Search Results (386)

This research presents a comprehensive spatial–temporal analysis of urban form and growth in Charlotte and Mecklenburg County, North Carolina, USA, from 1900 to 2017 at the land parcel level. Employing a data fusion framework, we integrate diverse datasets—including historical cadastral records, census data, remote sensing imagery, and infrastructure maps—to examine urban morphology through Euclidean and fractal geometries. Urban growth was reconstructed and visualized by decade and cumulatively, revealing dynamic patterns of expansion, densification, and fragmentation. Using scatterplot matrices and the Hausdorff box-counting algorithm, we quantified urban form across major land use types and temporal intervals. The fusion of socio-physical variables with mathematical functions enabled multi-scale modeling of urban transitions, aligning spatial, temporal, and thematic dimensions. Key findings include: (1) multidirectional spatial expansion resulting in a sprawling urban footprint at different rates over 117 years; (2) exponential growth between 1950 and 2000 with slower rates before and after manifesting a classic S-curve urban development by Northam; (3) a pivotal moment in 1993 when urbanized and rural lands reached parity, reflecting balanced urbanization in terms of population and land area for cities and rural areas for Mecklenburg; and (4) consistent quantitative relationships—linear, polynomial, exponential, logarithmic, and proportional—between urban form and growth metrics. This study’s novelty lies in its integrated spatial–temporal framework not only for combining both Euclidean and fractal geometric analyses with fused multi-source data to uncover the evolving structure of urban landscapes, but also for offering valuable insights into efficient land uses to assess equitable land and population dynamics, all aiming to achieve a good understanding of and sound policies for Charlotte, Mecklenburg and beyond. Full article

This study examines the spatiotemporal evolution and driving mechanisms of urban expansion in the Guangxi Zhuang Autonomous Region, China, from 2013 to 2023. Using Suomi-NPP VIIRS nighttime light (NTL) data, we combine Standard Deviational Ellipse (SDE) analysis, centroid migration, kernel density estimation (KDE), landscape metrics, Local Moran’s I (LISA), and system Generalised Method of Moments (system-GMM) estimation. The results show that the centroid of urban development remained within Binyang County while moving overall toward the southeast with recurrent north–south oscillations. The SDE results indicate a stable northeast–southwest orientation, with secondary expansion in other directions. The urban structure is dominated by a strong Nanning core, accompanied by secondary clusters in Liuzhou, Guilin, and other prefecture-level cities. Nanning recorded the largest absolute expansion, followed by secondary centres, including Liuzhou, Guilin, Yulin, Wuzhou, Fangchenggang, Qinzhou, and Beihai, whereas western and northern Guangxi expanded more slowly. The system-GMM results indicate that financial deepening has a marginally significant positive effect on built-up area expansion and fiscal pressure has a marginally significant constraining effect, both at the 10% level; land finance dependency does not emerge as an independent driver in this small panel. We interpret these findings through a Source–Channel–Valve framework, in which financial deepening provides the capital source, land finance represents a hypothesised institutional channel, and fiscal pressure acts as a regulatory constraint. The study provides empirical evidence for sustainable and regionally coordinated urban development in Guangxi and comparable geographically constrained regions. Full article

Sacred natural sites are often considered potential refugia for biodiversity in human-dominated landscapes, but their effects can be confounded by present-day habitat structure. We tested whether extant Cistercian monasteries in western Poland influence breeding-bird assemblages at two spatial scales by conducting standardized 5 min point counts during two visits at 234 stations across 23 plots and comparing Cistercian plots with environmentally matched Control and Post-Cistercian plots. We recorded 133 breeding-bird species, numerically dominated by widespread farmland and synanthropic taxa. Neither plot category nor station placement within versus outside monastery grounds explained variation in Shannon diversity or rarefied species richness. In contrast, both diversity metrics increased with contemporary landscape complexity, especially along gradients from arable land toward grasslands and urban habitats and with increasing heterogeneous agriculture, while community composition was significantly associated with current landcover structure. These findings indicate that present-day habitat structure, rather than monastic legacy, is the main driver of breeding-bird diversity in this system. Conservation and land-use policy in agricultural regions should therefore prioritize the maintenance and restoration of heterogeneous landscapes, including mosaics of semi-natural habitat elements. Full article

