Journal articles: 'Spatio-Temporal heterogeneity'

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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 (March 27, 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 here using a hypertemporal Sea Surface Temperature image dataset of the North Atlantic. Validation with Underway-derived temperature data showed higher heterogeneity areas were associated with stronger surface temperature gradients, or an increased presence of locally extreme temperature values. Using four exploratory case studies, spatio-temporal heterogeneity values were related to a range of region-specific surface and sub-surface characteristics including fronts, currents and bathymetry. The values conveyed the interactions between these parameters as a single metric. Such over-arching heterogeneity information is virtually impossible to map from in-situ instruments, or less temporally dense satellite datasets. This study demonstrated the OHMA approach is a useful and robust tool to explore, examine, and describe the ocean’s surface. It advances our capability to map biologically relevant measures of ocean surface heterogeneity. It can support ongoing efforts in Ocean Surface Partitioning, and attempts to understand marine species distributions. The study highlighted the need to establish dedicated spatio-temporal ocean validation sites, specifically measured using surface transits, to support advances in hypertemporal ocean data use, and exploitation. A number of future research avenues are also highlighted.

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Ji, Jiahao, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, and Yu Zheng. "Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 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 parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.

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Atkinson, Peter M. "Pierre Dutilleul: Spatio-temporal Heterogeneity: Concepts and Analyses." Mathematical Geosciences 46, no. 4 (March 21, 2014): 513–15. http://dx.doi.org/10.1007/s11004-014-9528-z.

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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 (March 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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Zhu, Jun. "Spatio‐Temporal Heterogeneity: Concepts and Analyses by DUTILLEUL, P.R.L." Biometrics 69, no. 2 (June 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 (May 7, 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 heterogeneity into consideration. This new method integrates the spatial heterogeneity constraints in the STRE model by constructing extra-constrained spatial bases for the heterogeneous area. The effectiveness of this method is verified by using simulated data and real land surface deformation data. The results show that eSTRE can reduce the root mean square (RMS) of InSAR interpolation results by 14% and 23% on average for a simulation experiment and Los Angeles experiment, respectively, indicating that the new proposed method (eSTRE) is substantially better than the previous STRE fusion model.

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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 (November 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: neighboring links have similar traffic patterns and lead to similar travel speeds. To this end, we build a link-connection graph to capture such heterogeneity and proximity. Temporal-wise, the weekly/daily periodicity of temporal background information (e.g., rush hours) and dynamic traffic conditions have significant impact on the travel time, which result in static and dynamic spatio-temporal features respectively. To capture such impacts, we regard the travel time/speed as a combination of static and dynamic parts, and extract many spatio-temporal relevant features for the prediction task. Talking about the methodology, it remains an open problem to build a generic learning model to boost the estimation accuracy. Hence, we design a novel encoder-decoder framework - The encoder uses the sequence attention model to encode dynamic features from the temporal-wise perspective. The decoder first uses the heterogeneous graph attention model to decode the static part of travel speed based on static spatio-temporal features, and then leverages the sequence attention model to decode the estimated travel time from spatial-wise perspective. Extensive experiments on real datasets verify the superiority of our method as well as the importance of the four aspects outlined above.

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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 (December 10, 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 empirical study was conducted in Nanjing, China, by incorporating smart card data with metro route data and built environment data. Comparative results show GTWR had a better performance of goodness-of-fit and achieved more accurate predictions, compared to traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR). The spatio-temporal relationship between travel distances and built environment was further analyzed by visualizing the average variation of local coefficients distributions. Effects of built environment variables on metro travel distances were heterogeneous over space and time. Non-commuting activity and exurban area generally had more influences on the heterogeneity of travel distances. The proposed framework can address the issue of spatio-temporal autocorrelation and enhance our understanding of impacts of built environment on travel behaviors, which provides useful guidance for transit agencies and planning departments to implement targeted investment policies and enhance public transit services.

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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 (September 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 (May 12, 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 predominantly urban areas with little apparent seasonal influence, while statistically significant cryptosporidiosis clusters were detected in spring, in areas with high livestock land use. The location and timing of cryptosporidiosis clusters suggest an influence of livestock production practices, while urban exposures and host behaviour are likely to influence giardiasis clusters. This approach provides a resource-efficient method for public health authorities to prioritize future research needs and areas for intervention.

