Making Sense of Cities Using Social Media Requirements for Hyper-Local Data Aggregation Tools (original) (raw)

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.

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.

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.

CitySense: Combining Geolocated Data for Urban Area Profiling

International Journal On Advances in Software

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, we developed 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.

Twitter Activity Timeline as a Signature of Urban Neighborhood

arXiv (Cornell University), 2017

Modern cities are complex systems, evolving at a fast pace. Thus, many urban planning, political, and economic decisions require a deep and up-to-date understanding of the local context of urban neighborhoods. This study shows that the structure of openly available social media records, such as Twitter, offers a possibility for building a unique dynamic signature of urban neighborhood function, and, therefore, might be used as an efficient and simple decision support tool. Considering New York City as an example, we investigate how Twitter data can be used to decompose the urban landscape into self-defining zones, aligned with the functional properties of individual neighborhoods and their social and economic characteristics. We further explore the potential of these data for detecting events and evaluating their impact over time and space. This approach paves a way to a methodology for immediate quantification of the impact of urban development programs and the estimation of socioeconomic statistics at a finer spatial-temporal scale, thus allowing urban policy-makers to track neighborhood transformations and foresee undesirable changes in order to take early action before official statistics would be available.

The livehoods project: Utilizing social media to understand the dynamics of a city

2012

Abstract Studying the social dynamics of a city on a large scale has traditionally been a challenging endeavor, often requiring long hours of observation and interviews, usually resulting in only a partial depiction of reality. To address this difficulty, we introduce a clustering model and research methodology for studying the structure and composition of a city on a large scale based on the social media its residents generate.

Situation Monitoring of Urban Areas Using Social Media Data Streams

2016

The continuous growth of social networks and the active use of social media services result in massive amounts of user-generated data. Our goal is to leverage social media users as "social sensors" in order to increase the situational awareness within and about urban areas. In addition to the well-known challenges of event and topic detection and tracking, this task involves a spatial and temporal dimension. In this article, we present a visualization that supports analysts in monitoring events/topics and emotions both in time and in space. The visualization uses a clock-face metaphor to encode temporal and spatial relationships, a color map to reflect emotion, and tag clouds for events and topics. A hierarchy of these clock-faces supports drilling down to finer levels of granularity as well as rolling up the vast and fast flow of information. In order to showcase these functionalities of our visualization, we discuss several case studies that use the live data stream of the Twitter microblogging service. Finally, we demonstrate the usefulness and usability of the visualization in a user study that we conducted.

GeoSocial Gauge: A System Prototype for Knowledge Discovery from Social Media,

International Journal of Geographical Information Science. 27(12): 2483-2508., 2013

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content due to the proliferation of location-aware devices, thus fostering the emergence of geosocial media - a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this paper presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our paper addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds, as well as a corresponding general system architecture. We present these key components, and demonstrate their implementation in our system prototype, GeoSocial Gauge.

The Image of a Data City: Studying the Hyperlocal with Social Media

Architectural Design, 2017

Sharing photos, videos and comments on social media may seem an idle pastime, but it is not without its uses where urban design is concerned. Analysing such posts can yield helpful indicators as to how people experience the built environment. Lev Manovich and Agustin Indaco, of the Software Studies Lab at the University of California, San Diego and the Graduate Center, City University of New York, here outline two of the Lab's recent research projects, which have involved examining extensive Instagram data from various cities around the globe.

ConnectiCity: Real-Time Observation and Interaction for Cities Using Information Harvested from Social Networks

International Journal of Art, Culture and Design Technologies (IJACDT), 2012

This paper presents the approaches, methodologies and results of a multidisciplinary research project which, over the last 4 years, was able to investigate on the possibility to observe the behavior of city dwellers using information harvested from social networks which was then analyzed using Natural Language Processing techniques to gather insights about the emotions and themes expressed in the messages, and Geo-Referencing, Geo-Parsing and Geo-Coding techniques to understand their relevancy to ...