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Relevant bibliographies by topics / Spatiotemporal granularitie

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Journal articles

Academic literature on the topic 'Spatiotemporal granularitie'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Spatiotemporal granularitie"

1

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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2

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 histo

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3

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-spatiotempo

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4

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

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5

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 ca

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6

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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7

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 fro

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8

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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9

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

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10

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

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