Geospatial big data and cartography: research challenges and opportunities for making maps that matter

Robinson, Anthony C.; Demšar, Urška; Moore, Antoni; Buckley, Aileen; Jiang, Bin; Field, Kenneth; Kraak, M.J.; Camboim, Silvana Philippi; Sluter, Claúdia Robbi

doi:10.1080/23729333.2016.1278151

Cited by 116 publications

(78 citation statements)

References 114 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, a comprehensive review of even the subset of VA focused on classification tasks is beyond the scope of this paper; for some recent overview papers see [4,[89][90][91][92][93][94]. A VA agenda is provided in [95,96], and then for geovisual analytics and related topics in [97]. Here, we focus specifically on the role of VA interfaces helping analysts understand M&DL, and then in Section 3.3 we review the recent efforts that are specifically focused on the integration of VA with AL methods.…”

Section: Visual Analytics (Va) and Human-in-the-loopmentioning

confidence: 99%

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

Yang

MacEachren

Mitra

et al. 2018

IJGI

View full text Add to dashboard Cite

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components.

show abstract

Section: Visual Analytics (Va) and Human-in-the-loopmentioning

confidence: 99%

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

Yang

MacEachren

Mitra

et al. 2018

IJGI

View full text Add to dashboard Cite

show abstract

“…The methodological component includes gathering real, verified and organized statistical information so it could be processed through GIS. Creating the data base (Robinson et al, 2017) usig the information from website 1 facilitates later on, according to the elements taken into consideration, the mapping of the European football space generated by complex geographical and statistical elements of qualitative and quantitative importance (Reilly & Gilbourne, 2003;Ilieș et al, 2014;Kozma et al, 2015;Herman et al, 2016a;Ilieș et al, 2016a;2016c) Thus, original maps are provided which, due to the methods and principles used in elaborating them (Ilieș et al, 2015;O'Brien & Cheshire, 2016;Gartner & Huang, 2016;Herman et al, 2016b;Roth et al, 2017;Raisch, 2018), are very useful in outlining in time and space of a certain phenomenon, event or sports competition (Bairner, 2011;Buhaș et al, 2017;Ilieș et al, 2015;Ilieș, et al, 2016a;Gaffney, 2016). Through the cartographic method and representation ways (dots, circles, cartograms, symbols, etc.…”

Section: Methodsmentioning

confidence: 99%

Cartographic representation of a sports (football) competition – UEFA Youth League (2013-2019)

Dehoorne¹,

Wendt²,

Mikhaylov³

et al. 2019

GSS

View full text Add to dashboard Cite

The sports phenomenon, football in this case, is analyzed through the angle of the inter-clubs' European competition UEFA Youth League, for teams with players aged under 19. By means of the used cartographic methods, our purpose was to build a useful tool in elaborating territorial planning and development strategies, applicable on local, regional and even continental level. The spatial representation, through adjusted methods, of certain quantitative elements (reflected through number) combined with the qualitative ones (performance) in graphic and cartographic synthetic materials, set in chronological order, finalizes our endeavor with the elaboration of synthetic products of great practical utility. There are analyzed the affiliated federations, participating teams, represented localities etc., the relations between them, their temporal activity sequence and their performances. The resulting maps are thus representations of territorial realities and they faithfully portray the condition of the sports phenomenon through temporal sequences and through its spatial distribution, as a reflection of the support human society.

show abstract

“…Big data are often characterized by a set of so-called V's, corresponding to the challenges associated with volume, velocity, variety, and veracity, among others (Gandomi and Haider 2015;Laney 2001). Broadly, geovisual analytics approaches to handling big spatial data need to address problems associated with analysis, representation, and interaction (Robinson et al 2017), similar to the challenges faced by geovisualization designers. New computational methods are needed to support real-time analysis of big spatial data sources.…”

Section: Big Data Digital Earth and Geovisual Analyticsmentioning

confidence: 99%

Geospatial Information Visualization and Extended Reality Displays

Çöltekin

Griffin

Slingsby

et al. 2019

Manual of Digital Earth

Self Cite

View full text Add to dashboard Cite

In this chapter, we review and summarize the current state of the art in geovisualization and extended reality (i.e., virtual, augmented and mixed reality), covering a wide range of approaches to these subjects in domains that are related to geographic information science. We introduce the relationship between geovisualization, extended reality and Digital Earth, provide some fundamental definitions of related terms, and discuss the introduced topics from a human-centric perspective. We describe related research areas including geovisual analytics and movement visualization, both of which have attracted wide interest from multidisciplinary communities in recent years. The last few sections describe the current progress in the

show abstract

Geospatial big data and cartography: research challenges and opportunities for making maps that matter

Cited by 116 publications

References 114 publications

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

Cartographic representation of a sports (football) competition – UEFA Youth League (2013-2019)

Geospatial Information Visualization and Extended Reality Displays

Contact Info

Product

Resources

About