A carregar...
Publicação
Modelo espacial integrado das percepções dos turistas no Litoral Alentejano
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 16.32 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
O turismo é considerado um meio de promover o crescimento económico e de revitalizar economias locais e regionais, proporcionando um conjunto de benefícios. No entanto, a atividade turística quando não bem planeada, pode trazer consigo vários impactos negativos, que prejudicam o ciclo de vida dos destinos turísticos. Neste contexto a necessidade de dados em turismo, é especialmente importante para fins de planeamento, previsão da procura turística, marketing e medição de impactos.
Os Big Data surgem como uma oportunidade devido à combinação de dois elementos: a dificuldade de extração de dados sobre o comportamento turístico da estatística oficial e a quantidade de novas fontes da Web 2.0 relacionadas com a atividade turística. Além disso, algumas destas fontes, como as redes sociais, permitem aceder a fotografias georreferenciada com alta resolução espacial e temporal, existindo um elevado número de utilizadores e níveis de participação elevados.
Para analisar o comportamento espácio-temporal da procura turística no Alentejo Litoral, foram utilizadas como fonte de dados mais de 40 000 fotografias de turistas, tendo estas sido extraídas das redes sociais Panoramio e Flickr, abrangendo o período de 2007 a 2017. As fotografias foram divididas em turistas e locais, como base nas datas de carregamento nas respetivas redes sociais, sendo o critério utilizado para a distinção a estada média do destino referido pelo Instituto Nacional de Estatística.
Nesta dissertação o principal foco foi analisar a localização das fotografias, identificar os padrões espaciais dos turistas e através da contabilização do número de fotos, localizar os locais mais atrativos. Os principais clusters identificados localizam-se ao longo do litoral, correspondendo a centros urbanos e às praias mais próximas do mesmo, como é o caso de Vila Nova de Milfontes, Troia, Sines e Porto Covo. Sendo igualmente pretendido avaliar a relação entre as perceções dos turistas e as perspetivas dos decisores, foi utlizada informação referente aos locais identificados pelos decisores como: locais atrativos, locais com potencial de atratividade e locais menos atrativos, recolhida através da realização do workshop realizado no âmbito do Plano Operacional Estratégico para o Turismo de Sol e Mar do Alentejo (2015). A análise de stakeholders, revelou que os clusters mais identificados pelos decisores coincidem na maioria com os locais de preferência dos turistas. No entanto, existem locais subvalorizados pelos decisores, principalmente no interior da sub-região, onde, apesar de se apresentarem de forma mais dispersa e por vezes pontual, existem registos de atratividade estatisticamente significativos.
Tourism is considered a mean to promote economic growth and to revitalize local and regional economies, providing several benefits. However, tourism activity should be well planned, as it can bring with it negative impacts that undermine the tourist destinations' life cycle. In this context, the need for data in tourism is crucial for planning, predicting tourism demand, marketing and impact measurement. The Big Data comes as an opportunity due to the combination of two elements: the difficulty of extracting data on the tourist behavior from official statistics and the number of new sources of Web 2.0 related to the tourist activity. In addition, some of these sources, such as social networks, allow access to georeferenced photographs with high spatial and temporal resolution, with a high number of users and high levels of participation. In order to analyze the spacio-temporal behavior of tourist demand in Alentejo Litoral, more than 40 000 photographs of tourists were used as data. The photos were extracted from Panoramio and Flickr social networks, covering the period from 2007 to 2017. The data was divided into tourists and locals, based on the date that the users uploaded the photos into the social networks. To distinguish it was used the average stay in the destination, referred to by the National Statistics Institute. In this dissertation, the focus was to analyze the location of the photographs, identify the spatial patterns of the tourists and by counting the number of photos, locate the most attractive places. The main clusters identified are located along the coast, corresponding to urban centers and the beaches closest to it, such as Vila Nova de Milfontes, Troia, Sines and Porto Covo. It was also intended to evaluate the relationship between tourists 'perceptions and decision-makers' perspectives. To do that, it was used information regarding the places identified by decision-makers as: attractive places, potentially attractive places and less attractive places, collected through a workshop concerning the Strategic Operational Plan for the Sun and Sea Tourism of Alentejo (2015). Stakeholder analysis revealed that the places most identified by decision-makers, mostly coincide with the places of tourist preference. However, there are places that are undervalued by decision-makers, especially within the subregion, where, despite being more dispersed and sometimes punctual, there are statistically significant attractiveness registers.
Tourism is considered a mean to promote economic growth and to revitalize local and regional economies, providing several benefits. However, tourism activity should be well planned, as it can bring with it negative impacts that undermine the tourist destinations' life cycle. In this context, the need for data in tourism is crucial for planning, predicting tourism demand, marketing and impact measurement. The Big Data comes as an opportunity due to the combination of two elements: the difficulty of extracting data on the tourist behavior from official statistics and the number of new sources of Web 2.0 related to the tourist activity. In addition, some of these sources, such as social networks, allow access to georeferenced photographs with high spatial and temporal resolution, with a high number of users and high levels of participation. In order to analyze the spacio-temporal behavior of tourist demand in Alentejo Litoral, more than 40 000 photographs of tourists were used as data. The photos were extracted from Panoramio and Flickr social networks, covering the period from 2007 to 2017. The data was divided into tourists and locals, based on the date that the users uploaded the photos into the social networks. To distinguish it was used the average stay in the destination, referred to by the National Statistics Institute. In this dissertation, the focus was to analyze the location of the photographs, identify the spatial patterns of the tourists and by counting the number of photos, locate the most attractive places. The main clusters identified are located along the coast, corresponding to urban centers and the beaches closest to it, such as Vila Nova de Milfontes, Troia, Sines and Porto Covo. It was also intended to evaluate the relationship between tourists 'perceptions and decision-makers' perspectives. To do that, it was used information regarding the places identified by decision-makers as: attractive places, potentially attractive places and less attractive places, collected through a workshop concerning the Strategic Operational Plan for the Sun and Sea Tourism of Alentejo (2015). Stakeholder analysis revealed that the places most identified by decision-makers, mostly coincide with the places of tourist preference. However, there are places that are undervalued by decision-makers, especially within the subregion, where, despite being more dispersed and sometimes punctual, there are statistically significant attractiveness registers.
Descrição
Palavras-chave
Geotagged fotos Redes sociais Alentejo Litoral Análise Espacial Análise de stakeholders