Journal articles: 'Spatio-temporal space'

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Guesgen, Hans W., and Stephen Marsland. "Spatio-Temporal Footprints." International Journal of Ambient Computing and Intelligence 2, no. 1 (January 2010): 52–58. http://dx.doi.org/10.4018/jaci.2010010104.

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The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this paper, we introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. We discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used.

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Liou, Shih-Ping, and Ramesh C. Jain. "Motion detection in spatio-temporal space." Computer Vision, Graphics, and Image Processing 45, no. 1 (January 1989): 131. http://dx.doi.org/10.1016/0734-189x(89)90079-0.

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Liou, Shih-Ping, and Ramesh C. Jain. "Motion detection in spatio-temporal space." Computer Vision, Graphics, and Image Processing 45, no. 2 (February 1989): 227–50. http://dx.doi.org/10.1016/0734-189x(89)90134-5.

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Baffelli, Erica, and Frederik Schröer. "Spatio-Temporal Translations." Anthropology in Action 28, no. 1 (March 1, 2021): 57–62. http://dx.doi.org/10.3167/aia.2021.280111.

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During the COVID-19 pandemic, access to space has been strictly regulated and restricted. Many of us feel acutely disconnected from our relationships, while at the same time new forms of (virtual) intimacies have become ubiquitous. In the pandemic present, nearly all interpersonal relations are now characterised by a double absence that is concrete and material, and also emotional and felt. This article offers a theoretical reflection on how conditions of absence create new practices of intimacy and new strategies of coping. It does so by discussing how pre-pandemic emotional repertoires are translated into new forms of intimacy that can synchronise or throw out of sync. It highlights the centrality of spatial and temporal relations under absence in uncovering new mediated practices.

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Ma, Chunsheng. "Spatio-temporal variograms and covariance models." Advances in Applied Probability 37, no. 3 (September 2005): 706–25. http://dx.doi.org/10.1239/aap/1127483743.

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Variograms and covariance functions are the fundamental tools for modeling dependent data observed over time, space, or space-time. This paper aims at constructing nonseparable spatio-temporal variograms and covariance models. Special attention is paid to an intrinsically stationary spatio-temporal random field whose covariance function is of Schoenberg-Lévy type. The correlation structure is studied for its increment process and for its partial derivative with respect to the time lag, as well as for the superposition over time of a stationary spatio-temporal random field. As another approach, we investigate the permissibility of the linear combination of certain separable spatio-temporal covariance functions to be a valid covariance, and obtain a subclass of stationary spatio-temporal models isotropic in space.

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Ma, Chunsheng. "Spatio-temporal variograms and covariance models." Advances in Applied Probability 37, no. 03 (September 2005): 706–25. http://dx.doi.org/10.1017/s0001867800000434.

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Variograms and covariance functions are the fundamental tools for modeling dependent data observed over time, space, or space-time. This paper aims at constructing nonseparable spatio-temporal variograms and covariance models. Special attention is paid to an intrinsically stationary spatio-temporal random field whose covariance function is of Schoenberg-Lévy type. The correlation structure is studied for its increment process and for its partial derivative with respect to the time lag, as well as for the superposition over time of a stationary spatio-temporal random field. As another approach, we investigate the permissibility of the linear combination of certain separable spatio-temporal covariance functions to be a valid covariance, and obtain a subclass of stationary spatio-temporal models isotropic in space.

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Jago, Roland, Raül Perea-Causin, Samuel Brem, and Ermin Malic. "Spatio-temporal dynamics in graphene." Nanoscale 11, no. 20 (2019): 10017–22. http://dx.doi.org/10.1039/c9nr01714c.

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van Inwagen, Peter. "Modes of Being and Quantification." Disputatio 6, no. 38 (May 1, 2014): 1–24. http://dx.doi.org/10.2478/disp-2014-0001.

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Abstract If Pegasus existed, he would indeed be in space and time, but only because the word ‘Pegasus’ has spatio-temporal connotations, and not because ‘exists’ has spatio-temporal connotations. If spatio-temporal reference is lacking when we affirm the existence of the cube root of 27, that is simply because a cube root is not a spatio-temporal kind of thing.

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Song, Bingbing, Yanlin Wang, and Fang Li. "The Visualization Representation of Space-Time-Path in The Space-Time-Cube." IOP Conference Series: Earth and Environmental Science 906, no. 1 (November 1, 2021): 012030. http://dx.doi.org/10.1088/1755-1315/906/1/012030.

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Abstract Map is a traditional visualization tool to represent distribution and interaction of spatial objects or spatial phenomenon. However, with the continuous development of acquisition and processing technologies for spatio-temporal data, traditional map can hardly meet the visualization requirement for this type of data. In other words, the dynamic information about spatial object or phenomenon cannot be expressed fully by traditional map. The Space-Time-Cube (STC), as a three-dimensional visualization environment, whose base represents the two-dimensional geographical space and whose height represents the temporal dimension, can simultaneously represent the spatial distribution as well as the temporal changes of spatio-temporal data. For some spatial object or phenomenon, its moving trajectory can be visualized in STC as a Space-Time-Path (STP), by which the speed and state of motion can be clearly reflected. Noticeably, the problem of visual clutter about STP is inevitably due to the complexity of three-dimensional visualization. In order to reduce the impact of visual clutter, this paper discusses different aspects about visualization representation of STP in the STC. The multiple scales representation and the multiple views display can promote interactive experience of users, and the application of different visual variables can help to represent different kinds of attribute information of STP. With the visualization of STP, spatio-temporal changes and attributive characters of spatial object or phenomenon can be represented and analysed.

