The Presence of Absence in Geovisual Analytics of Big Data

Anthony C. Robinson

doi:10.1109/BGDDS.2018.8626811

The Presence of Absence in Geovisual Analytics of Big Data

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

New spatial data sources are emerging that have attributes of big data. These sources challenge our ability to handle high degrees of velocity, volume, and variety, and also frequently include a wide range of attributes that stretch our ability to characterize their veracity. While it remains quite challenging to visually represent, interact with, and reason about these data, our focus in this work is to suggest what analysts may be able to learn from the gaps in what we measure or the absences suggested by certain attributes. Put simply, we need to know more about how to show the missingness and absence in spatial data and to understand how people reason with that type of information in geovisual analytics environments.

Original language	English (US)
Title of host publication	BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538614372
DOIs	https://doi.org/10.1109/BGDDS.2018.8626811
State	Published - Jul 2 2018
Event	2018 International Workshop on Big Geospatial Data and Data Science, BGDDS 2018 - Wuhan, China Duration: Sep 22 2018 → Sep 23 2018

Publication series

Name	BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science

Conference

Conference	2018 International Workshop on Big Geospatial Data and Data Science, BGDDS 2018
Country/Territory	China
City	Wuhan
Period	9/22/18 → 9/23/18

All Science Journal Classification (ASJC) codes

Information Systems and Management
Computers in Earth Sciences
Information Systems
Management, Monitoring, Policy and Law

Access to Document

10.1109/BGDDS.2018.8626811

Cite this

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title = "The Presence of Absence in Geovisual Analytics of Big Data",

abstract = "New spatial data sources are emerging that have attributes of big data. These sources challenge our ability to handle high degrees of velocity, volume, and variety, and also frequently include a wide range of attributes that stretch our ability to characterize their veracity. While it remains quite challenging to visually represent, interact with, and reason about these data, our focus in this work is to suggest what analysts may be able to learn from the gaps in what we measure or the absences suggested by certain attributes. Put simply, we need to know more about how to show the missingness and absence in spatial data and to understand how people reason with that type of information in geovisual analytics environments.",

author = "Robinson, {Anthony C.}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Workshop on Big Geospatial Data and Data Science, BGDDS 2018 ; Conference date: 22-09-2018 Through 23-09-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/BGDDS.2018.8626811",

language = "English (US)",

series = "BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science",

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}

Robinson, AC 2018, The Presence of Absence in Geovisual Analytics of Big Data. in BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science., 8626811, BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science, Institute of Electrical and Electronics Engineers Inc., 2018 International Workshop on Big Geospatial Data and Data Science, BGDDS 2018, Wuhan, China, 9/22/18. https://doi.org/10.1109/BGDDS.2018.8626811

The Presence of Absence in Geovisual Analytics of Big Data. / Robinson, Anthony C.
BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science. Institute of Electrical and Electronics Engineers Inc., 2018. 8626811 (BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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PY - 2018/7/2

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N2 - New spatial data sources are emerging that have attributes of big data. These sources challenge our ability to handle high degrees of velocity, volume, and variety, and also frequently include a wide range of attributes that stretch our ability to characterize their veracity. While it remains quite challenging to visually represent, interact with, and reason about these data, our focus in this work is to suggest what analysts may be able to learn from the gaps in what we measure or the absences suggested by certain attributes. Put simply, we need to know more about how to show the missingness and absence in spatial data and to understand how people reason with that type of information in geovisual analytics environments.

AB - New spatial data sources are emerging that have attributes of big data. These sources challenge our ability to handle high degrees of velocity, volume, and variety, and also frequently include a wide range of attributes that stretch our ability to characterize their veracity. While it remains quite challenging to visually represent, interact with, and reason about these data, our focus in this work is to suggest what analysts may be able to learn from the gaps in what we measure or the absences suggested by certain attributes. Put simply, we need to know more about how to show the missingness and absence in spatial data and to understand how people reason with that type of information in geovisual analytics environments.

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T3 - BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science

BT - BGDDS 2018 - 2018 International Workshop on Big Geospatial Data and Data Science

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Workshop on Big Geospatial Data and Data Science, BGDDS 2018

Y2 - 22 September 2018 through 23 September 2018

ER -

The Presence of Absence in Geovisual Analytics of Big Data

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