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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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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 (September 17, 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 (June 30, 2005): 174. http://dx.doi.org/10.1007/s00442-005-0020-9.

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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 (October 14, 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 dimensional reduction structure, and the resulting estimator is consistent. Then, a reweighting estimation of the parametric component can be obtained via taking the estimated variance function into account. The rate of convergence and the asymptotic normality of the new estimators are established under mild conditions. Simulation studies are conducted to evaluate the efficacy of the proposed methodologies, and a case study about the estimation of the air quality evaluation index in Nanjing is provided for illustration.

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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 (April 18, 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 differentiation characteristics with a prominent east-to-west disparity from 2000 to 2022. The trade links between various regions of the country have gradually strengthened, with a corresponding high correlation to the level of economic development. GDP, financial expenditure, freight transportation volume, technology market turnover, foreign investment, and disposable income of all residents, significantly influence the per capita export and import volume. In general, it is suggested that China and developing countries should take effective measures to promote balanced trade development, strengthen regional cooperation and coordination, and promote green trade and sustainable development.

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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 (March 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 examine the effect of spatial heterogeneity on the spatio-temporal dynamics of the phytoplankton–zooplankton system. The phytoplankton specific growth rate is assumed to be a linear function of the depth of the water body. It is found that the spatio-temporal dynamics of an aquatic system is governed by three important factors: (i) intensity of interference between the zooplankton, (ii) rate of fish predation and (iii) the spatial heterogeneity. In an homogeneous environment, the temporal dynamics of prey and predator species are drastically different. While prey species density evolves chaotically, predator densities execute a regular motion irrespective of the intensity of fish predation. When the spatial heterogeneity is included, the two species oscillate in unison. It has been found that the instability observed in the model aquatic system is diffusion driven and fish predation acts as a regularizing factor. We also observed that spatial heterogeneity stabilizes the system. The idea contained in the paper provides a better understanding of the pattern formation in aquatic systems.

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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 (August 28, 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 two pillars of land use efficiency research, which complement each other but also have their own applicability and limitations. Remote sensing technology can provide large-scale and multi-scale land use information, but with limited resolution, while GIS is suitable for spatial analysis of land use and integration of multiple geographic data, but relies on accurate datasets. Time series analysis and spatial analysis modeling are key methods in land use change studies. Time series analysis is used to reveal historical trends and future projections of land use, while spatial analysis models emphasize the spatial linkages between land uses. These two methods have their own focus and can be selected and combined according to the research questions. The study of land use efficiency and spatio-temporal heterogeneity needs to consider its important impact on the environment. Good land management can protect ecosystems, combat climate change and maintain resource sustainability. In addition, these studies provide guidance for practical applications of sustainable land management and resource planning, including urban planning, agricultural improvement, ecosystem protection, and climate change adaptation. However, land use efficiency and spatial and temporal heterogeneity studies suffer from limitations and uncertainties such as data uncertainty, data availability, and modeling assumptions. Future research could explore more integrated approaches, especially in the context of climate change and socioeconomic factors. An in-depth study of the variability and best practices in different regions can help improve the scientific basis for land management decisions. This paper summarizes the coupled research methods on land use efficiency and spatio-temporal heterogeneity and their importance. These studies provide certain references for sustainable land management and resource planning, and help to realize sustainable resource utilization, environmental protection, and sustainable socio-economic development.

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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 (December 26, 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 of urban expansion, mainly due to changes in land substrate; 2. the spatio-temporal model performs best, indicating that urban morphology and surface thermal environment have obvious spatio-temporal heterogeneity; obvious scale differences exist in each index influencing the heat island effect; and 3. floor area ratio (FAR) and building density (BD) negatively and positively correlate with surface thermal conditions, with gradually increasing effect, respectively. Normalized difference vegetation index (NDVI) and distance from the nearest water body (Dis_W) negatively and positively correlate with surface thermal conditions separately; good ecological infrastructure reduces surface temperatures but shows a gradually weakening effect. Proximity to roads is associated with warmer thermal environment. This study elucidates how urban form influences surface thermal environments and suggests measures to reduce surface temperatures in the main urban Nanjing area.

