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Tourism is a powerful economic driver for communities, but there are dangers of overtourism in tourism-dependent destinations. The study proposes an approach to detect early signs of overtourism by integrating census and industry data with residents' perceptions of tourism benefits. The indicators include industry performance, its economic impacts, and indirect measures related to quality of life. The public perceptions are collected through surveying four different tourism-dependent communities in Florida. We found that in general local residents are highly positive about the tourism industry benefits to their communities, yet many respondents would like the tourism levels reduced. Especially concerning are negative sentiments toward tourism among the younger population groups and racial minorities. The findings are interpreted as early signs of overtourism. [ABSTRACT FROM AUTHOR]

Tourism is one of the sectors of the economy that is most dependent on climate, creating multiple vulnerabilities and new opportunities arising with changing climate. Even though the links between tourism and climate have been well researched, this scientific knowledge has not percolated into policies and the ability to act. This disconnect between scientific knowledge and practices is frequently blamed on inadequate climate change communication to the public in mass media. We studied the mass media framing of climate change and tourism by analyzing English newspaper publications worldwide over the past 30 years. The paper presents a Big Data analysis of the content, geographical patterns, and temporal changes in newspapers' publications on climate change and tourism. [ABSTRACT FROM AUTHOR]

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970–2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship. [ABSTRACT FROM AUTHOR]

Interest in applying Big Data to tourism is increasing, and automated sentiment analysis has been used to extract public opinion from various sources. This article evaluates the suitability of different types of automated classifiers for applications typical in tourism, hospitality, and marketing studies by comparing their performance to that of human raters. While the commonly used performance indices suggest that on easier-to-classify data sets machine learning methods demonstrate performance comparable to that by human raters, other performance measures such as Cohen’s kappa show that the results of machine learning are still inferior to manual processing. On more difficult and noisy data sets, automated analysis has poorer performance than human raters. The article discusses issues pertinent to selection of appropriate sentiment analysis software and offers a word of caution against using automated classifiers uncritically. [ABSTRACT FROM AUTHOR]

Content analysis involves classification of textual, visual, or audio data. The inter-coder agreement is estimated by making two or more coders to classify the same data units, with subsequent comparison of their results. The existing methods of agreement estimation, e.g., Cohen’s kappa, require that coders place each unit of content into one and only one category (one-to-one coding) from the pre-established set of categories. However, in certain data domains (e.g., maps, photographs, databases of texts and images), this requirement seems overly restrictive. The restriction could be lifted, provided that there is a measure to calculate the inter-coder agreement in the one-to-many protocol. Building on the existing approaches to one-to-many coding in geography and biomedicine, such measure, fuzzy kappa, which is an extension of Cohen’s kappa, is proposed. It is argued that the measure is especially compatible with data from certain domains, when holistic reasoning of human coders is utilized in order to describe the data and access the meaning of communication. [ABSTRACT FROM AUTHOR]

Terminal lakes are impacted by regional changes in climate. Devils Lake ( DL), North Dakota, United States (U.S.), is a case in which a prolonged shift in the precipitation pattern resulted in a 10-m water-level rise over the past two decades, which cost over one billion U.S. dollars in mitigation. Currently, DL is 1.5 m from an uncontrolled overspill to the nearby Sheyenne River, which could lead to unprecedented environmental, social, and economic costs. Water outlets recently implemented in the lake to slow the water-level rise and prevent an uncontrolled overspill are subject to significant concerns over the introduction of invasive species and downstream water quality. We developed a hydrological model of the DL basin using the soil and water assessment tool and analyzed DL's overspill probability using an ensemble of statistically downscaled General Circulation Model ( GCM) projections of the future climate. The results indicate a significant likelihood (7.3-20.0%) of overspill in the next few decades in the absence of outlets; some members of the GCM integration ensemble suggest an exceedance probability of over 85.0 and 95.0% for the 2020s and 2050s, respectively. Full-capacity outlets radically reduce the probability of DL overspill and are able to partially mitigate the problem by decreasing the average lake level by approximately 1.9 and 1.5 m in the 2020s and 2050s, respectively. [ABSTRACT FROM AUTHOR]

The article proposes a new methodological approach to facilitate content analysis of electronic textual data in a more efficient and transparent way. The textual data are processed iteratively by two software products, CATPAC and WORDER. This approach permits smoothing of the original textual data, identification of the variables of interest, frequency count of the occurrences of these variables in the texts being processed, storage of frequency results in general purpose statistical packages, and subsequent dimensional reduction of word-frequency data by means of factor analysis. Application of this methodology is illustrated on three examples of destination-image studies, which cover content analyses of open-ended responses to e-survey questions, texts from tourism Web sites, and newspaper articles. Advantages and disadvantages of the proposed research technique, its contribution to tourism studies, and the place of the approach within the quantitative paradigm of content analysis are also discussed. [ABSTRACT FROM AUTHOR]

