Gender Matters! Analyzing Global Cultural Gender Preferences for Venues Using Social Sensing

Gender differences is a phenomenon around the world actively researched by social scientists. Traditionally, the data used to support such studies is manually obtained, often through surveys with volunteers. However, due to their inherent high costs because of manual steps, such traditional methods do not quickly scale to large-size studies. We here investigate a particular aspect of gender differences: preferences for venues. To that end we explore the use of check-in data collected from Foursquare to estimate cultural gender preferences for venues in the physical world. For that, we first demonstrate that by analyzing the check-in data in various regions of the world we can find significant differences in preferences for specific venues between gender groups. Some of these significant differences reflect well-known cultural patterns. Moreover, we also gathered evidence that our methodology offers useful information about gender preference for venues in a given region in the real world. This suggests that gender and venue preferences observed may not be independent. Our results suggests that our proposed methodology could be a promising tool to support studies on gender preferences for venues at different spatial granularities around the world, being faster and cheaper than traditional methods, besides quickly capturing changes in the real world

On the Potential of Social Media Data in Urban Planning: Findings from the Beer Street in Curitiba, Brazil

Social media makes available vast amounts of data for various types of analyses. Cities have the opportunity to explore this new data source to study urban dynamics and complement traditional data used for urban planning. We investigate Untappd social media data in the context of urban planning in Curitiba, Brazil. We analyze the project to create a Craft Beer Street, recently announced by the municipality to promote local beers in Curitiba, in order to study the potential of exploring social media data to support the planning of this project. Our results indicate that social media data could have helped to guide the decision of the Beer Street creation and can potentially become a strategic urban planning tool

A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform

Walking is a fundamental part of a physically active lifestyle, it is one of everyday activities that positively impacts health and wellbeing. In this paper we describe the challenges and experiences of conducting a sensing campaign in the wild. We make use of mk-sense; a software platform to facilitate the deployment of collaborative sensing campaigns. We elaborate on two cross-cultural studies conducted in four different countries (Mexico, Turkey, Spain, and Switzerland) with a total of 77 participants. We present a detailed description of the data collected from one of the studies aimed at measuring walkability around three different university campuses. The analysis of the data shows that walkability can be assessed using information from the sensors in the smartphones and results from surveys answered by participants. In addition, we analyze issues about data sharing and privacy awareness

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Social Media data: Challenges, opportunities and limitations in urban studies

Analysing the city through data retrieved from Location Based Social Networks (LBSNs) has received considerable attention as a promising method for applied research. However, the use of these data is not without its challenges and has given rise to a stream of polemical arguments over the validity of this source of information. This paper addresses the challenges and opportunities as well as some of the limitations and biases associated with the collection and use of LBSN data from Foursquare, Twitter, Google Places, Instagram and Airbnb in the context of urban phenomena research. The most recent research that uses LBSN data to understand city dynamics is presented. A method is proposed for LBSN data retrieval, selection, classification and analysis. In addition, key thematic research lines are identified given the data variables offered by these LBSNs. A comprehensive and descriptive framework for the study of urban phenomena through LBSN data is the main contribution of this study.This work was supported by the Council of Education, Research, Culture and Sports – Generalitat Valenciana (Spain). Project: Valencian Community cities analysed through Location-Based Social Networks and Web Services Data. Ref. no. AICO/2017/018

Modeling and Analyzing Collective Behavior Captured by Many-to-Many Networks

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