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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 heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London’s road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on L<sub>1</sub> penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.

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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 (March 10, 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 morning in control mice. In the afternoon, aerobic glycolysis is decreased and oxidative phosphorylation increased. In the dorsal hippocampus, the metabolic activity varies less between these two times but is weaker than in the ventral. Thus, the energy metabolism is different along the dorsoventral axis and changes as a function of time in control mice. In an experimental model of epilepsy, we find a large alteration of such spatiotemporal organization. In addition to a general hypometabolic state, the dorsoventral difference disappears in the morning, when seizure probability is low. In the afternoon, when seizure probability is high, the aerobic glycolysis is enhanced in both parts, the increase being stronger in the ventral area. We suggest that energy metabolism is tailored to the functions performed by brain networks, which vary over time. In pathological conditions, the alterations of these general rules may contribute to network dysfunctions.

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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 (October 17, 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 and typicality) and their associated indicators. We hypothesized that these criteria would correctly discriminate the post-restoration changes in soil morphology, and that these changes correspond to patterns of vascular plant diversity. Soil diversity and dynamism increased 5 yr after the restoration, but some typical soils of braided rivers were still missing. Soil typicality and dynamism were correlated to vegetation changes. These results suggest a limited success of the project, in agreement with evaluations carried out at the same site using other, more resource-demanding, methods (e.g., soil fauna, fish diversity, ecosystem functioning). Soil morphology provides structural and functional information on floodplain ecosystems. The spatio-temporal heterogeneity of soil morphology represents a cost-efficient ecological indicator that could easily be integrated into rapid assessment protocols of floodplain and river restoration projects. The follow-up assessment after several major floods (≥ HQ20) should take place to allow for testing the longer-term validity of our conclusion for the River Thur site. More generally, it would be useful to apply the soil morphology indicator approach in different settings to test its broader applicability.

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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 (April 5, 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) and their associated indicators. We hypothesized that these criteria would correctly discriminate the post-restoration changes in soil morphology within the study site, and that these changes correspond to patterns of vascular plant diversity. Soil diversity and dynamism increased five years after the restoration, but typical soils of braided rivers were still missing. Soil typicality and dynamism correlated to vegetation changes. These results suggest a limited success of the project in agreement with evaluations carried out at the same site using other, more resource demanding methods (e.g. soil fauna, fish, ecosystem functioning). Soil morphology provides structural and functional information on floodplain ecosystems and allows predicting broad changes in plant diversity. The spatio-temporal heterogeneity of soil morphology represents a cost-efficient ecological indicator that could easily be integrated into rapid assessment protocols of floodplain and river restoration projects.

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Molnár, Zsolt. "Perception and Management of Spatio-Temporal Pasture Heterogeneity by Hungarian Herders." Rangeland Ecology & Management 67, no. 2 (March 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 (December 10, 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 cities in China, providing a reference basis for sustainable development. The results showed that China’s population, total GDP, and construction land area increased from 1990 to 2018. The rate of construction land expansion was larger in the eastern coastal and western regions than in the northeastern and central regions, but the population growth rate was not significantly different among these regions. According to the decoupling analysis, the relationships of population–GDP, construction land–GDP, and population–construction land were mainly weak decoupling, indicating that both the population growth and the construction land expansion lagged behind the economic development, and the population growth lagged behind construction land expansion. In addition, the results were analyzed based on China’s four economic regions. Population and construction land area changes in the northeastern provinces experienced a shift from weak decoupling to expansive negative decoupling, then presented a strong decoupling. The decoupling state of population–construction land in the west region was relatively stable. The relationship between population and construction land in the central regions was mainly weak decoupling, and some cities developed into strong decoupling. The relationship between population and construction land in the east region experienced a shift from strong decoupling to weak decoupling, then demonstrated expansive negative decoupling, mainly manifested in the Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations. Therefore, the northeast region should take measures to promote regional population growth while reasonably controlling the expansion of construction land, the west region should focus on ecological protection and moderately attract population, the central region should control their population development and reasonably allocate land, and the east region should pay attention to and solve the citizenship problem of migrant workers in second–tier and third–tier cities when promoting new urbanization.

