Smart cities offer services to their inhabitants which make everyday life easier beyond providing a feedback channel to the city administration. For instance, a live timetable service for public transportation or real-time traffic jam notification can increase the efficiency of travel planning substantially. Traditionally, the implementation of these smart city services require the deployment of some costly sensing and tracking infrastructure. As an alternative, the crowd of inhabitants can be involved in data collection via their mobile devices. This emerging paradigm is called mobile crowd-sensing or participatory sensing. In this paper, we present our generic framework built upon XMPP (Extensible Messaging and Presence Protocol) for mobile participatory sensing based smart city applications. After giving a short description of this framework we show three use-case smart city application scenarios, namely a live transit feed service, a soccer intelligence agency service and a smart campus application, which are currently under development on top of our framework.

Using data from a Swiss German dialect syntax survey, this study aims to explore, in a spatially differentiated manner, the correlation between dialectal variation and geographic distances. A linguistic distance was expressed by a measure aggregated from 60 survey questions. To operationalize the possibility of language contact, Euclidean distance, as well as travel times in 2000, 1950 and 1850 between survey sites were used. Going beyond previous work by others, we also explore the covariation of geographic and linguistic distances at the local level, focusing on spatial subsets and individual survey sites, thus being able to paint a more differentiated picture. With the diverse physical landscape of Switzerland making an impact on potential language contact, we find that travel times are a better predictor than Euclidean distance for the syntactic variation in Swiss German dialects. However, on the local scale the difference is not always significant, depending on prevalent topography. AbstractUsing data of a Swiss German dialect syntax survey this study aims at exploring, in a spatially differentiated manner, the correlation between dialectal variation and geographic distances. A linguistic distance was expressed by a measure aggregated from 60 survey questions. To operationalise the possibility of language contact, Euclidean distance and travel times in 2000, 1950 and 1850 between survey sites were used. Going beyond previous work by others, we also explore the covariation of geographic and linguistic distances at the local scale, focusing on spatial subsets and individual survey sites, thus being able to paint a more differentiated picture. With the diverse physical landscape of Switzerland making an impact on potential language contact, we find that travel times are a better predictor than Euclidean distance for the syntactic variation in Swiss German dialects. However, on the local scale the difference is not always significant, depending on prevalent topography.

Linguistic data collection typically involves conducting interviews with participants in close proximity. The safety precautions related to the COVID-19 pandemic brought such data collection to an abrupt halt: Social distancing forced linguistic fieldwork into involuntary hibernation in many parts of the world. Such hardship, however, can inspire innovation. In this contribution, we present an approach that – we believe – enables a reliable switch from in-person, face-to-face interviews to virtual, online data collection. In this approach, participants remain at home and navigate a smartphone application, enabling high-quality audio recordings and multisensory presentation of linguistic material, while they are being supervised via videoconferencing (Zoom 2020https://zoom.us/ (accessed 11 August 2020)). The smartphone app and the infrastructure presented are open source, accessible, and adaptable to researchers’ specific needs. To explore whether participants’ experiences of in-person data collection are different from participation in a virtual setting, we conducted a study with 36 participants. Overall, findings revealed a substantial degree of overlap in interview experience, setting a methodological baseline for future work. We end this contribution by discussing the benefits and pitfalls of this new approach.

In this paper, we analyse spatial variation in the Japanese dialectal lexicon by assembling a set of methodologies using theories in variationist linguistics and GIScience, and tools used in historical GIS. Based on historical dialect atlas data, we calculate a linguistic distance matrix across survey localities. The linguistic variation expressed through this distance is contrasted with several measurements, based on spatial distance, utilised to estimate language contact potential across Japan, historically and at present. Further, administrative boundaries are tested for their separation effect. Measuring aggregate associations within linguistic variation can contrast previous notions of dialect area formation by detecting continua. Depending on local geographies in spatial subsets, great circle distance, travel distance and travel times explain a similar proportion of the variance in linguistic distance despite the limitations of the latter two. While they explain the majority, two further measurements estimating contact have lower explanatory power: least cost paths, modelling contact before the industrial revolution, based on DEM and sea navigation, and a linguistic influence index based on settlement hierarchy. Historical domain boundaries and present day prefecture boundaries are found to have a statistically significant effect on dialectal variation. However, the interplay of boundaries and distance is yet to be identified. We claim that a similar methodology can address spatial variation in other digital humanities, given a similar spatial and attribute granularity.

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.