Internal migration is a pivotal driver of population dynamics and socio-economic transformation. This study investigates spatial patterns of population movements across Iranian counties, exploring the influence of environmental and socio-economic drivers on in-migration and out-migration flows. Methodologically, the research employs Spatial Descriptive Statistics, Ordinary Least Squares (OLS), and Multiscale Geographically Weighted Regression (MGWR) to capture the non-stationary nature of migration drivers. MGWR results reveal that socio-economic factors exert a predominant and consistent influence on in-migration, whereas demographic and environmental stressors, particularly drought and the Normalized Difference Vegetation Index (NDVI), significantly predict out-migration with notable spatial variability. These findings underscore that socio-economic pull factors are relatively stable across regions, while environmental push factors exhibit localized effects. From a Spatial Information Science perspective, this study demonstrates the efficacy of multiscale spatial modeling and provides a framework for targeted, location-specific regional planning.
This study provides the first systematic evaluation of the Meta Movement Distribution Maps (MDM) dataset for analysing hazard-related mobility. Using a full year of data for Türkiye (2023), we assess MDM’s sensitivity to both routine disruptions—such as weekends, holidays, and school breaks—and acute hazard events, including a medium-scale storm and a localized wildfire. MDM, which aggregates anonymized Facebook users’ geolocation data, effectively captures shifts in immobility, short-range (0–10 km), and medium-range (10–100 km) mobility categories. Statistical analyses reveal significant associations between mobility patterns and socio-demographic factors, with population size, age, and literacy emerging as the strongest predictors. The July storm generated clear regional-scale mobility anomalies, while the August wildfire, though smaller in scale, still produced detectable short-range displacements. Limitations remain, notably demographic bias toward urban and literate users and the absence of information on the motivations behind movements. Despite these constraints, MDM offers a valuable, transparent, and near-real-time proxy for monitoring mobility during natural hazards. Its integration with complementary data sources could enhance disaster preparedness, targeted aid delivery, and recovery planning.
This study assessed flash-flood susceptibility across five Haor-dominated upazilas (subdistricts) of Kishoreganj, Bangladesh, using Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and an equal-weight ensemble. A Sentinel-1 Synthetic Aperture Radar (SAR)-derived flood inventory was integrated with twelve hydrological and environmental predictors. Models were trained using an 80/20 stratified split and evaluated using spatial block cross-validation (SBCV). RF, XGBoost, and the ensemble showed comparable performance, with receiver operating characteristic area under the curve (ROC-AUC) values of 0.950, 0.950, and 0.952 and accuracies of 87.5–87.6
Reliable InSAR-based monitoring of ground deformation is often hindered by atmospheric disturbances and phase noise, particularly in regions with rugged topography and high vegetation cover. However, limited work has explored integrated phase filtering and atmospheric correction strategies in complex, landslide-prone terrains like Mizoram, India. This study proposes an enhanced methodology combining a Modified Goldstein phase filter and Atmospheric Phase Screen (APS) removal to improve Line of Sight (LOS) displacement estimation. Mizoram, characterized by frequent landslides and challenging climatic conditions, provides an ideal testbed to evaluate these improvements. Twenty Sentinel-1 SAR images (10 ascending, 10 descending) were processed using SRTM DEM-based co-registration, with corrections addressing height distortions and DEM voids. This study modifies the standard Goldstein filter that incorporates Local Incidence Angle (LIA) information to enhance phase unwrapping stability, combined with Atmospheric Phase Screen (APS) removal applied before phase filtering. Furthermore, an ensemble learning framework using a Random Forest regressor was employed to model and refine time-series displacement trends. The proposed method constrained phase values within a range of -3 to 3 radians, improved displacement map interpretability, and achieved a LOS displacement of±80 mm/month. Validation against ground-collected levelling survey data showed a strong correlation of 76
Artificial Light at Night (ALAN) imagery has surfaced as a transformative tool for monitoring urban development, offering objective, spatially continuous insights into human socioeconomic dynamics from space. Guided by the PRISMA framework, this systematic literature review aims to clarify the influence of ALAN on urban spatial structure, two- and three-dimensional morphological analysis, and environmental impact assessment. Drawing on datasets from DMSP, VIIRS, and the high-resolution imageries SDGSAT-1, alongside methodological approaches spanning thresholding, machine learning, and deep learning, this review identifies critical gaps including intra-night temporal limitations, the “blue light” detection gap from LED transitions, sensor saturation, and light blooming effects. Multisource geospatial data fusion and advanced deep learning architectures are highlighted as promising solutions. Findings underscore ALAN’s growing role in supporting the UN Sustainable Development Goals, particularly SDG 11; sustainable cities and communities, while calling for harmonised long-term datasets and standardised global protocols for equitable urban monitoring.
