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Multi-scale analysis of linear data in a two-dimensional space

(2014) INFORMATION VISUALIZATION. 13(3). p.248-265
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Abstract
Many disciplines are faced with the problem of handling time-series data. This study introduces an innovative visual representation for time series, namely the continuous triangular model. In the continuous triangular model, all subintervals of a time series can be represented in a two-dimensional continuous field, where every point represents a subinterval of the time series, and the value at the point is derived through a certain function (e. g. average or summation) of the time series within the subinterval. The continuous triangular model thus provides an explicit overview of time series at all different scales. In addition to time series, the continuous triangular model can be applied to a broader sense of linear data, such as traffic along a road. This study shows how the continuous triangular model can facilitate the visual analysis of different types of linear data. We also show how the coordinate interval space in the continuous triangular model can support the analysis of multiple time series through spatial analysis methods, including map algebra and cartographic modelling. Real-world datasets and scenarios are employed to demonstrate the usefulness of this approach.
Keywords
information, GIS, time intervals, decision-making, triangular model, multi-criteria analysis, information visualization, multi-scale analysis, Time series, linear data

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Chicago
Qiang, Yi, Seyed Hossein Chavoshi, Steven Logghe, Philippe De Maeyer, and Nico Van de Weghe. 2014. “Multi-scale Analysis of Linear Data in a Two-dimensional Space.” Information Visualization 13 (3): 248–265.
APA
Qiang, Yi, Chavoshi, S. H., Logghe, S., De Maeyer, P., & Van de Weghe, N. (2014). Multi-scale analysis of linear data in a two-dimensional space. INFORMATION VISUALIZATION, 13(3), 248–265.
Vancouver
1.
Qiang Y, Chavoshi SH, Logghe S, De Maeyer P, Van de Weghe N. Multi-scale analysis of linear data in a two-dimensional space. INFORMATION VISUALIZATION. 2014;13(3):248–65.
MLA
Qiang, Yi, Seyed Hossein Chavoshi, Steven Logghe, et al. “Multi-scale Analysis of Linear Data in a Two-dimensional Space.” INFORMATION VISUALIZATION 13.3 (2014): 248–265. Print.
@article{5908674,
  abstract     = {Many disciplines are faced with the problem of handling time-series data. This study introduces an innovative visual representation for time series, namely the continuous triangular model. In the continuous triangular model, all subintervals of a time series can be represented in a two-dimensional continuous field, where every point represents a subinterval of the time series, and the value at the point is derived through a certain function (e. g. average or summation) of the time series within the subinterval. The continuous triangular model thus provides an explicit overview of time series at all different scales. In addition to time series, the continuous triangular model can be applied to a broader sense of linear data, such as traffic along a road. This study shows how the continuous triangular model can facilitate the visual analysis of different types of linear data. We also show how the coordinate interval space in the continuous triangular model can support the analysis of multiple time series through spatial analysis methods, including map algebra and cartographic modelling. Real-world datasets and scenarios are employed to demonstrate the usefulness of this approach.},
  author       = {Qiang, Yi and Chavoshi, Seyed Hossein and Logghe, Steven and De Maeyer, Philippe and Van de Weghe, Nico},
  issn         = {1473-8716},
  journal      = {INFORMATION VISUALIZATION},
  keyword      = {information,GIS,time intervals,decision-making,triangular model,multi-criteria analysis,information visualization,multi-scale analysis,Time series,linear data},
  language     = {eng},
  number       = {3},
  pages        = {248--265},
  title        = {Multi-scale analysis of linear data in a two-dimensional space},
  url          = {http://dx.doi.org/10.1177/1473871613477853},
  volume       = {13},
  year         = {2014},
}

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