Spatio-temporal dynamics and trend forecasting of urban green high-quality development: An examination of 287 cities in China. by Tonghui Yu, Shanshan Jia, Xufeng Cui

“…The research findings indicate: (1) A steady annual increase in the overall level of UGHQD, with a geographic pattern showing high levels in the east, moderate in the center, and low in the west. (2) A spatial agglomeration in UGHQD, predominantly in the eastern region, demonstrating spatio-temporal inertia. (3) Varying degrees of a "right-tail" phenomenon in the UGHQD across China and its four sub-regions, indicating a polarization trend or even a weak multi-polarization trend. (4) A forecast of continuous, steady growth in UGHQD from 2021 to 2025, with the eastern region maintaining its leading position. …”
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Spatio-temporal variation of dung beetle (Coleoptera: Scarabaeidae) assemblages in a community ecological reserve of southeastern Mexico by Diana A. Ahuatzin, Wesley Dáttilo, Fernando Escobar-Hernández, Albina Demeza, Lucrecia Arellano

“…When evaluating the spatial turnover, we found that the most important component of β diversity was β2 (between environments), which suggests that the environmental variation provided by the great spatial heterogeneity of the environments modulates the diversity patterns of beetles in this reserve. …”
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Spatio-temporal evolution and coupling relationship between biodiversity and urbanization in the areas along the Yellow River of Shandong province by Yingjun Sun, Wenxue Meng, Fang Wang, Haifeng Han, Mingxin Sui, Zhen Jian

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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

“…Then, a dynamic adjacency matrix is adaptively learned to capture hidden spatial dependencies, while temporal convolution modules extract multi-scale temporal patterns, and the node sequences with integrated corrosion features are input into the adaptive MTGNN for prediction. …”
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Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach by Marc Semper, Manuel Curado, Jose Luis Oliver, Jose F. Vicent

“…Each technique contributes specific strengths to the modeling of spatiotemporal series: CNNs are effective at capturing local spatial patterns, while LSTM networks excel at modeling long-term temporal dependencies. …”
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Spatial Modeling of Yellowfin Tuna in the Banda Sea Based on Oceanographic Factors Using MaxEnt by Sunarwan Asuhadi, Mukti Zainuddin, Safruddin Safruddin, Musbir Musbir

“…This study models the spatial distribution of yellowfin tuna (YFT) in the Banda Sea using the MaxEnt approach, addressing critical questions about its predictive capability, the influence of environmental variables such as sea surface temperature (SST) and chlorophyll-a concentration, and temporal patterns. …”
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Automated violence monitoring system for real-time fistfight detection using deep learning-based temporal action localization by Baolong Qi, Baoyuan Wu, Bailing Sun

“…However, essential individual features can be extracted by examining human skeletal movements’ progression and temporal patterns. This paper presents a novel multimodal spatio-temporal fistfight detection model (MSTFDet) that integrates RGB images and human skeletal data to identify violent behaviors accurately. …”
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Spatio-Temporal Heterogeneity of Ecological Quality in a Typical Dryland of Northern China Driven by Climate Change and Human Activities by Shuai Li, Junliang Gao, Pu Guo, Ge Zhang, Yu Ren, Qi Lu, Qinwen Bai, Jiahua Lu

“…This study compares the efficacy of RSEIs in evaluating dryland ecological quality, identifies spatio-temporal change patterns, and elucidates driving mechanisms, offering scientific evidence and policy recommendations for targeted conservation and restoration measures to address future changes in dryland regions.…”
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Blocked quadrat variance analysis for spatial relation of Ectropis oblique hypulina larva and spider natural enemies by Liu Feifei, Ke Shengbing, Wang Jianpan, Bi Shoudong, Zou Yunding, Zhou Xiazhi, Dang Fenghua, Xu Jinfeng, Yu Kun, Zhao Xuejuan

“…The relationships between insect pests and their natural enemies include the quantitative relations of their occurrence, the temporal relationships of their occurrence as well as their spatial distribution patterns. …”
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Spatio-Temporal Characteristics and Driving Forces of Landscape Ecological Risks in Highly Urbanized Areas: A Case Study of Suzhou City by Lingyue LU, Dawei SHAO, Dianming WU

