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Journal articles on the topic 'Spatio-Temporal heterogeneity'

Author: Grafiati

Published: 15 June 2024

Last updated: 31 July 2025

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1

Li, Zetao, Zheng Hu, Peng Han, Yu Gu, and Shimin Cai. "SSL-STMFormer Self-Supervised Learning Spatio-Temporal Entanglement Transformer for Traffic Flow Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 12130–38. https://doi.org/10.1609/aaai.v39i11.33321.

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Traffic flow prediction remains a critical issue in intelligent transport systems. Despite significant efforts in traffic flow modeling, existing approaches exhibit several notable limitations: (i) Most models fail to capture traffic flow similarities over long distances and extended periods; (ii) They struggle to account for spatio-temporal heterogeneity induced by varying traffic flow patterns; (iii) Due to their static modeling approach, they struggle to effectively capture the intricate spatio-temporal entanglement. To address these challenges, we propose a traffic flow prediction framewor

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Song, Jianxiang, Jiafu Liu, Xinyue Zhang, Xin Chen, Yingtao Shang, and Fengjie Gao. "Spatio-Temporal Fluctuation Analysis of Ecosystem Service Values in Northeast China over Long Time Series: Based on Bayesian Hierarchical Modeling." Land 13, no. 6 (2024): 833. http://dx.doi.org/10.3390/land13060833.

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Ecosystems are undergoing continuous degradation due to the dual perturbation of global climate change and human activities, posing unprecedented threats and challenges to the ecosystem services they provide. To gain a deeper understanding of the spatio-temporal evolution of ecosystem service value (ESV), it is essential to accurately capture the characteristics of its spatial and temporal changes and its influencing factors. However, traditional spatio-temporal statistical methods are limited to analyzing the heterogeneity of ESV in a single temporal or spatial dimension, which fails to meet

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Liu, Qingxiang, Sheng Sun, Yuxuan Liang, Min Liu, and Jingjing Xue. "Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 12192–200. https://doi.org/10.1609/aaai.v39i11.33328.

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The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) methods to model the spatio-temporally variant representations. While contrastive learning is promising in tackling spatio-temporal heterogeneity, the existing methods are noneffective in distinguishing positive and negative pairs and can hardly apply to PFL paradigm. To tackle this limitation, we propose a novel PFL method, named Federated dUal sEmantic aLignment-based contraStive learning (FUELS), which can adaptively a

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Scarrott, Rory Gordon, Fiona Cawkwell, Mark Jessopp, Caroline Cusack, Eleanor O’Rourke, and C. A. J. M. de Bie. "Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change." Remote Sensing 13, no. 7 (2021): 1283. http://dx.doi.org/10.3390/rs13071283.

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Mapping heterogeneity of the ocean’s surface waters is important for understanding biogeographical distributions, ocean surface habitat mapping, and ocean surface stability. This article describes the Ocean-surface Heterogeneity MApping (OHMA) algorithm—an objective, replicable approach that uses hypertemporal, satellite-derived datasets to map the spatio-temporal heterogeneity of ocean surface waters. The OHMA produces a suite of complementary datasets—a surface spatio-temporal heterogeneity dataset, and an optimised spatio-temporal classification of the ocean surface. It was demonstrated her

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Ji, Jiahao, Jingyuan Wang, Chao Huang, et al. "Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4356–64. http://dx.doi.org/10.1609/aaai.v37i4.25555.

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Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared p

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Feng, Jian, Cailing Du, and Qi Mu. "Traffic Flow Prediction Based on Federated Learning and Spatio-Temporal Graph Neural Networks." ISPRS International Journal of Geo-Information 13, no. 6 (2024): 210. http://dx.doi.org/10.3390/ijgi13060210.

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In response to the insufficient consideration of spatio-temporal dependencies and traffic pattern similarity in traffic flow prediction methods based on federated learning, as well as the neglect of model heterogeneity and objective heterogeneity, a traffic flow prediction model based on federated learning and spatio-temporal graph neural networks is proposed. The model is divided into two stages. In the road network division stage, the traffic road network is divided into subnetworks by the dynamic time warping algorithm and the K-means algorithm, to ensure the same subnetwork has the similar

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Atkinson, Peter M. "Pierre Dutilleul: Spatio-temporal Heterogeneity: Concepts and Analyses." Mathematical Geosciences 46, no. 4 (2014): 513–15. http://dx.doi.org/10.1007/s11004-014-9528-z.

