My area of work is Applied Data Science with specific expertise on Social/Urban Computing and Computer-Supported Cooperative Work.
I have published over 70 peer-reviewed journal papers, conference proceeding articles, and book chapters. My h-index is 20, my work has been cited over 1,400 times (source: Google Scholar).
S. Jain, D. Proserpio, G. Quattrone, D. Quercia.
Nowcasting Gentrification Using Airbnb Data.
In ACM CSCW, (Virtually Co-located with UIST), 2021.
Abstract: There is a rumbling debate over the impact of gentrification: presumed gentrifiers have been the target of protests and attacks in some cities, while they have been welcome as generators of new jobs and taxes in others. Census data fails to measure neighborhood change in real-time since it is usually updated every ten years. This work shows that Airbnb data can be used to quantify and track neighborhood changes. Specifically, we consider both structured data (e.g., number of listings, number of reviews, listing information) and unstructured data (e.g., user-generated reviews processed with natural language processing and machine learning algorithms) for three major cities, New York City (US), Los Angeles (US), and Greater London (UK). We find that Airbnb data (especially its unstructured part) appears to nowcast neighborhood gentrification, measured as changes in housing affordability and demographics. Overall, our results suggest that user-generated data from online platforms can be used to create socioeconomic indices to complement traditional measures that are less granular, not in real-time, and more costly to obtain.
A. Venerandi, G. Quattrone, L. Capra.
A scalable method to quantify the relationship between urban form and socio-economic indexes.
In EPJ Data Science, 2018.
Abstract: The world is undergoing a process of fast and unprecedented urbanisation. It is reported that by 2050 66% of the entire world population will live in cities. Although this phenomenon is generally considered beneficial, it is also causing housing crises and more inequality worldwide. In the past, the relationship between design features of cities and socio-economic levels of their residents has been investigated using both qualitative and quantitative methods. However, both sets of works had significant limitations as the former lacked generalizability and replicability, while the latter had a too narrow focus, since they tended to analyse single aspects of the urban environment rather than a more complex set of metrics. This might have been caused by the lack of data availability. Nowadays, though, larger and freely accessible repositories of data can be used for this purpose. In this paper, we propose a scalable method that delves deeper into the relationship between features of cities and socio-economics. The method uses openly accessible datasets to extract multiple metrics of urban form and then models the relationship between urban form and socio-economic levels through spatial regression analysis. We applied this method to the six major conurbations (i.e., London, Manchester, Birmingham, Liverpool, Leeds, and Newcastle) of the United Kingdom (UK) and found that urban form could explain up to 70% of the variance of the English official socio-economic index, the Index of Multiple Deprivation (IMD). In particular, results suggest that more deprived UK neighbourhoods are characterised by higher population density, larger portions of unbuilt land, more dead-end roads, and a more regular street pattern.
A. Venerandi, G. Quattrone, L. Capra, D. Quercia, D. Saez-Trumper.
Measuring Urban Deprivation from User Generated Content.
In ACM CSCW, Vancouver, BC, Canada, 2015.
Abstract: Measuring socioeconomic deprivation of cities in an accurate and timely fashion has become a priority for governments around the world, as the massive urbanization process we are witnessing is causing high levels of inequalities which require intervention. Traditionally, deprivation indexes have been derived from census data, which is however very expensive to obtain, and thus acquired only every few years. Alternative computational methods have been proposed in recent years to automatically extract proxies of deprivation at a fine spatio-temporal level of granularity; however, they usually require access to datasets (e.g., call details records) that are not publicly available to governments and agencies. To remedy this, we propose a new method to automatically mine deprivation at a fine level of spatio-temporal granularity that only requires access to freely available user-generated content. More precisely, the method needs access to datasets describing what urban elements are present in the physical environment; examples of such datasets are Foursquare and OpenStreetMap. Using these datasets, we quantitatively describe neighborhoods by means of a metric, called Offering Advantage, that reflects which urban elements are distinctive features of each neighborhood. We then use that metric to (i) build accurate classifiers of urban deprivation and (ii) interpret the outcomes through thematic analysis. We apply the method to three UK urban areas of different scale and elaborate on the results in terms of precision and recall.
G. Quattrone, A. Nocera, L. Capra and D. Quercia.
Social Interactions or Business Transactions? What customer reviews disclose about Airbnb marketplace.
In WWW, Taiwan, 2020.
