spreading patterns of the COVID-19 pandemic in Malaysia. The study results indicated significant changes in the COVID-19 hotspots over time. At the beginning of 2020, the state of Selangor and Sarawak were the first locality to become a significant COVID-19 hotspot. Furthermore, this research showed all affected areas during the study period. Overall, a non-random distribution of COVID-19 occurrences was detected, thus suggesting a positive spatial autocorrelation. Many parties are affected by the COVID-19 pandemic, especially those involved in healthcare provision, financial assistance allocation, and law enforcement. Other sectors such as the economy, education, and religion are also affected. Therefore, the findings from this study will provide useful information to all the related governmental and private agencies, as well as policymakers and researchers.

Heckman Sampel Selection Model (PSSM) has been adopted widely in the study of labour work. This model contains exogenous, endogenous and standard error variables. However, this model is constantly exposed to high inaccuracy of estimation result. Therefore, to obtain an accurate and precise estimation, the bootstrap approach is introduced. The bootstrap approach will be hybrid with PSSM model known as BPSSM to achieve estimation result that is more precise. Then, the BPSSM is applied to Malaysian Population and Family Survey 1994 (MPFS-1994) data. The results showed that BPSSM provide a smaller standard error and shorter confidence intervals.

Loligo Duvauceli, is known as squid in the local name. Squid is a species of high value and a good source of protein besides fish. In this paper, a logistic growth model with constant and periodic harvesting are used to find the optimum sustainable population of Loligo Duvauceli in order to preserve the resource. The data used for this study was obtained from Department of Fisheries. The aims of this study are to estimate to determine the stability of the equilibrium point. Then, the effect of the constant and the periodic harvesting on the population of squids analysed. Finally, to compare the results obtained between the two strategies. The best harvesting selected squids is periodic harvesting.

Geographical Weighted Regression (GWR) is used to improve decisionmaking in spatial analysis. Instead of the Ordinary Least Square (OLS) regression method that gives a single estimated parameter, the GWR method can provide unique estimated parameters in each location. This study aims to conduct a formal statistical inferential framework on the violent crime rate using the GWR. This analysis discovers the geographical distribution and pattern of criminal cases in Peninsular Malaysia using the average crime rates from 2000-2009, with focus on on violent crime. The comparison of OLS regression, known as Multiple Linear Regression (MLR) with the GWR method, was done to show that GWR was the best model. The GWR output suggests that about 30% of districts showed a significant correlation between violent crime and non-citizen rates. These findings contradict the result from the MLR model, also known global model. The global model could not create any other connection to explain the lack of parameter-location correspondence. Finally, the importance of local relationships in crime studies is necessary to understand the actual crime rate.

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