Quantitative land cover records for geopolitically restricted regions remain extremely scarce, particularly for the pre-Landsat era. This study reconstructs long-term urban land cover dynamics in Pyongyang, Democratic People’s Republic of Korea (DPRK), over a 55-year span (1967–2022) by combining deep learning colorization of declassified CORONA KH-4 panchromatic imagery with the GLC_FCS30D global 30 m land cover dynamics dataset. The GLC_FCS30D nine-epoch time series (1985–2022) revealed that built-up area expanded from 65.0 km² (36.0%) to a peak of 103.2 km² (57.1%) in 2015, driven almost entirely by the conversion of agricultural land, before declining to 92.7 km² (51.3%) by 2022. The 1967 colorization-based classification yielded a built-up proportion of 35.9%, closely approximating the 1985 baseline. Integration of these results identified three urbanization phases: post-reconstruction consolidation (1967–1985), sustained expansion at the expense of agricultural land (1985–2015), and stabilization coinciding with intensified international sanctions and pandemic-related isolation (2015–2022). The near-halving of agricultural land within the capital’s vicinity during chronic national food insecurity is consistent with a fundamental tension between showcase urban modernization and food production imperatives in state-planned economies. As perhaps the last continuously state-planned socialist city, Pyongyang’s trajectory offers a rare empirical counterpoint to market-driven urbanization processes. Full article

Vertical land motion in urban areas is a critical manifestation of groundwater, directly affecting infrastructure stability and groundwater sustainability. While land subsidence caused by groundwater extraction has been widely investigated, the opposite process—surface uplift induced by groundwater recovery—remains poorly documented or understood, particularly regarding its hydrological mechanisms and potential hazards. Here, we integrate InSAR time-series analysis of Sentinel-1 imagery (2017–2025) with groundwater well records to quantify the spatial–temporal characteristics of uplift in Xi’an, China, and to evaluate its hydrogeological drivers. Results reveal a persistent surface uplift zone south of the ancient city in Xi’an, with rates up to 20 mm/yr. The uplift correlates closely with rising groundwater levels in the shallow confined aquifer, indicating a strong coupling between aquifer recharge and surface uplift. Calculated storage coefficients and hydraulic diffusivity values highlight marked spatial variations, constrained by some ground fissures that act as both mechanical discontinuities and hydrological barriers controlling pressure diffusion. Time-series analysis further identifies the eastward propagation of subsidence-to-uplift reversal in Yuhuazhai, an urban village with groundwater injection, which is used to quantify the diffusivity coefficients. Field investigations show that rapid groundwater rebound can lead to uplift-related hazards, such as basement seepage, underscoring that surface uplift must be considered alongside subsidence in urban water management. Full article

Urbanization, together with shifts in rainfall patterns, has become an increasingly important driver of hydrological extremes in many rapidly developing tropical regions. In the Cimanceuri River Basin, Tangerang Regency, Indonesia, these processes have intensified over the last decade, raising concerns regarding flood risk. This study examines the combined influence of urban expansion and rainfall variability on flood dynamics over 2013–2025. Multi temporal land use classification based on Landsat imagery indicates a pronounced growth of impervious surfaces, primarily driven by rapid urban development and the conversion of agricultural land. To assess the hydrological consequences of these changes, rainfall–runoff processes and flood inundation were simulated using the Soil Conservation Service Curve Number (SCS–CN) method within a coupled HEC-HMS and HEC-RAS 2D modelling framework. Simulations were performed for multiple temporal conditions and design rainfall scenarios. Model calibration relied on observed flood events recorded in March 2025 in the Mustika Residential Area, Tangerang. The results suggest that urbanization has contributed to measurable increases in both peak discharge and inundation extent. Between 2013 and 2025, impervious surface coverage expanded by approximately 67%, accompanied by a rise in the composite Curve Number from 85.86 to 86.63 and an estimated 5.2% increase in flood extent. Also, the design rainfall increased from 85.01 to 90.95 with an average increase of 7.34%. Comparison between simulated inundation patterns and aerial imagery shows satisfactory agreement, with an average deviation of less than 10%, indicating acceptable model performance. Hydrologic analyses generated two discharge scenarios, consisting of event-based flow from the 5 March 2025 rainfall data and return-period flows derived from design rainfall under different rainfall-shift periods. The rainfall-shift analysis quantified changes in design rainfall and corresponding discharge using progressively updated rainfall records. Together, the results emphasize the combined effects of urban expansion and shifting rainfall patterns on flood dynamics, underscoring the need for adaptive land-use planning and climate-responsive water management in rapidly urbanizing catchments. Full article

