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

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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Interactive Exploratory Analysis of Spatio-Temporal Data

Compstat ◽

10.1007/978-3-642-57489-4_61 ◽

2002 ◽

pp. 407-412

Author(s):

Johannes M. Dreesman

Keyword(s):

Exploratory Analysis ◽

Temporal Data ◽

Spatio Temporal

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Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

Sustainability ◽

10.3390/su12229662 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9662 ◽

Cited By ~ 1

Author(s):

Disheng Yi ◽

Yusi Liu ◽

Jiahui Qin ◽

Jing Zhang

Keyword(s):

Traffic Management ◽

Spatial Clustering ◽

Spatial Interaction ◽

The Other ◽

Temporal Data ◽

Trajectory Data ◽

Data Field ◽

Spatio Temporal ◽

The Impact

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

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Using treemaps for variable selection in spatio-temporal visualisation

Information Visualization ◽

10.1057/palgrave.ivs.9500185 ◽

2008 ◽

Vol 7 (3-4) ◽

pp. 210-224 ◽

Cited By ~ 23

Author(s):

Aidan Slingsby ◽

Jason Dykes ◽

Jo Wood

Keyword(s):

Variable Selection ◽

Visual Analysis ◽

Temporal Distribution ◽

Temporal Data ◽

Data Set ◽

Raster Maps ◽

Categorical Attributes ◽

Interactive Interfaces ◽

Road Maps ◽

Spatio Temporal

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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RESEARCH ON VISUAL ANALYSIS METHODS OF TERRORISM EVENTS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b2-191-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 191-196

Author(s):

Wenyue Guo ◽

Haiyan Liu ◽

Anzhu Yu ◽

Jing Li

Keyword(s):

Visual Analysis ◽

Temporal Distribution ◽

Development Trend ◽

Event Data ◽

Thematic Map ◽

The World ◽

Spatio Temporal ◽

Global Terrorism Database ◽

The Development Trend ◽

Security Incidents

Under the situation that terrorism events occur more and more frequency throughout the world, improving the response capability of social security incidents has become an important aspect to test governments govern ability. Visual analysis has become an important method of event analysing for its advantage of intuitive and effective. To analyse events’ spatio-temporal distribution characteristics, correlations among event items and the development trend, terrorism event’s spatio-temporal characteristics are discussed. Suitable event data table structure based on “5W” theory is designed. Then, six types of visual analysis are purposed, and how to use thematic map and statistical charts to realize visual analysis on terrorism events is studied. Finally, experiments have been carried out by using the data provided by Global Terrorism Database, and the results of experiments proves the availability of the methods.

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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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Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage

Applied Sciences ◽

10.3390/app11041636 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1636

Author(s):

Javier Sevilla ◽

Pablo Casanova-Salas ◽

Sergio Casas-Yrurzum ◽

Cristina Portalés

Keyword(s):

Ad Hoc ◽

Computer Architectures ◽

Visualization Tool ◽

Temporal Data ◽

Use Of Data ◽

Source Data ◽

Visualization Tools ◽

Spatio Temporal ◽

Interactive Map

Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present STMaps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. STMaps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. STMaps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As STMaps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. STMaps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.

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