The call of the crowd: Event participation in location-based social services

Understanding the social and behavioral forces behind event participation is not only interesting from the viewpoint of social science, but also has important applications in the design of personalized event recommender systems. This paper takes advantage of data from a widely used location-based social network, Foursquare, to analyze event patterns in three metropolitan cities. We put forward several hypotheses on the motivating factors of user participation and confirm that social aspects play a major role in determining the likelihood of a user to participate in an event. While an explicit social filtering signal accounting for whether friends are attending dominates the factors, the popularity of an event proves to also be a strong attractor. Further, we capture an implicit social signal by performing random walks in a high dimensional graph that encodes the place type preferences of friends and that proves especially suited to identify relevant niche events for users. Our findings on the extent to which the various temporal, spatial and social aspects underlie users' event preferences lead us to further hypothesize that a combination of factors better models users' event interests. We verify this through a supervised learning framework. We show that for one in three users in London and one in five users in New York and Chicago it identifies the exact event the user would attend among the pool of suggestions.We acknowledge the support of Microsoft Research and EPSRC through grant GALE (EP/K019392).This is the final published version. It's also available from AAAI at http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8068. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved

Comparing cities’ cycling patterns using online shared bicycle maps

Bicycle sharing systems are increasingly being deployed in urban areas around the world, alongside online maps that disclose the state (i.e., location, number of bicycles/number of free parking slots) of stations in each city. Recent work has demonstrated how regularly monitoring these online maps allows for a granular analysis of a city’s cycling trends; further, the literature indicates that different cities have unique spatio-temporal patterns, reducing the generalisability of any insights or models derived from a single system. In this work, we analyse 4.5 months of online bike-sharing map data from 10 cities which, combined, have 996 stations. While an aggregate comparison supports the view of cities having unique usage patterns, results of applying unsupervised learning to the temporal data shows that, instead, only the larger systems display heterogeneous behaviour, indicating that many of these systems share intrinsic similarities. We further show how these similarities are reflected in the predictability of stations’ occupancy data via a cross-city comparison of the error that a variety of approaches achieve when forecasting the number of bicycles that a station will have in the near future.We close by discussing the impact of uncovering these similarities on how future bicycle sharing systems can be designed, built, and managed.This is the accepted manuscript. The final published version is available at http://link.springer.com/article/10.1007%2Fs11116-015-9599-9

Mining open datasets for transparency in taxi transport in metropolitan environments.

Uber has recently been introducing novel practices in urban taxi transport. Journey prices can change dynamically in almost real time and also vary geographically from one area to another in a city, a strategy known as surge pricing. In this paper, we explore the power of the new generation of open datasets towards understanding the impact of the new disruption technologies that emerge in the area of public transport. With our primary goal being a more transparent economic landscape for urban commuters, we provide a direct price comparison between Uber and the Yellow Cab company in New York. We discover that Uber, despite its lower standard pricing rates, effectively charges higher fares on average, especially during short in length, but frequent in occurrence, taxi journeys. Building on this insight, we develop a smartphone application, OpenStreetCab, that offers a personalized consultation to mobile users on which taxi provider is cheaper for their journey. Almost five months after its launch, the app has attracted more than three thousand users in a single city. Their journey queries have provided additional insights on the potential savings similar technologies can have for urban commuters, with a highlight being that on average, a user in New York saves 6 U.S. Dollars per taxi journey if they pick the cheapest taxi provider. We run extensive experiments to show how Uber's surge pricing is the driving factor of higher journey prices and therefore higher potential savings for our application's users. Finally, motivated by the observation that Uber's surge pricing is occurring more frequently that intuitively expected, we formulate a prediction task where the aim becomes to predict a geographic area's tendency to surge. Using exogenous to Uber data, in particular Yellow Cab and Foursquare data, we show how it is possible to estimate customer demand within an area, and by extension surge pricing, with high accuracy.This is the final version of the article. It was first available from Springer via http://dx.doi.org/10.1140/epjds/s13688-015-0060-

Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning

Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to o er gen- erous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as it can reduce nan- cial costs in terms of planning and improve quality of life in the city, more generally. In this paper, we make use of a large longitudinal dataset of user location check-ins from the online social network WeChat to develop a data-driven framework for culture planning in the city of Beijing. We exploit rich spatio-temporal representations on user activity at cultural venues and use a novel extended version of the traditional latent Dirichlet allocation model that incorporates temporal information to identify latent patterns of urban cultural interactions. Using the characteristic typologies of mobile user cul- tural activities emitted by the model, we determine the levels of demand for di erent types of cultural resources across urban areas. We then compare those with the corresponding levels of supply as driven by the presence and spatial reach of cultural venues in local areas to obtain high resolution maps that indicate urban re- gions with lack or oversupply of cultural resources, and thus give evidence and suggestions for further urban cultural planning and investment optimisation.Cambridge Trus

Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges.

As modern transportation systems become more complex, there is need for mobile applications that allow travelers to navigate efficiently in cities. In taxi transport the recent proliferation of Uber has introduced new norms including a flexible pricing scheme where journey costs can change rapidly depending on passenger demand and driver supply. To make informed choices on the most appropriate provider for their journeys, travelers need access to knowledge about provider pricing in real time. To this end, we developed OpenStreetcab a mobile application that offers advice on taxi transport comparing provider prices. We describe its development and deployment in two cities, London and New York, and analyse thousands of user journey queries to compare the price patterns of Uber against major local taxi providers. We have observed large heterogeneity across the taxi transport markets in the two cities. This motivated us to perform a price validation and measurement experiment on the ground comparing Uber and Black Cabs in London. The experimental results reveal interesting insights: not only they confirm feedback on pricing and service quality received by professional drivers users, but also they reveal the tradeoffs between prices and journey times between taxi providers. With respect to journey times in particular, we show how experienced taxi drivers, in the majority of the cases, are able to navigate faster to a destination compared to drivers who rely on modern navigation systems. We provide evidence that this advantage becomes stronger in the centre of a city where urban density is high

How regulating and cultural services of ecosystems have changed over time in Italy

In this experimental study, different components are computed for three different ecosystem services (ES). Specifically, supply, demand and use are estimated for pollination service, flood risk regulation service and nature-based tourism. These are analysed and assessed in 2012 and 2018 for the Italian context, in order to estimate the evolution over this period and to allow a significant comparison of results. The same methodology and models are applied for the selected accounting years and accounting tables and tend to reflect as closely as possible the System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA EA), which is the international standard endorsed by the United Nations to compile Natural Capital Accounting in 2021. Both biophysical and monetary assessments are performed using the ARIES technology, an integrated modelling platform providing automatic and flexible integration of data and models, via its semantic modelling nature. Models have been run adjusting the components of the global modelling approach to the Italian context and, whenever available, prioritising the use of local data to carry out the study. This approach is particularly useful to analyse trends over time, as potentially biased components of models and data are substantially mitigated when the same biases is constant over time. This study finds an increase in benefits over the period analysed for the ES examined. The main contribution of this pioneering work is to support the idea that ES accounting or Natural Capital Accounting can provide a very useful tool to improve economic and environmental information at national and regional level. This can support processes to provide the necessary incentives to steer policy-making towards preventative rather than corrective actions, which are usually much less effective and more costly, both at environmental and economic levels. Nevertheless, particular attention must be paid to the meaning of the estimates and the drivers of these values to derive a direct or indirect relationship between the benefits observable and the actual Italian ecosystems condition. © Capriolo A et al