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

Mochamad Firman Ghazali(1*), Anggun Tridawati(2), Mamad Sugandi(3), Aqilla Fitdhea Anesta(4), Ketut Wikantika(5)

(1) Teknik Geodesi dan Geomatika, Universitas Lampung, Jl. Prof. Dr. Ir. Sumantri Brojonegoro, No: 1, Gedong Meneng, Kec. Rajabasa, Kota Bandar Lampung, Lampung 35141
(2) Teknik Geodesi dan Geomatika, Institut Teknologi Bandung
(3) Teknik Geodesi dan Geomatika, Universitas Lampung, Jl. Prof. Dr. Ir. Sumantri Brojonegoro, No: 1, Gedong Meneng, Kec. Rajabasa, Kota Bandar Lampung, Lampung 35141
(4) Teknik Geodesi dan Geomatika, Universitas Lampung, Jl. Prof. Dr. Ir. Sumantri Brojonegoro, No: 1, Gedong Meneng, Kec. Rajabasa, Kota Bandar Lampung, Lampung 35141
(5) Kelompok Keahlian Penginderaan Jauh dan Sains Informasi Geografis (INSIG), Institut Teknologi Bandung
(*) Corresponding Author


COVID-19 is currently the hot topic of discussion by scientists because of its ability to quickly spread, in line with everyday human activities. One of the environmental factors related to climatic parameters, such as the air temperature, contributed to the spreading of COVID-19 in the last four months. Its distribution ability is no longer local as it successfully halts the important activities in many countries globally. This study aims to explain the opportunity of geospatial analysis in handling the COVID-19 distribution locally based on the characteristics of demographic data. Various data, including the confirmed positive for COVID-19, age-based population, and Landsat 8 satellite imagery data were used to determine the spatial characteristics of the COVID-19 distribution per September 2020 in Bandung, Indonesia. An inverse distance weighted (IDW), Moran's I index and local indicator spatial association (LISA), and a proposed ratio of the elderly population against the population with confirmed positive for COVID-19 (CoVE) were used as the approach to determine its distribution characteristics. The information derived from Landsat 8 satellite imagery, such as the residential area, surface temperature, and humidity based on the supervised classification, land surface temperature (LST), and the normalized difference water index (NDWI) was used to perform the analysis.  The results showed that the positive population of COVID-19 was concentrated in Bandung city. However, with a Moran's I value of 0.316, not all are grouped into the same category. There are only 8, 2, 5, and 3 districts categorized as HH, HL, LL, and LH. However, the areas with a large or small number of elderlies do not always correlate with the high number of confirmed positives for COVID-19. There are only 3, 1, and 3 districts classified as HH, HL, and LL. They were represented by the values of Moran's I, for about 0.057. The positive relationship between confirmed positive for COVID-19 and the built-up area, surface temperature, humidity, and the elderly population based on the coefficient of determination (R2) were 0.03, 0.28, 0.25, and 0.019, respectively. The study also shows that the vulnerability of those areas is relatively low. The study shows that the vulnerabilities in these areas are relatively low and the recommendation for COVID-19 widespread mitigation has to consider the demographic characteristics precisely in the large scale social restrictions (LSSR).


COVID-19, inverse distance weighted, old people population, spatial distribution, mitigation, PSBB

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