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Tian, Miao, Xinxin Hu, Jiakai Huang, Kai Ma, Haiyan Li, Shuai Zheng, Liufeng Tao, and Qinjun Qiu. "Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning." ISPRS International Journal of Geo-Information 12, no. 9 (September 1, 2023): 359. http://dx.doi.org/10.3390/ijgi12090359.

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The growing proliferation of geographic information presents a substantial challenge to the traditional framework of a geographic information analysis and service. The dynamic integration and representation of geographic knowledge, such as triples, with spatio-temporal information play a crucial role in constructing a comprehensive spatio-temporal knowledge graph and facilitating the effective utilization of spatio-temporal big data for knowledge-driven service applications. The existing knowledge graph (or geographic knowledge graph) takes spatio-temporal as the attribute of entity, ignoring the role of spatio-temporal information for accurate retrieval of entity objects and adaptive expression of entity objects. This study approaches the correlation between geographic knowledge and spatio-temporal information as a text classification problem, with the aim of addressing the challenge of establishing meaningful connections among spatio-temporal data using advanced deep learning techniques. Specifically, we leverage Wikipedia as a valuable data source for collecting and filtering geographic texts. The Open Information Extraction (OpenIE) tool is employed to extract triples from each sentence, followed by manual annotation of the sentences’ spatio-temporal relevance. This process leads to the formation of quadruples (time relevance/space relevance) or quintuples (spatio-temporal relevance). Subsequently, a comprehensive spatio-temporal classification dataset is constructed for experiment verification. Ten prominent deep learning text classification models are then utilized to conduct experiments covering various aspects of time, space, and spatio-temporal relationships. The experimental results demonstrate that the Bidirectional Encoder Representations from Transformer-Region-based Convolutional Neural Network (BERT-RCNN) model exhibits the highest performance among the evaluated models. Overall, this study establishes a foundation for future knowledge extraction endeavors.

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Sánchez-Montañés, Manuel A., Julian W. Gardner, and Timothy C. Pearce. "Spatio-temporal information in an artificial olfactory mucosa." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2092 (February 7, 2008): 1057–77. http://dx.doi.org/10.1098/rspa.2007.0140.

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Deploying chemosensor arrays in close proximity to stationary phases imposes stimulus-dependent spatio-temporal dynamics on their response and leads to improvements in complex odour discrimination. These spatio-temporal dynamics need to be taken into account explicitly when considering the detection performance of this new odour sensing technology, termed an artificial olfactory mucosa. For this purpose, we develop here a new measure of spatio-temporal information that combined with an analytical model of the artificial mucosa, chemosensor and noise dynamics completely characterizes the discrimination capability of the system. This spatio-temporal information measure allows us to quantify the contribution of both space and time to discrimination performance and may be used as part of optimization studies or calculated directly from an artificial mucosa output. Our formal analysis shows that exploiting both space and time in the mucosa response always outperforms the use of space alone and is further demonstrated by comparing the spatial versus spatio-temporal information content of mucosa experimental data. Together, the combination of the spatio-temporal information measure and the analytical model can be applied to extract the general principles of the artificial mucosa design as well as to optimize the physical and operating parameters that determine discrimination performance.

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Boucekkine, Raouf, Giorgio Fabbri, Salvatore Federico, and Fausto Gozzi. "Growth and agglomeration in the heterogeneous space: a generalized AK approach." Journal of Economic Geography 19, no. 6 (August 21, 2018): 1287–318. http://dx.doi.org/10.1093/jeg/lby041.

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Abstract We provide an optimal growth spatio-temporal setting with capital accumulation and diffusion across space to study the link between economic growth triggered by capital spatio-temporal dynamics and agglomeration across space. The technology is AK, K being broad capital. The social welfare function is Benthamite. In sharp contrast to the related literature, which considers homogeneous space, we derive optimal location outcomes for any given space distributions for technology and population. Both the transitional spatio-temporal dynamics and the asymptotic spatial distributions are computed in closed form. Concerning the latter, we find, among other results, that: (i) due to inequality aversion, the consumption per capital distribution is much flatter than the distribution of capital per capita; (ii) endogenous spillovers inherent in capital spatio-temporal dynamics occur as capital distribution is much less concentrated than the (pre-specified) technological distribution; (iii) the distance to the center (or to the core) is an essential determinant of the shapes of the asymptotic distributions, that is relative location matters.

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Chatzidakis, Andreas. "Chronotopic dilemmas: Space–time in consumer movements of the Greek crisis." Environment and Planning D: Society and Space 38, no. 2 (August 28, 2019): 325–44. http://dx.doi.org/10.1177/0263775819871301.

