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Stable Hotspot Analysis for Intra-Urban Heat Islands

Bruns, Julian; Simko, Viliam

Abstract:

The Urban Heat Island (UHI) effect describes the difference in temperature between cities and their surrounding areas. However, temperature differences within city limits, so-called Intra-Urban Heat Islands (IUHI), affect human health as well as the energy demands in local areas. In order to anticipate and mitigate the resulting impacts of heat through urban planning, a method to reliably detect these local areas is needed. Existing methods from the geo-statistical field can identify these areas. But these statistics, depending on their parametrization, can be unstable in their detection of hotspots, in particular temperature hotspots. In this paper, we propose a modification of the well-known Getis-Ord (G$^{*}$) statistic, called the Focal G$^{*}$ statistic. This modification replaces the computation of the global mean and standard deviation with their focal counterparts. We define the stability of our approach by introducing a stability metric called Stability of Hotspot (SoH), which requires that hotspots have to be in similar areas regardless of the chosen weight matrix. The results are evaluated on real-world temperature data for the city of Karlsruhe.


Volltext §
DOI: 10.5445/IR/1000071483
Originalveröffentlichung
DOI: 10.1553/giscience2017_01_s79
Dimensions
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Universität Karlsruhe (TH) – Einrichtungen in Verbindung mit der Universität (Einrichtungen in Verbindung mit der Universität)
FZI Forschungszentrum Informatik (FZI)
Fakultät für Wirtschaftswissenschaften – Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2017
Sprache Englisch
Identifikator ISSN: 2308-1708
urn:nbn:de:swb:90-714832
KITopen-ID: 1000071483
Erschienen in GI-Forum
Verlag ÖAW Verlag, Wien
Band 1
Seiten 79-92
Schlagwörter Urban Heat Island, Intra-Urban Heat Island, Hotspot Analysis, Stability Metric
Nachgewiesen in Dimensions
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