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Zhao, Bin, Gusheng Wu, Junjie Li, Qianhong Wu, and Min Deng. "Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence." Applied Sciences 14, no. 18 (2024): 8330. http://dx.doi.org/10.3390/app14188330.

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The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heterogeneity of land subsidence is not well considered, and (ii) the prediction performance of individual models is unsatisfactory when the data do not meet their assumptions. To address these issues, we developed a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. Firstly, a two-stage hybrid spatio-temporal cluster

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Ren, Siyuan, Bin Guo, Qinfen Wang, and Zhiwen Yu. "Non-IID spatio-temporal prediction in smart cities." XRDS: Crossroads, The ACM Magazine for Students 28, no. 3 (2022): 38–41. http://dx.doi.org/10.1145/3522692.

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Non-IID spatio-temporal prediction research points toward emerging directions and fundamental solutions to address various complexities from the perspective of both data couplings and heterogeneity. Delving into the non-IID challenge and opportunity of spatio-temporal prediction in smart cities, this article also addresses current solutions to bring some inspiration to future researchers.

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Zhang, Huibing, Qianxin Xie, Zhaoyu Shou, and Yunhao Gao. "Dynamic Spatial-Temporal Memory Augmentation Network for Traffic Prediction." Sensors 24, no. 20 (2024): 6659. http://dx.doi.org/10.3390/s24206659.

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Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogeneity. To better manage the spatio-temporal correlations inherent in traffic flow, we present a novel model called Dynamic Spatio-Temporal Memory-Augmented Network (DSTMAN). Firstly, we design three spatial–temporal embeddings to capture dynamic spatial–temporal contexts and encode the unique characteristics of time units and spatial states. Secondl

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Zhu, Jun. "Spatio‐Temporal Heterogeneity: Concepts and Analyses by DUTILLEUL, P.R.L." Biometrics 69, no. 2 (2013): 557–58. http://dx.doi.org/10.1002/biom.12055.

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Shi, Qiang, Wujiao Dai, Rock Santerre, Zhiwei Li, and Ning Liu. "Spatially Heterogeneous Land Surface Deformation Data Fusion Method Based on an Enhanced Spatio-Temporal Random Effect Model." Remote Sensing 11, no. 9 (2019): 1084. http://dx.doi.org/10.3390/rs11091084.

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The spatio-temporal random effect (STRE) model, a type of spatio-temporal Kalman filter model, can be used for the fusion of the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data to generate high spatio-temporal resolution deformation series, assuming that the land deformation is spatially homogeneous in the monitoring area. However, when there are multiple deformation sources in the monitoring area, complex spatial heterogeneity will appear. To improve the fusion accuracy, we propose an enhanced STRE fusion method (eSTRE) by taking spatial het

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Dou, Zeping, and Danhuai Guo. "DPSTCN: Dynamic Pattern-Aware Spatio-Temporal Convolutional Networks for Traffic Flow Forecasting." ISPRS International Journal of Geo-Information 14, no. 1 (2024): 10. https://doi.org/10.3390/ijgi14010010.

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Accurate forecasting of multivariate traffic flow poses formidable challenges, primarily due to the ever-evolving spatio-temporal dynamics and intricate spatial heterogeneity, where the heterogeneity signifies that the correlations among locations are not just related to distance. However, few of the existing models are designed to fully and effectively integrate the above-mentioned features. To address these complexities head-on, this paper introduces a novel solution in the form of Dynamic Pattern-aware Spatio-Temporal Convolutional Networks (DPSTCN). Temporally, the model introduces a novel

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Yuan, Haitao, Guoliang Li, and Zhifeng Bao. "Route Travel Time Estimation on a Road Network Revisited." Proceedings of the VLDB Endowment 16, no. 3 (2022): 393–405. http://dx.doi.org/10.14778/3570690.3570691.

