Geographic Data Science
TLDR
The articles selected for this special issue represent a mix of theoretical approaches and novel applications of geographic data science.Abstract:
Data science methods and approaches address all stages of transition from data to knowledge and action Visualization of this data is essential for human understanding of the subject under study, analytical reasoning about it, and generating new knowledge Geographic data science deals with data that incorporates spatial and, often, temporal elements The articles selected for this special issue represent a mix of theoretical approaches and novel applications of geographic data scienceread more
Citations
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Journal ArticleDOI
Smart cities, big data and urban policy: Towards urban analytics for the long run
Jens Kandt,Michael Batty +1 more
TL;DR: A theoretical perspective on urban analytics as a practice that is part of a new smart urbanism is developed and the particular tension of opposed temporalities of high-frequency data and the long duree of structural challenges facing cities is identified.
Journal ArticleDOI
Geographic Data Science
TL;DR: It is argued for the positive role that Geography can have on Data Science when being applied to spatially explicit problems; and inversely, it is made the case that there is much that Ge geography and Geographical Analysis could learn from Data Science.
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Opening practice: supporting reproducibility and critical spatial data science
Chris Brunsdon,Alexis Comber +1 more
TL;DR: It is argued that this closed form software is problematic and considers a number of ways in which issues identified in spatial data analysis could be overlooked when working with closed tools, leading to problems of interpretation and possibly inappropriate actions and policies based on these.
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Exploring new ways of visitor tracking using big data sources: Opportunities and limits of passive mobile data for tourism
Julian Reif,Dirk Schmücker +1 more
TL;DR: The study found that, at the current state of research, PMD can measure the mobility of people in space and time but are not suitable for correctly identifying tourists and distinguishing them from non-tourists.
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A Data Science Framework for Movement
TL;DR: This paper introduces a data science paradigm with the aim of advancing research on movement and proposes a new approach to visualize, model, and analyze movement as a multidimensional process that involves space, time, and context.
References
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Journal ArticleDOI
Space, time and visual analytics
Gennady Andrienko,Natalia Andrienko,Urška Demšar,Doris Dransch,Jason Dykes,Sara Irina Fabrikant,Mikael Jern,Menno-Jan Kraak,Heidrun Schumann,Christian Tominski +9 more
TL;DR: Researchers should find approaches to deal with the complexities of the current data and problems and find ways to make analytical tools accessible and usable for the broad community of potential users to support spatio-temporal thinking and contribute to solving a large range of problems.
Journal ArticleDOI
Evaluating the effect of visually represented geodata uncertainty on decision-making: systematic review, lessons learned, and recommendations
TL;DR: This paper presents a comprehensive review of uncertainty visualization assessments from geovisualization and related fields, and presents a categorization of research foci related to evaluating the effects of uncertainty visualize on decision-making.
Book ChapterDOI
A Taxonomy of Dirty Time-Oriented Data
TL;DR: This work addresses ‘dirty’ time-oriented data, i.e., time- oriented data with potential quality problems with categorized information related to existing taxonomies, to establish a basis for further research in the field of dirty time-orientation data, and for the formulation of essential quality checks when preprocessing time-driven data.
Journal ArticleDOI
Visual Encodings of Temporal Uncertainty: A Comparative User Study
TL;DR: A comprehensive study comparing alternative visual encodings of intervals with uncertain start and end times: gradient plots, violin plots, accumulated probability plots, error bars, centered error Bars, and ambiguation reveals significant differences in error rates and completion time.
Journal ArticleDOI
Understanding movement data quality
TL;DR: This work reviews the key properties of movement data and creates a typology of possible data quality problems and suggests approaches to identify these types of problems.