Changing temperature and precipitation pattern and increasing concentrations of atmospheric CO2 are likely to drive significant modifications in natural and modified forests. Our review is focused on recent publications that discuss the changes in commercial forestry, excluding the ecosystem functions of forests and nontimber forest products. We concentrate on potential direct and indirect impacts of climate change on forest industry, the projections of future trends in commercial forestry, the possible role of biofuels, and changes in supply and demand. [ABSTRACT FROM AUTHOR]

Landowners need a simple-to-use, readily available tool to contribute to sustainable management of family forests. We developed an Internet-based decision-support system, 4S Tool (Forest Stand Software Support System), which is designed to encourage informed forest management and to bridge the gap between a new generation of family forest owners (e.g. exurbanites) and natural resources professionals. We describe the technical and educational components of the 4S Tool and present several considerations for application to other geographic regions. The tool requires users to provide information on the species group, typical tree size, and density of trees in their forest. Users also select a forest management technique to apply to their “virtual forest. ” The tool then estimates forest dynamics and wildlife habitat availability for up to 40years into the future. The output provides projected changes in commercial timber, as well as changes in the diversity of berries, ferns, herbs, flowers, birds, and mammals as a consequence of the selected management option. Projected changes are based on primary and secondary data sources and modeled via the Forest Vegetation Simulator. 4S Tool provides a means of enhancing landowner awareness of options associated with management of forest property and provides users with access to additional resources and natural resource professionals to aid in future forest planning efforts. [Copyright &y& Elsevier]

Argues that transaction taxes can have negative effects on price discovery, volatility and market liquidity in securities market. Literature on securities transaction taxes; Importance of trading; Causes of price volatility; Importance of the volume of transactions.

This study expands the empirical evidence amassed to date concerning tourists' decision-making processes in a situation of conflict between a country of origin's market and country of destination. It uses the constructs of animosity, national attachment, consumer ethnocentrism, country image, and perceptions of the state of bilateral relations in order to examine three intent to visit scenarios. To obtain the robust results, the study uses two methods of analysis from distinctly different repertoires of techniques, such as traditional statistical and data-mining approaches, and applies them to each scenario. The results consistently indicate a high level of influence of general animosity and ethnocentric tendencies in addition to country image and bilateral relation variables in tourists' decision making. The situational context of the study is the conflict between China, the country of origin, and South Korea, the country of destination, over the Terminal High Altitude Area Defense (THAAD) system. [ABSTRACT FROM AUTHOR]

The flooding of Devils Lake, North Dakota, is a multi-decade, multi-billion-dollar, and yet unsolved water management issue along the US–Canada border. In this study, we define this situation as a 'wicked problem' and suggest a 'green paradiplomacy'–based framework that fosters multiactor, multiscale collaboration across jurisdictions as a management strategy. We interviewed stakeholders and combined their perceptions with currently employed management strategies to assess the potential for green paradiplomacy to address the Devils Lake problem. This study may encourage further discussion of green paradiplomacy as a strategy to manage other transboundary watershed problems along the US–Canada border and elsewhere. [ABSTRACT FROM AUTHOR]

Following the 2007-2009 financial crisis, governments around the world passed laws that marked the beginning of new period of enhanced regulation of the financial industry. These laws called for a myriad of new regulations, which in the U.S. are created through the so-called notice-and-comment process. Through examining the text documents generated through this process, we study the formation of regulations to gain insight into how new regulatory regimes are implemented following major laws like the landmark Dodd-Frank Wall Street Reform and Consumer Protection Act. Due to the variety of constituent preferences and political pressures, we find evidence that the government implements rules strategically to extend the regulatory boundary by first pursuing procedural rules that establish how economic activities will be regulated, followed by specifying who is subject to the procedural requirements. Our findings together with the unique nature of the Dodd-Frank Act translate to a number of stylized facts that should guide development of formal models of the rule-making process. [ABSTRACT FROM AUTHOR]

Destination photography communicates images that shape and reshape destination perceptions of past and potential tourists and, consequently, influence their decision-making process. Using theoretical underpinnings provided in works on culture, this exploratory study examines whether destination photographs taken by tourists and posted on social-network websites are reflective of culture to which those tourists belong. Content analysis of American and Korean photographs of Russia posted on Flickr and in Korean travel blogs, respectively, was followed by chi-square and co-occurrence analyses of destination attributes, as well as geospatial analysis of image locations using ARC GIS software. Although the core of Russia’s destination image—urban, contemporary, architecturally interesting, and spacious—is similar for both cultural groups, the study found differences in the way that American and Korean tourists represent Russia as a destination. These differences are discussed from the perspective of tourists’ respective cultures. [ABSTRACT FROM AUTHOR]