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In this paper, we revisit the problem of route travel time estimation on a road network and aim to boost its accuracy by capturing and utilizing spatio-temporal features from four significant aspects: heterogeneity, proximity, periodicity and dynamicity. Spatial-wise, we consider two forms of heterogeneity at link level in a road network: the turning ways between different links are heterogeneous which can make the travel time of the same link various; different links contain heterogeneous attributes and thereby lead to different travel time. In addition, we take into account the proximity: ne

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Chen, Enhui, Zhirui Ye, and Hui Bi. "Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances." Sustainability 11, no. 24 (2019): 7069. http://dx.doi.org/10.3390/su11247069.

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The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial and temporal autocorrelation and heterogeneity issues among research zones have not been sufficiently addressed. A framework integrating geographically and temporally weighted regression (GTWR) and the Shannon entropy index (SEI) was thus proposed to investigate the spatio-temporal relationship between travel behaviors and built environment. An empi

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Wang, Hongxia, Zihan Zhao, Hongxia Hao, and Chao Huang. "Estimation and Inference for Spatio-Temporal Single-Index Models." Mathematics 11, no. 20 (2023): 4289. http://dx.doi.org/10.3390/math11204289.

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To better fit the actual data, this paper will consider both spatio-temporal correlation and heterogeneity to build the model. In order to overcome the “curse of dimensionality” problem in the nonparametric method, we improve the estimation method of the single-index model and combine it with the correlation and heterogeneity of the spatio-temporal model to obtain a good estimation method. In this paper, assuming that the spatio-temporal process obeys the α mixing condition, a nonparametric procedure is developed for estimating the variance function based on a fully nonparametric function or d

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Zhang, Yan, Bak Koon Teoh, Limao Zhang, and Jiayu Chen. "Spatio-temporal heterogeneity analysis of energy use in residential buildings." Journal of Cleaner Production 352 (June 2022): 131422. http://dx.doi.org/10.1016/j.jclepro.2022.131422.

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Banerjee, Bidisha, Dipanjan Bhattacharya, and G. V. Shivashankar. "Chromatin Structure Exhibits Spatio-Temporal Heterogeneity within the Cell Nucleus." Biophysical Journal 91, no. 6 (2006): 2297–303. http://dx.doi.org/10.1529/biophysj.105.079525.

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LAL, A., and S. HALES. "Heterogeneity in hotspots: spatio-temporal patterns in neglected parasitic diseases." Epidemiology and Infection 143, no. 3 (2014): 631–39. http://dx.doi.org/10.1017/s0950268814001101.

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SUMMARYCryptosporidiosis and giardiasis have been recognized by the WHO as ‘Neglected Diseases’. Minimal attention has been paid to the spatial and temporal distribution of disease incidence patterns. Using disease notification data, we detected spatio-temporal clusters of cryptosporidiosis and giardiasis across three time periods: (i) 1997–2000, (ii) 2001–2004, (iii) 2005–2008. There was substantial variation in the geographical location and timing of recurrent cryptosporidiosis and giardiasis clusters. Statistically significant (P < 0·05) giardiasis clusters tended to occur in predominant

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Chu, Hone-Jay, Bo Huang, and Chuan-Yao Lin. "Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship." Atmospheric Environment 102 (February 2015): 176–82. http://dx.doi.org/10.1016/j.atmosenv.2014.11.062.

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21

Vandekerkhove, Jochen, Steven Declerck, Erik Jeppesen, José Maria Conde-Porcuna, Luc Brendonck, and Luc De Meester. "Dormant propagule banks integrate spatio-temporal heterogeneity in cladoceran communities." Oecologia 142, no. 1 (2004): 109–16. http://dx.doi.org/10.1007/s00442-004-1711-3.

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Vandekerkhove, Jochen, Steven Declerck, Erik Jeppesen, José Maria Conde-Porcuna, Luc Brendonck, and Luc De Meester. "Dormant propagule banks integrate spatio-temporal heterogeneity in cladoceran communities." Oecologia 145, no. 1 (2005): 174. http://dx.doi.org/10.1007/s00442-005-0020-9.

