Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens' ideas and feedback in participatory sensing approaches like "People as Sensors". As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens' emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none). Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab's performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens' perceptions of the city.
Even though much research has been conducted on the safety of cycling infrastructures, most previous approaches only make use of traditional and proven methods based upon datasets such as accident statistics, road infrastructure data, or questionnaires. Apart from typical surveys, which are known to face numerous limitations from a psychological and sociological viewpoints, the question of how perceived safety can best be assessed is still widely unexplored. Thus, this paper presents an approach for bio-physiological sensing to identify places in urban environments which are perceived as unsafe by cyclists. Specifically, a number of physiological parameters like ECG, skin conductance, skin temperature and heart rate variability are analysed to identify moments of stress. Together with data gathered through a People as Sensors app, these stress levels can be mapped to specific emotions. This method was tested in a pilot study in Cambridge, MA (USA), which is presented in this paper. Our findings show that our method can identify places with emotional peaks, particularly fear and anger. Although our results can be qualitatively interpreted and used in urban planning, more research is necessary to quantitatively and automatically generate recommendations from the measurements for urban planners.
This chapter introduces the 'Urban Emotions' approach. It focuses on integrating humans' emotional responses to the urban environment into planning processes. The approach is interdisciplinary and anthropocentric, i.e. citizens and citizens' perceptions are highlighted in this concept. To detect these emotions/perceptions, it combines methods from spatial planning, geoinformatics and computer linguistics to give a better understanding of how people perceive and respond to static and dynamic urban contexts in both time and geographical space. For collecting and analyzing data on the emotional perception to urban space, we use technical and human sensors as well as georeferenced social media posts, and extract contextual emotion information from them. The resulting novel information layer provides an additional, citizen-centric perspective for urban planners. In addition to technical and methodological aspects, data privacy issues and the potential of wearables are discussed in this chapter. Two case studies demonstrate the transferability of the approach into planning processes. This approach will potentially reveal new insights for the perception of geographical spaces in spatial planning.
In contrast to previous approaches, Urban Emotions defines Smart Cities not only as technology-enriched cities, but also emphasises the human factor -Smart Citizens. This is due to the fact that the question of how people perceive a city and how they feel about it has always been important in urban planning and management. In this paper we present our approach for crowdsourcing physiological conditions and subjective emotions by combining data from technical sensors (measuring psycho-physiological parameters) and human sensors ("People as Sensors" contributing subjectively perceived emotions). Furthermore, we couple this crowdsourcing approach with a technical architecture to harmonise and integrate the sensor data via standardised service interfaces (Sensor Web Enablement -SWE) to allow for generic access for further analysis and visualisation. Finally, we discuss the use of emotion information in urban planning and point out related privacy issues together with a number of strategies to mitigate those.
Urban and transport planners need to assess the stressful conditions experienced by cyclists, considering that highly stressful situations can discourage people from cycling as a transport mode. Therefore, this study has two objectives: (1) to present a method for monitoring stress and other environmental factors along cycling routes using smart sensors; and (2) to analyze the influence of noise, vibration, presence of cycle paths, and the period of the day on stress experienced by cyclists. Data were collected in the city of São Carlos, Brazil, using stress and noise sensors, accelerometers, and Global Positioning System (GPS). Primarily, heat maps generated from the data made it possible to identify critical points of stress along the routes. In addition, the results of a logistic regression model were analyzed to identify the influence of the studied variables on stress. Although high levels of noise increased the odds of experiencing stress by 4%, very uncomfortable vibrations increased the odds by 14%, and the presence of cycle paths reduced the odds by 8%, an analysis of p-values and odds ratio confidence intervals shows, with a 95% confidence level, that only the period of the day influenced stress, as confirmed by the data. In this case, the odds of having stress increased by 24% in the afternoon rush hour compared to the morning rush hour.
Walking as a transport mode is still often underrepresented in the overall transport system. Consequently, pedestrian mobility is usually not recorded statistically in the same manner as it is performed for motorised traffic which leads to an underestimation of its importance and positive effects on people and cities. However, the integration of walkability assessments is potentially a valuable complement in urban planning processes through considering important quantitative and qualitative aspects of walking in cities. Recent literature shows a variety of approaches involving discrepancies in the definition of walkability, the factors which contribute to it, and methods of assessing them. This paper provides a new understanding of the concept of walkability in the European context. Our approach relies on the extension of methodological competence in transportation, spatial planning and geography by linking new measurement methods for evaluating walkability. We propose an integrated approach to assessing walkability in a comprehensive methodology that combines existing qualitative and GIS-based methods with biosensor technologies and thus captures the perceptions and emotions of pedestrians. This results in an increased plausibility and relevance of the results of walkability analysis by considering the spatial environment and its effect on people.
There is substantial evidence that the environment has an important impact on the use of bicycles. Changes in the built environment, such as cycling infrastructure provision, usually aim at improving the efficiency, enjoyability and safety of cycling. They can also shape affective responses, for instance by triggering or preventing stress situations during cycling. The repeated occurrence of intensely stressful events may make actual cyclists more likely to abandon cycling and deter prospective cyclists from actually taking up this form of mobility. Therefore, using a novel approach, based on stress biomarker measurements obtained directly from cyclists, the objective of this study is to investigate the relationship between urban environment and cyclists' stress. It also investigates if different types of cycling infrastructures in the contexts of two different countries and in five different cities have different relationships with stress. Using a stress sensor, 70 young adults were invited to cycle along a standard route in Oxford, London (the United Kingdom), Amsterdam, Houten and Groningen (the Netherlands). These routes were around 6 km long and had a wide range of characteristics. Multilevel logistic regression analysis indicates that the probability of stressful events occurring is significantly lower on physically segregated cycle paths than on cycle paths on streets, with cycling on general use streets falling in-between these extremes. We also find higher probabilities of stress for primary roads compared to tertiary roads, at intersections than on straight roads, on cobbled and off-road surfaces compared to asphalt, and in noisier places. Models for the individual cities suggested that the relationship between cycling infrastructure and the likelihood of stressful events occurring may depend on the local context. Only for noise conditions, intersection types and cycling infrastructures were the effects consistent across the cities. These findings may be useful for urban infrastructure planning and management, indicating specific attributes that should be adjusted to make cycling less stressful.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.