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Robustness analyses for repeated mobility surveys in outdoor advertising

 
: Hecker, Dirk; Körner, Christine; May, Michael

:
Volltext urn:nbn:de:0011-n-1944515 (317 KByte PDF)
MD5 Fingerprint: 2af44cd2f42274270a4d229168d229d7
© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 8.3.2012


Leung, Y. ; Institute of Electrical and Electronics Engineers -IEEE-:
1st IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2011. Proceedings : In conjunction with Eighth International Workshop on Geographical Information Science, BJ-IWGIS 2011, June 29 2011-July 1 2011, Fuzhou
New York, NY: IEEE, 2011
ISBN: 978-1-4244-8352-5
ISBN: 978-1-4244-8349-5 (Print)
ISBN: 978-1-4244-8350-1 (CD-ROM)
S.148-153
International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) <1, 2011, Fuzhou>
International Workshop on Geographical Information Science (IWGIS) <8, 2011, Fuzhou>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
mobility mining; robustness analysis; sampling; GPS; outdoor advertising; bootstrap; standard error

Abstract
A growing number of companies use mobility data in their day-to-day business. However, as the data grows older, new data has to be collected in order to keep applications up-to-date. Consequently, it is of great importance to know the impact that a different mobility sample may cause. This aspect of analysis has been largely neglected in mobility data mining research so far. In this paper we therefore analyze the robustness of performance measures with respect to a changed GPS sample in outdoor advertisement. The evaluation of outdoor advertising campaigns is a challenging application because it requires the evaluation of mobility data on a very fine spatial level. Thus, the application has a higher dependency on routes of individual test persons than classical mobility surveys. In our rob ustness analysis we apply bootstrapping and subsampling in order to measure the effect of a) a repeated mobility survey and b) a mobility survey of smaller size. We conduct our experiments on a real-world data set from Swiss outdoor advertising. Our results show that the effect is comparably small for a typical campaign and may be mitigated further by increasing the campaign size.

: http://publica.fraunhofer.de/dokumente/N-194451.html