Urban Computing Leveraging Location-Based Social Network Data (original) (raw)
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Urban Computing Leveraging Location-Based Social Network Data: a Survey
ACM Computing Surveys, 2019
Urban computing is an emerging area of investigation in which researchers study cities using digital data. Location-Based Social Networks (LBSNs) generate one specific type of digital data, which offers unprecedented geographic and temporal resolutions. We discuss fundamental concepts of urban computing leveraging LBSN data and present a survey of recent urban computing studies that make use of LBSN data. Besides, we point out the opportunities and challenges that those studies open up.
Sensing the Urban Using location-based social network data in urban analysis Working
2011
Location-based services (LBS) are generating vast bodies of data relating to the whereabouts of their users. This is due to the ease with which modern mobile phones can communicate their precise location via the global positioning system (GPS). Online social networks have begun using LBS to aid social encounter and place discovery in cities. A spatial analysis of the aggregate activity generated by such networks can show us how social activity in a city is distributed, revealing fine-grained spatial patterns evident in the social life of cities. The nature of this new data is discussed in relation to existing urban data sets. Large-scale data from one such network is analysed across three cities in order to produce an inter-urban analysis. Comparative measures of polycentricity and fragmentation are used to discuss the spatial structure of the three cities in question. Finally, the impact of LBS technologies are discussed in the context of urban analysis.
Could Data from Location-Based Social Networks Be Used to Support Urban Planning
A great quantity of information is required to support urban planning. Usually there are many (not integrated) data sources, originating from different government bodies, in distinct formats and variable properties (e.g. reliability, completeness). The effort to handle these data, integrate and analyze them is high, taking to much time for the information to be available to help decision making. We argue that data from location-based social networks (LBSN) could be used to provide useful information in reasonable time, despite several limitations they have. To asses this, as a case study, we used data from different LBSN to calculate the Local Availability Index (IOL) for a Brazilian city. This index is part of a methodology to estimate quality of urban life inside cities and is used to support urban planning. The results suggest that data from LBSN are useful and could be used to provide insights for local governments.
Urban Enclave Location-Aware Social Computing
2006
Urban enclaves have richly interconnected social and physical geographies, much of which is hidden from casual observation. As a result, interpersonal collaboration, coordination, and socio-physical navigation is often suboptimal. We are exploring how ubiquitous social computing applications that seamlessly provide location-aware information about people and places could help address this situation.
It would be very difficult even for a resident to characterise the social dynamics of a city and to reveal to foreigners the evolving activity patterns which occur in its various areas. To address this problem, however, large amount of data produced by location-based social networks (LBSNs) can be exploited and combined effectively with techniques of user profiling. The key idea we introduce in this demo is to improve city areas and venues classification using semantics extracted both from places and from the online profiles of people who frequent those places. We present the results of our methodology in LiveCities 1 , a web application which shows the hidden character of several italian cities through clustering and information visualisations paradigms. In particular we give in-depth insights of the city of Florence, IT, for which the majority of the data in our dataset have been collected. The system provides personal recommendation of areas and venues matching user interests and allows the free exploration of urban social dynamics in terms of people lifestyle, business, demographics, transport etc. with the objective to uncover the real 'pulse' of the city. We conducted a qualitative validation through an online questionnaire with 28 residents of Florence to understand the shared perception of city areas by its inhabitants and to check if their mental maps align to our results. Our evaluation shows how considering also contextual semantics like people profiles of interests in venues categorisation can improve clustering algorithms and give good insights of the endemic characteristics and behaviours of the detected areas.
ACM Transactions on Intelligent Systems and Technology, 2014
Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing...
A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior
2013
Social media systems allow a user connected to the Internet to provide useful data about the context in which they are at any given moment, such as Instagram and Foursquare, which are called participatory sensing systems. Location sharing services are examples of participatory sensing systems. The sensed data is a check-in of a particular place that indicates, for instance, a restaurant in a specific location, and also a signal from a user expressing his/her preference. From a participatory sensing system we can derive a participatory sensor network. In this work we compare two different participatory sensor networks, one derived from Instagram, and another one derived from Foursquare. In Instagram, the sensed data is a picture of a specific place. On the other hand, in Foursquare the sensed data is the actual location associated with a specific category of place (e.g., restaurant). Using those social networks we can extract information in many ways. In this work we are interested in comparing two datasets of Foursquare and two datasets of Instagram. We analyze those datasets to investigate whether we can observe the same users' movement pattern, the popularity of regions in cities, the activities of users who use those social networks, and how users share their content along the time. In answering those questions, we want to better understand location-related information, which is an important aspect of the urban phenomena.
Scaling spatial big data in a location-based social network
Revista Brasileira de Administração Científica, 2014
The widespread of the World Wide Web has resulted in a high volume of volunteered generated information using different formats including text, photography and video. The technological advances of recent years enabled the emergence and the popularization of various mobile devices equipped with GPS and connectivity to the Internet. This scenario contributed to the advent of several location-based applications and aroused the interest of many users in the geographical context of the information. An example of such applications are the Location-Based Social Networks (LBSN), in which the users interact with information classified by geographic region, as in the context of Smart Cities, in which citizens can interact pinning their criticisms, opinions and comments on various topics related to their city or neighborhood. The LBSNs have increasingly attracted the interest of the population and have consequently registered an increase in both the number of users interacting and the volume o...
Digital Urban Networks and Social Media
2019
The new gold rush in today’s day and age is that of the urban mining of data for commercial usage. In the aim of monetizing on this, ICT corporations are actively, and aggressively, offering services, often at the expense of the general population, which are then disguised to increase public acceptance. Along with the Smart City, the safe city concept is an example of this and can be argued to stand as a data mining strategy for the enrichment of ICT Corporations. However, those dimensions can be recalibrated, in particular the former, so that they include dimensions of liveability and contribute to building safer, more inclusive, sustainable cities as prescribed by the Sustainable Development Goal 11 by the United Nations and through the New Urban Agenda.