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This paper explores the spatio-temporal dimensions of consumer activism during the Greek crisis. Existing work has provided valuable insights into the figure of the political consumer and the socio-spatial contexts in which consumer activism is enacted. The paper presents original six-year ethnographic work that extends current knowledge through exploring how the spatial and temporal dimensions of consumer activism are unsettled and reconfigured during an acute economic crisis. It builds on the concept of chronotopic dilemmas to illustrate the ideological tensions and contradictions between old and new spatio-temporal logics and practices. In doing so, the current study complements prior research focused on how distinct cultural and institutional settings mediate discourses and actions of consumer activism, by highlighting their inherently spatio-temporal (chronotopic) nature.

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Li, Qing, William H. K. Lam, and Mei Lam Tam. "Vehicle Travel Time Prediction in Spatio-Temporal Space." Applied Mechanics and Materials 253-255 (December 2012): 1662–65. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1662.

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It is recognized that travel times on a link are temporally correlated with its travel times of previous time periods. Also, the link travel time are spatially correlated by travel times on its neighboring links. Based on such temporal and spatial correlations, a new method is proposed for travel time prediction in urban roads. The proposed method is capable of rapidly predicting the link travel time in the near future. For validation of the proposed method, the temporal and spatial variance-covariance of travel times on related links are employed together with historical travel time data. It is found that the proposed method is able to provide more accurate travel time prediction.

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Pal, Swadesh, Malay Banerjee, and Vitaly Volpert. "Spatio-temporal Bazykin’s model with space-time nonlocality." Mathematical Biosciences and Engineering 17, no. 5 (2020): 4801–24. http://dx.doi.org/10.3934/mbe.2020262.

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Qing, X., Z. Yang, Z. Baoming, and L. ChaoZhen. "SPACE MOVING OBJECTS SPATIO-TEMPORAL MODELING AND VISUALIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-5/W16 (September 10, 2012): 155–62. http://dx.doi.org/10.5194/isprsarchives-xxxviii-5-w16-155-2011.

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Demšar, Urška, A. Stewart Fotheringham, and Martin Charlton. "Exploring the spatio-temporal dynamics of geographical processes with geographically weighted regression and geovisual analytics." Information Visualization 7, no. 3-4 (September 2008): 181–97. http://dx.doi.org/10.1057/palgrave.ivs.9500187.

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The paper examines the potential for combining a spatial statistical methodology – Geographically Weighted Regression (GWR) – with geovisual analytical exploration to help understand complex spatio-temporal processes. This is done by applying the combined statistical – exploratory methodology to a simulated data set in which the behaviour of regression parameters was controlled across space and time. A variety of complex spatio-temporal processes was captured through space-time (i.e. as spatio-temporal) varying parameters whose values were known. The task was to see if the proposed methodology could uncover these complex processes from the data alone. The results of the experiment confirm that the combined methodology can successfully identify spatio-temporal patterns in the local GWR parameter estimates that correspond to the controlled behaviour of the original parameters.

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Song, W., and F. Zhang. "Spatio-temporal topological relationships between land parcels in cadastral database." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 89–92. http://dx.doi.org/10.5194/isprsarchives-xl-6-89-2014.

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There are complex spatio-temporal relationships among cadastral entities. Cadastral spatio-temporal data model should not only describe the data structure of cadastral objects, but also express cadastral spatio-temporal relationships between cadastral objects. In the past, many experts and scholars have proposed a variety of cadastral spatio-temporal data models, but few of them concentrated on the representation of spatiotemporal relationships and few of them make systematic studies on spatiotemporal relationships between cadastral objects. The studies on spatio-temporal topological relationships are not abundant. In the paper, we initially review current approaches to the studies of spatio-temporal topological relationships, and argue that spatio-temporal topological relation is the combination of temporal topology on the time dimension and spatial topology on the spatial dimension. Subsequently, we discuss and develop an integrated representation of spatio-temporal topological relationships within a 3-dimensional temporal space. In the end, based on the semantics of spatiotemporal changes between land parcels, we conclude the possible spatio-temporal topological relations between land parcels, which provide the theoretical basis for creating, updating and maintaining of land parcels in the cadastral database.

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Poem, E., T. Hiemstra, A. Eckstein, X. M. Jin, and I. A. Walmsley. "Free-space spectro-temporal and spatio-temporal conversion for pulsed light." Optics Letters 41, no. 18 (September 13, 2016): 4328. http://dx.doi.org/10.1364/ol.41.004328.

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Song, Yuanbin, and D. K. H. Chua. "Detection of spatio-temporal conflicts on a temporal 3D space system." Advances in Engineering Software 36, no. 11-12 (November 2005): 814–26. http://dx.doi.org/10.1016/j.advengsoft.2005.03.025.

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Zhao, Ling, Hanhan Deng, Linyao Qiu, Sumin Li, Zhixiang Hou, Hai Sun, and Yun Chen. "Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding." Symmetry 12, no. 2 (February 1, 2020): 199. http://dx.doi.org/10.3390/sym12020199.

