– Revealing the Demographics of Location Data

2012

Abstract In recent years, social media users are voluntarily making large volume of personal data available on the social networks. Such data (eg, professional associations) can create opportunities for users to strengthen their social and professional ties. However, the same data can also be used against the user for viral marketing and other unsolicited purposes. The invasion of privacy occurs due to privacy unawareness and carelessness of making information publicly available.

Future Internet

Nowadays, location-sharing applications (LSA) within social media enable users to share their location information at different levels of precision. Users on their side are willing to disclose this kind of information in order to represent themselves in a socially acceptable online way. However, they express privacy concerns regarding potential malware location-sharing applications, since users’ geolocation information can provide affiliations with their social identity attributes that enable the specification of their behavioral normativity, leading to sensitive information disclosure and privacy leaks. This paper, after a systematic review on previous social and privacy location research, explores the overlapping of these fields in identifying users’ social attributes through examining location attributes while online, and proposes a targeted set of location privacy attributes related to users’ socio-spatial characteristics within social media.

The rise of smart phone use, and its convergence with mapping infrastructures and large search and social media corporations, has led to a commensurate rise in the importance of location. While locations are still defined by fixed longitude/latitude coordinates, they now increasingly ‘acquire dynamic meaning as a consequence of the constantly changing location-based information that is attached to them’ becoming ‘a near universal search string for the world’s data’. As the richness of this geocoded information increases, so the commercial value of this location information also increases. This article examines the growing commercial significance of location data. Informed by recent calls for ‘medium-specific analysis’, we build on earlier work to argue that social media companies actively extract location data for commercial advantage in quite specific ways. By not paying due and careful attention to the specifics of data extraction strategies, political and cultural economic analyses of new media services risk eliding key differences between new media platforms, and their respective software systems, patterns of consumer use, and individual revenue models. In response, we develop a comparative analysis of two platforms – Foursquare and Google – and examine how each extracts and uses geocoded user data. From this comparative exploration of platform specificity, we aim to draw conclusions concerning marketing (economic) surveillance, and how Foursquare’s and Google’s operations work in the service of fostering the securitization of mobility - the process by which the capacity to track and predict mobility and associated patterns of consumption is directly productive of value.

2013

Use of social media continues to increase day by day, with implications for the creation of ‘big’ data – Twitter alone was forecast to have created 1.8 zettabytes of data in 2011 (equivalent to the information storage capacity of 150bn Apple iPads). Users of the likes of Twitter, Facebook, Flickr, LinkedIn, Bebo, and Orkut are frequently mobile users of the developing range of smartphones and tablet devices. The availability of such data has profound implications for the geodemographic analysis of human settlement structure. Hitherto, small area measures of neighbourhood conditions have been based only upon the night-time socioeconomic characteristics of residential areas and selected physical characteristics of their built environments (Martin et al 2012). Although useful for guiding resource allocation decisions for many private and public goods (Longley 2005), these static and essentially cross sectional views provide only limited insights into the functioning of settlement syste...

arXiv (Cornell University), 2023

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications. The recent availability of large-scale human movement data collected from mobile devices have enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting fair performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (90 days) dataset of 100,000 individuals' human mobility trajectories, using mobile phone location data. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users' privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency. To promote the use of the dataset, we will host a human mobility prediction data challenge ('HuMob Challenge 2023') using the human mobility dataset, which will be held in conjunction with ACM SIGSPATIAL 2023.

Applied Spatial Analysis and Policy

Location-aware mobile phone handsets have become increasingly common in recent years, giving rise to a wide variety of location based services that rely on a person's mobile phone reporting its current location to a remote service provider. Previous research has demonstrated that services that geo-code status updates may permit the estimation of both the rough location of users' home locations and those of their workplaces. The paper investigates the disclosure risks of a priori knowledge of a person's home and workplace locations, or of their current and previous home locations. Detailed interaction data sets published from censuses or other sources are characterised by the sparsity of the contained data, such that unique combinations of two locations may often be observed. In the most detailed 2011 migration data 37% of migrants had a unique combination of origin and destination, whilst in the most detailed journey to work data, 58% of workers had a unique combination of home and workplace. The amount of additional attribute data that might be disclosed is limited. When more coarse geographies are used their still remain a non-trivial number of persons with unique location combinations, with considerably more attributes potentially disclosable.

EPJ Data Science, 2015

We analyse a large mobile phone activity dataset provided by Telecom Italia for the Telecom Big Data Challenge contest. The dataset reports the international country codes of every call/SMS made and received by mobile phone users in Milan, Italy, between November and December 2013, with a spatial resolution of about 200 meters. We first show that the observed spatial distribution of international codes well matches the distribution of international communities reported by official statistics, confirming the value of mobile phone data for demographic research. Next, we define an entropy function to measure the heterogeneity of the international phone activity in space and time. By comparing the entropy function to empirical data, we show that it can be used to identify the city's hotspots, defined by the presence of points of interests. Eventually, we use the entropy function to characterize the spatial distribution of international communities in the city. Adopting a topological data analysis approach, we find that international mobile phone users exhibit some robust clustering patterns that correlate with basic socio-economic variables. Our results suggest that mobile phone records can be used in conjunction with topological data analysis tools to study the geography of migrant communities in a global city. al.; licensee Springer. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Applied Network Science

We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we re...

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 2013

In recent years, geo-referenced community-contributed multimedia data that is available from services such as Flickr/YouTube, has been used to help understand patterns of human mobility, behavior and habits. While this data is freely available for much larger regions of the world, it is understood that the quality of such data is lower than that of data that can be obtained from mobile phone operators. This is probably the reason why public data has not been considered for studies attempting to identify basic laws that govern human mobility. In this study we explore the possibility of using Flickr data as an alternative to mobilephone-generated data when it comes to analyzing human mobility. To do this, we apply a recently published approach to analysis of mobile phone data to the trajectories of 6404 Fickr users, derived from a dataset of 1 million images pertinent to the San Francisco/San Diego area. Our goal is to show that regularities that can be observed using mobile phone data are present in the Flickr data and that the publicly available data has the potential to enable researchers to conduct similar analysis at larger (continent/world wide) scales, with possible applications to urban planning, traffic forecasting and the spread of biological and mobile-phone viruses. The results presented show that Flickr data is suitable for such studies, and can be used as an alternative to proprietary mobile-phone-use related data.

2011

The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users.