Search Results (6)

Cultivated land fragmentation (CLF) has evolved from a physical landscape phenomenon into a systemic constraint on agricultural sustainability, especially in rapidly urbanizing regions such as the Yangtze River Delta (YRD). Existing studies are limited by static “snapshot” comparisons that obscure continuous trajectories and by linear models that fail to capture nonlinear interactions and threshold effects. This study integrates the Space–Time Cube (STC) model with an interpretable machine learning framework (Extreme Gradient Boosting–Shapley Additive Explanations, XGBoost–SHAP) to explore the spatiotemporal dynamics and driving mechanisms of CLF in the YRD (1990–2020) at a 1 km² resolution. The STC identifies a distinct north–south gradient, with persistent hotspots in low-lying plains and intensifying fragmentation at peri-urban interfaces. SHAP interpretation suggests a “Base–Stabilizer–Amplifier” structure in the modeled relationships: hydrological accessibility and soil fertility form the dominant background linked to higher CLF, whereas topography correlates with lower CLF, and socioeconomic variables exhibit nonlinear, threshold-like increases in fragmentation beyond higher development levels. Overall, CLF reflects coupled natural–anthropogenic interactions with pronounced nonlinear responses. This mechanism-oriented framework provides actionable guidance for adaptive farmland governance. It also offers a transferable methodology for analyzing land system changes in other deltaic agricultural regions worldwide. Full article

The transition to renewable energy is crucial in order to attain sustainable development, lower greenhouse gas emissions, and secure long-term energy security. This study examines spatial–temporal trends in electricity generation (both renewable and non-renewable) across EU-28 countries using monthly Eurostat data (2008–2025) at the NUTS0 level. Two harmonized Space–Time Cubes (STCs) were constructed for renewable and non-renewable electricity covering the fully comparable 2017–2024 interval, while 2008–2016 data were used for descriptive validation, and 2025 data were used for one-step-ahead forecasting. In this paper, the authors present a novel multi-method approach to energy transition dynamics in Europe, integrating forecasting (ESF), hot-spot detection (EHSA), and clustering (TSC) with the help of a new spatial–temporal modeling framework. The methodology is a step forward in the development of methodological literature, since it regards predictive and exploratory GIS analytics as comparative energy transition evaluation. The paper uses Exponential Smoothing Forecast (ESF) and Emerging Hot Spot Analysis (EHSA) in a GIS-based analysis to uncover the dynamics in the region and the possible production pattern. The ESF also reported strong predictive performance in the form of the mean Root Mean Square Errors (RMSE) of renewable and non-renewable electricity generation of 422.5 GWh and 438.8 GWh, respectively. Of the EU-28 countries, seasonality was statistically significant in 78.6 per cent of locations that relied on hydropower, and 35.7 per cent of locations exhibited structural outliers associated with energy-transition asymmetries. EHSA identified short-lived localized spikes in renewable electricity production in a few Western and Northern European countries: Portugal, Spain, France, Denmark, and Sweden, termed as sporadic renewable hot spots. There were no cases of persistent or increase-based hot spots in any country; therefore, renewable growth is temporally and spatially inhomogeneous in the EU-28. In the case of non-renewable sources, a hot spot was evident in France, with an intermittent hot spot in Spain and sporadic increases over time, but otherwise, there was no statistically significant activity of hot or cold spots in the rest of Europe, indicating structural stagnation in the generation of fossil-based electricity. Time Series Clustering (TSC) determined 10 temporal clusters in the generation of renewable and non-renewable electricity. All renewable clusters were statistically significantly increasing (p < 0.001), with the most substantial increase in Cluster 4 (statistic = 9.95), observed in Poland, Finland, Portugal, and the Netherlands, indicating a transregional phase acceleration of renewable electricity production in northern, western, and eastern Europe. Conversely, all non-renewable clusters showed declining trends (p < 0.001), with Cluster 5 (statistic = −8.58) showing a concerted reduction in the use of fossil-based electricity, in line with EU decarbonization policies. The results contribute to an improved understanding of the spatial dynamics of the European energy transition and its potential to support energy security, reduce fossil fuel dependency, and foster balanced regional development. These insights are crucial to harmonize policy measures with the objectives of the European Green Deal and the United Nations Sustainable Development Goals (especially Goals 7, 11, and 13). Full article