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Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

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Sang, Neil. "Does Time Smoothen Space? Implications for Space-Time Representation." ISPRS International Journal of Geo-Information 12, no. 3 (March 9, 2023): 119. http://dx.doi.org/10.3390/ijgi12030119.

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The continuous nature of space and time is a fundamental tenet of many scientific endeavors. That digital representation imposes granularity is well recognized, but whether it is possible to address space completely remains unanswered. This paper argues Hales’ proof of Kepler’s conjecture on the packing of hard spheres suggests the answer to be “no”, providing examples of why this matters in GIS generally and considering implications for spatio-temporal GIS in particular. It seeks to resolve the dichotomy between continuous and granular space by showing how a continuous space may be emergent over a random graph. However, the projection of this latent space into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a “smooth” spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation.

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Suhartono, Suhartono, Dedy Dwi Prastyo, Heri Kuswanto, and Muhammad Hisyam Lee. "Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production." MATEMATIKA 34, no. 1 (May 28, 2018): 103–11. http://dx.doi.org/10.11113/matematika.v34.n1.1040.

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Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

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SEGEL, LEE A. "SOME SPATIO-TEMPORAL MODELS IN IMMUNOLOGY." International Journal of Bifurcation and Chaos 12, no. 11 (November 2002): 2343–47. http://dx.doi.org/10.1142/s021812740200590x.

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Brief descriptions of spatio-temporal structures that arose in the author's explorations of theoretical immunology are provided. Topics range from singular perturbation theory for a biological assay and a model for skin rashes (reaction–diffusion patterns), through immunotherapy for allergy and selection of appropriate antibody types (compartment models), to a somewhat abstract "shape space".

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Wang, Chen, Kai Du, Yin Li Jin, and Ling Yun He. "Han Ning Highway Traffic Accident Spatio-Temporal Analysis." Applied Mechanics and Materials 135-136 (October 2011): 560–64. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.560.

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Collecting the traffic accidents data of Han Ning highway in 2008, 2009 and 2010 three years, through two aspects of time and space to do statistical analysis and data mining of the traffic accidents of this highway. The result of the analysis is that accident rates have a certain relevance to time and space. From the time perspective, holidays and vacation days are accident high-risk days. From the space perspective, accident rate in the ascent and in the downhill are higher than on the straight road, cars go straight have a higher accident rate than in the corners, traffic accident rate in single-km single-lane tunnel is higher than outside the tunnel. Through the time and space distribution rule of the traffic accident, educating traffic participants follow the time and space distribution rule to restrict their behavior. Educating traffic managers obey the time and space distribution rule of traffic accident to adopt targeted management measures and engineering measures. Both of these two aspects have very important significance in reducing traffic accidents.

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Sun, Yue Lin, Lei Bao, and Yi Hang Peng. "Study of the Spatio-Temporal Data Model in Sea Battlefield." Applied Mechanics and Materials 246-247 (December 2012): 744–48. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.744.

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An effective analysis of the battlefield situation and spatio-temporal data model in a sea battlefield has great significance for the commander to perceive the battlefield situation and to make the right decisions. Based on the existing spatio-temporal data model, the present paper gives a comprehensive analysis of the characteristics of sea battlefield data, and chooses the object-oriented spatio-temporal data model to modify it; at the same time this paper introduces sea battlefield space-time algebra system to define various data types formally, which lays the foundation for the establishment of the sea battlefield spatio-temporal data model.

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EL-GERESY, BAHER A., ALIA I. ABDELMOTY, and CHRISTOPHER B. JONES. "EPISODES IN SPACE: QUALITATIVE REPRESENTATION AND REASONING OVER SPATIO-TEMPORAL OBJECTS." International Journal on Artificial Intelligence Tools 09, no. 01 (March 2000): 131–52. http://dx.doi.org/10.1142/s0218213000000100.

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There is growing interest in many application domains for the temporal treatment and manipulation of spatially referenced objects. Handling the time dimension in spatial databases can greatly enhance and extend their functionality and usability by offering means of understanding the spatial behaviour in time. Few works, to date, have been directed towards the development of formalisms for representation and reasoning in this domain. In this paper, a new approach is presented for the representation and reasoning over spatio-temporal relationships. The approach is simple and aims to satisfy the requirements of coherency, expressiveness and reasoning power. Consistent behaviours of spatial objects in time are denoted episodes. The topology of the domain is defined by decomposing episodes into representative components and relationships are defined between those components. Spatio-temporal reasoning is achieved by composing the relationships between the object components using constraint networks. New composition tables between simple spatio-temporal regions and between regions and volumes are also derived and used in the reasoning process.

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Dong, Qifen, Yu Li, Ziwan Zheng, Xun Wang, and Guojun Li. "ST3DNetCrime: Improved ST-3DNet Model for Crime Prediction at Fine Spatial Temporal Scales." ISPRS International Journal of Geo-Information 11, no. 10 (October 18, 2022): 529. http://dx.doi.org/10.3390/ijgi11100529.

