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.
People are able to detect up to 1 trillion smells @cite_2 . Despite that, there are limited maps of this potentially vast urban smellscape. One reason is that smell is problematic to record, to analyse and to depict visually. Here we review a variety of methodological approaches for recording urban smells.
@cite_2
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Humans can discriminate several million different colors and almost half a million different tones, but the number of discriminable olfactory stimuli remains unknown. The lay and scientific literature typically claims that humans can discriminate 10,000 odors, but this number has never been empirically validated. We determined the resolution of the human sense of smell by testing the capacity of humans to discriminate odor mixtures with varying numbers of shared components. On the basis of the results of psychophysical testing, we calculated that humans can discriminate at least 1 trillion olfactory stimuli. This is far more than previous estimates of distinguishable olfactory stimuli. It demonstrates that the human olfactory system, with its hundreds of different olfactory receptors, far outperforms the other senses in the number of physically different stimuli it can discriminate. Bad smells are signs of potential problems in code. Detecting and resolving bad smells, however, remain time-consuming for software engineers despite proposals on bad smell detection and refactoring tools. Numerous bad smells have been recognized, yet the sequences in which the detection and resolution of different kinds of bad smells are performed are rarely discussed because software engineers do not know how to optimize sequences or determine the benefits of an optimal sequence. To this end, we propose a detection and resolution sequence for different kinds of bad smells to simplify their detection and resolution. We highlight the necessity of managing bad smell resolution sequences with a motivating example, and recommend a suitable sequence for commonly occurring bad smells. We evaluate this recommendation on two nontrivial open source applications, and the evaluation results suggest that a significant reduction in effort ranging from 17.64 to 20 percent can be achieved when bad smells are detected and resolved using the proposed sequence. List of Figures and Charts Acknowledgements Glossary Chatper One: Introduction PART I: SMELL, SOCIETY AND CITIES Chapter Two: Perspectives on Smell and the City: Part I Chapter Three: Perspectives on Smell and the City: Part II Chapter Four: Smellwalking and Representing Urban Smellscapes PART 2: SMELL SOURCES IN THE CITY Chapter Five: Air Quality, Pollution and Smell Chapter Six: Food and Smell Chapter Seven: Urban Policy and Smell PART 3: SMELLSCAPE CONTROL, DESIGN AND PLACEMAKING Chapter Eight: Processes of Odour Management and Control in the City Chapter Nine: Designing with Smell - Restorative Environments and Design Tools Chapter Ten: Odour, Placemaking and Urban Smellscape Design Conclusion References Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we present a framework that spots the presence of acoustic events, such as horns and sirens, using a two-stage approach. We first model the urban soundscape and use anomaly detection to identify the presence of an anomalous sound, and later determine the nature of this sound. As the audio samples are affected by copious non-stationary and unstructured noise, which can degrade classification performance, we propose a noise-removal technique to obtain a clean representation of the data we can use for classification and waveform reconstruction. The method is based on the idea of analysing the spectrograms of the incoming signals as images and applying spectrogram segmentation to isolate and extract the alerting signals from the background noise. We evaluate our framework on four hours of urban sounds collected driving around urban Oxford on different kinds of road and in different traffic conditions. When compared to traditional feature representations, such as Mel-frequency cepstrum coefficients, our framework shows an improvement of up to 31 in the classification rate.