Measuring Urban Deprivation from User Generated Content

Venerandi, Alessandro; Quattrone, Giovanni; Capra, Licia; Quercia, Daniele; Sáez-Trumper, Diego

doi:10.1145/2675133.2675233

Cited by 58 publications

(45 citation statements)

References 35 publications

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“…In our case study, the Airbnb listing density was much higher in the new urban tourism hotspot Kreuzkölln. One could use this information, together with other data retrieved from Airbnb listings, to classify neighborhoods, similar to Venerandi et al's approach [123]. An open question that could be investigated using surveys and interviews is if hosts are aware of their role as producers of space, in particular in new urban tourism hotspots.…”

Section: Discussionmentioning

confidence: 99%

Constructing Urban Tourism Space Digitally

Stors

Baltes

2018

Proc. ACM Hum.-Comput. Interact.

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Over the past decade, Airbnb has emerged as the most popular platform for renting out single rooms or whole apartments. The impact of Airbnb listings on local neighborhoods has been controversially discussed in many cities around the world. The platform's widespread adoption led to changes in urban life, and in particular urban tourism. In this paper, we argue that urban tourism space can no longer be understood as a fixed, spatial entity. Instead, we follow a constructionist approach and argue that urban tourism space is (re-)produced digitally and collaboratively on online platforms such as Airbnb. We relate our work to a research direction in the CSCW community that is concerned with the role of digital technologies in the production and appropriation of urban space and use the concept of representations as a theoretical lens for our empirical study. In that study, we qualitatively analyzed how the two Berlin neighborhoods Kreuzkölln and City West are digitally constructed by Airbnb hosts in their listing descriptions. Moreover, we quantitatively investigated to what extend mentioned places differ between Airbnb hosts and visitBerlin, the city's destination management organization (DMO). In our qualitative analysis, we found that hosts primarily focus on facilities and places in close proximity to their apartment. In the traditional urban tourism hotspot City West, hosts referred to many places also mentioned by the DMO. In the neighborhood of Kreuzkölln, in contrast, hosts reframed everyday places such as parks or an immigrant food market as the must sees in the area. We discuss how Airbnb hosts contribute to the discursive production of urban neighborhoods and thus co-produce them as tourist destinations. With the emergence of online platforms such as Airbnb, power relations in the construction of tourism space might shift from DMOs towards local residents who are now producing tourism space collaboratively. close to major sights and tourism-related facilities, and venture into residential neighborhoods-a phenomenon called off the beaten track or new urban tourism [55,85,94,95,121]. Short-term rentals seem to foster this development [53,55,67]. They enable all kinds of temporary city-users [86], such as cultural tourists, exchange students, temporary migrants, or business travelers, to stay in private apartments. However, only a fraction of urban residents participates in the practice of renting out rooms or apartments. Some of them are annoyed by the large influx of visitors to their neighborhood and the consequences for the urban structure resulting from large-scale short term rentals [33,56,91]. The impact of the fast-growing number of Airbnb listings, particularly in residential neighborhoods, has been controversially discussed in many cities around the world. Central topics of this debate were, among others, Airbnb's contribution to the transformation of residential neighborhoods and resulting gentrification processes [56,60], coming along with rent increase [89,106], changes in the social structure of ...

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Section: Discussionmentioning

confidence: 99%

Constructing Urban Tourism Space Digitally

Stors

Baltes

2018

Proc. ACM Hum.-Comput. Interact.

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“…Our goal here is to use such data as a means to obtain indicators on population activity that can complement existing datasets in healthcare such as the ambulance calls data we analyse in the following sections. To our knowledge this is one of the first works that combines these types of data, building on the line of recent research works that exploit mobility traces of user collectives to understand crime patterns or deprivation levels of local communities [15,18,34,38]. Health Geography and environmental epidemiology: Our work also relates to literature in the field of health geography and environmental epidemiology.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Population Activity Dynamics to Predict Urban Epidemiological Incidence

Todorova

Noulas

2019

Proceedings of the 9th International Conference on Digital Public Health

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Ambulance services worldwide are of vital importance to population health. Timely responding to incidents by dispatching an ambulance vehicle to the location a call came from can offer significant benefits to patient care across a number of medical conditions. Moreover, identifying the reasons that drive ambulance activity at an area not only can improve the operational capacity of emergency services, but can lead to better policy design in healthcare. In this work, we analyse the temporal dynamics of 5.6 million ambulance calls across a region of 7 million residents in the UK. We identify characteristic temporal patterns featuring diurnal and weekly cycles in ambulance call activity. These patterns are stable over time and across geographies. Using a dataset sourced from location intelligence platform Foursquare, we establish a link between the spatio-temporal dynamics of mobile users engaging with urban activities locally and emergency incidents. We use this information to build a novel metric that assesses the health risk of a geographic area in terms of its propensity to yield ambulance calls. Formulating then an online classification task where the goal becomes to identify which regions will need an ambulance at a given time, we demonstrate how semantic information about real world places crowdsourced through online platforms, can become a useful source of information in understanding and predicting regional epidemiological trends. * Gergana Todorova was placed as a data scientist at the North West Ambulance Service, NHS during the period of the present work.

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“…Note that this threshold is mainly valid for the European context. To compute this metric, we use a formula adopted in a previous work (i.e., Offering Advantage) [38] that showed to be effective in capturing variations in the offering of urban elements across a city. In this paper, Offering Advantage is adapted to reflect to what extent a city area a k offers more historic property h i , compared to the average area.…”

Section: Urban Formmentioning

confidence: 99%

A scalable method to quantify the relationship between urban form and socio-economic indexes

Venerandi¹,

Quattrone²,

Capra³

2018

EPJ Data Sci.

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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.

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Measuring Urban Deprivation from User Generated Content

Cited by 58 publications

References 35 publications

Constructing Urban Tourism Space Digitally

Constructing Urban Tourism Space Digitally

Exploiting Population Activity Dynamics to Predict Urban Epidemiological Incidence

A scalable method to quantify the relationship between urban form and socio-economic indexes

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