This study entailed a review of 63 scientific articles on geospatial and spatial-statistical analysis of the geographical dimension of the 2019 coronavirus disease (COVID-19) pandemic. The diversity of themes identified in this paper can be grouped into the following categories of disease mapping: spatiotemporal analysis, health and social geography, environmental variables, data mining, and web-based mapping. Understanding the spatiotemporal dynamics of COVID-19 is essential for its mitigation, as it helps to clarify the extent and impact of the pandemic and can aid decision making, planning and community action. Health geography highlights the interaction of public health officials, affected actors and first responders to improve estimations of disease propagation and likelihoods of new outbreaks. Attempts at interdisciplinary correlation examine health policy interventions for the siting of health/sanitary services and controls, mapping/tracking of human movement, formulation of appropriate scientific and political responses and projection of spatial diffusion and temporal trends. This review concludes that, to fight COVID-19, it is important to face the challenges from an interdisciplinary perspective, with proactive planning, international solidarity and a global perspective. This review provides useful information and insight that can support future bibliographic queries, and also serves as a resource for understanding the evolution of tools used in the management of this major global pandemic of the 21 Century. It is hoped that its findings will inspire new reflections on the COVID-19 pandemic by readers.

Global warming and climate change is one of the most extensively researched and discussed topical issues affecting the environment. Although there are enough historical evidence to support the theory that climate change is a natural phenomenon, many research scientists are widely in agreement that the increase in temperature in the 20th century is anthropologically related. The associated effects are the variability of rainfall and cyclonic patterns that are being observed globally. In Southeast Asia the link between global warming and the seasonal atmospheric flow during the monsoon seasons shows varying degree of fuzziness. This study investigates the impact of climate change on the seasonality of monsoon Asia and its effect on the variability of monsoon rainfall in Southeast Asia. The comparison of decadal variation of precipitation and temperature anomalies before the 1970s found general increases which were mostly varying. But beyond the 1970s, global precipitation anomalous showed increases that almost corresponded with increases in global temperature anomalies for the same period. There are frequent changes and a shift westward of the Indian summer monsoon. Although precipitation is observed to be 70% below normal levels, in some areas the topography affects the intensity of rainfall. These shifting phenomenon of other monsoon season in the region are impacting on the variability of rainfall and the onset of monsoons in Southeast Asia and is predicted to delay for 15 days the onset of the monsoon in the future. The variability of monsoon rainfall in the SEA region is observed to be decadal and the frequency and intensity of intermittent flooding of some areas during the monsoon season have serious consequences on the human, financial, infrastructure and food security of the region

Purpose – To present a comprehensive flood management plan for Malaysia, the various planning stages and the proponents of the plan. It is also to expound and highlight the importance of spatial information technology in the strategy and to outline the critical decision-making at various levels of the plan. Design/methodology/approach – A review of flood disaster management aimed at providing an insight into the strategies for a comprehensive flood disaster management for Malaysia. Discussion of the framework of a spatial decision support system (SDSS) and its role in decision-making in a comprehensive disaster management plan. Findings – Provides information about a proposed comprehensive disaster management program for Malaysia and highlight the role of SDSS in improving decision-making. It recognizes the strength of SDSS in the collection and processing of information to speed up communication between the proponents of the disaster management program. A well-design SDSS for flood disaster management should present a balance among capabilities of dialog, data and modeling. Research limitations/implications – The study has outlined the links and components of SDSS and not its development processes; this may limit the used of this paper in in-depth study of the development if SDSS. Some source for detail study of the development of SDSS have, however, been cited. Practical implications – This paper is a very useful source of information about the preparation of a comprehensive disaster management program. It also sheds light on the role of SDSS in improving and speeding up communication between the various proponents of the program. Researcher and students will fine, it provides general guidelines and framework for disaster planning and management. Originality/value – This paper fulfills flood disaster study need for developing a comprehensive disaster management program. It presents the framework of SDSS, the interrelationship between their various components and how they play a role in decision-making.

This study integrates high spatial resolution remote sensor data with geographic information system (GIS) data and multi‐criteria analysis to develop a methodology to model disaster risk for flood risk management and in peat swamp forest fires in order to assist in providing decision support systems for emergency operations and disaster prevention. Landslides are the result of a wide variety of processes, including geological, geomorphological and meteorological factors. Spatial technology has the ability to assess and estimate regions of landslide hazard by creating thematic maps and overlapping them to produce a final hazard map which classifies regions according to three categories of risk.