Bantul and Yogyakarta are regions with earthquake-hazard risks in Indonesia. The earthquake that occurred in 2006 produced deaths, high economic losses, and significant damages to the housing and infrastructure. This research aimed to assess the urban growth in the earthquake-hazard zone in Bantul and Yogyakarta. The study used the remote sensing method of nighttime light (NTL), zonal statistics, and ClockBoard zone analysis. The combination of these analysis techniques for linking urban growth and earthquake hazards has not been widely discussed by previous studies. The earthquake-hazard data was retrieved from the United States Geological Survey website; meanwhile, the NTL data was based on the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite.The results indicated that those zone segments at very high earthquake-hazard levels were also areas with night-light intensities of more than ten units (meaning increasing urban growth). Based on these facts, local governments should evaluate spatial planning to limit the density of built-up areas in earthquake-hazard areas and ensure the effective implementation of urban sustainability and resilience.

The article’s primary purpose was to explore the potential of Internet searches for keywords that were related to the polish housing market in order to understand the public’s current preferences or reactions to changes in the market environment. The research used data that was downloaded from Google Trend (RSV) from 2010 through 2024. The Granger causality test was then applied to the relationship between RSV and housing prices, and a Bayesian structural time-series model was applied to examine the impact of the external intervention (COVID-19) on the RSV dynamics. The results indicated that significant changes in the market environment could influence fluctuations in interest in housing, as was evidenced by the changes in the online searches. The article respectfully suggests that a more nuanced understanding of market dynamics might be achieved through a thoughtful integration of classical economic data with non-classical Internet data.

Population expansion and climate change have significantly affected the coastal environment in Lampung, Indonesia, mainly through the conversion of mangroves into shrimp-farming ponds. This transformation requires effective monitoring to evaluate its impacts on coastal ecosystems and local livelihoods, as shrimp farming is a major income source in East Lampung. This research improves aquaculture detection and monitoring along the eastern coast of Lampung by integrating several water indices such as the normalized difference water index (NDWI), modified NDWI (MNDWI), water ratio index (WRI), and a newly developed water index (WI), within the cloud-based Google Earth Engine (GEE) platform to capture spatial and temporal variations. Reference data were derived from the 2019 Regional Medium-Term Development Planning Document (RPJMD) and high-resolution Google Earth imagery for accuracy assessment. Results showed that WRI combined with the Otsu’s thresholding method achieved the highest performance, with an overall accuracy (OA) of 93.3% and a kappa coefficient (κ) of 86.7%. Analysis from 2018 to 2022 showed a decline in aquaculture area from 8,407.35 ha to 3,415.50 ha, aligned with statistical data on shrimp production, which decreased from 24,202 t to 8,041 t. These results indicate that the method provides a rapid and effective tool for detecting aquaculture changes, enabling local authorities to strengthen coastal management for sustainable development, ecosystem protection, and livelihood support.

This study explores the relationship between population growth and urban expansion as well as their impacts on climate and environmental parameters in Berau Regency, Indonesia. Using night-light data and land use/land cover (LULC) analysis from 2019 through 2023, the research identified significant urban growth, with night-lit areas doubling and a population increase from 232,290 to 280,990. Urban expansion led to notable land conversion, reducing vegetated areas by 18,202.38 ha, while built-up and open land grew by 11,768.6 ha and 5,989.74 ha, respectively. These changes impacted environmental conditions, with non-vegetated areas experiencing higher land-surface temperatures (31–34°C) and lower rainfall (5,000–6,000 mm/year ) compared to the cooler and wetter vegetated areas (20–21°C; 7,000–8,000 mm/year ). The findings emphasized vegetation’s role in regulating temperature and rainfall, highlighting the environmental risks of urbanization and the need for sustainable land management to mitigate climate impacts in growing cities.

The interconnected porosity of soil provides conduit channels for the downward infiltration of water into the subsurface; this occurs in soil layers and within soil-less areas or geologic formations. The lithology and geological structure significantly influence the infiltration capacity of soils and are crucial in determining whether the infiltration water continuously reaches an aquifer or becomes stagnant in the saturated soil. An artificial neural network (ANN) algorithm was employed to model the actual infiltration rate, incorporating soil texture and soil moisture along with geological scores as inputs and actual infiltration rates as outputs. This study aimed to quantify qualitative geological data and incorporate it into ANN model parameters. The development of the ANN infiltration model involved two serial trial-and-error experiments to determine the optimal number of nodes in the hidden layer, ranging from nodes c(4,2) to c(12,2), one serial experiment with geological input, and the other without geological input. Throughout the model testing, metrics such as MAE, RMSE, and MSE were recorded, and the first and second optimum models were identified when employing c(9,2) nodes of hidden layers. The resulting model can be used to predict actual infiltration and will be beneficial for hydrometeorological-disaster mitigation and city-development planning.

