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.

Mangrove management is completely crucial for environmental sustainability. This study aimed (1) to examine the green and fair strategy in developing mangrove ecotourism in Bedul, Banyuwangi Regency, and (2) to examine the economic, social, and cultural improvement of the local community around the mangrove ecotourism area. The research method was qualitative descriptive using Internal Factor Analysis System (IFAS) and External Factor Analysis System (EFAS) matrix analysis from the SWOT approach. Data included primary and secondary data collected in 2020. Primary data were collected through observations and in-depth interviews with key informants and stakeholders around the study site. Secondary data came from literature reviews, documentation of the government institutions, and the local community. Based on IFAS and EFAS analysis, Bedul Mangrove Ecotourism belonged to Quadrant II with a diversification strategy. The diversification strategy in Quadrant II is located between concentric and conglomerate diversification that signifies a strong organization with strong threats. The analysis showed the need to diversify strategies or make modifications by integrating indicators for sustainable tourism in the mangrove ecotourism area of Bedul, Banyuwangi Regency, East Java. According to the identification results of tourism sustainability goals in accordance with the SDGs 2030 agenda, Mangrove Bedul Ecotourism requires the addition or modification of green and fair policies to address the limited aspect of economic development and the lack of guarantees for community welfare and stakeholders.

Learning geography in Indonesia philosophically aims to develop spatial literacy. Students must improve spatial literacy to form reasoning skills and apply spatial concepts in real life. Applying Gersmehl's spatial learning can improve students' spatial literacy through syntax arranged based on spatial aspects. The use of google earth helps students to complete case studies of disaster mitigation materials without the need to go directly to the field. This quasi-experimental research refers to the posttest-only control group design by comparing the posttest of spatial literacy in the experimental and control classes. This study involved students of class XI IPS SMAN 1 Blitar City. The instrument is a five-point description test based on indicators of spatial literacy in disaster mitigation materials standardized with validity and reliability tests. The research data were analyzed using normality, homogeneity, and hypothesis tests. The data tested were in the form of posttest scores for spatial literacy in the experimental and control classes. The results of Gersmehl's spatial learning influence spatial literacy. The use of LKPD bridges the implementation of spatial learning syntax with Google Earth as a geospatial medium. The implementation of syntax in spatial learning is interrelated with spatial literacy indicators, thereby increasing students' spatial literacy based on experience and spatial recognition.

This study aims to uncover GST's gaps and patterns (Geospatial Technology) knowledge transformation among Pre-service and experienced geography teachers using GST as an educational tool based on the Technological, Pedagogical, and Content Knowledge (TPACK) framework. GST integration into material content is implemented as a basis for thinking and acting to solve problems and decision-making, especially geographic content. This study is doing a cross-sectional survey with 600 respondents of the population. They were divided into five groups, such as Pre-service teachers, in the beginning, middle, final semester, and then beginner and experienced teachers, and each group consisting of 120 respondents. The measured GST component includes conceptual knowledge, implementation, and reasoning acquired by tests. The obtained test values were analyzed by the SPSS software for statistic descriptive and plotting gap knowledge value in the scatterplot-graph. Then, deviation values were obtained; the transformation model was mapped and interpreted in the Cartesian diagram. The results showed a GST knowledge gap between Pre-service and experienced geography teachers. The low implementation ability of experienced teachers means that GST is not optimal as a pedagogical tool in geography learning with the TPACK framework. Therefore, the TPACK framework needs to be practiced in continuous geography learning and changing the paradigm learning from GST Be learning with GST to strengthen the curriculum.

Penelitian ini bertujuan untuk mengembangkan modul ajar digital berbasis spasial yang memanfaatkan Google Earth Engine (GEE) untuk meningkatkan pemahaman siswa SMA terhadap fenomena kekeringan. Modul ini dirancang untuk menyajikan materi kekeringan secara interaktif, memadukan visualisasi spasial dan data penginderaan jauh melalui platform Google Sites. Penelitian ini menggunakan model pengembangan ADDIE (Analysis, Design, Development, Implementation, and Evaluation) dengan uji coba melibatkan tiga sekolah di Jawa Timur. Hasil validasi ahli, guru, dan siswa menunjukkan bahwa modul ini memperoleh penilaian sangat baik dalam hal cakupan materi, media, serta implementasi. Skor N-Gain sebesar 0,362 menempatkan peningkatan hasil belajar siswa pada kategori sedang dengan indikator identifikasi kekeringan memperoleh peningkatan tertinggi (N-Gain = 0,474). Meskipun modul ini menunjukkan efektivitas dalam meningkatkan pemahaman siswa terhadap kekeringan, penelitian ini juga menemukan bahwa siswa memerlukan waktu adaptasi lebih lama terhadap penggunaan data spasial. Selain itu, pengembangan lebih lanjut perlu difokuskan pada perluasan implementasi modul dalam skala luas serta integrasi strategi pembelajaran yang mendukung keterampilan dasar geografi, seperti kemampuan berpikir spasial, keterampilan pemecahan masalah, dan berpikir kritis.