Early classification of spatio-temporal events using partial information

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

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Classification of spatio-temporal data represented as multiset for tactical profiling in badminton

2017 2nd International Conference on Image, Vision and Computing (ICIVC) ◽

10.1109/icivc.2017.7984701 ◽

2017 ◽

Author(s):

Kokum Weeratunga ◽

Anuja Dharmaratne ◽

Khoo Boon How

Keyword(s):

Temporal Data ◽

Spatio Temporal

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AN EARLY CLASSIFICATION OF NEW ZEALAND COASTAL CLIMATES

The Professional Geographer ◽

10.1111/j.0033-0124.1964.00006.x ◽

1964 ◽

Vol 16 (2) ◽

pp. 6-10

Author(s):

R. P. Hargreaves ◽

W. J. Maunder

Keyword(s):

New Zealand ◽

Early Classification

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Early Classification of Time Series: Cost-based multiclass Algorithms

10.1109/dsaa53316.2021.9564134 ◽

2021 ◽

Author(s):

Paul-Emile Zafar ◽

Youssef Achenchabe ◽

Alexis Bondu ◽

Antoine Cornuejols ◽

Vincent Lemaire

Keyword(s):

Time Series ◽

Early Classification

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Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2923-2017 ◽

2017 ◽

Vol 21 (6) ◽

pp. 2923-2951 ◽

Cited By ~ 19

Author(s):

Laurie Caillouet ◽

Jean-Philippe Vidal ◽

Eric Sauquet ◽

Alexandre Devers ◽

Benjamin Graff

Keyword(s):

Climate Change ◽

Low Flow ◽

Reference Network ◽

Large Set ◽

Historical Events ◽

Variable Threshold ◽

Matching Procedure ◽

Spatio Temporal ◽

Temporal Events ◽

Rich Countries

Abstract. The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989–1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

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Supervised linear classification of Gaussian spatio-temporal data

Lietuvos matematikos rinkinys ◽

10.15388/lmr.2021.25214 ◽

2021 ◽

Vol 62 ◽

pp. 9-15

Author(s):

Marta Karaliutė ◽

Kęstutis Dučinskas

Keyword(s):

Time Moment ◽

Gaussian Random Field ◽

Covariance Structure ◽

Simulated Data ◽

Spatial Location ◽

Error Rates ◽

Prior Probabilities ◽

Structure Factors ◽

Spatio Temporal

In this article we focus on the problem of supervised classifying of the spatio-temporal Gaussian random field observation into one of two classes, specified by different mean parameters. The main distinctive feature of the proposed approach is allowing the class label to depend on spatial location as well as on time moment. It is assumed that the spatio-temporal covariance structure factors into a purely spatial component and a purely temporal component following AR(p) model. In numerical illustrations with simulated data, the influence of the values of spatial and temporal covariance parameters to the derived error rates for several prior probabilities models are studied.

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Sensing as a Service for the Internet of Things

International Journal of Recent Contributions from Engineering Science & IT (iJES) ◽

10.3991/ijes.v4i3.6087 ◽

2016 ◽

Vol 4 (3) ◽

pp. 28

Author(s):

Omar Subhi Aldabbas

Keyword(s):

Internet Of Things ◽

Cost Effective ◽

Sensor Data ◽

Detection Accuracy ◽

Heterogeneous Services ◽

Coordination Languages ◽

Spatio Temporal ◽

Temporal Events ◽

New Applications ◽

Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

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