Sustainable development is contingent upon the efficient management of land resources for resolving spatial challenges such as land-use conflicts and fragmentation. A land-suitability model offers a potential instrument for assessing land-use/land-cover (LULC) consistency with spatial plans. This study employed a data-driven probabilistic approach using a support vector machine (SVM) algorithm and error-correcting output codes (ECOCs) for incorporating 11 physical parameters to generate spatial grids that reflected land-suitability levels. The probabilistic outputs were derived by calibrating SVM decision values using Platt scaling within the ECOC framework, enabling a reliable estimation of class-wise landsuitability probabilities. The model achieved the highest probability value of 0.9952, with an average of 0.8251; this demonstrated its potential for assessing the consistency of land use/land cover with spatial plans. The model exhibited robust performance and substantial agreement between the predictions and actual data, with an overall accuracy of 88.56% and a kappa index of 0.873. Additionally, the study utilized a land-suitability model and non-weighted overlay relevance matrix to identify discrepancies in Bogor Regency’s spatial plan, quantifying the compliant and noncompliant land areas for each LULC class within specified spatial-plan zones. The evaluation revealed a significant misalignment, with 25–45% of agricultural land uses that included wetland and dryland agriculture, plantations, and inland fish farms being allocated within settlement zones; this indicated a mismatch between spatial plans and land suitability. These findings underscored the importance of evaluating and revising the spatial plan to enhance its alignment with land suitability.

Over the years, urban heat island (UHI) has emerged as a significant contributor to global warming, thereby necessitating considerable attention. Currently, satellite technology is a basic tool for the future – particularly, for its effective and efficient urban analysis. Thus, this study aims to assess the progress of existing satellite-based UHI studies by reviewing scientific publications that were released between 1972 and early 2024. Moreover, we observed that 1991 was a pivotal year, marking the integration of satellite technologies into the development of UHI monitoring and identification systems based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review methodology examines the UHI phenomenon by focusing on its characteristics based on sensors, algorithms, and accuracy. The results of the systematic review revealed that Landsat and MODIS were the most-deployed sensors for UHI identification and monitoring, while the land surface temperature (LST) indicator and normalized difference vegetation index (NDVI) were the most-deployed algorithms. Regarding accuracy, the integration of satellite sensors and algorithms into UHI studies provides a promising range of accuracies. The review found that the future of satellite-based UHI monitoring is promising, with technological advancements driving the development of effective techniques such as data fusion, gap filling, machine learning (ML), and deep learning. Additionally, Google Earth Engine (GEE) is a cloud-based platform for performing large-scale geospatial analyses, which facilitates the assessments of local, regional, and global-scale UHIs. Finally, the other review findings for future directions indicated that future satellite-based UHI studies will prioritize six crucial points: enhancing data resolution, integrating satellite data with ground-based sensors, artificial intelligence, and ML, climate change modeling, and a global study of UHIs and their impacts.

The extensive generation of waste and intensified geological processes that result from hard coal mining and active operations within mining regions have led to increases in the pollution levels of ecosystems. Most coal-mining wastes contain significant amounts of heavy metals and are, therefore, particularly hazardous to the environment. The soils around waste heaps can be contaminated with various pollutants. This article presents the results of a study of soils that were sampled in the impact zone of the waste heap of the Chervonohradska CPP of the Chervonohrad Mining District. Using statistical methods (including variogram modeling and spatial interpolation), we analyzed the spatial distributions of heavy metals in the affected soil zones. This approach allowed for an enhanced understanding of contamination-dispersion patterns and potential risk areas. The authors collected soil samples from the depth of the biotically active humus-accumulative horizon from the lower tier of the slope of the waste heap at distances of 20 m, 40 m, and 100 m from the spoil tip. We measured the contents of the studied elements in the soil using X-ray fluorescence analysis and assessed the quality of the soil by phytotesting using the Triticum aestivum L. and Lepidium sativum L. test species. It was found that the average concentrations of certain heavy metals in multiple samples exceeded the background values for the region and affected the inhibition of the development and growth of the test objects.

This research concerned the directions of changes in the real estate market in the area of private nursing homes for the elderly. The progressive decline in Poland’s population, the decline in the fertility rate, and demographic projections for the population structure have given rise to considerations about areas of the real estate market that will have to meet the expectations of elderly real estate participants. The growing number of privately owned nursing homes represents a commercial real estate sector that can be viewed as a profitable investment venture. The study examined investment performance indicators for several private nursing homes in Poland; the results indicated relatively high levels of EBIDTA margins for each company.

This study evaluates the preliminary performance of the dual-frequency multiconstellation satellite-based augmentation system (DFMC SBAS) prototype that was deployed in Thailand, focusing on key performance indicators such as positional accuracy and continuity. To this end, real data that was collected from 4, 8, and 12 ground tracking stations in Thailand was used to calculate SBAS corrections for the periods of January 1–7, April 1–7, August 1–7, and December 1–7, 2023. The accuracy of these corrections for single-point positioning was then tested using data from 20 continuously operating reference stations (CORS) in the region. The results showed that the correction data that was derived from the data from the 8 and 12 ground tracking stations significantly improved the efficiency of the single-point positioning, thus meeting the required standards for Category I (CAT-I) aviation operations. This initial assessment provides a solid foundation for the continued development of a fully operational DFMC SBAS that is tailored to Thailand’s specific requirements.