Spatial Analysis to Mitigate the Spread of Covid-19 Based on Regional Demographic Characteristics

Abstract

COVID-19 is currently the hot topic of conversation because of its ability to spread relatively quickly, in line with everyday human activities. It is unknown exactly the dominant environmental factors and their influence on the spread of COVID-19 in the last four months. Its distribution ability is no longer locally but has succeeded in making several countries stop its important activities globally. Non-spatial data such as positive confirmed population data, population-based on age, and Landsat 7 satellite imagery data were used to determine the spatial characteristics of the COVID-19 distribution until October September 2020. Inverse distance weighted (IDW), Moran's I and Local Indicator Spatial Association (LISA), as well as the ratio of the old population to the population, confirmed positive (+) were used as an approach to determine the characteristics of its distribution. Besides information on residential areas, surface temperature, and surface humidity based on supervised classification, land surface temperature (LST), and the normalized difference water index (NDWI) of Landsat 7 satellite imagery is used to enrich the spatial analysis carried out. The study results show a population concentration of COVID-19 towards the city of Bandung, with Moran's I result in not showing a good correlation. Meanwhile, the LISA results show that areas with a large or small number of elderly residents do not always have high positive COVID-19 numbers. The relation between the positive population (+) COVID-19 population and the built-up area (settlement), the surface temperature in the built-up area, surface humidity, and old age population based on the coefficient of determination (R2) is 0.03, 0.28, 0.25, and 0.019. This shows the level of vulnerability of the area is low. So, in the end, a recommendation for handling can be produced by taking into account the demographic characteristics of the area appropriately

Keywords

• covid-19, inverse distance weighted, old people population, spatial distribution, mitigation, psbb