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| Titel |
Relationship between the spatial distribution of SMS messages reporting needs and building damage in 2010 Haiti disaster |
| VerfasserIn |
C. Corbane, G. Lemoine, M. Kauffmann |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 2 ; Nr. 12, no. 2 (2012-02-02), S.255-265 |
| Datensatznummer |
250010493
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| Publikation (Nr.) |
 copernicus.org/nhess-12-255-2012.pdf |
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| Zusammenfassung |
Just 4 days after the M = 7.1 earthquake on 12 January 2010, Haitians could
send SMS messages about their location and urgent needs through the on-line
mapping platform Ushahidi. This real-time crowdsourcing of crisis
information provided direct support to key humanitarian resources on the
ground,
including Search and Rescue teams. In addition to its use as a knowledge
base for rescue operations and aid provision, the spatial distribution of
geolocated SMS messages may represent an early indicator on the spatial
distribution and on the intensity of building damage.
This work explores the relationship between the spatial patterns of SMS
messages and building damage. The latter is derived from the detailed damage
assessment of individual buildings interpreted in post-earthquake airborne
photos. The interaction between SMS messages and building damage is studied
by analyzing the spatial structure of the corresponding bivariate patterns.
The analysis is performed through the implementation of cross Ripley's
K-function which is suitable for characterizing the spatial structure of a
bivariate pattern, and more precisely the spatial relationship between two
types of point sets located in the same study area.
The results show a strong attraction between the patterns exhibited by SMS
messages and building damages. The interactions identified between the two
patterns suggest that the geolocated SMS can be used as early indicators of
the spatial distribution of building damage pattern. Accordingly, a
statistical model has been developed to map the distribution of building
damage from the geolocated SMS pattern.
The study presented in this paper is the first attempt to derive
quantitative estimates on the spatial patterns of novel crowdsourced
information and correlate these to established methods in damage assessment
using remote sensing data. The consequences of the study findings for rapid
damage detection in post-emergency contexts are discussed. |
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