Geo-visualization of Sarawak COVID-19 Publicly Available Data Employing Open-source Geospatial Software

Abstract

The state government of Sarawak with the help of the Sarawak Disaster Management Committee (SDMC) has continuously made the updated information on the state COVID-19 situation and its ensuing control measures available to general public in the form of daily press statements. However, these statements are merely providing textual information on daily basis though the data are in fact rich in temporal and spatial properties. Since the onset of COVID-19 pandemic, spatiotemporal analysis becomes the key element to better understand the spread of COVID-19 in various spatial levels worldwide. Hence, there is an urgent need to convert this textual information into more valuable insights by applying geo-visualization techniques and geospatial statistics. The paper demonstrates the prospect of retrieving geospatial data from publicly available document to locate, map and analyze the spread of COVID-19 up to division level of Sarawak. Specifically, map visualization and geospatial statistical analysis are performed for the list of exposed locations, which are indeed locations visited by COVID-19 patients prior to being tested positive in Kuching division, using open-source geospatial software QGIS. It is found that these exposed locations concentrate on the build-up areas in the division and are in south-west to north-east direction of the center of Kuching in September and October 2021. Despite the number of exposed locations published is relatively small compared to the number of confirmed cases reported, both are nearly strongly correlated. The insights gained from such geospatial analysis may assist the local public health authorities to impose applicable disease control interventions at division level.

Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19

“…Spatial error model. The Spatial Error Model (SEM) assumes that OLS error terms or residuals have spatially correlated or spatial dependence (22). Thus, residuals are disintegrated into random error terms, and the general form of the model is given as: (6) Where at county i, y i value for the dependent variable, ξ i specifies the spatial error component, λ specifies the level of correlation between these components, and ε i represent a spatially uncorrelated error term (17).…”