Mining Rainfall Spatio-Temporal Patterns inTwitterA Temporal Approach

Sidgley Camargo de Andrade1,2, Camilo Restrepo-Estrada3, Alexandre C. B.Delbem2, Eduardo Mario Mendiondo3,4, João Porto de Albuquerque2,5

May 10 2017

(1) Federal University of Technology - Paraná (UTFPR)(2) Institute of Mathematical and Computing Sciences (ICMC), University of São Paulo (USP)(3) São Carlos School of Engineering, University of São Paulo (USP)(4) Brazilian National Center of Monitoring and Early Warning of Natural Disasters (CEMADEN)(5) Centre for Interdisciplinary Methodologies (CIM), University of Warwick

Publication available at dx.doi.org/10.1007/978-3-319-56759-4_220th AGILE Conference, Wageningen University, The Netherlands

1

Agenda

• Context & Problem

• Motivation

• Research Question

• Hypothesis

• Case Study

• Methodology & Techniques

• Results

• Discussions & Conclusions

2

Context & Problem

There has been some research to explore the relationship betweensocial media data and sensor data in the context of disastermanagement. However, there is a lack of attention to temporalvalidity of the social network data.

3

Motivation – Detection and monitoring of flash floods

• There are an insufficient number of physical sensors distributed in theterritory of Brazil.

• There is a lack of accurate, fine and updated official data on the urbanareas at risk.

• There is a lack of qualitative information on the surroundings of thetargeted event. 4

Question

Is there a temporal relationship between the rainfallgauge time-series and time-series of social networkmessages?

5 5

What is the time of the social network messages?

Date/Time Rainfall-related tweets Translation Time2016-01-1206:11:39

“Escuro e quente, a tarde achuva vem, não se enganem,é verão!!!! (...)”

“Dark and warm, in the after-noon there will be rain; makeno mistake, i, it’s summer !!!!(...)”

Before

2016-01-2813:47:57

“A chuva de ontem. @ Em MOEMAhttps://t.co/3kggiLckGZ”

“Yesterday’s rain .@ In MOEMA cityhttps://t.co/3kggiLckGZ”

After

2016-01-2717:37:39

“Chuva chuva emais chuva....https://t.co/vzC9w01qQ8”

“Rain rain and more rain…https://t.co/vzC9w01qQ8”

During

Social network messages can occur at the beginning, middle or endof the targeted events.

6

Hypothesis

∀t∈T, ∃k∈Z : ρ(Pt,Qt+k) ̸= 0

where Pt and Qt+k are observations of the variables at time t andt+ k, and k is the lagged k-periods. Here, the function ρ is ameasure of correlation between both time series.

7

Case study and data

• Study area: Sao Paulo city, Brazil

• Social network data: 243,333geolocated tweets (620 on-topic -0.25%) collected from Jan 1st to30th 2016 via API Streaming Twitter

• Rainfall data: rainfall levels(millimeters) from 81 activerainfall gauges obtained from theCEMADEN via API RESTful –temporal window: 10 min (raining)and 60 min (otherwise).

8

Methodology

9

Techniques

10

Results (10-min time scale)

11

Comparison of cross-correlation (10, 20 and 30-min time-scales)

12

Discussions & Conclusions

• We provide evidence that the rainfall time series and the timeseries of rainfall-related tweets are associated with differentlag-times (with highest correlation from -1 to 1 lag-time).

• The lag-time can be used to define a threshold time forforecasting and monitoring a targeted area.

• We can adopt the temporal approach to fit or approximate thebest temporal scale between sensor data and social networkdata, since the individual data sources can be represented froma univariate time-series.

13

Thank you very much!

Sidgley Camargo de Andrade

PhD Student in computer science at University of Sao Paulosidgleyandrade@usp.br

http://www.agora.icmc.usp.br/

Lecturer at Federal University of Technology - Paranásidgleyandrade@utfpr.edu.br

http://pessoal.utfpr.edu.br/sidgleyandrade/

Acknowledgments

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