High resolution models from the High-Resolution Model Intercomparison Project (HighResMIP), part of CMIP6, have the capacity to allow a better representation of the climate system in tropical regions, but how different model resolutions affect hydrological outputs remains unclear. This research aims to evaluate projections of hydro-climatic change of the Johor River Basin (JRB) in southern Peninsular Malaysia between 1985 to 2015 and 2021 to 2050, focusing on uncertainty quantification of hydrological outputs from low (>1°), medium (0.5° to 1°) and high (≤0.5°) horizontal resolution models. These projections show future increases in annual precipitation of 0.4 to 3.1%, minimum and maximum temperature increases of 0.8 to 0.9 °C and 0.9 to 1.1 °C, respectively. These projected climate changes lead to increases in annual mean streamflow of 0.9% to 7.0% and surface runoff of 7.0% to 20.6% in the JRB. These annual mean changes are consistent with those during the wet period (November to December), e.g., streamflow increases of 4.9% to 10.8% and surface runoff of 28.8 to 39.9% in December. Disagreement in the direction of change is found during the dry seasons, (February to March and May to September), where high resolution models project a decrease in future monthly precipitation and streamflow, whilst increases are projected by the medium- and low-resolution models.

Landslides are one of the common natural disasters involving mostly movement of soil surfaces associated with gravitational attraction. Their adverse losses and significant damage, which always result in at least 17% of casualties and billions of dollars of financial losses worldwide, have made landslides the third most notorious phenomenon devastating many parts of the world. Malaysia has had multiple landslide occurrences, particularly in highly urbanized areas, such as Penang Island, owing to the declining vegetation cover in hilly terrains. Thus, this study aims to delineate the spatial relationship variances between landslide occurrences and the influencing factors in the area of interest. Ten influencing factors considered, including distance to roads, distance to rivers, distance to faults, slope angle, slope aspect, curvature, rainfall annual average, lithology, soil series, and land use. In this study, we use a software (GWR 4.0) as a medium for the analysis processing, coupled with GIS. A local statistical technique, Geographically Weighted Logistic Regression (GWLR), is primacy in capturing the geographical variation of the model coefficients that considers non-stationary variables and models their relationships, as well as processes regression coefficients over space. Goodness-of-fit criteria were used to evaluate the GWLR model, namely AICc that decrease from 872.202167 to 800.856998. Bayesian Information Criterion (BIC) shows a decrease in value from 925.784185 to 945.196942. Likewise, deviance decreased from 849.931675 to 739.175630, while pdev increased from 0.379457 to 0.460321. These goodness-of-fit criteria values express GWLR as the best model for local measure. The variances in both local parameter estimates and the t-values (negative and positive values) show the level of significance for each landslide factor in influencing landslide occurrences across the study area. The results of the local parameter estimates and the t-values also show that the slope angle and the slope aspect spatially affect landslide occurrences across the study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition must be established for future mitigation actions to support the agenda of SDG 15, which promotes resilience and disaster risk reduction.

In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.

The main objective was to identify common geographical buildup within the 100-meter buffer of severely injured based on injury severity score (ISS) among the motor vehicle crash (MVC) victims in Malaysia. This was a prospective cohort study from July 2011 until June 2013 and involved all MVC patients attending emergency departments (ED) of two tertiary centers in a district in Malaysia. A set of digital maps was obtained from the Town Planning Unit of the district Municipal Office (local district map). Vector spaces were spanned over these maps using GIS software (ARCGIS 10.1 licensed to the study center), and data from the identified severe injured cases based on ISS of 16 or more were added. Buffer analysis was performed and included all events occurring within a 100 -meter perimeter around a reference point. A total of 439 cases were recruited over the ten-month data collection period. Fifty two (11%) of the cases were categorized as severe cased based on ISS scoring of 16 and more. Further buffer analysis looking at the buildup areas within the vicinity of the severely injured locations showed that most of the severe injuries occurred at locations on municipal roads (15, 29%), straight roads (16, 30%) and within villages buildup (suburban) areas (18, 35%). This study has successfully achieved its objective in identifying common geographical factors and buildup areas within the vicinity of severely injured road traffic cases.