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Crime prediction is crucial for sustainable urban development and protecting citizens’ quality of life. However, there exist some challenges in this regard. First, the spatio-temporal correlations in crime data are relatively complex and are heterogenous in time and space, hence it is difficult to model the spatio-temporal correlation in crime data adequately. Second, crime prediction at fine spatial temporal scales can be applied to micro patrol command; however, crime data are sparse in both time and space, making crime prediction very challenging. To overcome these challenges, based on the deep spatio-temporal 3D convolutional neural networks (ST-3DNet), we devise an improved ST-3DNet framework for crime prediction at fine spatial temporal scales (ST3DNetCrime). The framework utilizes diurnal periodic integral mapping to solve the problem of sparse and irregular crime data at fine spatial temporal scales. ST3DNetCrime can, respectively, capture the spatio-temporal correlations of recent crime data, near historical crime data and distant historical crime data as well as describe the difference in the correlations’ contributions in space. Extensive experiments on real-world datasets from Los Angeles demonstrated that the proposed ST3DNetCrime framework has better prediction performance and enhanced robustness compared with baseline methods. In additon, we verify that each component of ST3DNetCrime is helpful in improving prediction performance.

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Li, Wu, Wu, and Zhao. "An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks." ISPRS International Journal of Geo-Information 8, no. 11 (November 12, 2019): 512. http://dx.doi.org/10.3390/ijgi8110512.

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Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.

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Spiegel, Elmar, Thomas Kneib, and Fabian Otto-Sobotka. "Spatio-temporal expectile regression models." Statistical Modelling 20, no. 4 (March 18, 2019): 386–409. http://dx.doi.org/10.1177/1471082x19829945.

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Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.

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Delmelle, Eric, Changjoo Kim, Ningchuan Xiao, and Wei Chen. "Methods for Space-Time Analysis and Modeling." International Journal of Applied Geospatial Research 4, no. 4 (October 2013): 1–18. http://dx.doi.org/10.4018/jagr.2013100101.

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With increasing availability of spatio-temporal data and the democratization of Geographical Information Systems (GIS), there has been a demand for novel statistical and visualization techniques which can explicitly integrate space and time. The paper discusses the nature of spatio-temporal data, the integration of time within GIS and the flourishing availability of spatial and temporal-explicit data over the Internet. The paper attempts to answer the fundamental question on how these large datasets can be analyzed in space and time to reveal critical patterns. The authors further elaborate on how spatial autocorrelation techniques are extended to deal with time, for point, linear, and areal features, and the impact of parameter selection, such as critical distance and time threshold to build adjacency matrices. The authors also discuss issues of space-time modeling for optimization problems.

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Fu, Wen, Camille Bonnet, Julie Figoni, Alexandra Septfons, and Raphaëlle Métras. "Exploratory Space–Time Analyses of Reported Lyme Borreliosis Cases in France, 2016–2019." Pathogens 10, no. 4 (April 8, 2021): 444. http://dx.doi.org/10.3390/pathogens10040444.

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In recent decades, the incidence of Lyme borreliosis (LB) in Europe seems to have increased, underpinning a growing public health concern. LB surveillance systems across the continent are heterogeneous, and the spatial and temporal patterns of LB reports have been little documented. In this study, we explored the spatio-temporal patterns of LB cases reported in France from 2016 to 2019, to describe high-risk clusters and generate hypotheses on their occurrence. The space–time K-function and the Kulldorf’s scan statistic were implemented separately for each year to evaluate space–time interaction between reported cases and searching clusters. The results show that the main spatial clusters, of radius size up to 97 km, were reported in central and northeastern France each year. In 2017–2019, spatial clusters were also identified in more southern areas (near the Alps and the Mediterranean coast). Spatio-temporal clustering occurred between May and August, over one-month to three-month windows in 2016–2017 and in 2018–2019. A strong spatio-temporal interaction was identified in 2018 within 16 km and seven days, suggesting a potential local and intense pathogen transmission process. Ongoing improved surveillance and accounting for animal hosts, vectors, meteorological factors and human behaviors are keys to further elucidate LB spatio-temporal patterns.

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Hussain, I., J. Pilz, and G. Spoeck. "Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach." Advances in Geosciences 25 (March 30, 2010): 97–102. http://dx.doi.org/10.5194/adgeo-25-97-2010.

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Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolation are used in order to predict precipitation in Pakistan during the monsoon period. Monthly average precipitation data whose time domain is the monsoon period for the years 1974–2000 and whose spatial domain are various regions in Pakistan are considered. The prediction of space-time precipitation is applicable in many sectors of industry and economy in Pakistan especially; the agricultural sector. Mean field maps and prediction error maps for both methods are estimated and compared. In this paper it is shown that the transformed hierarchical Bayesian model is providing more accuracy and lower prediction error compared to the spatio-temporal Bayesian maximum entropy method; additionally, the transformed hierarchical Bayesian model also provides predictive distributions.

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DAYA SAGAR, B. S., and C. BABU RAO. "EDITORIAL." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 02 (March 2003): 163–65. http://dx.doi.org/10.1142/s0218001403002289.

