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 flood disaster is a severe threat in Indonesia due to the enormous impacts on environmental degradation, social and economic sectors. One flood event due to the overflow is the Badeng River's flooding in 2018 at Singojuruh Subdistrict, Banyuwangi Regency. The flood had a detrimental impact on the local community, especially on agricultural land and residential. Anticipatory steps need to be taken to minimize losses due to flooding in the future. Inundation modelling in this research is purposed to predict flood hazards. Hence it can have appropriate anticipatory steps in the future. The software used to model the inundation in this study was the HEC-RAS Program. Data needed in this study are river geometry, manning coefficient, and maximum daily rainfall from the year 2010 until 2019. The research e stages in this study consist of (1) Calculation of watershed morphometry, (2) Calculation of average regional rainfall, (3) Calculation of rainfall plan, (4) Rain Data Suitability Test, (5) Calculation of Rain Intensity, (6) Calculation of Flood Discharge Plan, (7) Geometry Modelling, (8) Extraction of Manning Coefficient, and (9) Inundation Simulation. The results of the Gama 1 method's peak discharge plan showed an increase in each return period. The area with the highest level of susceptibility around the Badeng River occurs in Alasmalang Village, Singojuruh Subdistrict. This area has the smallest river storage capacity than other river crossings. Hence it has the most significant potential for flooding.Keywords: inundation modelling, flood, HEC-RAS, Badeng RiverBencana banjir menjadi ancaman serius bagi negara Indonesia karena memberikan dampak yang besar terhadap kerusakan lingkungan, sosial maupun ekonomi. Salah satu kejadiannya adalah banjir yang terjadi akibat luapan sungai Badeng pada tahun 2018 di Kecamatan Singojuruh, Kabupaten Banyuwangi. Kejadian Banjir tersebut memberikan dampak yang merugikan bagi masyarakat setempat, terutama pada lahan pertanian dan permukiman. Langkah antisipasi perlu dilakukan untuk meminimalisir kerugian akibat bencana banjir di masa mendatang. Pemodelan genangan dalam penelitian ini dibuat bertujuan untuk  memprediksi bahaya banjir, sehingga dapat dilakukan langkah antisipasi yang tepat. Software yang digunakan untuk memodelkan genangan dalam penelitian ini adalah Program HEC-RAS. Data yang dibutuhkan berupa data geometri sungai, koefisien manning dan curah hujan harian maksimum selama periode tahun 2010 sampai 2019. Beberapa tahapan dalam penelitian ini meliputi (1) Perhitungan morfometri DAS, (2) Perhitungan hujan rerata wilayah, (3) Perhitungan curah hujan rencana, (4) Uji Kesesuaian Data Hujan, (5) Perhitungan Intensitas Hujan, (6) Perhitungan Debit banjir rencana, (7) Pemodelan geometri, (8) Ekstraksi angka kekasaran manning, dan (9) Simulasi Genangan. Hasil perhitungan debit puncak rencana metode Gama 1 menunjukkan peningkatan pada setiap periode ulang. Daerah yang mempunyai tingkat kerawanan paling besar adalah areal sekitar Sungai Badeng yang berada di Desa Alasmalang Kecamatan Singojuruh. Daerah ini memiliki kapasitas tampung sungai yang paling kecil daripada penampang sungai yang lainnya, sehingga memiliki potensi terjadinya banjir paling besar. Kata kunci: pemodelan genangan, banjir, HEC-RAS, Sungai Badeng

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.

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