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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 (April 14, 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 (August 25, 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 (February 2024): 164a—165a. http://dx.doi.org/10.1016/j.bpj.2023.11.1101.

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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 (January 11, 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 temperature data gathered during 1961–2020 from 26 meteorological stations. The results revealed local cooling in the context of general warming in the CTMR. The amplitude of variation (AV) varied from −0.57 to 3.64 °C, with the average value of 1.19 °C during the last six decades. The lapse rates of the elevation-dependent warming that existed annually, and in spring, summer, and autumn are −0.5 °C/100 m, −0.5 °C/100 m, −0.7 °C/100 m, and −0.4 °C/100 m, respectively. The warming in the CTMR is characteristic of high temporal heterogeneity, as represented by the amplified warming at 10-d scale for more than half a year, and the values of AV were higher than 1.09 °C of the global warming during 2011–2020 (GWV2011–2020). Meanwhile, the amplitudes of warming differed greatly on a seasonal scale, with the rates in spring, autumn, and winter higher than that in summer. The large spatial heterogeneity of climate warming also occurred across the CTMR. The warming pole existed in the warm part, the Turpan-Hami basin (below 1000 m asl) where the air temperature itself was high. That is, the warm places were warmer across the CTMR. The cooling pole was also found in the Kuqa region (about 1000 m asl). This study could greatly improve the understanding of the spatio-temporal dynamics, patterns, and regional heterogeneity of climate warming across the CTMR and even northwest China.

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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 (October 2, 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 spatial-temporal clustering, hotspot and coldspot, spatial-temporal heterogeneity and risk factors Results: It was found from 64 articles that spatio-temporal analysis can effectively support the study of HFMD such as understanding of spatial patterns, hotspot, the spread and affecting factors. Conclusion: The spatial-temporal analysis can provide important information and contribute to development of effective measurements to control and prevent its transmission. The study contributes to current research on the spread and affecting factor of HFMD. Keywords: Spatio-temporal analysis, Spatial autocorrelation, GIS, Spatial patterns, Hand, foot and mouth disease, Review.

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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 the traffic domain. First, the time-series correlation module calculates the sequence similarity by fast Fourier transform and inverse fast Fourier transform, while obtaining multiple possible lengths as possible solutions for the sequence period length. Then, K possible periods fetches are selected and the corresponding sequences are weighted and aggregated to the target sequence. Then, the gated dual graph convolution recurrent unit uses the graph convolution operation, which combines the ideas of node embedding, and dual graph, as an operation inside the gated recurrent structure to capture the spatio-temporal heterogeneity relationship of long sequences. The gated decomposition recurrent module decomposes the time series into the period and trend terms, which are modelled by convolutional gated recurrent unit (ConvGRU) and then fused with features, respectively, and output after graph convolution. Finally, multi-step prediction of future traffic flow is performed in the form of encoder-decoder. Experimental evaluations are conducted on two real traffic datasets, and the results demonstrate the effectiveness of the proposed model.

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Jiménez-Sánchez, Juan, Álvaro Martínez-Rubio, Anton Popov, Julián Pérez-Beteta, Youness Azimzade, David Molina-García, Juan Belmonte-Beitia, Gabriel F. Calvo, and Víctor M. Pérez-García. "A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors." PLOS Computational Biology 17, no. 2 (February 10, 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 macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.

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Azovsky, A. I. "Concept of scale in marine ecology: linking the words or the worlds?" Web Ecology 1, no. 1 (April 14, 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. Broad comparison of patterns for these three different but interrelated dimensions can reveal some new regularities ("scaling laws") of ecological systems. It also allows us to look at the worlds of different organisms "through their own eyes". Some examples of applying the cross-scaling approach in marine ecology are considered: &#151; Patterns and scales of spatial heterogeneity; &#151; Species-area curves and body size; &#151; Co-occurrence of congeners as scale-dependent phenomenon; &#151; Spatio-temporal ranges of ecological hierarchies.