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Natural systems undergo several morphological changes with time. To study spatio-temporal dynamics of such natural systems, and to further understand the morphological dynamical behaviors, various images that show several macro- and micro-level phenomena, acquired by various types of sensors need to be analyzed in spatio-temporal scales. Such analyses, to facilitate the researcher to model the spatio-temporal organization of a desired phenomenon, evidently require the robust procedures to extract specific error-free features from multiscale-temporal images represented in discrete space. Geometry and topology based features, such as edges of unique type and general type, are the indicators to record the changes that occur temporally. Extraction of such information is essential prerequisite to develop cogent models to understand the spatio-temporal organization.

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Skøien, J. O., and G. Blöschl. "Catchments as space-time filters – a joint spatio-temporal geostatistical analysis of runoff and precipitation." Hydrology and Earth System Sciences Discussions 3, no. 3 (June 12, 2006): 941–85. http://dx.doi.org/10.5194/hessd-3-941-2006.

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Abstract. In this paper catchments are conceptualised as linear space-time filters. Catchment area A is interpreted as the spatial support and the catchment response time Tis interpreted as the temporal support of the runoff measurements. These two supports are related by T~Aκ which embodies the space-time connections of the rainfall-runoff process from a geostatistical perspective. To test the framework, spatio-temporal variograms are estimated from about 30 years of quarter hourly precipitation and runoff data from about 500 catchments in Austria. In a first step, spatio-temporal variogram models are fitted to the sample variograms for three catchment size classes independently. In a second step, variograms are fitted to all three catchment size classes jointly by estimating the parameters of a point/instantaneous spatio-temporal variogram model and aggregating (regularising) it to the spatial and temporal scales of the catchments. The exponential, Cressie-Huang and product-sum variogram models give good fits to the sample variograms of runoff with dimensionless errors ranging from 0.02 to 0.03, and the model parameters are plausible. This indicates that the first order effects of the spatio-temporal variability of runoff are indeed captured by conceptualising catchments as linear space-time filters. The scaling exponent κ is found to vary between 0.3 and 0.4 for different variogram models.

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Skøien, J. O., and G. Blöschl. "Catchments as space-time filters – a joint spatio-temporal geostatistical analysis of runoff and precipitation." Hydrology and Earth System Sciences 10, no. 5 (September 26, 2006): 645–62. http://dx.doi.org/10.5194/hess-10-645-2006.

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Abstract. In this paper catchments are conceptualised as linear space-time filters. Catchment area A is interpreted as the spatial support and the catchment response time T is interpreted as the temporal support of the runoff measurements. These two supports are related by T~Aκ which embodies the space-time connections of the rainfall-runoff process from a geostatistical perspective. To test the framework, spatio-temporal variograms are estimated from about 30 years of quarter hourly precipitation and runoff data from about 500 catchments in Austria. In a first step, spatio-temporal variogram models are fitted to the sample variograms for three catchment size classes independently. In a second step, variograms are fitted to all three catchment size classes jointly by estimating the parameters of a point/instantaneous spatio-temporal variogram model and aggregating (regularising) it to the spatial and temporal scales of the catchments. The exponential, Cressie-Huang and product-sum variogram models give good fits to the sample variograms of runoff with dimensionless errors ranging from 0.02 to 0.03, and the model parameters are plausible. This indicates that the first order effects of the spatio-temporal variability of runoff are indeed captured by conceptualising catchments as linear space-time filters. The scaling exponent κ is found to vary between 0.3 and 0.4 for different variogram models.

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Liu, Hong, Jining Yan, Jinlin Wang, Bo Chen, Meng Chen, and Xiaohui Huang. "HGST: A Hilbert-GeoSOT Spatio-Temporal Meshing and Coding Method for Efficient Spatio-Temporal Range Query on Massive Trajectory Data." ISPRS International Journal of Geo-Information 12, no. 3 (March 7, 2023): 113. http://dx.doi.org/10.3390/ijgi12030113.

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In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of location-based services (LBS). Spatio-temporal range query, as the basis of many services, remains a challenge in supporting efficient analysis and calculation of data, especially when large volumes of trajectory data have been accumulated. We propose a Hilbert-GeoSOT spatio-temporal meshing and coding method called HGST to improve the efficiency of spatio-temporal range queries on massive trajectory data. First, the method uses Hilbert to encode the grids obtained based on the GeoSOT space division model, and then constructs a unified time division standard to generate the space–time location identification of trajectory data. Second, this paper builds a novel spatio-temporal index to organize trajectory data, and designs an adaptive spatio-temporal scaling and coding method based on HGST to improve the query performance on indexed records. Finally, we implement a prototype system based on HBase and Spark, and develop a Spark-based algorithm to accelerate the spatio-temporal range query for huge trajectory data. Extensive experiments on a real taxi trajectory dataset demonstrate that HGST improves query efficiency levels by approximately 14.77% and 34.93% compared with GeoSOT-ST and GeoMesa at various spatial scales, respectively, and has better scalability under different data volumes.

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Nguyen Mau Quoc, Hoan, Martin Serrano, Han Mau Nguyen, John G. Breslin, and Danh Le-Phuoc. "EAGLE—A Scalable Query Processing Engine for Linked Sensor Data." Sensors 19, no. 20 (October 9, 2019): 4362. http://dx.doi.org/10.3390/s19204362.

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Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.

