Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia

Abstract

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses.

Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling

“…A regional spatiotemporal understanding of COVID-19 is critical to provide insight into how the pandemic occurred and its continued growth and decline [10][11][12]. Previous studies by Purwanto et al [10] demonstrated that it is important to determine the spatial and temporal resolution of the STC model because it affects the detailed information on the endemic/epidemic, and it is important for the hotspot analysis results. Al-Kindi et al [11] also used the spatiotemporal assessment to determine the virus spread in Oman for 2 months.…”

Spasial Autocorrelation Analysis On Coronavirus Transmission In East Java 2020

“…In Indonesia, the first case of a Covid-19 infected person discovered in March 2020. The virus then transmitted through all the provinces in Indonesia, including East Java (Purwanto et al, 2021).…”

“…However there were also neighboring areas that had relatively similar amount covid-19 confirmed cases like Probolinggo district with 2.213 confirmed cases and Pasuruan district 2.191 confirmed cases. This journal would find if there were significant clustering between East Java cities and districts covid-19 confirmed cases in year 2020 (Purwanto et al, 2021). The use spatial autocorrelation in this journal is to determine the mapping systematic spatial variation on confirmed cases of Covid-19 in East Java 2020 (Dhewantara et al, 2021).…”

Measuring of the COVID-19 Based on Time-Geography

“…These restrictive measures have the same meaning as the three constraints in time geography to a certain extent [ 8 , 9 ]. Similarly, two important tools in time geography—space-time path and space-time prism—can also be used to simulate the spread of the COVID-19 pandemic [ 10 , 11 , 12 , 13 ]. The time-space path is often used to simulate the life trajectory of the coronavirus carrier, which can be used to find close contacts of the virus carrier [ 14 , 15 ].…”