A Framework for Visualization and Exploration of Events

2007 ◽  
Vol 7 (2) ◽  
pp. 133-151 ◽  
Author(s):  
Kate Beard ◽  
Heather Deese ◽  
Neal R. Pettigrew
Keyword(s):  
Gulf Of Maine ◽  
Temporal Trends ◽  
Temporal Patterns ◽  
Sensor Data ◽  
Temporal Data ◽  
Spatial And Temporal Patterns ◽  
Interface Elements ◽  
Analysis Tools ◽  
Time Space ◽  
Spatio Temporal

The expanding deployment of sensor systems that capture location, time, and multiple thematic variables is increasing the need for exploratory spatio-temporal data analysis tools. Geographic information systems (GIS) and time series analysis tools support exploration of spatial and temporal patterns respectively and independently, but tools for the exploration of both dimensions within a single system are relatively rare. The contribution of this research is a framework for the visualization and exploration of spatial, temporal, and thematic dimensions of sensor-based data. The unit of analysis is an event, a spatio-temporal data type extracted from sensor data. The conceptual framework suggests an approach for design layout that can be flexibly modified to explore spatial and temporal trends, temporal relationships among events, periodic temporal patterns, the timing of irregularly repeating events, event–event relationships in terms of thematic attributes, and event patterns at different spatial and temporal granularities. Flexible assignment of spatial, temporal, and thematic categories to a set of graphical interface elements that can be easily rearranged provides exploratory power as well as a generalizable design layout structure. The framework is illustrated with events extracted from Gulf of Maine Ocean Observing System data but the approach has broad application to other domains and applications in which time, space, and attributes need to be considered in conjunction.

THE NEW RADIOCARBON DATASET FOR TRILITH MONUMENTS OF SOUTHEASTERN ARABIA

Radiocarbon ◽  
2020 ◽  
pp. 1-11
Author(s):  
R Garba ◽  
P Demján ◽  
I Svetlik ◽  
D Dreslerová
Keyword(s):  
Iron Age ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Coastal Plains ◽  
Spatial And Temporal Patterns ◽  
Two Phase ◽  
Stone Monuments ◽  
South Central ◽  
Geographical Expansion ◽  
Spatio Temporal

ABSTRACT Triliths are megalithic monuments scattered across the coastal plains of southern and southeastern Arabia. They consist of aligned standing stones with a parallel row of large hearths and form a space, the meaning of which is undoubtedly significant but nonetheless still unknown. This paper presents a new radiocarbon (14C) dataset acquired during the two field seasons 2018–2019 of the TSMO (Trilith Stone Monuments of Oman) project which investigated the spatial and temporal patterns of the triliths. The excavation and sampling of trilith hearths across Oman yielded a dataset of 30 new 14C dates, extending the use of trilith monuments to as early as the Iron Age III period (600–300 BC). The earlier dates are linked to two-phase trilith sites in south-central Oman. The three 14C pairs collected from the two-phase trilith sites indicated gaps between the trilith construction phases from 35 to 475 years (2 σ). The preliminary spatio-temporal analysis shows the geographical expansion of populations using trilith monuments during the 5th to 1st century BC and a later pull back in the 1st and 2nd century AD. The new 14C dataset for trilith sites will help towards a better understanding of Iron Age communities in southeastern Arabia.

scholarly journals RESEARCH ON CONSTRUCTION METHOD OF SPATIO-TEMPORAL DATA MODEL BASED ON BEHAVIOR-EVENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 591-594
Author(s):  
J. W. Li ◽  
Y. Ma ◽  
J. W. Jiang ◽  
W. D. Chen ◽  
N. Yu ◽  
...  
Keyword(s):  
Data Model ◽  
Logical Structure ◽  
Physical Structure ◽  
Temporal Data ◽  
Model Based ◽  
Time Space ◽  
Before And After ◽  
Spatio Temporal ◽  
Temporal Objects ◽  
Organic Combination

