Dynamic resource management of civil protection department for Smart and Safe Tourism Destination

Application Track:

Theme Driven

Code:

REACH-2021-THEMEDRIVEN-DEUSTO_6.3

Proposed by:

DEUSTO

Entity Logo:

Summary of the entity:

The University of Deusto, recently recognized as an International Excellence Campus, was founded in 1886 and comprises 6 Faculties: Psychology and Education, Human and Social Sciences, Engineering, Law, Business and Economic Sciences and Theology. The MORElab research group has worked in several projects dealing with Linked Data, Big Data and Data Analytics. In the Spanish research project THOFU, the group undertook activity recognition over the PlaceLab dataset and opinion mining regarding hotels. In the IES Cities project, the group worked on the creation of a set of urban apps that make use of data published by councils and that leverage the information generated and published by users (crowdsourcing). In WeLive H2020 project, our Open Data Stack has been extended with a repository of micro-services consuming open data and a wizard-like tool that enables citizens to co-create and co-define public services, giving place to an Open Government solution. In EDI – European Data Incubator, DEUSTO is in charge of the project coordination and has an important role in WP3-Big Data Infrastructure and Support, where we share with ENG the duty of providing a Big Data experimentation infrastructure for start-ups (sub-grantees) which tackle through Big Data large corporations’ data challenges.

Summary of the challenge:

The aim is to develop a predictive tool that provides support to civil protection departments’ managers when sizing, allocating and planning the emergency teams in order to contribute to the creation of Smart and Safe Tourism Destinations.

Description:

Smart Tourism Destinations is a concept that seeks to improve tourism services and experiences through innovative digital solutions. But a Smart Destination is also the one that provides safe experiences. And, in a climate change situation in which more frequent adverse weather events will happen, it is necessary to optimize the resources associated to civil protection and emergencies.
Tourism can significantly modify the amount and distribution of people over time and space in a city or a region, with the power of transforming a low-risk situation into a real emergency. Therefore, tourism information is a feature to be considered in the civil protection strategy.
Based on historical data on mobilisations, interventions and emergencies in the area of civil protection, weather alerts, tourism information and events in a city or region, the aim is to obtain a predictive geospatial model of the probability of an increase or decrease in the different types of emergency situations in a 15-day time frame, with the aim of providing support to managers when sizing emergency teams.

Data:

REACH Data Providers

Bilbao City Council – Security department: dataset with Firefighter interventions, events in Bilbao, and weather alerts in Bilbao area

JOT Internet Media: Keyword lists and statistics for mobile and laptop campaigns

PLAY&GO: Fallas (objectives & ratings). Two datasets providing mobility and user experience data related to the Fallas, one of the most relevant regional touristic festivities in Valencia city, brought from a dedicated outdoor gamified sightseeing guide from Play&Go. Data includes points of interest, ratings, geoposition, objectives achieved (as attractive).

External Data Provider:

Turisme Comunitat Valenciana – INVAT.TUR: Tourism Dataset Region of Valencia. Different datasets could be provided, under a private agreement with the applicants. Datasets dealing with: source of tourism flows to Valencia region and some of the factors influencing them (source countries, age, language, climate, safety, satisfaction), tourism products (main products, product satisfaction, sentiment analysis, gender and age distribution, language per product, …), attractiveness factors, accommodations (prices, customer reviews, satisfaction, flights (capacity, prices).

Open source datasets:

Open Source: Valencia City open data portal, with transport and tourism datasets http://gobiernoabierto.valencia.es/en/data/

Open Source: Valencia City public transport mobility data (EMT http://transitfeeds.com/p/emt-valencia/719 MetroValencia http://transitfeeds.com/p/ferrocarriles-de-la-generalidad-valenciana/1039

Open Source: Valencia Region data portal, including tourism data and others http://www.dadesobertes.gva.es/es/dataset

Open Source: Spain open data portal, including tourism data and others https://datos.gob.es/en/catalogo?theme_id=turismo

Other datasets could be used if needed, such as end-users bringing their daily data on which to train and test the solution developed.

Expected outcomes:

Predictive geospatial model of the increase or decrease of probability of the different types of interventions.

How do we apply?

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