Socio-environmental conflicts in mining regions are often examined through political, economic, or social lenses, while the role of land-system transformation remains less integrated into quantitative analysis. This study examines the co-evolution of socio-environmental conflict and territorial change in Las Bambas (Apurímac, Peru) as a socio-territorial process. Annual conflict records from the Peruvian Ombudsman’s Office (2007–2024) were combined with annual land-cover data from MapBiomas. Yearly conflict influence zones were reconstructed from reported affected communities and geographic features using buffered spatial entities and concave hull polygons. Clustering methods (K-medoids, DBSCAN, and agglomerative hierarchical clustering) and FP-Growth association rule mining were applied to 23 unique conflicts consolidated from the original records and encoded with 10 root causes. The most intense conflict phases were accompanied by measurable landscape transformations, including the emergence of mining-related land cover from 2012 onward, sustained loss of high-Andean natural vegetation, expansion of agricultural mosaics, urban growth along the Apurímac–Cusco corridor, and hydrological alterations in wetlands and headwaters. Three conflict typologies were identified, with unfulfilled company commitments emerging as the most recurrent co-occurring grievance. The dynamic polygon approach offers a replicable framework for linking conflict records with land-system change in extractive regions. Full article

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming the modeling of complex spatiotemporal urban processes such as urban growth, sprawl, shrinkage, redevelopment, and Land Use/Land Cover Change (LULCC). However, despite rapid methodological innovation, applications remain fragmented, and there is limited synthesis of how AI-based models complement, extend, or supersede conventional approaches. This study addresses this gap through a systematic review of 6356 records, from which 120 articles were selected for detailed analysis. It investigates: (i) how ML/DL techniques are embedded within spatiotemporal modeling frameworks; (ii) their use in simulating urbanization dynamics and land-use (LU) transitions; (iii) methodological and performance gains relative to traditional statistical and rule-based models; and (iv) emerging research frontiers and limitations. The review shows that LULCC dominates current applications, with Artificial Neural Networks (ANNs) as the most prevalent ML method, increasingly complemented by DL architectures. Across cases, AI is primarily used to learn non-linear transition dynamics, represent spatial and temporal dependencies, identify influential drivers, and improve classification performance and computational efficiency. Building on these insights, the paper synthesizes the roles of AI in spatiotemporal urban modeling and outlines forward-looking research directions to support more robust, transparent, and policy-relevant applications for urban sustainability. Full article

As a key link between short-distance urban mobility and public transport, dockless bike-sharing (DBS) systems have expanded rapidly in recent years. However, existing studies are limited by insufficient factor coverage, incomplete temporal analysis, and inadequate assessment of spatial-scale effects. To address these gaps, this study uses Shenzhen as a case study, integrating 40 million DBS trip records from August 2021 with multi-source geospatial data to develop a spatiotemporal analytical framework. First, it examines differences in riding patterns between weekdays and weekends, further segmenting trips into six time periods to capture intra-day temporal variations. Through multicollinearity and spatial autocorrelation tests, a 700-m grid was identified as the optimal analysis unit. Subsequently, a Multi-scale Geographically Weighted Regression (MGWR) model quantified how multiple sources of factors collectively shape DBS usage behavior. Results indicate that higher frequency, faster speeds, and longer distances during peak periods characterize weekday trips. Office POIs and transit accessibility positively affect DBS usage during weekday peaks, whereas Residential POIs and Convenience Service POIs have a greater influence on weekend trips. Population density and land-use mix consistently promote DBS use across all periods. Younger residents (<30 years) were the main users, especially during weekday peak and weekend no-peak periods, whereas gender and education had limited impact. These findings provide empirical evidence to optimize bike-sharing deployment, enhance multimodal transport integration, and support sustainable urban mobility planning. Full article

