Effects of mobility on dialect change: Introducing the linguistic mobility index.

Increased geographical mobility prompts dialectologists to factor in survey participants' exposure to linguistic variation in their research. Changing mobility patterns (e.g. longer-distance commuting; easier relocation to distant places for work, study or marriage) have caused linguistic connections to become much more diverse, potentially contributing to the acceleration of dialect change. In this methodological work we propose the Linguistic Mobility Index (LMI) to estimate long-term exposure to dialectal variation and thereby to provide a reference of "localness" about survey participants. Based on data about a survey participant's linguistic biography, an LMI may comprise combinations of influential agents and environments, such as the dialects of parents and long-term partners, the places where participants have lived and worked, and the participants' level of education. We encapsulate the linguistic effects of these agents based on linguistic differences, the intensity and importance of the relationship. We quantify the linguistic effects in three steps. 1) The linguistic effect of an agent is represented by a linguistic distance, 2) This linguistic distance is weighted based on the intensity of the participant's exposure to the agent, and 3) Further weighted according to the relationship embodied by the agent. LMI is conceptualised and evaluated based on 500 speakers from 125 localities in the Swiss German Dialects Across Time and Space (SDATS) corpus, and guidance is provided for establishing LMI in other linguistic studies. For the assessment of LMI's applicability to other studies, four LMI prototypes are constructed based on the SDATS corpus, employing different theoretical considerations and combinations of influential agents and environments to simulate the availability of biographical data in other studies. Using mixed-effects modelling, we evaluate the utility of the LMI prototypes as predictors of dialect change between historic and contemporary linguistic data of Swiss German. The LMI prototypes successfully show that higher exposure to dialectal variation contributes to more dialect change and that its effect is stronger than some sociodemographic variables that are often tested for affecting dialect change (e.g. sex and educational background).

A city-wide examination of fine-grained human emotions through social media analysis.

The proliferation of Social Media and Open Web data has provided researchers with a unique opportunity to better understand human behavior at different levels. In this paper, we show how data from Open Street Map and Twitter could be analyzed and used to portray detailed Human Emotions at a city wide level in two cities, San Francisco and London. Neural Network classifiers for fine-grained emotions were developed, tested and used to detect emotions from tweets in the two cites. The detected emotions were then matched to key locations extracted from Open Street Map. Through an analysis of the resulting data set, we highlight the effect different days, locations and POI neighborhoods have on the expression of human emotions in the cities.

Motivation moderates gender differences in navigation performance.

Gender differences in navigation performance are a recurrent and controversial topic. Previous research suggests that men outperform women in navigation tasks and that men and women exhibit different navigation strategies. Here, we investigate whether motivation to complete the task moderates the relationship between navigation performance and gender. Participants learned the locations of landmarks in a novel virtual city. During learning, participants could trigger a top-down map that depicted their current position and the locations of the landmarks. During testing, participants were divided into control and treatment groups and were not allowed to consult the map. All participants were given 16 minutes to navigate to the landmarks, but those in the treatment group were monetarily penalized for every second they spent completing the task. Results revealed a negative relationship between physiological arousal and the time required to locate the landmarks. In addition, gender differences in strategy were found during learning, with women spending more time with the map and taking 40% longer than men to locate the landmarks. Interestingly, an interaction between gender and treatment group revealed that women in the control group required more time than men and women in the treatment group to retrieve the landmarks. During testing, women in the control group also took more circuitous routes compared to men in the control group and women in the treatment group. These results suggest that a concurrent and relevant stressor can motivate women to perform similarly to men, helping to diminish pervasive gender differences found in the navigation literature.

Reduction of Survey Sites in Dialectology: A New Methodology Based on Clustering.

Many language change studies aim for a partial revisitation, i.e., selecting survey sites from previous dialect studies. The central issue of survey site reduction, however, has often been addressed only qualitatively. Cluster analysis offers an innovative means of identifying the most representative survey sites among a set of original survey sites. In this paper, we present a general methodology for finding representative sites for an intended study, potentially applicable to any collection of data about dialects or linguistic variation. We elaborate the quantitative steps of the proposed methodology in the context of the "Linguistic Atlas of Japan" (LAJ). Next, we demonstrate the full application of the methodology on the "Linguistic Atlas of German-speaking Switzerland" (Germ.: "Sprachatlas der Deutschen Schweiz"-SDS), with the explicit aim of selecting survey sites corresponding to the aims of the current project "Swiss German Dialects Across Time and Space" (SDATS), which revisits SDS 70 years later. We find that depending on the circumstances and requirements of a study, the proposed methodology, introducing cluster analysis into the survey site reduction process, allows for a greater objectivity in comparison to traditional approaches. We suggest, however, that the suitability of any set of candidate survey sites resulting from the proposed methodology be rigorously revised by experts due to potential incongruences, such as the overlap of objectives and variables across the original and intended studies and ongoing dialect change.

Large-scale simulation of traffic flow using Markov model.

Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps in understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic's dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic's dynamic together with the vehicles' distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory and superior to the frequency based maximum likelihood method. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques which, when used together to analyze traffic on large road networks, has not previously been reported.