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Cavazos Cohn, Teresa, Kate Berry, Kyle Powys Whyte, and Emma Norman. "Spatio-Temporality and Tribal Water Quality Governance in the United States." Water 11, no. 1 (January 9, 2019): 99. http://dx.doi.org/10.3390/w11010099.

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Hydrosocial spatio-temporalities—aspects of water belonging to space, time, or space-time—are central to water governance, providing a framework upon which overall hydrosocial relations are constructed, and are fundamental to the establishment of values and central to socio-cultural-political relationships. Moreover, spatio-temporal conceptions may differ among diverse governing entities and across scales, creating “variability” through ontological pluralism, as well as power asymmetries embedded in cultural bias. This paper explores spatio-temporal conceptions related to water quality governance, an aspect of water governance often biased toward technical and scientific space-time conceptions. We offer examples of different aspects of spatio-temporality in water quality issues among Tribes in the United States, highlighting several themes, including spatiotemporal cycles, technological mediation, and interrelationship and fluidity. Finally, we suggest that because water is part of a dynamic network of space-times, water quality may be best governed through more holistic practices that recognize tribal sovereignty and hydrosocial variability.

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Clement, L., O. Thas, P. A. Vanrolleghem, and J. P. Ottoy. "Spatio-temporal statistical models for river monitoring networks." Water Science and Technology 53, no. 1 (January 1, 2006): 9–15. http://dx.doi.org/10.2166/wst.2006.002.

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When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

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Wu, C., Q. Zhu, Y. T. Zhang, Z. Q. Du, Y. Zhou, X. Xie, and F. He. "AN ADAPTIVE ORGANIZATION METHOD OF GEOVIDEO DATA FOR SPATIO-TEMPORAL ASSOCIATION ANALYSIS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 29–34. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-29-2015.

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Public security incidents have been increasingly challenging to address with their new features, including large-scale mobility, multi-stage dynamic evolution, spatio-temporal concurrency and uncertainty in the complex urban environment, which require spatio-temporal association analysis among multiple regional video data for global cognition. However, the existing video data organizational methods that view video as a property of the spatial object or position in space dissever the spatio-temporal relationship of scattered video shots captured from multiple video channels, limit the query functions on interactive retrieval between a camera and its video clips and hinder the comprehensive management of event-related scattered video shots. GeoVideo, which maps video frames onto a geographic space, is a new approach to represent the geographic world, promote security monitoring in a spatial perspective and provide a highly feasible solution to this problem. This paper analyzes the large-scale personnel mobility in public safety events and proposes a multi-level, event-related organization method with massive GeoVideo data by spatio-temporal trajectory. This paper designs a unified object identify(ID) structure to implicitly store the spatio-temporal relationship of scattered video clips and support the distributed storage management of massive cases. Finally, the validity and feasibility of this method are demonstrated through suspect tracking experiments.

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Zhou, Chunlei, Xinwei Dong, Liang Ji, Bijun Zhang, Zhongping Xu, and Chengping Zhang. "Hierarchical mining algorithm for high dimensional spatiotemporal big data based on association rules." E3S Web of Conferences 256 (2021): 02040. http://dx.doi.org/10.1051/e3sconf/202125602040.

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The traditional data mining algorithm focuses too much on a single dimension of data time or space, ignoring the association between time and space, which leads to a large amount of computation and low processing efficiency of the mining algorithm and makes it difficult to guarantee the final data mining effect. In response to the above problems, a hierarchical mining algorithm based on association rules for high-dimensional spatio-temporal big data is proposed. Based on the traditional association rules, after establishing the association rules of spatio-temporal data, the data to be mined are cleaned for redundancy. After selecting the local linear embedding algorithm to reduce the dimensionality of the data, a hierarchical mining strategy is developed to realize high-dimensional spatio-temporal big data mining by searching frequent predicates to form a spatio-temporal transaction database. The simulation experiment results verify that the algorithm has high complexity and can effectively reduce the processing volume, which can improve the processing efficiency by at least 56.26% compared with other algorithms.

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Petrasova, Anna, J. Aaron Hipp, and Helena Mitasova. "Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams." ISPRS International Journal of Geo-Information 8, no. 12 (December 5, 2019): 559. http://dx.doi.org/10.3390/ijgi8120559.

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Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate pedestrians in the captured images is a promising technique for analyzing pedestrian activity. However, it is challenging to efficiently transform the time series of pedestrian locations in the images to information suitable for geospatial analytics, as well as visualize data in a meaningful way to inform urban design or decision making. In this study, we propose to use a space-time cube (STC) representation of pedestrian data to analyze the spatio-temporal patterns of pedestrians in public spaces. We take advantage of AMOS (The Archive of Many Outdoor Scenes), a large database of images captured by thousands of publicly available, outdoor webcams. We developed a method to obtain georeferenced spatio-temporal data from webcams and to transform them into high-resolution continuous representation of pedestrian densities by combining bivariate kernel density estimation with trivariate, spatio-temporal spline interpolation. We demonstrate our method on two case studies analyzing pedestrian activity of two city plazas. The first case study explores daily and weekly spatio-temporal patterns of pedestrian activity while the second one highlights the differences in pattern before and after plaza’s redevelopment. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time series of pedestrian occurrences captured by webcam images.

