Workshop @ CEMADEN

Bridging the gap between decision-making and emerging data sources in disaster managementFlávio E. A. Horita, MSc., CSM

12016-1-20

Bridging the gap between decision-making and emerging data sources in disaster management

Flávio E. A. Horita, MSc., CSM PhD Student, LABES, ICMC, University of São Paulo

Member of AGORA Research [email protected]

http://www.agora.icmc.usp.br | http://www.flaviohorita.com

Pró-alertas


22016-1-20

PhD candidate in Computer Science, ICMC/USP Research-stay, ERCIS, Münster University

MSc in Computer Science, UEL in 2013 Specialist in Software Engineering, UNIFIL in 2012BSc in Information Systems, UNILINS in 2009

Research interests: Decision Support Systems

Crowdsourcing and Volunteered Geographic Information (VGI)

Disaster and Emergency Management

Flávio Horita


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Agenda

• Introduction

• Lines of work and resultsReal-time Data Integration

The Structuring of Decision-making

• Future DirectionsCEMADEN


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Agenda

• Introduction





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Static Sensors

Mobile Sensors

Citizens as sensors

Dynamic Sensor

Management

Crowd Sensing

Social Media

Information Fusion and

ManagementDecision Support

Citizen Engagement

Information Quality

Assurance

Citizens

AGORA

Acquisition Integration Application

Social Information

Sharing

Collaborative Mapping

Semantic Enrichment &

Fusion

Collaborative Maps

VGI

Environmental Modelling

Scientists

Decision Makers


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IntroductionMotivation

• Humanitarian operations in 2015 Nepal Earthquake

Floods in Brazil

São Carlos/SP2014

Recife/PE2015


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IntroductionDecision-makers

• Coordination Agencies in Local Context

• Monitoring and Early warning systems

• Decision-makers’ information requirements• Overall information

• Infrastructure • Resources • Affected people

• Details of information on demand

• Quick and updated

• Coordination Agencies in Global Context

• Humanitarian logistics operations

• Decision-makers’ information requirements• Overall information

• Infrastructure • Resources • Affected people

• Details of information on demand

• Quick and updated


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Introduction

Hard sensors Exploratory teams

Data sources


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Introduction

Source: Elwood (2008); Goodchild & Glennon (2010); Niko et al., (2011); DeLongueville et al. (2012); Horita et al. (2013); Haworth & Bruce (2015)

Hard sensors Exploratory teams

Social Media Collaborative Platforms

Data sources & Volunteered geographic information

A lot of data about disasters


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IntroductionChallenges

80% of data is unstructured and rawSource: Vivacqua and Borges (2012); Vieweg et al. (2014).


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Big Data is about different data formats from distinct data streams in different periodicity

Source: Bacelar et al. (2013); Dolif et al. (2013)


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The data is not integrated into the decision-making Source: Crowley and Chan (2011); Tapia and Moore (2014); Vieweg et al. (2014); Horita et al. (2015)


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Agenda

• Introduction





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• Characteristics• Layer-based approach• Interoperability standards• Heterogeneous data sources• GeoDashboard

• Application Case• Wireless Sensor Network• Crowdsourcing Platform

• Results• Collaborative platforms complements data

provided by sensors• The layer-based approach and use of

adapters

Real-time Data IntegrationSpatial Decision Support System (SDSS)

Conceptual Model

SOS SOS


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Real-time Data IntegrationSpatial Decision Support System (SDSS)

GeoDashboard

Wireless Sensor Network Citizen Observatory


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1. Collaborative platforms complements data provided by sensors

a) Providing information from unmonitored areas and thus extending the monitored area

2. The layer-based approach and use of adapters

a) The distinct formats of data is encapsulated by the adapters

b) It makes flexible the inclusion of new elements

Real-time Data IntegrationResults of the application case in Brazil

Horita, F. E. A. et al. Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks. Computers & Geosciences 80, pp.

84-94, 2015. DOI:10.1016/j.cageo.2015.04.001.


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A lot data from the affected areas (integrated

or not)

Decision-making process or Reference Model


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• Common and well-known models for modelling business processes, decisions, observations and information models

• Business Process Modelling Notation (BPMN)

• Reference Task Model (RTM)

• Observation & Measurements (O&M)

• Decision Model and Notation (DMN)

• Infrastructure and Resource Category Systems


Source: (Blecken, 2010; OMG, 2013; OGC, 2013; OMG, 2014; Horita et al., 2014; Link and Hellingrath, 2014)


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Elements Connecting DMN and O&M



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2015 Nepal Earthquake



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1. Understand the relationship between tasks and information

i. Determine the impact of new (or lack) information on decisions and tasks

ii. Support targeted data collection (using mobile or social media)

2. Support combination and data integration among decision support systems

i. e.g. OpenStreetMap provides the road network while Twitter reports the condition of the roads

The Structuring of Decision-makingResults of the application case in 2015 Nepal Earthquake

Horita, F. E. A. et al. oDMN: An Integrated Model to Connect Decision-Making Needs to Emerging Data Sources in Disaster Management. Hawaii International Conference on System Sciences (HICSS 2016). Kauai, Hawaii, 2014.


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Agenda

• Introduction





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The use of the developed metamodel for:

...identifying information needed, tasks, and decisions

…structuring the Brazilian decision-making process in regarding the managing of warnings

Future DirectionsApplication in CEMADEN


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Bridging the gap between decision-making and emerging data sources in disaster management

Flávio E. A. Horita, MSc., CSM PhD Student, LABES, ICMC, University of São Paulo

Member of AGORA Research [email protected]

http://www.agora.icmc.usp.br | http://www.flaviohorita.com

Pró-alertas

Education

Workshop @ CEMADEN