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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 (March 31, 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 analysis. In addition, the estimation results of the spatial panel analysis demonstrate that the carbon emissions showed a distinct spatial lag effect and temporal lag effect. Moreover, the three traditional factors including population, affluence, technology are identified as the key influencing factors of carbon emissions in the YREB of China. Furthermore, the spatio-temporal heterogeneity is illustrated vividly by employing the GTWR-STIRPAT model. Finally, policy implications are provided to respond to the demand for low-carbon development.

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Ma, Le, and Chunlu Liu. "International Real Estate Review." International Real Estate Review 18, no. 4 (December 31, 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 distance and the scale of development of the urban housing market are used to construct temporal varying spatial measurements. The spatio-temporal model is then applied to investigate the long-run equilibrium in the house prices of Australian capital cities. The results confirm that house prices in Sydney approach a steady state in the long run, whereas house prices in Brisbane, Canberra, Melbourne and Perth are able to do with lower confidence. However, little evidence supports the existence of long-run equilibrium in the house prices of Adelaide, Darwin and Hobart.

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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 (September 20, 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 to explain these fundamental patterns of the observed local forest dynamics. We identified a large spatio-temporal variability in the local number of recruits as the main missing mechanism, whereas variability of mortality rates contributed to a lesser extent. We also found that local tree numbers stabilize at typical values which can be explained by a simple analytical model. Our study emphasized the importance of spatio-temporal variability in recruitment beyond demographic stochasticity for explaining the local heterogeneity of tropical forests.

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Jiang, Renhe, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, and Toyotaro Suzumura. "Spatio-Temporal Meta-Graph Learning for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8078–86. http://dx.doi.org/10.1609/aaai.v37i7.25976.

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Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

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Ma, Le, and Chunlu Liu. "SPATIO-TEMPORAL ANALYSIS OF HOUSE PRICE CONVERGENCE BASED ON A DEMOGRAPHICAL DISTANCE." International Journal of Strategic Property Management 17, no. 3 (September 23, 2013): 263–77. http://dx.doi.org/10.3846/1648715x.2013.822031.

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Convergences of house prices have been studied for over three decades, but yet have been confirmed because of spatial heterogeneity and autocorrelations in house prices. A spatio-temporal approach was recently proposed to address the spatial and temporal issues related to house prices. However, most previous studies placed the focus on the spatial heterogeneity and autocorrelations from geographical locations, which neglected other spatial factors. In order to overcome this shortfall, this research argued a demographical distance, constructed by demographical structure and housing market scales, to investigate the house price convergences in Australian capital cities. The results confirmed the house price levels in Canberra, Brisbane and Perth converged to the house price level in Sydney.

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Rumisha, Susan Fred, Thomas Smith, Salim Abdulla, Honorath Masanja, and Penelope Vounatsou. "Modelling heterogeneity in malaria transmission using large sparse spatio-temporal entomological data." Global Health Action 7, no. 1 (June 24, 2014): 22682. http://dx.doi.org/10.3402/gha.v7.22682.

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del Carmen, Sofía, José María Sayagués, Oscar Bengoechea, María Fernanda Anduaga, Jose Antonio Alcazar, Ruth Gervas, Jacinto García, et al. "Spatio-temporal tumor heterogeneity in metastatic CRC tumors: a mutational-based approach." Oncotarget 9, no. 76 (September 28, 2018): 34279–88. http://dx.doi.org/10.18632/oncotarget.26081.

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Questad, Erin J., and Bryan L. Foster. "Coexistence through spatio-temporal heterogeneity and species sorting in grassland plant communities." Ecology Letters 11, no. 7 (July 2008): 717–26. http://dx.doi.org/10.1111/j.1461-0248.2008.01186.x.

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González-Megías, Adela, José María Gómez, and Francisco Sánchez-Piñero. "Spatio-temporal change in the relationship between habitat heterogeneity and species diversity." Acta Oecologica 37, no. 3 (May 2011): 179–86. http://dx.doi.org/10.1016/j.actao.2011.01.011.

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Park, Kinam. "Spatio-temporal heterogeneity in tumor liposome uptake: Characterization of macro- and microdistribution." Journal of Controlled Release 207 (June 2015): 164. http://dx.doi.org/10.1016/j.jconrel.2015.04.043.

