The Creative City is now a classic and has been republished many times. It is an ambitious book and a clarion call for imaginative action in running urban life. It seeks to inspire people to think, plan and act imaginatively in the city and to get an ideas factory going that turns urban innovations into reality. Its aim is to make readers feel: ‘I can do that too’ and to spread confidence that creative and innovative solutions to urban problems are feasible however bad they may seem at first sight.

The creative city: A toolkit for urban innovators

Tourists' digital footprint in cities: Comparing Big Data sources

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

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HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.

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Measuring Urban Social Diversity Using Interconnected Geo-Social Networks

Urban computing is an emerging area of investigation in which researchers study cities using digital data. Location-Based Social Networks (LBSNs) generate one specific type of digital data that offers unprecedented geographic and temporal resolutions. We discuss fundamental concepts of urban computing leveraging LBSN data and present a survey of recent urban computing studies that make use of LBSN data. We also point out the opportunities and challenges that those studies open.

Urban Computing Leveraging Location-Based Social Network Data: A Survey

Social BPM fuses business process management practices with social networking applications, with the aim of enhancing the enterprise performance by means of a controlled participation of external stakeholders to process design and enactment. This project-centered demonstration paper proposes a model-driven approach to participatory and social enactment of business processes. The approach consists of defining a specific notation for describing Social BPM behaviors (defined as a BPMN 2.0 extension), a methodology, and a technical framework that allows enterprises to implement of social processes as Web applications integrated with public or private Web social networks. The presented work is performed within the BPM4People SME Capacities project

Combining social web and BPM for improving enterprise performances: the BPM4People approach to social BPM

Mapping the functional use of city areas (e.g., mapping clusters of hotels or of electronic shops) enables a variety of applications (e.g., innovative way-finding tools). To do that mapping, researchers have recently processed geo-referenced data with spatial clustering algorithms. These algorithms usually perform two consecutive steps: they cluster nearby points on the map, and then assign labels (e.g., 'electronics') to the resulting clusters. When applied in the city context, these algorithms do not fully work, not least because they consider the two steps of clustering and labeling as separate. Since there is no reason to keep those two steps separate, we propose a framework that clusters points based not only on their density but also on their semantic relatedness. We evaluate this framework upon Foursquare data in the cities of Barcelona, Milan, and London. We find that it is more effective than the baseline method of DBSCAN in discovering functional areas. We complement that quantitative evaluation with a user study involving 111 participants in the three cities. Finally, to illustrate the generalizability of our framework, we process temporal data with it and successfully discover seasonal uses of the city.

Taxonomy-Based Discovery and Annotation of Functional Areas in the City

Evolution of online social networks is driven by the need of their members to shareand consume content, resulting in a complex interplay between individual activity andattention received from others. In a context of increasing information overload andlimited resources, discovering which are the most successful behavioral patterns toattract attention is very important. To shed light on the matter, we look into thepatterns of activity and popularity of users in the Yahoo Meme microblogging service.We observe that a combination of different type of social and content-producingactivity is necessary to attract attention and the efficiency of users, namely theaverage attention received per piece of content published, for many users has adefined trend in its temporal footprint. The analysis of the user time series ofefficiency shows different classes of users whose different activity patterns giveinsights on the type of behavior that pays off best in terms of attention gathering.In particular, sharing content with high spreading potential and then supporting theattention raised by it with social activity emerges as a frequent pattern for usersgaining efficiency over time.

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Modeling dynamics of attention in social media with user efficiency

Easy access to different necessities of daily life makes a city more livable. This has motivated urban planning researchers to quantify urban accessibility from official city data. However, due to the manual nature of data collection, these earlier survey based analyses were limited in scope and scalability, and mostly offered insights on cities of developed countries like the UK and the USA. Using Google Places data that is crowd-sourced around the world, this paper gathers walkability information for twentyfive cities across five continents. We detail the collection methodology of this unprecedented dataset and show useful applications of this data in urban analysis: e.g., how different areas within a city compare against each other in terms of accessibility and which areas in a city would benefit the most from the least intervention.

Scalable Urban Data Collection from the Web.

Urban resources are allocated according to socio-economic indicators, and rapid urbanization in developing countries calls for updating those indicators in a timely fashion. The prohibitive costs of census data collection make that very difficult. To avoid allocating resources upon outdated indicators, one could partly update or complement them using digital data. It has been shown that it is possible to use social media in developed countries (mainly UK and USA) for such a purpose. Here we show that this is the case for Brazil too. We analyze a random sample of a microblogging service popular in that country and accurately predict the GDPs of 45 Brazilian cities. To make these predictions, we exploit the sociological concept of glocality, which says that economically successful cities tend to be involved in interactions that are both local and global at the same time. We indeed show that a city's glocality, measured with social media data, effectively signals the city's economic well-being.

/pdf/taking-brazil-s-pulse-tracking-growing-urban-economies-from-290k4fks5h.pdf

Carmen Karina Vaca Ruiz

Papers

Combining social web and BPM for improving enterprise performances: the BPM4People approach to social BPM

Taxonomy-Based Discovery and Annotation of Functional Areas in the City

Modeling dynamics of attention in social media with user efficiency

Scalable Urban Data Collection from the Web.

Taking Brazil's pulse: tracking growing urban economies from online attention