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Jahn, M. W., P. E. Bradley, M. Al Doori, and M. Breunig. "TOPOLOGICALLY CONSISTENT MODELS FOR EFFICIENT BIG GEO-SPATIO-TEMPORAL DATA DISTRIBUTION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 65–72. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-65-2017.

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Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.

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Meroz, Yasmine, Renaud Bastien, and L. Mahadevan. "Spatio-temporal integration in plant tropisms." Journal of The Royal Society Interface 16, no. 154 (May 2019): 20190038. http://dx.doi.org/10.1098/rsif.2019.0038.

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Tropisms, growth-driven responses to environmental stimuli, cause plant organs to respond in space and time and reorient themselves. Classical experiments from nearly a century ago reveal that plant shoots respond to the integrated history of light and gravity stimuli rather than just responding instantaneously. We introduce a temporally non-local response function for the dynamics of shoot growth formulated as an integro-differential equation whose solution allows us to qualitatively reproduce experimental observations associated with intermittent and unsteady stimuli. Furthermore, an analytic solution for the case of a pulse stimulus expresses the response function as a function of experimentally tractable variables, which we calculate for the case of the phototropic response of Arabidopsis hypocotyls. All together, our model enables us to predict tropic responses to time-varying stimuli, manifested in temporal integration phenomena, and sets the stage for the incorporation of additional effects such as multiple stimuli, gravitational sagging, etc.

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Henriksen, R. N. "Compressible turbulence or spatio-temporal chaos?" Astrophysics and Space Science 221, no. 1-2 (November 1994): 25–39. http://dx.doi.org/10.1007/bf01091140.

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Kuper, P. V., M. Breunig, M. Al-Doori, and A. Thomsen. "APPLICATION OF 3D SPATIO-TEMPORAL DATA MODELING, MANAGEMENT, AND ANALYSIS IN DB4GEO." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W1 (October 5, 2016): 163–70. http://dx.doi.org/10.5194/isprs-annals-iv-2-w1-163-2016.

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Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

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MISRA, A. K., MILAN TIWARI, and ANUPAMA SHARMA. "SPATIO-TEMPORAL PATTERNS IN A CHOLERA TRANSMISSION MODEL." Journal of Biological Systems 23, no. 03 (August 30, 2015): 471–84. http://dx.doi.org/10.1142/s0218339015500242.

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Cholera has been a public health threat for centuries. Unlike the biological characteristics, relatively less effort has been paid to comprehend the spatial dynamics of this disease. Therefore, in this paper, we have proposed a cholera epidemic model for variable population size and studied the spatial patterns in two-dimensional space. First, we have performed the equilibrium and local stability analysis of steady states obtained for temporal system. Afterwards, the local and global stability behavior of the endemic steady state in a spatially extended setting has been investigated. The numerical simulations have been done to investigate the spatial patterns. They show that dynamics of the cholera epidemic varies with time and space.

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Jiang, Mian, Hua Deng, and Chang Qing Huang. "Spectral Based Spatio-Temporal Modeling for Thermal Crown of Aluminium Alloy Hot Rolling Processes." Applied Mechanics and Materials 197 (September 2012): 696–702. http://dx.doi.org/10.4028/www.scientific.net/amm.197.696.

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A spectral based low-dimensional spatio-temporal modeling approach is proposed for thermal crown of work rolling in aluminium alloy rolling processes. Firstly，the Karhunen-Loève (KL) decomposition is used for dimension reduction and time/space separation. The neural networks are used for dynamic modeling. The simulations have demonstrated the effectiveness of the proposed spatio-temporal modeling approach..

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Bouwer, Johann M., Daniel N. Wilke, and Schalk Kok. "Spatio-Temporal Gradient Enhanced Surrogate Modeling Strategies." Mathematical and Computational Applications 28, no. 2 (April 8, 2023): 57. http://dx.doi.org/10.3390/mca28020057.

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This research compares the performance of space-time surrogate models (STSMs) and network surrogate models (NSMs). Specifically, when the system response varies over time (or pseudo-time), the surrogates must predict the system response. A surrogate model is used to approximate the response of computationally expensive spatial and temporal fields resulting from some computational mechanics simulations. Within a design context, a surrogate takes a vector of design variables that describe a current design and returns an approximation of the design’s response through a pseudo-time variable. To compare various radial basis function (RBF) surrogate modeling approaches, the prediction of a load displacement path of a snap-through structure is used as an example numerical problem. This work specifically considers the scenario where analytical sensitivities are available directly from the computational mechanics’ solver and therefore gradient enhanced surrogates are constructed. In addition, the gradients are used to perform a domain transformation preprocessing step to construct surrogate models in a more isotropic domain, which is conducive to RBFs. This work demonstrates that although the gradient-based domain transformation scheme offers a significant improvement to the performance of the space-time surrogate models (STSMs), the network surrogate model (NSM) is far more robust. This research offers explanations for the improved performance of NSMs over STSMs and recommends future research to improve the performance of STSMs.

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Journal articles on the topic 'Spatio-temporal space'

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