This document summarizes a study that used crowd sensing data and weather radar data to determine flooded areas in Sao Paulo, Brazil. The study analyzed crowd reports from January 16th and 21st, 2018 to identify 57 and 40 flooded clusters, respectively. Weather radar data from those dates showed heavy rainfall in the same locations as the crowd reports. The preliminary results indicate weather radar data can validate flooded areas identified through crowd sensing. The authors propose future work to analyze additional data sources and flooding vulnerabilities to improve spatial analysis of flooding.

1. 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
1
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

2. 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
▷ Introduction
▷ Research design
▷ Approach
▷ Methods
▷ Preliminary results
▷ Final remarks
Agenda

3. 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
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

4. 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
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)

5. 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
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.

6. 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
▷ RAdio Detection And Ranging (RADAR)
Problem statement
Research Design
rainfall
eletromagnetic waves
reflection

7. 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
7
10.11.2017
Research Question
How can weather radar data validate flooded areas
identified by crowd sensing data?

8. 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
8
10.11.2017
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.

9. 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
• 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

10. 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
10
10.11.2017
• 12 million inhabitants (9th
highest cities in World)
• An area of ~1.5 million km²
• A population density of ~7,400
inhabitants per km²

11. 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
11
10.11.2017
Case study – Geographic distribution
Preliminary Results
Jan 16th, 2018 Jan 21st, 2018

12. 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
12
10.11.2017
Case study – Crowd sensing data
Preliminary Results
Jan 16th, 2018 Jan 21st, 2018

13. 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
13
10.11.2017
Case study – Weather Radar Systems
Preliminary Results
Jan 16th, 2018 Jan 21st, 2018

14. 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
14
10.11.2017
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.

15. 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
15
10.11.2017
Case study
Preliminary Results

16. 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
16
10.11.2017
Case study
Preliminary Results

17. 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
17
10.11.2017
▷ 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

18. 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
18
10.11.2017
▷ 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

19. 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
• 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

20. 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
20
10.11.2017
• 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

21. 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
21
10.11.2017
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