Geomatics and Environmental Engineering

Google Trends as Indicator of Social Preferences: Causality and Intervention in Poland’s Housing Market

Mirosław Bełej — 2026-01-12

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.

Assessing Urban Growth Toward Earthquake-Hazard Zone in Yogyakarta and Bantul, Indonesia

Nur Miladan — 2026-01-14

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.

Integrating Water Indices and Cloud-Based Engine for Change Detection of Aquaculture Areas in Lampung, Indonesia

Marindah Yulia Iswari — 2026-01-04

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.

Geologic Control of Soil-Infiltration Rate Based on Artificial Neural Network Models

Totok Sulistyo — 2026-01-03

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.

Utilizing Remote Sensing for Land Change and Night Lights in Urbanization: Correlation between Built-Up Area Expansion in Berau City and Changes in Climate Parameters

Syaiful Muflichin Purnama — 2026-01-03

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.