Traffic crashes remain a critical public safety issue and are among the leading causes of mortality worldwide. Understanding, analyzing, and forecasting crash trends are essential for implementing effective countermeasures and reducing injury severity. In response to the growing number of crashes and their associated economic and social costs, this study presents a geospatial analytical framework for prioritizing and classifying roadway segments based on crash trends. The framework focuses on a major freeway corridor in the United States, covering a four-year period across 20 counties. This methodology employs spatiotemporal analysis, which integrates both spatial (geographic) and temporal (time-based) dimensions to better understand how crash patterns evolve over time and space. A central component of the analysis is Space–Time Cube (STC) modeling, a three-dimensional GIS-based visualization, and an analytical approach that organizes data into spatial locations (x and y) across a sequence of temporal bins (z-axis) to reveal patterns that may not be evident in a two-dimensional analysis. Additionally, emerging pattern analysis, specifically Emerging Hotspot Analysis (EHA), is used to identify statistically significant trends in crash frequency over time. The results indicate a significant spatial clustering of crashes, with high-risk segments predominantly located in densely populated urban areas with high traffic volumes. Crash hotspots were classified into five distinct categories: persistent, intensifying, new, sporadic, and diminishing, enabling transportation agencies to tailor interventions based on temporal dynamics. The proposed geospatial framework enhances decision making for roadway safety improvements and can be adapted for use in other regional corridors to support infrastructure investment and advance public safety. Full article

Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists in videos, which makes it difficult to accurately perceive anomalous activities. In this paper, we exploit the idea of a training model based on the “Cloze Test” strategy in natural language processing (NLP) and introduce a novel unsupervised learning framework to encode both motion and appearance information at an object level. Specifically, to store the normal modes of video activity reconstructions, we first design an optical stream memory network with skip connections. Secondly, we build a space–time cube (STC) for use as the basic processing unit of the model and erase a patch in the STC to form the frame to be reconstructed. This enables a so-called ”incomplete event (IE)” to be completed. On this basis, a conditional autoencoder is utilized to capture the high correspondence between optical flow and STC. The model predicts erased patches in IEs based on the context of the front and back frames. Finally, we employ a generating adversarial network (GAN)-based training method to improve the performance of VAD. By distinguishing the predicted erased optical flow and erased video frame, the anomaly detection results are shown to be more reliable with our proposed method which can help reconstruct the original video in IE. Comparative experiments conducted on the benchmark UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets demonstrate AUROC scores reaching 97.7%, 89.7%, and 75.8%, respectively. Full article

Based on the significant hotspots analysis method (Getis-Ord Gi* significance statistics), space-time cube model (STC) and the Mann–Kendall trend test method, this paper proposes a G-STC-M spatio-temporal analysis method based on Archaeological Sites. This method can integrate spatio-temporal data variable analysis and the space-time cube model to explore the spatio-temporal distribution of Archaeological Sites. The G-STC-M method was used to conduct time slice analysis on the data of Archaeological Sites in the study area, and the spatio-temporal variation characteristics of Archaeological Sites in East China from the Tang Dynasty to the Qing Dynasty were discussed. The distribution of Archaeological Sites has temporal hotspots and spatial hotspots. Temporally, the distribution of Archaeological Sites showed a gradual increasing trend, and the number of Archaeological Sites reached the maximum in the Qing Dynasty. Spatially, the hotspots of Archaeological Sites are mainly distributed in Jiangsu (30°~33° N, 118°~121° E) and Anhui (29°~31° N, 117°~119° E) and the central region of Zhejiang (28°~31° N, 118°~121° E). Temporally and spatially, the distribution of Archaeological Sites is mainly centered in Shanghai (30°~32° N, 121°~122° E), spreading to the southern region. Full article

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses. Full article