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23

Shi, Baixi, Lijie Yu, Qi Yang, Na Zhang, and Nanxi Yang. "Spatio-temporal heterogeneity of metro ridership under major epidemic conditions." PLOS One 20, no. 6 (2025): e0326114. https://doi.org/10.1371/journal.pone.0326114.

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The COVID-19 epidemic has significantly altered travelers' behavior, therefore influenced how land use impacts subway ridership. This paper investigates these changes by employing a Geographically and Temporally Weighted Regression (GTWR) model to analyze the spatial and temporal impacts throughout the pandemic. The findings reveal that the outbreak notably reduced metro trip generation across all land use types except residential. Post-pandemic, the influence of workplace, park and green space, and educational land uses in the city center increased. Additionally, workplace land use in rapidly

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Wang, Haisong, Yuhuan Wu, and Ning Zhu. "Spatio-temporal heterogeneity of China’s import and export trade, factors influencing it, and its implications for developing countries’ trade." PLOS ONE 19, no. 4 (2024): e0300307. http://dx.doi.org/10.1371/journal.pone.0300307.

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This study constructed a multidimensional indicator system to evaluate spatio-temporal heterogeneity of China’s import and export trade of 31 provinces from 2000 to 2022. This study describes the distribution of China’s import and export trade by using location Gini coefficient and exploratory spatial analysis. Additionally, Multiple linear regression was used to ascertain the extent of contribution by various factors on the spatio-temporal heterogeneity of import and export trade. The simulation results show that inter-provincial import and export trade displayed distinct spatio-temporal diff

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UPADHYAY, RANJIT KUMAR, N. K. THAKUR, and V. RAI. "DIFFUSION-DRIVEN INSTABILITIES AND SPATIO-TEMPORAL PATTERNS IN AN AQUATIC PREDATOR–PREY SYSTEM WITH BEDDINGTON–DEANGELIS TYPE FUNCTIONAL RESPONSE." International Journal of Bifurcation and Chaos 21, no. 03 (2011): 663–84. http://dx.doi.org/10.1142/s0218127411028684.

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Predator–prey communities are building blocks of an ecosystem. Feeding rates reflect interference between predators in several situations, e.g. when predators form a dense colony or perform collective motion in a school, encounter prey in a region of limited size, etc. We perform spatio-temporal dynamics and pattern formation in a model aquatic system in both homogeneous and heterogeneous environments. Zooplanktons are predated by fishes and interfere with individuals of their own community. Numerical simulations are carried out to explore Turing and non-Turing spatial patterns. We also examin

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Lu, Jieqiong, and Geon-Seok Yang. "A Review of Research Methods for Coupling Land Use Efficiency and Spatio-Temporal Heterogeneity." Journal of Innovation and Development 4, no. 2 (2023): 93–99. http://dx.doi.org/10.54097/jid.v4i2.12181.

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This paper synthesizes research methods on the coupling of land use efficiency and spatio-temporal heterogeneity, aiming to provide insights into the diversity and important findings in this important field. Land use efficiency is a key factor in sustainable land management and resource planning, while spatio-temporal heterogeneity emphasizes the variability between different regions and points in time. The article explores the methods and applications of land use efficiency research from different perspectives. Remote sensing technology and geographic information systems (GIS) are considered

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Chang, Jiahao, Jiali Yin, Yanrong Hao, and Chengxin Gao. "STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast." Sensors 25, no. 11 (2025): 3446. https://doi.org/10.3390/s25113446.

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The characteristics of multivariate heterogeneity in traffic flow forecasting exhibit significant variation, heavily influenced by spatio-temporal dynamics and unforeseen events. To address this challenge, we propose a spatio-temporal fusion graph neural network based on dynamic sparse graph convolution GRU for traffic flow forecast (STFDSGCN), which incorporates a spatio-temporal attention fusion scheme with a gating mechanism. The dynamic sparse graph convolution gated recurrent unit (DSGCN-GRU) in this model is a novel component that integrates adaptive dynamic sparse graph convolution into

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Zhao, Zidong, Ruhai Ye, Yingyin Wang, and Yiming Tao. "How Plot Spatial Morphology Drives Surface Thermal Environment: A Spatial and Temporal Analysis of Nanjing Main City." Sustainability 15, no. 1 (2022): 383. http://dx.doi.org/10.3390/su15010383.