This study is motivated by the empirical findings that news and social media Twitter messages (tweets) exhibit persistent predictive power on financial market movement. Based on the evidence that tweets are faster than news in revealing new market information, whereas news is regarded broadly a more reliable source of information than tweets, we propose a superior trading strategy based on the sentiment feedback strength between the news and tweets using generic programming optimization method. The key intuition behind this feedback strength based approach is that the joint momentum of the two sentiment series leads to significant market signals, which can be exploited to generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy using investors’ sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feedback based strategies yield superior market returns with low maximum drawdown over the period from 2012 to 2015. In comparison, the strategies based on the sentiment feedback indicator generate over 14.7% Sterling ratio compared with 10.4% and 13.6% from the technical indicator-based strategies and the basic buy-and-hold strategy respectively. After considering transaction costs, the sentiment indicator based strategy outperforms the technical indicator based strategy consistently. Backtesting shows that the advantage is statistically significant. The result suggests that the sentiment feedback indicator provides support in controlling loss with lower maximum drawdown. [ABSTRACT FROM AUTHOR]

Many market participants now employ algorithmic trading, commonly defined as the use of computer algorithms, to automatically make certain trading decisions, submit orders and manage those orders after submission. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. Advanced data feeds and audit trail information from market operators now allow for the full observation of market participants’ actions. A key question is the extent to which it is possible to understand and characterize the behaviour of individual participants from observations of trading actions. In this paper, we consider the basic problems of categorizing and recognizing traders (or, equivalently, trading algorithms) on the basis of observed limit orders. These problems are of interest to regulators engaged in strategy identification for the purposes of fraud detection and policy development. Methods have been suggested in the literature for describing trader behaviour using classification rules defined over a feature space consisting of summary trading statistics of volume and inventory, along with derived variables that reflect the consistency of buying or selling behaviour. Our principal contribution is to suggest an entirely different feature space that is constructed by inferring key parameters of a sequential optimization model that we take as a surrogate for the decision-making process of the traders. In particular, we model trader behaviour in terms of a Markov decision process. We infer the reward (or objective) function for this process from observations of trading actions using a process from machine learning known as inverse reinforcement learning (IRL). The reward functions learned through IRL then constitute a feature space that can be the basis for supervised learning (for classification or recognition of traders) or unsupervised learning (for categorization of traders). Making use of a real-world data-set from the E-Mini futures contract, we compare two principal IRL variants, linear IRL and Gaussian Process IRL, against a method based on summary trading statistics. Results suggest that IRL-based feature spaces support accurate classification and meaningful clustering. Further, we argue that, because they attempt to learn traders’ underlying value propositions under different market conditions, the IRL methods are more informative and robust than the summary statistic-based approach and are well suited for discovering new behaviour patterns of market participants. [ABSTRACT FROM PUBLISHER]

The climate of the United States Northern Great Plains region is highly variable. Modelling of agriculture in this region and similar locations depends on the availability and quality of satellite and ground data for agro-climate variables. We evaluated tropical rainfall measuring mission (TRMM) multi-satellite preparation analysis (TMPA) precipitation, atmospheric infrared sounder (AIRS) surface air temperature, and AIRS relative air humidity (RH). A significant bias was found within the temperature and RH products and no bias but an insufficient rain event detection skill in the precipitation product (probability of detection ∼0.3). A linear correction of the temperature product removed the bias as well as lowered the root mean square deviation (RMSD). The bias-corrections for RH led to increased RMSD or worse correlation. For precipitation, the correlation between the satellite product and ground data improved if cumulative precipitation or only precipitation during the growing season was used. [ABSTRACT FROM PUBLISHER]

Fire has been the dominant disturbance in boreal America since the Pleistocene, resulting in a spatial mosaic in which the most fire occurs in the continental northwest. Spatial variation in snowshoe hare (Lepus americanus) density reflects the fire mosaic. Because fire initiates secondary forest succession, a fire mosaic creates variation in the abundance of early successional plants that snowshoe hares eat in winter, leading to geographic variation in hare density. We hypothesize that fire is the template for a geographic mosaic of natural selection: where fire is greatest and hares are most abundant, hare browsing has most strongly selected juvenile-phase woody plants for defense. We tested the hypothesis at multiple spatial scales using Alaska birch (Betula neoalaskana) and white birch (Betula papyrifera). We also examined five alternative hypotheses for geographic variation in antibrowsing defense. The fire-hare-defense hypothesis was supported at transcontinental, regional, and local scales; alternative hypotheses were rejected. Our results link transcontinental variation in species interactions to an abiotic environmental driver, fire. Intakes of defense toxins by Alaskan hares exceed those by Wisconsin hares, suggesting that the proposed selection mosaic may coincide with a geographic mosaic of coevolution. [ABSTRACT FROM AUTHOR]