Smart City Data Analysis via Visualization of Correlated Attribute Patterns

Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among...

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Published in:arXiv.org 2021-04
Main Authors: Sasaki, Yuya, Hori, Keizo, Nishihara, Daiki, Ohashi, Sora, Wakuta, Yusuke, Harada, Kei, Onizuka, Makoto, Arase, Yuki, Shimojo, Shinji, Doi, Kenji, He Hongdi, Zhong-Ren, Peng
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Language:English
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Data analysis
Data mining
Interactive systems
Sensors
Smart cities
Traffic volume
Visualization
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creator Sasaki, Yuya
Hori, Keizo
Nishihara, Daiki
Ohashi, Sora
Wakuta, Yusuke
Harada, Kei
Onizuka, Makoto
Arase, Yuki
Shimojo, Shinji
Doi, Kenji
He Hongdi
Zhong-Ren, Peng
description Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among multiple attributes from the sets of sensors spatially close to each other and temporally correlated in their measurements. In this paper, we develop a visualization system for CAP mining and demonstrate analysis of smart city data. Our visualization system supports an intuitive understanding of mining results via sensor locations on maps and temporal changes of their measurements. In our demonstration scenarios, we provide four smart city datasets collected from China and Santander, Spain. We demonstrate that our system helps interactive analysis of smart city data.
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subjects Data analysis
Data mining
Interactive systems
Sensors
Smart cities
Traffic volume
Visualization
title Smart City Data Analysis via Visualization of Correlated Attribute Patterns
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