Visual Analysis of Spatio-Temporal Data: Applications in Weather Forecasting

We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km2 area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time-based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation.

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MAP-Vis: A Distributed Spatio-Temporal Big Data Visualization Framework Based on a Multi-Dimensional Aggregation Pyramid Model

Applied Sciences ◽

10.3390/app10020598 ◽

2020 ◽

Vol 10 (2) ◽

pp. 598

Author(s):

Xuefeng Guan ◽

Chong Xie ◽

Linxu Han ◽

Yumei Zeng ◽

Dannan Shen ◽

...

Keyword(s):

High Performance ◽

Visual Analysis ◽

Interactive Visualization ◽

Temporal Data ◽

Time Space ◽

Big Data Visualization ◽

Spatio Temporal ◽

And Performance ◽

Open Datasets ◽

Map Data

During the exploration and visualization of big spatio-temporal data, massive volume poses a number of challenges to the achievement of interactive visualization, including large memory consumption, high rendering delay, and poor visual effects. Research has shown that the development of distributed computing frameworks provides a feasible solution for big spatio-temporal data management and visualization. Accordingly, to address these challenges, this paper adopts a proprietary pre-processing visualization scheme and designs and implements a highly scalable distributed visual analysis framework, especially targeted at massive point-type datasets. Firstly, we propose a generic multi-dimensional aggregation pyramid (MAP) model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid. The proposed MAP model can support the simultaneous hierarchical aggregation of time, space, and attributes, and also later transformation of the derived aggregates into discrete key-value pairs for scalable storage and efficient retrieval. Using the generated MAP datasets, we develop an open-source distributed visualization framework (MAP-Vis). In MAP-Vis, a high-performance Spark cluster is used as a parallel preprocessing platform, while distributed HBase is used as the massive storage for the generated MAP data. The client of MAP-Vis provides a variety of correlated visualization views, including heat map, time series, and attribute histogram. Four open datasets, with record numbers ranging from the millions to the tens of billions, are chosen for system demonstration and performance evaluation. The experimental results demonstrate that MAP-Vis can achieve millisecond-level query response and support efficient interactive visualization under different queries on the space, time, and attribute dimensions.

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Geovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data

GEOMATICA ◽

10.1139/geomat-2020-0004 ◽

2020 ◽

pp. 1-23

Author(s):

Jacques Gautier ◽

Paule-Annick Davoine ◽

Claire Cunty

Keyword(s):

Visual Search ◽

Harmonic Analysis ◽

Ad Hoc ◽

Visual Analysis ◽

Spatial Clustering ◽

Exploratory Analysis ◽

Temporal Data ◽

Event Data ◽

Visual Approach ◽

Spatio Temporal

Many geovisualization environments integrate graphical representations of time. Some of them include representation of both linear and cyclic aspects of time, providing an exploratory analysis of spatio-temporal data through several temporal cyclic scales. However, few of them provide an exploratory analysis of localized cyclic recurrences in spatio-temporal data. Ad hoc temporal diagrams, representing both linear and cyclic aspects of time, provide a visual search for cyclic recurrences in temporal data when the possibility is left to the user to perform a gradual modification of the represented cyclic scale’s duration. The combination of these graphic representations of time, with cartographic representations, displaying the spatial distribution of such cyclic recurrences, could provide an exploratory analysis of localized cyclic recurrences in spatio-temporal data. Mathematical tools coming from other scientific fields, such as the harmonic analysis, offer another way to identify cyclic behaviors in temporal data. Combining the visual approach offered by specifically designed geovisualization environments, with a harmonic analysis that suggests searching paths to the user during its exploratory analysis, can then improve the visual search for localized cyclic recurrences. We propose a geovisualization environment, which combines, on one hand, a visual analysis of localized cyclic recurrences in spatio-temporal data, using ad hoc temporal diagrams, cartographic representations, and specific semiologic rules, and on the other hand, mathematical tools, such as harmonic analysis and spatial clustering, that provide searching paths to the user for its visual analysis. This approach is supported by a geovisualization environment, GrAPHiST, which provides an exploratory analysis of spatio-temporal event data.

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A visual analysis of spatio-temporal data associated with human movement

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '09 ◽

10.1145/1653771.1653829 ◽

2009 ◽

Author(s):

Benjamin Aeschliman ◽

Beomjin Kim ◽

Michael Burton

Keyword(s):

Visual Analysis ◽

Human Movement ◽

Temporal Data ◽

Spatio Temporal

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Statistical Analysis of Spatio-Temporal Data Based on Poisson Conditional Autoregressive Model

Informatica ◽

10.15388/informatica.2015.39 ◽

2015 ◽

Vol 26 (1) ◽

pp. 67-87

Author(s):

Yuriy Kharin ◽

Maryna Zhurak

Keyword(s):

Statistical Analysis ◽

Autoregressive Model ◽

Temporal Data ◽

Conditional Autoregressive Model ◽

Conditional Autoregressive ◽

Spatio Temporal

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Intelligent transport systems. Spatio-temporal data dictionary for cooperative ITS and automated driving systems

10.3403/30354876 ◽

2018 ◽

Cited By ~ 1

Keyword(s):

Transport Systems ◽

Data Dictionary ◽

Temporal Data ◽

Automated Driving ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

Spatio Temporal

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Spatial-temporal data collection process models using mobile sinks

Geodesy and Cartography ◽

10.22389/0016-7126-2018-942-12-22-28 ◽

2019 ◽

Vol 942 (12) ◽

pp. 22-28

Author(s):

A.V. Materuhin ◽

V.V. Shakhov ◽

O.D. Sokolova

Keyword(s):

Energy Consumption ◽

Operation Time ◽

Point Of View ◽

Process Models ◽

Temporal Data ◽

Network Nodes ◽

Mobile Sinks ◽

Autonomous Operation ◽

Data Collection Process ◽

Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

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