Smelly Maps: The Digital Life of Urban Smellscapes

Smell has a huge influence over how we perceive places. Despite its importance, smell has been crucially overlooked by urban planners and scientists alike, not least because it is difficult to record and analyze at scale. One of the authors of this paper has ventured out in the urban world and conducted smellwalks in a variety of cities: participants were exposed to a range of different smellscapes and asked to record their experiences. As a result, smell-related words have been collected and classified, creating the first dictionary for urban smell. Here we explore the possibility of using social media data to reliably map the smells of entire cities. To this end, for both Barcelona and London, we collect geo-referenced picture tags from Flickr and Instagram, and geo-referenced tweets from Twitter. We match those tags and tweets with the words in the smell dictionary. We find that smell-related words are best classified in ten categories. We also find that specific categories (e.g., industry, transport, cleaning) correlate with governmental air quality indicators, adding validity to our study.Comment: 11 pages, 7 figures, Proceedings of 9th International AAAI Conference on Web and Social Media (ICWSM2015

Towards a property graph generator for benchmarking

The use of synthetic graph generators is a common practice among graph-oriented benchmark designers, as it allows obtaining graphs with the required scale and characteristics. However, finding a graph generator that accurately fits the needs of a given benchmark is very difficult, thus practitioners end up creating ad-hoc ones. Such a task is usually time-consuming, and often leads to reinventing the wheel. In this paper, we introduce the conceptual design of DataSynth, a framework for property graphs generation with customizable schemas and characteristics. The goal of DataSynth is to assist benchmark designers in generating graphs efficiently and at scale, saving from implementing their own generators. Additionally, DataSynth introduces novel features barely explored so far, such as modeling the correlation between properties and the structure of the graph. This is achieved by a novel property-to-node matching algorithm for which we present preliminary promising results

Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information Scienc

Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie