CitySense: Combining Geolocated Data for Urban Area Profiling (original) (raw)
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CitySense: Retrieving, Visualizing and Combining Datasets on Urban Areas
Social networks, available open data and massive online APIs provide huge amounts of data about our surrounding location, especially for cities and urban areas. Unfortunately, most previous applications and research usually focused on one kind of data over the other, thus presenting a biased and partial view of each location in question, hence partially negating the benefits of such approaches. To remedy this, this work presents the CitySense framework that simultaneously combines data from administrative sources (e.g., public agencies), massive Point of Interest APIs (Google Places, Foursquare) and social microblogs (Twitter) to provide a unified view of all available information about an urban area, in an intuitive and easy to use web-application platform. This work describes the engineering and design challenges of such an effort and how these different and divergent sources of information may be combined to provide an accurate and diverse visualization for our use-case, the urban area of Chicago, USA.
Harnessing Heterogeneous Social Data to Explore, Monitor, and Visualize Urban Dynamics
Understanding the complexity of urban dynamics requires the combination of information from multiple city data sources. Besides traditional urban data, geo-localized social media provide human-generated content, which may reflect in (near) real time the activities people undertake in cities. The challenge is to devise methods and tools that enable the integration and analysis of such heterogeneous sources of information. Motivated by this, we developed SocialGlass, a novel web-based application framework to explore, monitor, and visualize urban dynamics. By deploying our platform in three real-world use cases, the paper elaborates on the benefits and limitations of integrating social media with related city datasets. It further shows how the inherent spatiotemporal, demographic, and contextual diversities of social data influence the interpretations of (dynamic) urban phenomena.
Making Sense of Cities Using Social Media Requirements for Hyper-Local Data Aggregation Tools
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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.
City of the People, for the People: Sensing Urban Dynamics via Social Media Interactions
Lecture Notes in Computer Science, 2018
Understanding the spatio-temporal dynamics of cities is in the heart of many applications including urban planning, zoning, and real-estate construction. So far, much of our understanding about urban dynamics came from traditional surveys conducted by persons or by leveraging mobile data in the form of Call Detailed Records. However, the high financial and human cost associated with these methods make the data availability very limited. In this paper, we investigate the use of large scale and publicly available user contributed content, in the form of social media posts to understand the urban dynamics of cities. We build activity time series for different cities, and different neighborhoods within the same city to identify the different dynamic patterns taking place. Next, we conduct a cluster analysis on the time series to understand the spatial distribution of patterns in the city. Our instantiation for the two cities of London and Doha shows the effectiveness of our method.
Establishing the Process of Spatial Informatization Using Data from Social Network Services
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 2016
Prior knowledge about the SNS (Social Network Services) datasets is often required to conduct valuable analysis using social media data. Understanding the characteristics of the information extracted from SNS datasets leaves much to be desired in many ways. This paper purposes on analyzing the detail of the target social network services, Twitter, Instagram, and YouTube to establish the spatial informatization process to integrate social media information with existing spatial datasets. In this study, valuable information in SNS datasets have been selected and total 12,938 data have been collected in Seoul via Open API. The dataset has been geocoded and turned into the point form. We also removed the overlapped values of the dataset to conduct spatial integration with the existing building layers. The resultant of this spatial integration process will be utilized in various industries and become a fundamental resource to further studies related to geospatial integration using social media datasets.
The purpose of this thesis clarifies a methodology of analyzing and visualizing the spatio-temporal distributions of human behaviors related to daily life by using Twitter. Although a number of field study methods of human behaviors in a city, little research was related to geo-tagged social networking services. Twitter has the possibility to analyze user’s behavior. Two programs were fabricated: making a list of geo-tagged users, and gleaning its users’ data. Excel sorted the determined keywords of “Verb” and “Object” related daily life and outputted into files. In “Verb,” three-dimensional kernel density estimation evaluated the time-space density of its “Verb.” In “Object,” kernel density estimation evaluated the density of its “Object.” These estimations were visualized in Voxler and Google Earth. In conclusion, this methodology indicates the locality of each area by comparison between adjacent areas or inner area. However, three limitations occurred in this method. First, unconscious bias remains because only smart phone users can embed geo-location data. Second, discontinuous history of users’ data disproves the human behavior. Finally, this method cannot analyze real-time. By the overcoming of these limitations, the digital research will be a tool for not only architects or urban planner but also citizens to design their city.
Urban Computing Leveraging Location-Based Social Network Data
ACM Computing Surveys, 2020
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 that 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. We also point out the opportunities and challenges that those studies open.
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.
Understanding the dynamics of urban areas of interest through volunteered geographic information
Journal of Geographical Systems
Obtaining insights about the dynamics of urban structure is crucial to the framing of the context within the smart city. This paper focuses on urban areas of interest (UAOI), a concept that provides functional definitions of a city's spatial structure. Traditional sources of social data can rarely capture these aspects at scale while spatial information on the city alone does not capture how the population values different parts of the city and in different ways. Hence, we leverage volunteered geographic information (VGI) to overcome some of the limits of traditional sources in providing urban structural and functional insights. We use a special type of VGImetadata from geotagged Flickr images-to identify UAOIs and exploit their temporal and spatial attributes. To do this, we propose a methodological strategy that combines hierarchical density-based spatial clustering for applications with noise and the 'α-shape' algorithm to quantify the dynamics of UAOIs in Inner London for a period 2013-2015 and develop an innovative visualisation of UAOI profiles from which UAOI dynamics can be explored. Our results expand and improve upon the previous literature on this topic and provide a useful reference for urban practitioners who might wish to include more timely information when making decisions.