AGILE 2016 Conference, Helsinki - 15th June 2016

Identification of disaster-affected areas using exploratory visual analysis of georeferenced Tweets:

application to a flood eventV. Cerutti1, G. Fuchs2, G. Andrienko2, N. Andrienko2, F.Ostermann1

1 ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, The Netherlands 2 Fraunhofer IAIS, Sankt Augustin, Germany

IN THIS PRESENTATION:

INTRODUCTION METHODS CASE STUDY CONCLUSIONS AND FUTURE WORK

AKNOWLEDGMENT1. 2. 3. 4. 5.

1. INTRODUCTION

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RESEARCH CONTEXT AND MOTIVATION

Social media for Disaster Management - Disaster response phase

Enable decision makers in rapid assessment of the situation

Geographic and temporal extent of disaster effects

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RESEARCH OBJECTIVES

Conceptual management of crisis information

Geospatial footprint of disaster

Situational awareness and decision making

Combination of data mining and visual analysis

Detection of areas affected by a disaster

Twitter as data source

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CASE STUDY OBJECTIVE

Help decision makers to assess the spatio-temporal footprint of a disaster

2. METHODS

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Tweets pre-processing:

• removal of geographically unrelated data

• pattern-based removal of machine-generated data

• natural language processing to mine the data and classify and annotate its content

DATA PREPROCESSING

CLUSTERING + VISUAL ANALYSIS

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Clustering techniques to understand how affected places are represented

Tight integration between computational and visualization

functionality

V-Analytics toolkit (http://geoanalytics.net/)

Need parameterization

space-time cube frequency histograms time graphs qualitative colouring animated maps

Density-based clustering Distance-bounded spatio-temporal event

clustering Data-driven territory tessellation

Visual analysis techniques

3. CASE STUDY

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DATA DESCRIPTION AND PREPARATION

Case study: Sardinia flood, 18-19 November 2013

Original dataset: georeferenced Tweets (Dec 2012 - Apr 2014), bounding box: Italy, collected from Twitter streaming API

Query: Lexicon of flood-related keywords in Italian language

Demographic data to normalize the results

Ground truth information from official reports

Final dataset: 3,000 Tweets (Nov 2013) 897 Tweets (18-20 Nov 2013)

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DATA ANALYSIS

18-19 Nov 2013

Random sample (5%) of georeferenced Tweets generated during the month of November 2013

Impossible to detect the flood event

After keyword-based filtering:

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Spatio-temporal density-based clustering (OPTICS) + visual analysis to select optimal parameters

DATA ANALYSIS

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Distance-bounded event clustering + visual analysis to select optimal parameters

DATA ANALYSIS

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Data-driven territory tessellation + time series of Tweets frequency

DATA ANALYSIS

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Spatio-temporal density-based clustering (OPTICS)

Distance-bounded event clustering

Data-driven territory tessellation

DATA ANALYSIS

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RESULTS COMPARISON AND EVALUATION

False negative

Ground truth data Combination of clustering results

False positive

4. CONCLUSIONS AND FUTURE WORK

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CONCLUSIONS

First steps towards an approach to define the geospatial footprint of a flood event using georeferenced Tweets

Data mining techniques + exploratory visual analysis of georeferenced Tweets to identify the areas affected by a flood

Intuitive and fast procedure

User evaluation needed

False positive and false negative need to be addressed

Further analysis required to obtain more precise footprints

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Extraction of locations (and other potentially useful information) mentioned in social media

Comparison locations of origin (geotag) and content

Contextualization and validation of social media information with authoritative data

FUTURE WORK

Conceptualization of disasters

Content analysis

Geospatial footprint in near real time

5. AKNOWLEDGEMENT

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AKNOWLEDGEMENT

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COST ACTION IC 1203• STSM Grant

Fraunhofer Institute for Intelligent Analysis and Information Systems• Knowledge• Software• Dataset

THANKS FOR YOUR ATTENTION

QUESTIONS?

v.cerutti@utwente.nl