Abstract: Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is an hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers' reviews. Using a combination of thematic analysis and machine learning techniques, we first build a platform-specific dictionary for guests' reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different city neighbourhoods.
G. Quattrone, A. Greatorex, D. Quercia, L. Capra and M. Musolesi.
Analyzing and Predicting the Spatial Penetration of Airbnb in U.S. Cities.
In EPJ Data Science, 2018.
Abstract: 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 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.
G. Quattrone, D. Proserpio, D. Quercia, L. Capra and M. Musolesi.
Who benefits from the "sharing" economy of Airbnb?
In WWW, Montreal, Canada. 2016.
Abstract: 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".
M. Dittus, G. Quattrone, L. Capra.
Mass participation during emergency response: event-centric crowd-sourcing in humanitarian mapping.
In ACM CSCW, Portland, USA, 2017. (honorable mention)
Abstract: Crowdsourcing platforms have become important information providers after disaster events. While they can build on some prior experiences, it is not yet well understood how contributor capacity for such activities is constituted. To what extent are initiatives building a dormant task force that springs to action when it is needed? Alternatively, do they mainly rely on the recruitment of new contributors during disaster events, possibly at the expense of contribution quality? We seek to develop a better understanding of these relationships, using the example of the Humanitarian OpenStreetMap Team. In a large-scale quantitative study, we assess the outcomes of 26 campaigns with almost 20,000 participants. We find that event-centric campaigns can be significant recruiting and reactivation events, however that this is not guaranteed. Our analytical methods provide a means of interpreting key differences in outcomes. We close with recommendations relating to the promotion and coordination of event-centric campaigns in HOT and related platforms.
M. Dittus, G. Quattrone, L. Capra.
Analysing volunteer engagement in humanitarian mapping: building contributor communities at large scale.
In ACM CSCW, San Francisco, USA, 2016.
Abstract: Organisers of large-scale crowdsourcing initiatives need to consider how to produce outcomes with their projects, but also how to build volunteer capacity. The initial project experience of contributors plays an important role in this, particularly when the contribution process requires some degree of expertise. We propose three analytical dimensions to assess first-time contributor engagement based on readily available public data: cohort analysis, task analysis, and observation of contributor performance. We apply these to a large-scale study of remote mapping activities coordinated by the Humanitarian OpenStreetMap Team, a global volunteer effort with thousands of contributors. Our study shows that different coordination practices can have a marked impact on contributor retention, and that complex task designs can be a deterrent for certain contributor groups. We close by providing recommendations about how to build and sustain volunteer capacity in these and comparable crowdsourcing systems.
G. Quattrone, M. Dittus, L. Capra.
Work Always in Progress: Analysing Maintenance Practices in Spatial Crowd-sourced Datasets.
In ACM CSCW, Portland, USA, 2017.
Abstract: Crowd-mapping is a form of collaborative work that empowers users to share geographic knowledge. Despite geographic information being intrinsically evolving, little research has so far gone into analysing maintenance practices in these domains. In this paper, we 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, who engages in these practices and where. We apply this method to 117 countries in OpenStreetMap, one of the most successful examples of geographic crowd-sourced datasets. Furthermore, we explore what triggers maintenance, by means of an online survey to which 96 OpenStreetMap contributors took part. 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, by whom, and what triggers them.
G. Quattrone, L. Capra, P. De Meo.
There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets.
In ACM CSCW, Vancouver, BC, Canada, 2015.
Abstract: Crowd-sourcing has become a popular form of computer mediated collaborative work and OpenStreetMap represents one of the most successful crowd-sourcing systems, where the goal of building and maintaining an accurate global map of the world is being accomplished by means of contributions made by over 1.2M citizens. However, within this apparently large crowd, a tiny group of highly active users is responsible for the mapping of almost all the content. One may thus wonder to what extent the information being mapped is biased towards the interests and agenda of this group of users. In this paper, we present a method to quantitatively measure cont ent bias in crowd-sourced geographic information. We then apply the method to quantify content bias across a three-year period of OpenStreetMap mapping in 40 countries. We find almost no content bias in terms of what is being mapped, but significant geographic bias; furthermore, we find that bias in terms of meticulousness varies with culture.
Dr. Giovanni Quattrone
Senior Lecturer
Department of Computer Science
Middlesex University, UK
E-mail: g.quattrone [@] mdx.ac.uk
Associate Professor
Department of Computer Science
University of Turin, Italy
E-mail: giovanni.quattrone [@] unito.it