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Rapid urban development has changed urban substrate conditions, greatly affecting urban ecology and heating urban environment. Mitigating urban temperature rises by optimizing urban morphology is considered a promising approach; most studies ignore spatial and temporal heterogeneity. This study analyzes how plot spatial form influences urban thermal environment in the main Nanjing area from 2001, 2006, 2011, 2016, and 2021, based on geographically weighted regression models (spatio-temporal- and multi-scale). Results show that: 1. The formation of geothermal heat islands matches the direction

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Haworth, J., and T. Cheng. "A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2 (November 11, 2014): 7–12. http://dx.doi.org/10.5194/isprsarchives-xl-2-7-2014.

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Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of

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Amirah Halim, Aida, Bhismer Shakaran, and Nur Shameen Ezanee. "Spatio-temporal Analysis of Hand, Foot and Mouth Disease: A Systematic Review." International Journal of Science and Healthcare Research 8, no. 3 (2023): 455–64. http://dx.doi.org/10.52403/ijshr.20230360.

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Background: Hand, foot and mouth disease (HFMD) is a widespread pediatric disease caused primarily by human enterovirus 71 (EV-A71) and Coxsackievirus A16 (CV-A16). Over the past two decades, our understanding of HFMD has greatly improved and it has received significant attention. This study reports a systematic review of spatio-temporal analysis of HFMD. Materials and Methods: Systematic and comprehensive searches were conducted in the PubMed, Scopus, Web of Science, and Google Scholar. Four different issues related this issue are presented under four sub-sections; including the analysis of s

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Zhao, Shanchun, and Xu Li. "An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction." Journal of Advanced Transportation 2022 (April 9, 2022): 1–17. http://dx.doi.org/10.1155/2022/7682274.

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Accurate traffic prediction is a powerful factor of intelligent transportation systems to make assisted decisions. However, existing methods are deficient in modeling long series spatio-temporal characteristics. Due to the complex and nonlinear nature of traffic flow time series, traditional methods of prediction tasks tend to ignore the heterogeneity and long series dependencies of spatio-temporal data. In this paper, we propose an attentional encoder-decoder dual graph convolution model with time-series correlation (AED-DGCN-TSC) for solving the spatio-temporal sequence prediction problem in

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Li, Xuemei, Bo Zhang, Rui Ren, Lanhai Li, and Slobodan P. Simonovic. "Spatio-Temporal Heterogeneity of Climate Warming in the Chinese Tianshan Mountainous Region." Water 14, no. 2 (2022): 199. http://dx.doi.org/10.3390/w14020199.

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The Chinese Tianshan mountainous region (CTMR) is a typical alpine region with high topographic heterogeneity, characterized by a large altitude span, complex topography, and diverse landscapes. A significant increase in air temperature had occurred in the CTMR during the last five decades. However, the detailed, comprehensive, and systematical characteristics of climate warming, such as its temporal and spatial heterogeneity, remain unclear. In this study, the temporal and spatial heterogeneity of climate warming across the CTMR had been comprehensively analyzed based on the 10-day air temper

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Brancati, Giulio E., Chahinaz Rawas, Antoine Ghestem, Christophe Bernard, and Anton I. Ivanov. "Spatio-temporal heterogeneity in hippocampal metabolism in control and epilepsy conditions." Proceedings of the National Academy of Sciences 118, no. 11 (2021): e2013972118. http://dx.doi.org/10.1073/pnas.2013972118.

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The hippocampus’s dorsal and ventral parts are involved in different operative circuits, the functions of which vary in time during the night and day cycle. These functions are altered in epilepsy. Since energy production is tailored to function, we hypothesized that energy production would be space- and time-dependent in the hippocampus and that such an organizing principle would be modified in epilepsy. Using metabolic imaging and metabolite sensing ex vivo, we show that the ventral hippocampus favors aerobic glycolysis over oxidative phosphorylation as compared to the dorsal part in the mor

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Fournier, B., C. Guenat, G. Bullinger-Weber, and E. A. D. Mitchell. "Spatio-temporal heterogeneity of riparian soil morphology in a restored floodplain." Hydrology and Earth System Sciences 17, no. 10 (2013): 4031–42. http://dx.doi.org/10.5194/hess-17-4031-2013.

