NDLI: Analysis of urban heat island effect using k-means clustering

Content Provider	IEEE Xplore Digital Library
Author	Yufeng Luo Yunlu Jiang Khan, Shahbaz Shizhang Peng Yuehua Feng Bin Han
Copyright Year	2010
Description	Author affiliation: Division of Water Sciences, UNESCO, Paris 75732, France (Khan, Shahbaz) \|\| State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China (Yufeng Luo; Yunlu Jiang; Shizhang Peng; Bin Han) \|\| Huibei Water Science Experiment Station, East Henan, Water Projects Management Bureau, Kaifeng 475100, China (Yuehua Feng)
Abstract	The characteristics of the urban heat island effect were often examined using urban heat island intensity (UHII). We collected daily mean, maximum and minimum temperature data from the Kaifeng Weather Station in the Kaifeng City and Huibei Station in the surrounding rural area for 2004–2008. Daily UHIIs calculated from daily mean, maximum and minimum temperature were regarded as components of vectors in a 3-D space and they clustered using the k-means algorithm. Clustering results showed that there was usually a slight UHI for most days, while sometimes nocturnal UHI effect was significant. There were very few cases with extremely obvious UHI effect and cooling effect was unignorable. The clustering results confirmed the graphical interpretations. The clustering method presented in this paper provides an alternative way for analysis of UHI effect.
Starting Page	3543
Ending Page	3546
File Size	412152
Page Count	4
File Format	PDF
ISBN	9781424476169
e-ISBN	9781424476183
DOI	10.1109/ICISE.2010.5691908
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cooling Laboratories Urban heat island effect Clustering algorithms Cities and towns Classification algorithms Thermal pollution Data mining Daily temperature K-means clustering Meteorology
Content Type	Text
Resource Type	Article

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