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Добірка наукової літератури з теми "Spatiotemporal granularitie"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Spatiotemporal granularitie"

Timko, Igor, Michael Böhlen, and Johann Gamper. "Sequenced spatiotemporal aggregation for coarse query granularities." VLDB Journal 20, no. 5 (2011): 721–41. http://dx.doi.org/10.1007/s00778-011-0247-5.

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Jiang, Man, Qilong Han, Haitao Zhang, and Hexiang Liu. "Spatiotemporal Data Prediction Model Based on a Multi-Layer Attention Mechanism." International Journal of Data Warehousing and Mining 19, no. 2 (2023): 1–15. http://dx.doi.org/10.4018/ijdwm.315822.

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Spatiotemporal data prediction is of great significance in the fields of smart cities and smart manufacturing. Current spatiotemporal data prediction models heavily rely on traditional spatial views or single temporal granularity, which suffer from missing knowledge, including dynamic spatial correlations, periodicity, and mutability. This paper addresses these challenges by proposing a multi-layer attention-based predictive model. The key idea of this paper is to use a multi-layer attention mechanism to model the dynamic spatial correlation of different features. Then, multi-granularity historical features are fused to predict future spatiotemporal data. Experiments on real-world data show that the proposed model outperforms six state-of-the-art benchmark methods.

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Wang, Pengyuan, Xiao Huang, Joseph Mango, Di Zhang, Dong Xu, and Xiang Li. "A Hybrid Population Distribution Prediction Approach Integrating LSTM and CA Models with Micro-Spatiotemporal Granularity: A Case Study of Chongming District, Shanghai." ISPRS International Journal of Geo-Information 10, no. 8 (2021): 544. http://dx.doi.org/10.3390/ijgi10080544.

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Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotemporal granularity based on the long short-term memory (LSTM) and cellular automata (CA) models. We aim at designing a hybrid data-driven model with good adaptability and scalability, which can be used in more refined population prediction. We not only try to integrate these two models, aiming to fully mine the spatiotemporal characteristics, but also propose a method that fuses multi-source geographic data. We tested its functionality using the data from Chongming District, Shanghai, China. The results demonstrated that, among all scenarios, the model trained by three consecutive days (ordinary dates), with the granularity of one hour, incorporated with road networks, achieves the best performance (0.905 as the mean absolute error) and generalization capability.

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Kragh-Furbo, Mette, and Gordon Walker. "Electricity as (Big) Data: Metering, spatiotemporal granularity and value." Big Data & Society 5, no. 1 (2018): 205395171875725. http://dx.doi.org/10.1177/2053951718757254.

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Electricity is hidden within wires and networks only revealing its quantity and flow when metered. The making of its properties into data is therefore particularly important to the relations that are formed around electricity as a produced and managed phenomenon. We propose approaching all metering as a situated activity, a form of quantification work in which data is made and becomes mobile in particular spatial and temporal terms, enabling its entry into data infrastructures and schemes of evaluation and value production. We interrogate the transition from the pre-digital into the making of bigger, more spatiotemporally granular electricity data, through focusing on those actors selling and materialising new metering technologies, data infrastructures and services for larger businesses and public sector organisations in the UK. We examine the claims of truth and visibility that accompany these shifts and their enrolment into management techniques that serve to more precisely apportion responsibility for, and evaluate the status of, particular patterns and instances of electricity use. We argue that whilst through becoming Big Data electricity flow is now able to be known and given identity in significantly new terms, enabling new relations to be formed with the many heterogeneous entities implicated in making and managing energy demand, it is necessary to sustain some ambivalence as to the performative consequences that follow for energy governance. We consider the wider application of our conceptualisation of metering, reflecting on comparisons with the introduction of new metering systems in domestic settings and as part of other infrastructural networks.

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Kupfer, John A., Zhenlong Li, Huan Ning, and Xiao Huang. "Using Mobile Device Data to Track the Effects of the COVID-19 Pandemic on Spatiotemporal Patterns of National Park Visitation." Sustainability 13, no. 16 (2021): 9366. http://dx.doi.org/10.3390/su13169366.

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Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph as part of their Social Distancing Metric dataset. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively capture park visitation patterns but with much finer spatiotemporal granularity. In general, triggers of visitation changes corresponded well with the parks’ management responses to COVID-19, with all six parks showing dramatic decreases in the number of visitors (compared to 2019) beginning in March 2020 and continuing through April and May. As restrictions were eased to promote access to the parks and the benefits associated with outdoor recreation, visitation in 2020 approached or even passed that from 2019 by late summer or early autumn at most of the parks. The results also revealed that parks initially saw the greatest increases in visitation after reopening originating from nearby states, with visitorship coming from a broader range of states as time passed. Our study highlights the capability of mobility data for providing spatiotemporally explicit knowledge of place visitation.

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Ma, Jun, Yuexiong Ding, Vincent J. L. Gan, Changqing Lin, and Zhiwei Wan. "Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM." IEEE Access 7 (2019): 107897–907. http://dx.doi.org/10.1109/access.2019.2932445.

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Ottaviano, Flavia, Fabing Cui, and Andy H. F. Chow. "Modeling and Data Fusion of Dynamic Highway Traffic." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (2017): 92–99. http://dx.doi.org/10.3141/2644-11.

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This paper presents a data fusion framework for processing and integrating data collected from heterogeneous sources on motorways to generate short-term predictions. Considering the heterogeneity in spatiotemporal granularity in data from different sources, an adaptive kernel-based smoothing method was first used to project all data onto a common space–time grid. The data were then integrated through a Kalman filter framework build based on the cell transmission model for generating short-term traffic state prediction. The algorithms were applied and tested with real traffic data collected from the California I-880 corridor in the San Francisco Bay Area from the Mobile Century experiment. Results revealed that the proposed fusion algorithm can work with data sources that are different in their spatiotemporal granularity and improve the accuracy of state estimation through incorporating multiple data sources. The present work contributed to the field of traffic engineering and management with the application of big data analytics.

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Wang, Ruxin, Hongyan Wu, Yongsheng Wu, Jing Zheng, and Ye Li. "Improving influenza surveillance based on multi-granularity deep spatiotemporal neural network." Computers in Biology and Medicine 134 (July 2021): 104482. http://dx.doi.org/10.1016/j.compbiomed.2021.104482.

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Chen, F., C. Jing, H. Zhang, and X. Lv. "WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 493–99. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-493-2022.

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Abstract. Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern. Moreover, the visualization methods are weak in wholeness, intuitiveness and interactivity perspectives. In this paper, we design a geospatial dashboard-based student behavior analysis and visualization system considering students characteristics and spatiotemporal pattern. This system includes four components: user monitoring, data mining analysis, behavior prediction and spatiotemporal visualization. Furthermore, a deep learning model based on LSTNet to predict student behaviour. Our work takes WiFi log data of a university in Beijing as dataset. The results show that this system can identify student behavior patterns at a finer granularity by visualization method, which is helpful in improving learning and living efficiency.

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Jian, Yang, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting." Journal of Advanced Transportation 2022 (April 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/4260244.

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As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic speed forecasting. In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). DeepWalk is used to extract semantic information from road networks. Three publicly available datasets with different time granularities of 15, 30, and 60 min are used to validate the short- and long-time prediction effect of this model. The results show that the ST-DWGRU model significantly outperforms the state-of-the-art baselines.

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