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Abstract. Floodplains have been intensively altered in industrialized countries, but are now increasingly being restored. It is therefore important to assess the effect of these restoration projects on the aquatic and terrestrial components of ecosystems. However, despite being functionally crucial components of terrestrial ecosystems, soils are generally overlooked in floodplain restoration assessments. We studied the spatio-temporal heterogeneity of soil morphology in a restored (riverbed widening) river reach along the River Thur (Switzerland) using three criteria (soil diversity, dynamism

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Fournier, B., C. Guenat, G. Bullinger-Weber, and E. A. D. Mitchell. "Spatio-temporal heterogeneity of riparian soil morphology in a restored floodplain." Hydrology and Earth System Sciences Discussions 10, no. 4 (2013): 4337–67. http://dx.doi.org/10.5194/hessd-10-4337-2013.

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Abstract. Floodplains have been intensively altered in industrialized countries, but are now increasingly being restored and it is therefore important to assess the effect of these restoration projects on the aquatic and terrestrial components of ecosystems. Soils are a functionally crucial component of terrestrial ecosystems but are generally overlooked in floodplain restoration assessment. We studied the spatio-temporal heterogeneity of soil morphology in a restored (riverbed widening) river reach along River Thur (Switzerland) using three criteria (soil diversity, dynamism and typicality) a

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Molnár, Zsolt. "Perception and Management of Spatio-Temporal Pasture Heterogeneity by Hungarian Herders." Rangeland Ecology & Management 67, no. 2 (2014): 107–18. http://dx.doi.org/10.2111/rem-d-13-00082.1.

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Liu, Shuangshuang, Qipeng Liao, Yuan Liang, Zhifei Li, and Chunbo Huang. "Spatio–Temporal Heterogeneity of Urban Expansion and Population Growth in China." International Journal of Environmental Research and Public Health 18, no. 24 (2021): 13031. http://dx.doi.org/10.3390/ijerph182413031.

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Urbanization has become one of the hot issues of global sustainable development, and is mainly characterized by urban population growth and construction land expansion. However, the inharmonious development of urban expansion and population migration has brought serious challenges to urban planning and management. China is the largest developing country in the world, and the urbanization process has accelerated over the past decades. In this paper, decoupling analysis was used to demonstrate the spatio–temporal relationship between urban expansion and population growth in 321 prefecture–level

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Seuront, Laurent, and Yvan Lagadeuc. "Spatio-temporal structure of tidally mixed coastal waters: variability and heterogeneity." Journal of Plankton Research 20, no. 7 (1998): 1387–401. http://dx.doi.org/10.1093/plankt/20.7.1387.

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Pearce, Ian G., Mark A. J. Chaplain, Pietà G. Schofield, Alexander R. A. Anderson, and Stephen F. Hubbard. "Chemotaxis-induced spatio-temporal heterogeneity in multi-species host-parasitoid systems." Journal of Mathematical Biology 55, no. 3 (2007): 365–88. http://dx.doi.org/10.1007/s00285-007-0088-4.

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Jakoby, O., M. F. Quaas, S. Baumgärtner, and K. Frank. "Adapting livestock management to spatio-temporal heterogeneity in semi-arid rangelands." Journal of Environmental Management 162 (October 2015): 179–89. http://dx.doi.org/10.1016/j.jenvman.2015.07.047.

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Zhu, Rui, Xiaohu Zhang, Dániel Kondor, Paolo Santi, and Carlo Ratti. "Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility." Computers, Environment and Urban Systems 81 (May 2020): 101483. http://dx.doi.org/10.1016/j.compenvurbsys.2020.101483.

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Silvin, Aymeric, and Florent Ginhoux. "Microglia heterogeneity along a spatio-temporal axis: More questions than answers." Glia 66, no. 10 (2018): 2045–57. http://dx.doi.org/10.1002/glia.23458.

