Presentation @ ISCRAM 2018

Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil
Dr. Flávio E. A. Horita | http://www.flaviohorita.com | CMCC/UFABC, Santo André, Brazil
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10.11.2017
Flávio E. A. Horita1, Ricardo B. Vilela2, Renata G. Martins2, Danielle A. Bressiani2,
Gilca Palma2, João Porto de Abuquerque3
1 CMCC, Federal University of ABC (UFABC), Santo André, Brazil
2 Labs, Brazilian Meteorological Agency, São José dos Campos, Brazil
3 CIM, University of Warwick, Coventry, UK
flavio.horita@ufabc.edu.br | http://www.flaviohorita.com
Determining flooded areas using crowd
sensing data and weather radar precipitation: a
case study in Brazil
ISCRAM 2018
15th International Conference on Information Systems
for Crisis Response and Management

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▷ Introduction
▷ Research design
▷ Approach
▷ Methods
▷ Preliminary results
▷ Final remarks
Agenda

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Crowdsourcing and VGI for disaster risk management
Introduction
Source: Elwood (2008); Goodchild & Glennon (2010); Niko et al., (2011); Horita et al. (2013); Haworth & Bruce (2015)
Hard sensors Exploratory teams
Social Media Collaborative Platforms

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Categories of crowdsourcing
Introduction
▷ Social media: information produced using social
media platforms;
▷ Collaborative mapping: information about
geographic features collected from mapping platforms;
▷ Crowd sensing: information collected from dedicated
applications and platforms;
Source: De Albuquerque et al. (2016)

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Problem statement
Hard sensors have been
used for filtering (and pre-
processing) volunteered
information;
Provided data are
restricted to geographic
location of these sensors
and thus relevant
information may be
eliminated.

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▷ RAdio Detection And Ranging (RADAR)
Problem statement
Research Design
rainfall
eletromagnetic waves
reflection

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Research Question
How can weather radar data validate flooded areas
identified by crowd sensing data?

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Approach
Crowd sensing
data analysis
Weather radar
systems data
analysis
Data validation Flooded areas
Clusters
Rain intensity
category
• Riverbasin
catchments
• Rainfall data
• Output: 0) no, 1)
low, 2) moderate,
and 3) high
• Kernel-density
estimator (KDE);
• Bandwidth: 200
meters of
distance.
• More than 3
elements;
• Lag time of 30
minutes.

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• 12 million inhabitants (9th
highest cities in World)
• An area of ~1.5 million km²
• A population density of ~7,400
inhabitants per km²
Case study
City of São Paulo

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• 12 million inhabitants (9th
highest cities in World)
• An area of ~1.5 million km²
• A population density of ~7,400
inhabitants per km²

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Case study – Geographic distribution
Preliminary Results
Jan 16th, 2018 Jan 21st, 2018

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Case study – Crowd sensing data
Preliminary Results
Jan 16th, 2018 Jan 21st, 2018

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Case study – Weather Radar Systems
Preliminary Results
Jan 16th, 2018 Jan 21st, 2018

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Case study
Preliminary Results
Nro of generated
clusters
Jan 21st, 2018
40 clusters
Jan 16th, 2018
57 clusters
Kernel-density
estimator (KDE);
Bandwidth: 200
meters of distance.
More than 3
elements;
Lag time of 30
minutes.

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Case study
Preliminary Results

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Case study
Preliminary Results

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▷ Weather radar systems are of great value for validating
volunteered information;
▷ Weather radar systems may supplement authoritative
data provided by rainfall gauges and hydrological
stations for pre-processing volunteered information;
Final Remarks

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▷ Employment of further methods for spatial data analysis
like DBSCAN and Moran’s I;
▷ Consideration of community and flood vulnerability
variables in the data analysis;
▷ Conduction of more case studies
▷ Different context settings; e.g., heavy weather and KDE’s
threshold on 120m.
▷ Other collaborative platforms (e.g., Twitter and Instagram).
Future directions
Final Remarks

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• ELWOOD, S. Volunteered geographic information: future research directions motivated by critical,
participatory, and feminist GIS. GeoJournal, v. 72, n. 3-4, p. 173–183, 2008.
• HORITA, F. E. A.; DEGROSSI, L. C.; ASSIS, L. F. G.; ZIPF, A.; ALBUQUERQUE, J. P. The use of volunteered
geographic information (VGI) and crowdsourcing in disaster management: a systematic literature review. In:
Proceedings of the 19th Americas Conference on Information Systems (AMCIS). [S.l.: s.n.], 2013. p. 1–10.
• GOODCHILD, M. F.; GLENNON, J. A. Crowdsourcing geographic information for disaster response: a research
frontier. International Journal of Digital Earth, v. 3, n. 3, p. 231–241, 2010.
• HAWORTH, B.; BRUCE, E. A review of volunteered geographic information for disaster management. Geography
Compass, v. 9, n. 5, p. 237–250, 2015.
• NIKO, D. L.; HWANG, H.; LEE, Y.; KIM, C. Integrating User-generated Content and Spatial Data into Web GIS for
Disaster History. Computers, Networks, Systems, and Industrial Engineering 2011, v. 365, p. 245–255, 2011.
• DE ALBUQUERQUE, J. P.; HERFORT, B.; ECKLE, M.; ZIPF, A. (2016). Crowdsourcing geographic information for
disaster management and improving urban resilience: an overview of recent developments and lessons learned.
In C. Capineri, M. Haklay, H. Huang, V. Antoniou, J. Kettunen, F. Ostermann, & R. Purves (Eds.), European
handbook on crowdsourced geographic information (pp. 309–321).
References

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• http://obeyproximity.com/2017/06/22/report-13-million-
proximity-sensors-now-deployed-globally/
• http://www.tiemporojas.com/la-provincia-adhiere-al-sistema-
nacional-de-radares-meteorologicos/
• http://chuvaonline.iag.usp.br/
• http://www.starnet.iag.usp.br/chuvaonline/sobre_chuva.php
• https://en.wikipedia.org/wiki/Radar
Images
References

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Determining flooded areas using crowd sensing data
and weather radar precipitation:
a case study in Brazil
Dr. Flávio E. A. Horita
Center for Mathematics, Computation and Cognition (CMCC)
Federal University of ABC (UFABC), Santo André/SP, Brazil
e-mail: flavio.horita@ufabc.edu.br
website: http://flavio.horita.com

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