The Digital Life of Walkable Streets

Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.Comment: 10 pages, 7 figures, Proceedings of International World Wide Web Conference (WWW 2015

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England

How effective is the Forestry Commission Scotland's woodland improvement programme--'Woods In and Around Towns' (WIAT)--at improving psychological well-being in deprived urban communities? A quasi-experimental study

Introduction: There is a growing body of evidence that suggests that green spaces may positively influence psychological well-being. This project is designed to take advantage of a natural experiment where planned physical and social interventions to enhance access to natural environments in deprived communities provide an opportunity to prospectively assess impacts on perceived stress and mental well-being.<p></p> Study design and methods: A controlled, prospective study comprising a repeat cross-sectional survey of residents living within 1.5 km of intervention and comparison sites. Three waves of data will be collected: prephysical environment intervention (2013); postphysical environment intervention (2014) and postwoodland promotion social intervention (2015). The primary outcome will be a measure of perceived stress (Perceived Stress Scale) preintervention and postintervention. Secondary, self-report outcomes include: mental well-being (Short Warwick-Edinburgh Mental Well-being Scale), changes in physical activity (IPAQ-short form), health (EuroQoL EQ-5D), perception and use of the woodlands, connectedness to nature (Inclusion of Nature in Self Scale), social cohesion and social capital. An environmental audit will complement the study by evaluating the physical changes in the environment over time and recording any other contextual changes over time. A process evaluation will assess the implementation of the programme. A health economics analysis will assess the cost consequences of each stage of the intervention in relation to the primary and secondary outcomes of the study.<p></p> Ethics and dissemination: Ethical approval has been given by the University of Edinburgh, Edinburgh College of Art Research, Ethics and Knowledge Exchange Committee (ref. 19/06/2012). Findings will be disseminated through peer-reviewed publications, national and international conferences and, at the final stage of the project, through a workshop for those interested in implementing environmental interventions.<p></p&gt

Mining large-scale human mobility data for long-term crime prediction

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

Mobile Service Affordability for the Needy, Addiction, and ICT Policy Implications

This paper links communications and media usage to social and household economics boundaries. It highlights that in present day society, communications and media are a necessity, but not always affordable, and that they furthermore open up for addictive behaviours which raise additional financial and social risks. A simple and efficient methodology compatible with state-of-the-art social and communications business statistics is developed, which produces the residual communications and media affordability budget and ultimately the value-at-risk in terms of usage and tariffs. Sensitivity analysis provides precious information on communications and media adoption on the basis of affordability. Case data are surveyed from various countries. ICT policy recommendations are made to support widespread and responsible communications access.addiction;ICT policy;communications affordability;mobile service

Measuring Social Well Being in The Big Data Era: Asking or Listening?

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.Comment: 40 pages, 2 figures. arXiv admin note: text overlap with arXiv:1512.0156