Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time

Demšar, Urška; Buchin, Kevin; Loon, E.E. van; Shamoun‐Baranes, Judy

doi:10.1007/s10707-014-0207-5

Cited by 31 publications

(45 citation statements)

References 51 publications

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“…While point density estimation is widely used to quantify hazard of disasters with concentric impact, a line-based density estimation is imperative to map the hazard of disasters with linear distribution. Density estimation techniques have been applied to examine spatiotemporal dynamics in some domains [19][20][21][22][23][24]. ese types of techniques generally use a density kernel to spread the values of the samples out over a surface.…”

Section: Introductionmentioning

confidence: 99%

“…Steiniger and Hunter [22] extend the pointbased kernel density estimation (KDE) approach to work with sequential GPS-point tracks, the outcome of which is a line-based KDE. Demšar et al [23] present an alternative geovisualization method for spatiotemporal aggregation of trajectories of tagged animals: stacked space-time densities. Given tracks of tropical cyclones are represented as lines, and in this study, we propose a track density algorithm to map the density of tropical cyclones.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Exploration and Hazard Mapping of Tropical Cyclones along the Coastline of China

Zhong

Wang

et al. 2018

Advances in Meteorology

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Spatiotemporal patterns are one of the greatest interests and provide valuable insights into chronological events occurring in space. A tropical cyclone (TC) track is defined as a sequence of successive points, and several different types of analyses are performed to explore the temporal and spatial patterns of the TCs in the Northwest Pacific and along the coastline of China during . Results show that (1) the number of TCs is getting more frequent from April to August and less frequent from August to October with the peak occurring in August almost every year; (2) the mean of the sizes of the annual temporal clusters during 1949-2014 is 52.5 (days), the standard deviation is 17.0 (days), and the average starting point is the 210.5th day; (3) the spatial clusters are located in two areas: the boundary of Guangxi and Guangdong provinces and the boundary of Fujian and Zhejiang provinces; and (4) the within-strata variance is less than the between-strata variance, which implies the locational and seasonal factors are the potential determinants of the heterogeneity of the TCs. Furthermore, several maps representing the hazards of TCs are produced. According to the resultant maps, 12 coastal prefectures (Zhanjiang, Maoming, Fuzhou, Huizhou, Yangjiang, Qinzhou, Ningde, Quanzhou, Jiangmen, Nanning, Zhangzhou, and Hangzhou) have return periods of less than two years, and the two island provinces of Hainan and Taiwan are visited by TCs the most. Guangdong, Guangxi, Fujian, and Zhejiang provinces in particular suffered severely from the destructive TCs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Exploration and Hazard Mapping of Tropical Cyclones along the Coastline of China

Zhong

Wang

et al. 2018

Advances in Meteorology

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“…Recent ubiquity and widespread use of modern positioning and context-aware devices have enabled the acquisition of movement positions and attributes of almost any type of moving object, and thus, have produced large amounts of trajectories data [1]. These data are usually collected as a series of trajectories; that is, when an object moves in the basic 3D geographic space of our physical world, the movement of each object can be presented as a tilted 3D polyline in space [2].…”

Section: Introductionmentioning

confidence: 99%

“…Analysis and exploration of large movement datasets is of great significance to many fields, such as the exploration of the movement laws of a particle, the analysis of human behavior, and the search for 'bottlenecks' in transportation networks [3]. They study the movement of groups or individuals on different spatial scales, different time scales, and with varying degrees of complexity [1,4].…”

Section: Introductionmentioning

confidence: 99%

4D Time Density of Trajectories: Discovering Spatiotemporal Patterns in Movement Data

Zou

Chen

et al. 2018

IJGI

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Abstract:Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common way to visualize and explore trajectory data is to use the 3D cube (e.g., space-time cube), where trajectories are presented as a tilted 3D polyline. As larger movement datasets become available, this type of display can easily become confusing and illegible. In addition, movement datasets are often unprecedentedly massive, high-dimensional, and complex (e.g., implicit spatial and temporal relations and interactions), making it challenging to explore and analyze the spatiotemporal movement patterns in space. In this paper, we propose 4D time density as a visualization method for identifying and analyzing spatiotemporal movement patterns in large trajectory datasets. The movement range of the objects is regarded as a 3D geographical space, into which the fourth dimension, 4D time density, is incorporated. The 4D time density is derived by modeling the movement path and velocity separately. We present a time density algorithm, and demonstrate it on the simulated trajectory and a real dataset representing the movement data of aircrafts in the Hong Kong International and the Macau International Airports. Finally, we consider wider applications and further developments of time density.

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“…In its basic appearance, the STC's horizontal plane represents space (geographic map), while the vertical axis represents time. A number of works have adopted the STC (Kraak 2008, Huisman et al 2009, Orellana et al 2012, Turdukulov et al 2014, Demšar et al 2015 and different analytical functionalities have been integrated in this visualization (Bach et al 2014). Since visual exploration mainly focuses on data discovery but not on DM; hence, integrating them will further enhance the ability of knowledge discovery.…”

Section: Introductionmentioning

confidence: 99%

Interactive discovery of sequential patterns in time series of wind data

Yusof

Zurita-Milla²,

Kraak³

et al. 2016

International Journal of Geographical Information Science

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Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth's surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time.ARTICLE HISTORY

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Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time

Cited by 31 publications

References 51 publications

Spatiotemporal Exploration and Hazard Mapping of Tropical Cyclones along the Coastline of China

Spatiotemporal Exploration and Hazard Mapping of Tropical Cyclones along the Coastline of China

4D Time Density of Trajectories: Discovering Spatiotemporal Patterns in Movement Data

Interactive discovery of sequential patterns in time series of wind data

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