Severe weather events can impact negatively on tourism and put tourists at risk. To reduce vulnerability, tourists should be aware of and be prepared for possible severe weather. Seeking risk information, a type of protective action behaviour, is an important way to reduce vulnerability. This paper presents the results of a study that investigated the role of Locus of Responsibility (LoR) for protection behaviour for severe weather, by linking it with Information Seeking and related intra-personal antecedents. LoR has previously been found to impact protective action decisions, but not within the context of severe weather and tourism. Our survey research among tourists in New Zealand provided evidence for three Loci of Responsibility; " Internal" , " Shared" and " External" Significant differences between these groups were found for Information Seeking antecedents, though not for Information Seeking. Next, significant differences were found for weather information preferences, both source and content. Findings and implications for tourism and safety management in New Zealand are discussed.
LINK
In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
DOCUMENT
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
DOCUMENT
