During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city’s center and low in the outskirts. This contrasts with the city’s population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. ‘What-if’ scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal.

Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the spatio-temporal trend of COVID-19 cases and fatalities to get a better understanding of the spread as well as to mitigate it. However, such a comprehensive study at national level is not to be seen in Indonesia with limited health infrastructure. This study aims to analyse the spatio-temporal distribution and clusters of COVID-19 in Indonesia for a year period. COVID-19 cases, as well as the fatalities as a consequence of this disease, were collected from the government through publicly shared data. A geographic information system (GIS) was used to manage and analyse the data on demographics, cases, and fatalities. The case fatality rate (CFR) was produced based on the number of cases and deaths per province weekly. The spatio-temporal data of both cases and fatalities were generated from the data. Finally, K-means clustering was employed to classify the cluster of Indonesia based on the proportion of vulnerable age groups, cases, and CFR. The results show that most of the provinces in Indonesia are affected by COVID-19, but the fatalities are not distributed evenly throughout the country. Based on the K-means clustering, two provinces are classified as moderate, namely the Province of East Kalimantan and North Kalimantan. The Province of Jakarta is classified as high, because the vulnerable age group there is highly correlated with the number of cases and deaths.

This study develops a spatial synthetic population (SSP)-based computational model to produce realis-tic, high-resolution flood-risk maps for the Upper Bengawan Solo Watershed. It combines Global Hu-man Settlement (spatial distribution) with local population statistics (attributes). The SSP is created for flood risk mapping in the Upper Bengawan Solo Watershed (BSH) using a 100 m grid from the Global Human Settlement Layer (GHSL) GHS-POP R2023A. Synthetic individuals are strategically placed around the pixel centre (radius ≤ 100 m), and each is assigned demographic attributes (age, gender, ed-ucation, occupation) validated against official county-level data. Social vulnerability is calculated through weighted aggregation (AHP) across four attributes; individual scores are combined with flood hazard intensity at each location to produce a risk index for each person. Validation shows that (i) the SSP aligns closely with reference statistics: gender and age are nearly identical (MAE ≈ 0.01–0.02%), with slight deviations in occupation (MAE 6.52%) and education (MAE 4.89%), (ii) the overall suita-bility of the SSP compared to GHS counts at pixel samples, and (iii) location plausibility testing using ESRI Sentinel‑2 Land Cover (10 m). Results indicate that (i) the SSP aligns well for gender, moderately for education and occupation, but shows significant misalignment in age, (ii) 91.96% of SSP points are in built-up land, suggesting high spatial accuracy. Medium- to high-risk patterns are mainly along the main river corridors and peri-urban areas, while rural non-built zones are mostly low- to medium-risk. These findings suggest that this methodology is scalable, reproducible, and suitable for data limited regions, enabling the production of detail risk maps that can guide mitigation and preparedness efforts.

The value of terrestrial carbon sequestration in urban areas, due to lack of vegetation as a carbon sink, is rarely studied. In fact, urban areas have high carbon emission values, which must be minimised. On the other hand, the value of carbon sequestration in urban areas is very dynamic due to natural factors from the environment and non-natural factors from anthropogenic activities. The main objectives of this study are to identify the carbon dioxide sequestration in urban areas, especially in tropical climates, and to determine the dynamics of carbon sequestration in urban areas for a year. The results show that carbon sequestration in tropical urban areas has a significant value compared with urban areas in temperate climates. This condition happens because there are still green open spaces in gardens and agricultural lands. The value of carbon sequestration in this tropical urban area experiences monthly dynamics caused by rainfall variation and anthropogenic activities, such as land conversion and plant type conversion in agricultural lands.

Kesehatan merupakan salah satu aspek yang penting bagi kehidupan. Hingga saat ini, penyampaian data terkait kesehatan umumnya terbatas dalam bentuk tabel, grafik, dan deskripsi. Oleh karena itu perlu adanya inovasi untuk menampilkan data kesehatan secara lebih menarik, efektif, dan membagikannya secara lebih luas. Tujuan dari penelitian ini adalah (1) mevisualisasikan data fasilitas kesehatan dengan media web, dan (2) menganalisis ketersediaan fasilitas kesehatan di Boyolali. Data bersumber dari profil kesehatan Boyolali tahun 2020. Tampilan data fasilitas kesehatan dilakukan menggunakan fitur yang ada di aplikasi ArcGIS StoryMaps, sedangkan analisis ketersediaan fasilitas dilakukan secara kualitatif dengan pendekatan spasial. Dari data yang digunakan, dihasilkan tampilan web (https://arcg.is/1mC1z50) berisi grafik dan empat peta untuk sebaran rumah sakit, sebaran puskesmas, keterjangkauan fasilitas, dan sebaran kasus Covid-19. Analisis menunjukkan bahwa fasilitas kesehatan lebih banyak berada di Kecamatan Boyolali dengan keterjangkauan fasilitas yang masih kurang di wilayah barat dan utara Kabupaten Boyolali. Hasil ini diharapkan dapat membantu pemerintah lokal dalam merencanakan pembangunan fasilitas kesehatannya.