scholarly journals Tourist Mobility at the Destination Toward Protected Areas: The Case-Study of Extremadura

2018 ◽  
Vol 10 (12) ◽  
pp. 4853 ◽  
Author(s):  
José Sánchez Martín ◽  
Juan Rengifo Gallego ◽  
Luz Martín Delgado

The use of natural protected areas has been analyzed abundantly in the relevant literature, although on many occasions these areas are studied from the viewpoint of their role as a tourist destination in themselves, while neglecting their role as a tourist attraction that can be visited from the main destination of their stay. In certain specific areas, as in the case of Extremadura, protected areas are often a complementary destination for visitors who are staying in popular tourist hubs. This study is based on data from 4 different spaces (with different degrees in their status as protected areas) about the flow of tourists they generate at the destination. In light of the data, this paper identifies the 41 towns and villages with the most tourists, later to determine their mobility towards natural protected areas. Information was collected from almost 14,000 surveys that were filled at 52 tourist offices. This information allowed us to map the flow of tourists from their places of stay to the protected areas analyzed here, which resulted in the mapping of relevant networks by means of a Geographic Information System following the criterion of shortest path available. The results here clearly demonstrate that each area has a varying capacity to attract visitors, although in a large proportion of cases, the 60-min isochrone is a boundary beyond which the number of visitors drops significantly.

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Recchioni ◽  
V Castello ◽  
S Del Vecchio ◽  
V Ciaccio ◽  
A M Donia ◽  
...  

Abstract Issue Geographic information systems (GIS) and remote sensing technologies are increasingly used in Public Health epidemiology, showing a great potential in anticipating and responding to actual and future challenges for the public health system and in improving health services' excellence. According to the evidences collected within a wide meta-research carried on of relevant literature (”GIS geographic information system” and “GIS geographic information system and training” on Pubmed; “epidemiologist use of GIS and training” and “epidemiologist use of gis” on Google Scholar),GIS and new sensing technologies are mostly used to: map air and water pollution, map diseases prevalence, predict infection diseases and vector-spread diseases in big areas, study health service coverage and preparedness in emergencies, map cities and study urban health, study climate changes for decision making. Description of the Problem Specific skills and training are required to address the use of GIS and new sensing technologies.The specific aim of our study is to identify the professional profile of a new figure, called 'Geomatic Epidemiologist' and to define its professional and educational standards, as well as the relevant training programs. Results Data collection and analysis of INAPP and ESCO databases about existing professional profiles (starting from 2016) has allowed drafting a first qualification schema and profile. The profile has been defined according to the 4C model (elaborated by Univaq) distinguishing between Hard Skills (technical knowledge and skills),Soft Skills (cognitive, individual and social) and interpersonal behaviors. Conclusions Profile will be validated with relevant stakeholders and Public Health professionals in order to deepen the understanding of the main competences required to study health issues with GIS and related technologies; to this extent, a questionnaire has been elaborated to evaluate relevance, frequency and complexity of each component of the profile Key messages Developing cross-disciplinary profiles, (i.e. the Geomatic Epidemiologist) integrating clusters of competences (holistic approach). Public health research challenges and excellence.


2020 ◽  
Vol 13 (3) ◽  
pp. 1145
Author(s):  
Fabiano Peixoto Freiman ◽  
Camila De Oliveira Carvalho

A identificação de áreas suscetíveis a inundações é essencial para o gerenciamento de desastres e definição de políticas públicas. O objetivo deste trabalho é a apresentação de um método para identificação de áreas suscetíveis a inundações através da integração de informações geográficas provenientes de técnicas do Sensoriamento Remoto, as ferramentas do Sistema de Informação Geográfica (SIG), a lógica Fuzzy e a aplicação de Métodos de Análise Multicritério (MAM) Analytical Hierarchy Process (AHP). Para atingir o objetivo foi proposto um estudo de caso, localizado na Bacia do Rio Bengalas, nos municípios de Nova Friburgo e Bom Jardim (Região Serrana do Rio de Janeiro). A modelagem espacial multicritério foi realizada a partir da seleção de um conjunto de dados composto por informações geomorfológicas, hidrológicas e de uso e ocupação do solo. Como resultado, obteve-se um mapa de suscetibilidade a inundações para a região. A coerência do modelo gerado foi verificada a partir do histórico de inundações da bacia do Rio Bengalas. A metodologia, apresentou-se eficiente e adequada para a determinação de áreas suscetíveis a inundações, prevendo com sucesso a distribuição espacial de áreas com riscos a inundações.  Spatial modelling of flood-susceptible areas based on a hybrid multi-criteria model and Geographic Information System: a case study applied to the Bengalas River basin A B S T R A C TThe identification of areas susceptible to flooding is essential for disaster management and public policy making. The objective of this work is the presentation of a method for the identification of areas susceptible to floods through the integration of geographic information from Remote Sensing techniques, Geographic Information System (GIS) tools, Fuzzy logic and the application of Multicriteria Analysis Methods (MAM) Analytical Hierarchy Process (AHP). In order to achieve the objective, a case study was proposed, located in the Bengalas River Basin, in the municipalities of Nova Friburgo and Bom Jardim (Mountain Region of Rio de Janeiro). Multicriteria spatial modeling was performed by selecting a data set composed of geomorphological, hydrological and land use information. As a result, a flood susceptibility map was obtained for the region. The coherence of the generated model was verified from the flood history of the Bengalas River basin. The methodology was efficient and adequate for the determination of areas susceptible to floods, successfully predicting the spatial distribution of areas at risk of flooding.Keywords: flood susceptibility. Fuzzy logic. MAM. AHP. GIS. 


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