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Geoinformatics in Citizen Science
Article

A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams

1
Centre for Geoinformation Science, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
2
Earth Observation Science and Information Technology, Meraka Institute, Council for Scientific and Industrial Research, Pretoria 0001, South Africa
3
School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2000, South Africa
*
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(12), 475; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120475
Received: 30 September 2018 / Revised: 28 November 2018 / Accepted: 6 December 2018 / Published: 11 December 2018
(This article belongs to the Special Issue Spatial Stream Processing )
Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes. View Full-Text
Keywords: Sensor observation; data streaming; spatio-temporal data; geovisual analyitcs Sensor observation; data streaming; spatio-temporal data; geovisual analyitcs
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MDPI and ACS Style

Sibolla, B.H.; Coetzee, S.; Van Zyl, T.L. A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams. ISPRS Int. J. Geo-Inf. 2018, 7, 475. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120475

AMA Style

Sibolla BH, Coetzee S, Van Zyl TL. A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams. ISPRS International Journal of Geo-Information. 2018; 7(12):475. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120475

Chicago/Turabian Style

Sibolla, Bolelang H., Serena Coetzee, and Terence L. Van Zyl 2018. "A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams" ISPRS International Journal of Geo-Information 7, no. 12: 475. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120475

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