“…The formation mechanism, spatio-temporal heterogeneous driving effect and scale response law of LER in highly urbanized areas still need to be systematically explored.MethodsUtilizing multi-source data spanning the period from 1995 to 2020, including Landsat TM/ETM imagery, digital elevation model (DEM) and NASA datasets, this research focuses on exploring the characteristics of spatio-temporal variability of LER and corresponding drivers in Suzhou at the optimal scale, utilizing the LER evaluation model and the geographically and temporally weighted regression (GTWR) framework.Results1) The landscape pattern of Suzhou City is scale-dependent, with significant variations in the landscape pattern index for each land type at the optimal scale. …”
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Fine-Resolution Satellite Remote Sensing Improves Spatially Distributed Snow Modeling to Near Real Time by Graham A. Sexstone, Garrett A. Akie, David J. Selkowitz, Theodore B. Barnhart, David M. Rey, Claudia León-Salazar, Emily Carbone, Lindsay A. Bearup

“…The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. …”
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A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems by Andrii Liashenko, Larysa Globa

“…Modern dynamic systems, such as transportation networks and IoT infrastructures, generate massive volumes of interrelated temporal data represented as temporal graphs. Conventional methods – like clustering, statistical thresholds, and classical time series analysis – often fail to account for the spatial-temporal dependencies inherent in these systems, leading to high false positive rates or missed complex anomalies. …”
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An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification by Aaqib Raza, Mohd Zuki Yusoff

“…Moreover, advanced deep learning models often utilize rigid architectures with fixed spatial-temporal filters, restricting their adaptability to dynamic EEG patterns. …”
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A Multi-Modal Approach Using a Hybrid Vision Transformer and Temporal Fusion Transformer Model for Stock Price Movement Classification by Ibanga Kpereobong Friday, Sarada Prasanna Pati, Debahuti Mishra

“…Our approach adequately captures potential price reversals or continuations by utilizing two modalities (candlestick chart patterns and historical price data). Specifically, the proposed framework converts the historical data into candlestick charts of <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula>-pixel images where both modalities are effectively integrated and processed. …”
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Spatio-temporal dynamics of urban medical system carrying capacity and their obstacle factors: A case study of Yangtze River Delta urban agglomeration. by Chengshuang Sun, Ke Zhou, Guangxia Li, Weina Zhu, Dongjun Wan

“…The frequent occurrence of various urban disasters poses risks to human survival and welfare, and it is of great significance to evaluate the urban medical system carrying capacity (UMSCC), monitor the spatial and temporal patterns of the UMSCC and identify their obstacle factors to improve the public medical system and rationalize medical resource allocation. …”
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Quantifying water supply–demand relationship and spatial flow in Qinghai–Xizang Plateau: Case study of Lhasa River Basin by Jun Wang, Xiaochi Liu, Xiao Zhang, Yan Gao

“…The Lhasa River Basin is the political, economic, and cultural core area and main cultivation area of Qinghai–Xizang Plateau and is considered ecologically fragile. With uneven spatial and temporal distribution of water resources, mismatched supply and demand may accentuate differences in distribution and affect the security of regional water resources. …”
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A Temporal Attention-Based SARIMA-BiLSTM Residual Learning Model Tuned by Grey Wolf Optimizer for Parallel Urban Traffic Forecasting by Manas Kamal Das, Christopher Columbus Chinnappan, E. Elakiya

“…However, urban traffic exhibits high levels of temporal variability, spatial complexity, and nonlinear dependencies, posing challenges to traditional time series and machine learning models. …”
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Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model by Sicong He, Yanbin Yuan, Zhen Li, Heng Dong, Xiaopang Zhang, Zili Zhang, Lan Luo

“…Evaluated with in-situ NO2 measurements, the results have demonstrated the robust and excellent performance of the T-XGB (R2: 0.920–0.948; MAE: 2.89–3.67 µg/m3/h), as well as the accuracy of the temporally conserved downscaling technique (R2 > 0.973). …”
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Evaluation of the Effectiveness of Downscaled Precipitation Data in Drought Monitoring by Ping Ji, Qunming Wang

“…The results indicated that the SPI calculated from 1-km downscaled results can reproduce more detailed spatial distribution patterns of meteorological drought compared to the SPI derived from the 10-km original GPM. …”
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Spatio-temporal evolution of land subsidence and susceptibility zonation of associated ground fissures in the urban area of loess plateau: a case in Xianyang city, China by Yuemin Sun, Jiewei Zhan, Fei Qiang, Quanzhong Lu, Cong Liu, Zhaowei Yao, Jianbing Peng

“…Then, the gradient of land subsidence and its potential relation to ground fissure hazards were evaluated. The results highlight a strong spatial correlation between high subsidence gradient areas and dense ground fissure distribution. …”
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