Exploring maintenance practices in crowd-mapping

Crowd-mapping is a form of collaborative work that empowers users to gather and share geographic knowledge. OpenStreetMap is one of the most successful examples of such paradigm, where the goal of building a global map of the world is collectively performed by over 2M contributors. Despite geographic information being intrinsically evolving, little research has so far gone into analysing maintenance practices in these domains. In this paper, we perform a preliminary exploration to quantitatively capture maintenance dynamics in geographic crowd-sourced datasets, in terms of: the extent to which different maintenance actions are taking place, the type of spatial information that is being maintained, and who engages in these practices. We apply this method to 117 countries in OSM, over one year of mapping activity. Our findings reveal that, although maintenance practices vary substantially from country to country in terms of how widespread they are, strong commonalities exist in terms of what metadata is being maintained and by whom

Social contribution settings and newcomer retention in humanitarian crowd mapping

Organisers of crowd mapping initiatives seek to identify practices that foster an active contributor community. Theory suggests that social contribution settings can provide important support functions for newcomers, yet to date there are no empirical studies of such an effect. We present the first study that evaluates the relationship between colocated practice and newcomer retention in a crowd mapping community, involving hundreds of first-time participants. We find that certain settings are associated with a significant increase in newcomer retention, as are regular meetings, and a greater mix of experiences among attendees. Factors relating to the setting such as food breaks and technical disruptions have comparatively little impact. We posit that successful social contribution settings serve as an attractor: they provide opportunities to meet enthusiastic contributors, and can capture prospective contributors who have a latent interest in the practice

"...when you’re a Stranger": Evaluating Safety Perceptions of (un)familiar Urban Places

What makes us feel safe when walking around our cities? Previous research has shown that our perception of safety strongly depends on characteristics of the built environment; separately, research has also shown that safety perceptions depend on the people we encounter on the streets. However, it is not clear how the two relate to one another. In this paper, we propose a quantitative method to investigate this relationship. Using an online crowd–sourcing approach, we collected 5452 safety ratings from over 500 users about images showing various combinations of built environment and people inhabiting it. We applied analysis of covariance (ANCOVA) to the collected data and found that familiarity of the scene is the single most important predictor of our sense of safety. Controlling for familiarity, we identified then what features of the urban environment increase or decrease our safety perception

Science Education for Citizenship and a Sustainable Future

In this article Jerry Wellington argues very strongly in favour of the role of science in citizenship education. He emphasizes the need for knowledge, skills and action and suggests areas and ways in which pupils can be engaged in the struggle for a sustainable future where interdependence and interconnectedness mesh well with notions of equity and justice

Volunteers in public health and emergency management at outdoor music festivals

This article will report on a study undertaken involving volunteers at an outdoor music festival in Australia. The study was designed to assess the volunteers’ knowledge and skills in emergency management. The findings are based predominantly on self-report data. Findings from the study indicated that a major proportion of the volunteers in the study expressed some level of confidence in dealing with an emergency situation within their work locations at the festival. This level of confidence was associated with volunteer training and knowledge of public health and emergency management. However, less than half of the study participants had knowledge of emergency and public health management for the festival. Furthermore, less than one quarter had knowledge of the festival’s emergency management plan. It was evident that there was a need to increase the number of volunteers with knowledge of public health and emergency management for the festival. All these findings support continued volunteer training programs to improve emergency and public health management at the festival

A survey of the use of crowdsourcing in software engineering

The term 'crowdsourcing' was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering

Cluster of legionnaires’ disease in an Italian prison

Background: Legionella pneumophila (Lp) is the most common etiologic agent causing Legionnaires’ Disease (LD). Water systems offer the best growth conditions for Lp and support its spread by producing aerosols. From 2015 to 2017, the Regional Reference Laboratory of Clinical and Environmental Surveillance of Legionellosis of Palermo monitored the presence of Lp in nine prisons in Western Sicily. During this investigation, we compared Lp isolates from environmental samples in a prison located in Palermo with isolates from two prisoners in the same prison. Methods: We collected 93 water samples from nine Sicilian prisons and the bronchoalveolar lavages (BALs) of two prisoners considered cases of LD. These samples were processed following the procedures described in the Italian Guidelines for the Prevention and Control of Legionellosis of 2015. Then, genotyping was performed on 19 Lp colonies (17 from water samples and 2 from clinical samples) using the Sequence-Based Typing (SBT) method, according to European Study Group for Legionella Infections (ESGLI) protocols. Results: Lp serogroup (sg) 6 was the most prevalent serogroup isolated from the prisons analyzed (40%), followed by Lp sg 1 (16%). Most of all, in four penitentiary institutions, we detected a high concentration of Lp >104 Colony Forming Unit/Liter (CFU/L). The environmental molecular investigation found the following Sequence Types (STs) in Lp sg 6: ST 93, ST 292, ST 461, ST 728, ST 1317 and ST 1362, while most of the isolates in sg 1 belonged to ST 1. We also found a new ST that has since been assigned the number 2451 in the ESGLI-SBT database. From the several Lp sg 1 colonies isolated from the two BALs, we identified ST 2451. Conclusions: In this article, we described the results obtained from environmental and epidemiological investigations of Lp isolated from prisons in Western Sicily. Furthermore, we reported the first cluster of Legionnaires’ in an Italian prison and the molecular typing of Lp sg 1 from one prison’s water system and two BALs, identified the source of the contamination, and discovered a new ST

Analyzing and predicting the spatial penetration of Airbnb in U.S. cities

In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725

Who benefits from the "sharing" economy of Airbnb?

Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of “algorithmic regulation”