Exploratory analysis of spatial and temporal data - a systematic approach

By Natalia V. Andrienko. 2005

524 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/37316a323a2808f3d4dd6a5086e72b56ba960a96

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geography, Planning and Development

In Information Systems

In Transportation

Citations in the corpus

Interactive Visual Profiling of Musicians (2016, background): “An overview of various visual analytics approaches to dynamically explore spatio-temporal data with the help of maps and timelines is given by Andrienko et al. [4].”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, methodology): “The Andrienkos[7] point out that a common goal in explorative data analysis is to ‘get acquainted with the data’.”
PySAL: A Python Library of Spatial Analytical Methods (2010, background): “Chief among these are GeoDa, GeoVista Studio (Takatsuka and Gahegan 2002), CommonGIS (Andrienko and Andrienko 2005; Andrienko, Andrienko, and Voss 2003), among others.”
A Taxonomy and Survey of Dynamic Graph Visualization (2017, background): “Kerracher et al. [KKC15] extend the generic task framework by Andrienko and Andrienko [AA06] and describe the design space of dynamic graph visualization tasks as a combinationoftwodimensions:ﬁrst,taskcategoriessuchaslookup, comparisonandrelationseeking;second,thedataitemsinvolved…”
SensePlace2: GeoTwitter analytics support for situational awareness (2011, background): “…that supports foraging and sensemaking activities generally along with more specific SA tasks such as information chunking and the identification of patterns and relationships (for background on place-time-object models for information representation and use, see [9], [10, 11], and [12, 13]).”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, unknown): “Automated analysis methods are often applied in order to abstract time-related data characteristics, for example, by computing temporal data trends [21] or statistical aggregates such as mean values or standard deviations [15].”
Visual Analytics of Movement (2013, background): “A task may be elementary with respect to one component of a phenomenon and synoptic with respect to another component (Andrienko and Andrienko 2006).”
Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines (2010, methodology): “However, the work of Ellis and Dix ignores the potentially useful distinction between performing abstraction in data space and in visual space that is highlighted by Cui et al. [22].”
Online Evaluation for Information Retrieval (2016, methodology): “, 2011], and visualization [Tufte and Graves-Morris, 1983, Andrienko and Andrienko, 2006, Unwin, 2015]. Testing the effectiveness of algorithmic changes in large-scale Web applications is only one of the many important uses of controlled experiments in IR. When information retrieval initially requir”
Space, time and visual analytics (2010, methodology): “Interactive maps and other techniques and tools for interactive visual analysis of spatial and spatio-temporal data and for spatio-temporal decision-making are designed, developed and evaluated predominantly in field of geovisualization (Dykes et al. 2005b, Andrienko and Andrienko 2006).”
A Multi-Level Typology of Abstract Visualization Tasks (2013, methodology): “…systems; we acknowledge that their scope is often deliberately constrained , with some focusing on low-level tasks, interactions, or operations [2] [5] [10] [12] [13] [14] [15] [19] [29] [37] [59] [61] [72] [73] [75] [78] [82] [83], while others focus on high-level tasks or goals [3] [11] [31]…”