Urban pluvial flooding presents growing challenges for disaster risk management, yet most susceptibility studies rely on watershed-based frameworks that inadequately capture the localized dynamics of urban systems. This study proposes a city-scale flood susceptibility modeling framework for Cincinnati, Ohio. Cincinnati was chosen because it is a city with a documented history of severe urban flooding, including a once-in-a-century storm in 2016. Multi-source historical flood data were compiled from NOAA storm event records and crowdsourced reports to enhance spatial coverage. Four machine learning algorithms (Random Forest, Support Vector Machine, XGBoost, and Logistic Regression) were implemented to identify the most effective approach for urban pluvial flood prediction. Random Forest (RF) and Support Vector Machine (SVM) achieved the highest accuracy (0.84) and demonstrated strong discriminatory power. RF was selected as the optimal model because it had a higher AUC (90%) and the lowest RMSE (0.35). To assess generalizability, the RF model was validated on updated land use data and flood records from a 2020 storm event. It demonstrated robust performance (accuracy = 0.89, RMSE = 0.36, precision = 0.75, recall = 1, and AUC = 0.95), despite urban development changes. This study’s novelty lies in combining multi-source flood records with a grid-based machine learning framework and rigorously validating model robustness under evolving urban conditions. The results advance urban pluvial flood susceptibility modeling and offer actionable guidance for evidence-based flood risk management worldwide. Full article

Urban accessibility is commonly evaluated using static spatial indicators, which assume stable travel conditions throughout the day. Road congestion, network saturation, and service variability change the function and experience of the built environment (BE). This study tests the Temporal City Framework (TCF) by examining how time of day (TOD) reshapes urban accessibility and travel behaviour with varying levels of congestion. Using 30,288 trip records from the 2022 US National Household Travel Survey (NHTS), duration is operationalised as a sixth dimension of the BE. A time-normalised impedance metric, measured in minutes per mile (MPM), is used that captures realised congestion independently of distance. Temporal impedance (TI) varies strongly with TOD, with substantially higher MPM during peak and midday periods than at night. Compared with nighttime conditions, midday travel requires approximately 19% more time per mile. This indicates a measurable contraction in functional accessibility under identical BE conditions. The TI model outperforms duration-only models, with impedance remaining dominant when both measures are included. These results support interpreting duration as a structural dimension of urban accessibility. TI significantly increases the relative likelihood of active and public transport compared to private cars, even after accounting for absolute trip duration. Hired transport modes (taxi and ride-hailing services) are most prevalent at night, reflecting a greater reliance on on-demand services outside regular daytime schedules. This study tests duration as a structural dimension of the BE by operationalising time-normalised TI. Associations are interpreted as trip-level behavioural constraints rather than causal effects. Planning frameworks based on static travel times systematically misrepresent exposure, equity, and travel mode feasibility. Time-stratified accessibility metrics should therefore be integrated into transport and land-use evaluation and associated policies. Full article

This study examines land surface temperature (LST) variability over Malta, a small island in the central Mediterranean, using satellite observations compared with in situ near-surface air temperature (NSAT) measurements. The analysis focuses on the comparison between satellite-derived LST and local atmospheric thermal conditions for urban and rural land cover types. LST data from Landsat-8, MODIS (Terra and Aqua), and Sentinel-3A and 3B were analysed over a six-month period (September 2024 to February 2025). Monthly morning and evening field campaigns were conducted at 19 monitoring sites distributed across the island, during which NSAT, relative humidity, wind speed, and wind direction were recorded. Morning comparisons showed strong correlations between satellite-derived LST and in situ NSAT, i.e., Pearson’s correlation coefficient, r, in the range of 0.82–0.85. Landsat-8 exhibited a slight positive bias (+1.04 °C), while MODIS and Sentinel-3 Level-2 products showed negative biases (−3.82 °C and −1.89 °C, respectively). Nighttime comparisons revealed larger negative biases for MODIS (−6.91 °C) and Sentinel-3 (−6.89 °C). After empirical-based harmonisation, these discrepancies were reduced to near-zero mean bias, maintaining strong correlations. Spatial analysis indicated a persistent nocturnal urban heat island (UHI) effect, with urban areas retaining more heat than rural zones. Morning patterns showed seasonal modulation: during late summer and early autumn, rural areas exhibited higher surface temperatures due to sparse vegetation and exposed soils, whereas during cooler months the urban signal became more pronounced as vegetation recovery enhanced rural cooling. Overall, the results demonstrate the usefulness of multi-sensor satellite observations, interpreted alongside ground-based measurements for characterising thermal behaviour in small island environments. Full article