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Busch, Karin B., and Jimmy Villalta Villalobus. "Spatio-temporal heterogeneity of mitochondria during differentiation of hiPSC-derived neurons." Biophysical Journal 123, no. 3 (2024): 164a—165a. http://dx.doi.org/10.1016/j.bpj.2023.11.1101.

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Tian, Mingming, Jingqiao Mao, Kang Wang, and Diandian Xu. "Spatio-temporal heterogeneity of ecological water level in Poyang Lake, China." Ecological Informatics 82 (September 2024): 102694. http://dx.doi.org/10.1016/j.ecoinf.2024.102694.

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Wei, Chunzhu, Xufeng Liu, Wei Chen, Lupan Zhang, Ruixia Chao, and Wei Wei. "How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China." Land 14, no. 1 (2024): 59. https://doi.org/10.3390/land14010059.

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Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across differ

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Jiménez-Sánchez, Juan, Álvaro Martínez-Rubio, Anton Popov, et al. "A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors." PLOS Computational Biology 17, no. 2 (2021): e1008266. http://dx.doi.org/10.1371/journal.pcbi.1008266.

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Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of ma

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Azovsky, A. I. "Concept of scale in marine ecology: linking the words or the worlds?" Web Ecology 1, no. 1 (2000): 28–34. http://dx.doi.org/10.5194/we-1-28-2000.

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Abstract. The concept of scale (in sensu lato) is considered to be very promising as the integrative basis for modern ecology. Nowadays it is not a full-blown theory but rather a flexible and progressively developing methodology to outline future unifying theories. It provides a powerful conceptual framework for generating testable hypotheses and studying a wide range of ecological phenomena related with such themes as heterogeneity, hierarchy and size. Spatio-temporal heterogeneity, organizational hierarchies and body size are the main scaling factors for ecological patterns and processes. Br

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Zhang, Jingxue, Yanchao Feng, and Ziyi Zhu. "Spatio-Temporal Heterogeneity of Carbon Emissions and Its Key Influencing Factors in the Yellow River Economic Belt of China from 2006 to 2019." International Journal of Environmental Research and Public Health 19, no. 7 (2022): 4185. http://dx.doi.org/10.3390/ijerph19074185.

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The Yellow River Economic Belt (YREB) performs an essential function in the low-carbon development of China as an important ecological protection barrier, and it is of great importance to identify its spatio-temporal heterogeneity and key influencing factors. In this study, we propose a comprehensively empirical framework to conduct this issue. The STIRPAT model was applied to determine the influencing factors of carbon emissions in the YREB from 2006 to 2019. The results show that the carbon emissions in the YREB had significant clustering characteristics in the spatial auto-correlation analy

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Ma, Le, and Chunlu Liu. "International Real Estate Review." International Real Estate Review 18, no. 4 (2015): 503–21. http://dx.doi.org/10.53383/100211.

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In order to explore the long-run equilibrium in the house prices of different cities, studies on house price convergence have been conducted by a number of researchers. However, the majority of previous studies have neglected the effects of spatial heterogeneity and autocorrelation on house prices. This research improves on the investigation of house price convergence by developing a spatio-temporal autoregressive model based on a framework of panel regression methods. Both spatial heterogeneity and autocorrelation of house prices in different cities are taken into account. Geographical distan

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Wiegand, Thorsten, Felix May, Martin Kazmierczak, and Andreas Huth. "What drives the spatial distribution and dynamics of local species richness in tropical forest?" Proceedings of the Royal Society B: Biological Sciences 284, no. 1863 (2017): 20171503. http://dx.doi.org/10.1098/rspb.2017.1503.

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Understanding the structure and dynamics of highly diverse tropical forests is challenging. Here we investigate the factors that drive the spatio-temporal variation of local tree numbers and species richness in a tropical forest (including 1250 plots of 20 × 20 m 2 ). To this end, we use a series of dynamic models that are built around the local spatial variation of mortality and recruitment rates, and ask which combination of processes can explain the observed spatial and temporal variation in tree and species numbers. We find that processes not included in classical neutral theory are needed

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