This report summarizes the result of the comparison between 4 weather stations: 2 Kestrels 5400 Heat Stress and 2 Davis Vantage Pro2. The measurements were performed from the 08/04/2019 to 11/04/2019 on the rooftop of the Benno Premselahuis from the Hogeschool van Amsterdam.
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As one of its main resources, weather is an integral part of tourism. Yet little is known about how individual tourists experience the weather and how it affects the subjective perception of their holidays. The weather appears to have a prominent place in language and the use of the weather in narratives of tourists can provide insight into how the weather affects tourist experiences. Based on a qualitative analysis of online travel blogs written by Dutch tourists, 16 weather themes could be distinguished, representing how the weather was narrated about by tourists. Moreover, different impact themes emerged describing how the weather impacted the tourists: tourists revealed positive, negative or neutral evaluations about the weather impacts. The findings of this study can be used for future research on tourist behaviour and how specific weather types and impacts influence the decision making of tourists in terms of itineraries and activities.
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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.
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Purpose: This study analyses how weather shocks influence agricultural entrepreneurs’ risk perception and how they manage these risks. It explores what risks agricultural entrepreneurs perceive as important, and how they face climate change and related weather shock risks compared to the multiple risks of the enterprise. Design/methodology: This paper uses qualitative data from several sources: eight semi-structured interviews with experts in agriculture, three focus groups with experts and entrepreneurs, and 32 semi-structured interviews with agricultural entrepreneurs. Findings: not published yet Originality and value: This study contributes to the literature about risk management by small- and medium-sized agricultural enterprises: it studies factors that shape perceptions about weather shocks and about climate change and how these perceptions affect actions to manage related risks, and it identifies factors that motivate agricultural entrepreneurs to adapt to climate change and changing weather shock risks. Practical implications can lay the foundation for concrete actions and policies to improve the resilience and sustainability of the sector, by adjusting risk management strategies, collaboration, knowledge sharing, and climate adaptation policy support.
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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.
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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.
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Extant research on the role of weather in COVID-19 has produced ambiguous results and much methodological debate. Following advice emerging from this methodological debate, we take a step further in modeling effects of weather on COVID-19 spread by including interactions between weather, behavior, baseline cases, and restrictions in our model. Our model was based on secondary infection, hospitalization, restriction, weather, and mobility data per day nested with safety region in the Netherlands. Our findings show significant but inconsistent interactions. The robust effects of weather on COVID-19 spread persisted over and above these interactions, highlighting the need to account for weather with nuance and caution in public policy, communication, and forecasting
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% 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 metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
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Ageing brings about physiological changes that affect people’s thermal sensitivity and thermoregulation. The majority of older Australians prefer to age in place and modifications to the home environment are often required to accommodate the occupants as they age and possibly become frail. However, modifications to aid thermal comfort are not always considered. Using a qualitative approach this study aims to understand the thermal qualities of the existing living environment of older South Australians, their strategies for keeping cool in hot weather and warm in cold weather and to identify existing problems related to planning and house design, and the use of heating and cooling. Data were gathered via seven focus group sessions with 49 older people living in three climate zones in South Australia. The sessions yielded four main themes, namely ‘personal factors’, ‘feeling’, ‘knowing’ and ‘doing’. These themes can be used as a basis to develop information and guidelines for older people in dealing with hot and cold weather. Original publication at MDPI: https://doi.org/10.3390/ijerph16060935 © 2018 by the authors. Licensee MDPI.
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Older people are often over-represented in morbidity and mortality statistics associated with hot and cold weather, despite remaining mostly indoors. The study “Improving thermal environment of housing for older Australians” focused on assessing the relationships between the indoor environment, building characteristics, thermal comfort and perceived health/wellbeing of older South Australians over a study period that included the warmest summer on record. Our findings showed that indoor temperatures in some of the houses reached above 35 °C. With concerns about energy costs, occupants often use adaptive behaviours to achieve thermal comfort instead of using cooling (or heating), although feeling less satisfied with the thermal environment and perceiving health/wellbeing to worsen at above 28 °C (and below 15 °C). Symptoms experienced during hot weather included tiredness, shortness of breath, sleeplessness and dizziness, with coughs and colds, painful joints, shortness of breath and influenza experienced during cold weather. To express the influence of temperature and humidity on perceived health/wellbeing, a Temperature Humidity Health Index (THHI) was developed for this cohort. A health/wellbeing perception of “very good” is achieved between an 18.4 °C and 24.3 °C indoor operative temperature and a 55% relative humidity. The evidence from this research is used to inform guidelines about maintaining home environments to be conducive to the health/wellbeing of older people. Original publication at MDPI: https://doi.org/10.3390/atmos13010096 © 2022 by the authors. Licensee MDPI.
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