Chile’s economic development model, which was shattered by the military coup, restructured under the dictatorship, and institutionalised under democracy as a neoliberal model, gave rise to a liberalisation process that affects the country’s natural resources and commercial dynamics and, by extension, places society itself at the service of the system. This model, enshrined in the 1980 Political Constitution, was founded on the principles of external openness, private investment, and deregulation. Against this backdrop, this paper examines and analyses the impact of strengthening private ownership over tangible assets on the increase in land transactions in the rural commune of Hualaihué. The research, based on a quantitative and qualitative analysis of land ownership records from 2005, 2015, 2021, and 2022, as well as information from 23 semi-structured interviews with different territorial stakeholders, reveals the impact of territorial commodification in the area of study. The results indicate that the sale of rural land, the increase in land sales, and the reduction in the size of plots acquired since 2021 constitute an emerging and latent problem, which confirms that rural land is undergoing a subdivision process that presents urban development characteristics in certain parts of the commune. This needs to be critically examined to develop urgent, comprehensive planning dynamics, thereby reducing emerging socio-territorial conflicts. Full article

Agriculture in dryland regions faces increasing pressure from climate variability, water scarcity, and competing urban and environmental demands. A recent basin-wide technical analysis for the Rio Grande/Rio Bravo in the United States of America (USA) and Mexico shows that consumptive water use in the river system overall is on an unsustainable path. The Middle Rio Grande Basin (MRGB) of central New Mexico (USA) exemplifies these sustainability challenges, where irrigated agriculture persists despite low precipitation, high evaporative demand, and prolonged drought. This study provides analytical spatial description of irrigated agriculture in the MRGB, examining farm size distribution, crop composition, groundwater access, and consumptive water use measured by evapotranspiration (ET) and effective ET. Using 2021 remotely sensed crops and ET data, groundwater well records, and GIS-based aggregation to the irrigator farm level, the analysis reveals a highly fragmented agricultural landscape dominated numerically by micro-scale and small farms, which together account for 55.9% of total agricultural ET. Alfalfa and other hay crops occupy nearly three-quarters of irrigated acreage and consume 74% of total ET, reflecting the prevalence of forage production. Groundwater access is highly uneven, with most wells concentrated among large farms, creating resilient disparities. The findings highlight that consumptive agricultural water use in the MRGB is diffuse rather than concentrated: non-commercial farms (<12 hectares) account for 55.9% of basin-wide ET, while commercial farms contribute only 14.4% despite occupying about one-fifth of irrigated land. This complicates water conservation efforts. Resilient management strategies must therefore engage thousands of small, largely non-commercial irrigators through mechanisms that recognize both hydrological and spatial realities. The study provides an empirical basis for designing sustainable irrigation and water-management strategies in dryland agricultural systems facing increasing climatic and institutional pressures. Full article

Urban flood risk management in southern Chile is often constrained by fragmented territorial information, discontinuous hydrological records, and weak integration between hazard assessment and formal land-administration systems. These limitations are particularly evident in persistently cloudy cities such as Temuco, where optical satellite imagery is frequently unusable. This study examines how satellite-derived flood observations can be incorporated into municipal land-administration practices. Flood-prone areas were identified using multitemporal Sentinel-1 SAR imagery (2018–2025) and integrated into a municipal multipurpose cadastre structured according to the ISO 19152 Land Administration Domain Model (LADM). Rather than remaining as standalone GIS maps, detected inundation areas were translated into standardized cadastral entities representing spatial units and hazard-related planning constraints. The analysis identified recurrent flooding along the Cautín River floodplain, characterized by strong winter seasonality and increasing exposure linked to urban expansion. More importantly, the results demonstrate that satellite-based hazard observations can be structured as interoperable administrative information with defined semantics, temporal validity, and traceable data sources. The proposed framework enables flood information to support territorial planning, emergency preparedness, and municipal risk management without altering property legal status. By linking Earth observation data with cadastral information infrastructures, the study provides a replicable approach for integrating environmental observations into land-administration systems in regions affected by institutional fragmentation and recurring hydrometeorological hazards. Full article