Malaysia Recovery Movement Control Order (RMCO) aims to bring the business, education, tourism and other industry sectors back into operation. Due to movement constraints that result in local economic patterns, individual mobility patterns are expected to occur. However, this matter needs further investigation from people's spatial behaviour during the RMCO. Therefore, this research proposed a new technique for analysing people's spatial behaviour patterns via geo-tagged data. The data from social media users are gathered using data mining techniques. Geographical Information System (GIS) is used to show the geolocation of social media users and analyse their spatial behaviour. The finding of this analysis shows higher people's movement recorded when the RMCO was enforced; a distinctive pattern where spatial trajectory length is high but spatial area coverage is low. It is noticed that the focal points are concentrated in urban areas and tourism attractions.
Despite the government's policies and objectives, Malaysia lags behind in sustainable waste management techniques, particularly recycling. Bins should be located conveniently to encourage recycling and reduce waste. The current model of bin location-allocation is mostly determined by distance. However, it has been identified that previous studies excluded an important factor: litter pattern identification. Litter pattern is important to identify waste generation hotspots and litter distribution. Thus, we proposed the within cluster pattern identification (WCPI) approach to optimize the recycle point distribution. WCPI gathers the information on litter distribution using geotagged images and analyses the pattern distribution. The optimal location for recycle bin can be identified by incorporating k-means clustering to the pattern distribution. Since k-means faces the non-deterministic polynomial-time-hard challenge of selecting the ideal cluster and cluster centre, WCPI used the total within-cluster sum of square on top of k-means clustering. The proposed location by WCPI is validated in terms of accessibility and suitability. Furthermore, this study provides further analysis of carbon footprint that can be reduced by simulating the data from geotagged images. The results show that 10,323.55 kg of carbon emission can be reduced if the litter is sent for recycling. Thus, we believe that locating bins at an optimal location will embark on consumer motivation to dispose of recycled waste, reduce litter and lessen the carbon footprint. At the same time, these efforts could transform Malaysia into a clean and sustainable nation that aims to achieve Agenda 2030.
This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings.