| Records |
| Author |
Sérgio Freire; Christoph Aubrecht |
| Title |
Assessing spatio-temporal population exposure to tsunami hazard in the Lisbon Metropolitan Area |
Type |
Conference Article |
| Year |
2011 |
Publication |
8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 |
Abbreviated Journal |
ISCRAM 2011 |
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Hazards; Information systems; Mapping; Population distribution; Population statistics; Land use and land cover; Lisbon; Metropolitan area; Population exposure; Spatio-temporal; Spatiotemporal distributions; Tsunami hazards; Tsunami inundation; Tsunamis |
| Abstract |
The coastal region of Lisbon, Portugal, is potentially subject to tsunami hazard. Mapping and assessing tsunami risk requires giving adequate consideration to the population exposure. In the present work we model and map the spatio-temporal distribution of population in the daily cycle and analyze it with a tsunami hazard map to better assess tsunami risk in the Lisbon Metropolitan Area. New high-resolution daytime and nighttime population distribution surfaces are developed using 'intelligent dasymetric mapping' to combine best-available census data and statistics with land use and land cover data. Mobility statistics are considered for mapping daytime distribution. Finally, the population distribution maps are combined with the Tsunami Inundation Susceptibility map to assess potential human exposure to tsunami in daytime and nighttime periods. Results show that a significant amount of population is potentially at risk, and its numbers increase from nighttime to daytime, especially in the zones of high susceptibility. |
| Address |
New University of Lisbon, E-GEO, Geography and Regional Planning Research Center, Portugal; AIT Austrian Institute of Technology, Foresight and Policy Development Department, Austria |
| Corporate Author |
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Thesis |
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| Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Lisbon |
Editor |
M.A. Santos, L. Sousa, E. Portela |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
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| ISSN |
2411-3387 |
ISBN |
9789724922478 |
Medium |
|
| Track |
Geographic Information Science |
Expedition |
|
Conference |
8th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
no |
| Call Number |
|
Serial |
511 |
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| Author |
Benjamin Herfort; João Porto De Albuquerque; Svend-Jonas Schelhorn; Alexander Zipf |
| Title |
Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013 |
Type |
Conference Article |
| Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
| Volume |
|
Issue |
|
Pages |
747-751 |
| Keywords |
Catchments; Data mining; Information systems; Social networking (online); Spatial distribution; Water levels; Crisis management; Digital elevation model; Geographical features; Situational awareness; Social media; Social media platforms; Spatiotemporal distributions; Twitter; Floods |
| Abstract |
In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring. |
| Address |
GIScience Department, Heidelberg University, Germany; Dept. of Computer Systems/ICMC, University of Sao Paulo, Brazil |
| Corporate Author |
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Thesis |
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| Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9780692211946 |
Medium |
|
| Track |
Social Media in Crisis Response and Management |
Expedition |
|
Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
no |
| Call Number |
|
Serial |
572 |
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