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Carrivick, Jonathan L., Lee E. Brown, David M. Hannah, and Andy G. D. Turner. "Numerical modelling of spatio-temporal thermal heterogeneity in a complex river system." Journal of Hydrology 414-415 (January 2012): 491–502. http://dx.doi.org/10.1016/j.jhydrol.2011.11.026.

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Dementyev, A. N., A. N. Novikov, K. V. Arsenyev, A. N. Kurkin, А. O. Zhukov, and I. N. Kartsan. "Mathematical model of spatial and temporal processing of broadband signals in satellite radio systems of broadband access and radio navigation." Spacecrafts & Technologies 7, no. 1 (March 24, 2023): 68–74. http://dx.doi.org/10.26732/j.st.2023.1.08.

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To effectively receive a useful broadband signal, it is necessary to compensate for distortions caused by the complex frequency-dependent coefficient of the receiving and transmitting antenna, multiplicative interference and noise from the heterogeneity of the propagation medium, additive interference and noise. Using only correlation processing under certain conditions will not allow receiving a useful broadband signal with the required quality. The article develops a mathematical model of spatio-temporal processing of broadband signals in satellite broadband access systems, showing that spatio-temporal processing should be carried out in two stages. At the first stage, space-time processing is carried out in the channel adaptively to the antenna array based on the formation of a complex frequency-dependent vector of weighting coefficients, which should change with a change in the signal-interference situation in real time, taking into account the change in the direction of radiation sources. At the second stage, correlation processing of the broadband signal is performed based on the use of long-length code sequences. This model is the basis for the development of methods for the formation and correlation (temporal) processing of broadband signals and methods of spatio-temporal processing of broadband signals under conditions of intentional and unintentional exposure. Based on the presented mathematical model of spatio-temporal processing of broadband signals, the main directions of increasing the noise immunity of radio-electronic systems are determined.

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Ozturk, Derya. "Fractal analysis of spatio-temporal changes of forest cover in Istanbul, Turkey." Acta geographica Slovenica 62, no. 1 (June 21, 2022): 7–20. http://dx.doi.org/10.3986/ags.10206.

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In this study, the spatio-temporal changes in forest cover in Istanbul, one of the provinces with the most changes in forest areas in Turkey due to the pressure of urbanization and industrialization, were investigated using fractal analysis. The areal changes and changes in spatial patterns were determined to assess the spatio-temporal changes in the period 2000–2017. Fragmentation/compactness and heterogeneity/homogeneity of forest cover were determined by fractal dimension and lacunarity index, respectively. The results show that the forest areas have significantly decreased and become more fragmented and heterogeneous. In conclusion, this study reveals that fractal analysis can provide considerable information in the examination and interpretation of spatial changes in forest areas.

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Lu, Jingfang, Xianqing Lv, and Honghua Shi. "Spatio-Temporal Heterogeneity and Cumulative Ecological Impacts of Coastal Reclamation in Coastal Waters." Remote Sensing 15, no. 6 (March 8, 2023): 1495. http://dx.doi.org/10.3390/rs15061495.

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The coastal reclamation, as one of the most extreme transformations of the ocean space by humans, still lacks scientific quantitative evaluating methods to a large extent, compared with the evolution of land use patterns. A cumulative ecological impacts of reclamation (RCEI) was established in our study based on ecological influence characteristics of different reclamation types, and the attenuation effect of reclamation on adjacent areas. It was characterized by spatio-temporal features in decades. Here, we estimated that the cumulative reclamation area in the Bohai Sea from 1985 to 2018 was 5839.5 km2. Under the influence of human activity, proportions of the industrial and urban boundary, marine construction boundaries (e.g., ports, wharves, and bridges), and protective dams were increased significantly, which led to a sharp increase of the RCEI. In addition, spatio-temporal changes of reclamation were affected by the combination of population growth, economic development, urbanization, industrialization, and marine industry development in coastal cities. These results provided an important historical reference for tracking future development of the Bohai Sea by humans and provided basic data support for the development and protection of the ocean.

Journal articles on the topic 'Spatio-Temporal heterogeneity'

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