Abstract. Starting from the object-oriented idea, this paper analyses the existing event-based models and the logical relationship between behavioral cognition and events, and discusses the continuity of behavioral cognition on the time axis from the perspective of temporal and spatial cognition. A geospatial data model based on behavioral-event is proposed. The physical structure and logical structure of the model are mainly designed, and the four-dimensional model of “time, space, attribute and event” is constructed on the axis. The organic combination of the four models can well describe the internal mechanism and rules of geographical objects. The expression of data model based on behavior-event not only elaborates the basic information of geospatial objects, but also records the changes of related events caused by the changes of geographic Entities' behavior, and expresses the relationship between spatial and temporal objects before and after the changes of behavior cognition. This paper also designs an effective method to organize spatio-temporal data, so as to realize the effective management and analysis of spatio-temporal data and meet the requirements of storage, processing and mining of large spatio-temporal data.

scholarly journals Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

2020 ◽  
Vol 17 (7) ◽  
pp. 2563 ◽  
Author(s):  
Wentao Yang ◽  
Min Deng ◽  
Chaokui Li ◽  
Jincai Huang
Keyword(s):  
Temporal Dynamics ◽  
Spatial Interaction ◽  
Temporal Trends ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Hubei Province ◽  
Incidence Rates ◽  
County Level ◽  
Spatial Clusters ◽  
Spatio Temporal

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

scholarly journals Estimating Spatial and Temporal Trends in Environmental Indices Based on Satellite Data: A Two-Step Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 361 ◽  
Author(s):  
Brigitte Colin ◽  
Kerrie Mengersen
Keyword(s):  
Satellite Data ◽  
Temporal Trends ◽  
Regression Tree ◽  
Spatial And Temporal Patterns ◽  
Boosted Regression Tree ◽  
Environmental Indices ◽  
Study Region ◽  
Explanatory Variables ◽  
Green Vegetation ◽  
Spatio Temporal

This paper presents a method for employing satellite data to evaluate spatial and temporal patterns in environmental indices of interest. In the first step, linear regression coefficients are extracted for each area in the image. These coefficients are then employed as a response variable in a boosted regression tree with geographic coordinates as explanatory variables. Here, a two-step approach is described in the context of a substantive case study comprising 30 years of satellite derived fractional green vegetation cover for a large region in Queensland, Australia. In addition to analysis of the entire image and timeframe, separate analyses are undertaken over decades and over sub-regions of the study region. The results demonstrate both the utility of the approach and insights into spatio-temporal trends in green vegetation for this site. These findings support the feasibility of using the proposed two-step approach and geographic coordinates in the analysis of satellite derived indices over space and time.

scholarly journals Merging diaries and GPS records: The method of data collection for spatio-temporal research

2015 ◽  
Vol 23 (2) ◽  
pp. 12-25
Author(s):  
Martin Šveda ◽  
Michala Madajová
Keyword(s):  
Data Collection ◽  
Traditional Method ◽  
Temporal Data ◽  
Proof Of Concept ◽  
Data Frame ◽  
Gps Technology ◽  
Time Space ◽  
Spatio Temporal ◽  
Transportation Research ◽  
Space Data

Abstract The results of a ‘proof-of-concept’ study that examined a new opportunity for using GPS technology in activity surveys are presented in this article. The aim is to demonstrate the method of collection and processing of individual time-space data via the dual records of a time-space diary and the GPS locator. The GPS technology here is not treated as a substitute for the traditional method of diaries; rather, the paper concentrates on the potential existing in a combination of these two techniques. The time-geographical approach and the corresponding methodology are used in order to assess the complexities of an individual’s everyday life, and to capture the spectrum of human activities in a data frame applicable to different analyses in behavioural, social and transportation research. This method not only improves the quality and robustness of spatio-temporal data, but also reduces under-reporting and the burdens on the respondents.

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

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.

Mining Flow Patterns in Spatio-Temporal Data

2008 ◽  
pp. 157-188
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Temporal Patterns ◽  
Flow Patterns ◽  
Optimization Techniques ◽  
Temporal Data ◽  
Generation Algorithm ◽  
Spatio Temporal

In this chapter, we describe flow patterns and the design of the algorithm called FlowMiner to find such flow patterns. FlowMiner incorporates a new candidate generation algorithm and employs various optimization techniques for better efficiency. The discovery of generalized spatio-temporal patterns will be described in the next chapter.

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