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.

There has been an increasing trend of land area being brought under human’s use over time. This situation has led the community to carry out land-use development activities in landslide hazard-prone areas. The use of land can have a positive impact by increasing economic conditions, but it can have negative impacts on the environment. Therefore, this study aimed to identify the landslide hazard, focusing on the development of a landform map to reduce the risk of landslide disaster in JLS, Malang Regency. The integration of remote sensing and geographic information systems, as well as field observation, were used to create a landform map and a landslide susceptibility map. Using the geomorphological approach as a basic concept in landform mapping, the morphology, morphogenesis, and morphoarrangement conditions were obtained from the remote sensing data, GIS, and field observation, while morphochronological information was obtained from a geological map. The landslide susceptibility map was prepared using 11 landslide conditioning factors by employing the index of entropy method. Thirty-nine landform units were successfully mapped into four landslide susceptibility classes. The results showed that the study area is dominated by a high level of landslide susceptibility with a majority of moderate to strongly eroded hill morphology. It also reaffirms that landform mapping is a reliable method by which to investigate landslide susceptibility in JLS, Malang Regency.

The Singojuruh flash flood incident have diverse effects on residential and agriculture areas. As result of the 2018 flood, two houses collapsed and there were severe also effects on agriculture. This paper focuses on understanding the river capacity (Badeng River) as well as the modeling to the next flood. We used Hydrological Engineering Center-River Analysis System (HEC-RAS) version 5.0.7 to analyze the river storage capacity. The factor in this analysis i.e. river morphometry (river length, river width, river depth and river slope) and manning coefficient. The sampling conducted in three point along the Badeng River. The result showed that each of point location have different characteristic hence influence its capacity. The smallest capacity has the biggest potential of flooding.