Sensing and Mining Urban Qualities in Smart Cities

The emergence of the Internet of Things in Smart Cities questions how the future citizens will perceive their predominant living and working environments and what quality of living they can experience within it, for instance the level of everyday stress. However, perception and experienced stress levels are challenging metrics to measure and are even more challenging to correlate with an underlying causal-effectual relationship in such stimulus abundant environments. The Internet of Things, enabled by several pervasive and ubiquitous devices such as smart phones and smart sensors, can provide real-time contextual information that can be used by advanced data science methodologies to generate new insights about urban qualities in Smart Cities and how they can be improved. The goal of this study is to show the predominant factors, which influence perceptual qualities of inhabitants in a Smart City equipped with sensing capabilities by the Internet of Things. To serve this goal, a novel data collection process for Smart Cities is introduced that involves (i) environmental data, such noise, dust, illuminance, temperature, relative humidity, (ii) location/mobility data, such as GNSS and citizens density detected via WiFi, and (iii) perceptual social data collected by citizens' responses in smart phones. These fine-grained real-time data can provide invaluable insights about the spatial correlations of the sensor measurements as well as the spatial and citizens' similarity illustrated. The data analysis illustrated reveals significant links between stress level and environmental changes observed

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Smart schools in sentient cities

What makes a city “smart?” And, in a “smart city,” what makes a “smart school?” Designers, researchers and commercial technology companies are increasingly concerned with the development of "smarter cities," "programmable cities" and "sentient cities"that are augmented with big data, sensor networks, and other computationally programmable processes and software-supported practices. The smart city is an urban environment with a computational "nervous system." It appears to have some form of awareness, intelligence, and thoughtfulness, along with some ability to learn and to transform itself. In many smart city programs, themes such as "smarter education"are emerging as important points of focus for various kinds of imaginings and product developments. It is surprising, then, that educational research has, to date, said very little about such data-intensive, spatially sentient and programmable cities. This leaves a lot of questions to be addressed. What kind of educational future are we facing in sentient cities that are already becoming automated, plastered in data and augmented with context-aware devices offering mediated sensory experiences; in which billions of objects and machines can interrelate with one another via the Internet of Things; and in which the urban environment itself is able to track, trace and even "think of us?" What does it mean for people to learn in a city that can learn

The Digital Life of Walkable Streets

Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.Comment: 10 pages, 7 figures, Proceedings of International World Wide Web Conference (WWW 2015

Experiential Probes : probing for emerging behavior patterns in everyday life

With the rise of highly interactive and intelligent product-systems it becomes increasingly more difficult for design researchers to understand and predict the impact, meaning and value of their designs for society. As the meaning of these products is often created in interaction, design researchers learn about their designs, and the user acceptance or rejection, only after product launch. Valuable insights and inspiration for design researchers are not incorporated in the design process, but come as an afterthought. Probing is a useful technique to inform and inspire design researchers, allowing them to gain early insights in their designs. The nature of most probing techniques is to record, analyze and understand current ‘static’ situations (i.e. ethnography), and to obtain information or inspiration for design researchers. Recent technological developments offer opportunities to probe in ‘dynamic’ i.e. changing or emerging, situations, and to merge analysis with design synthesis. This conceptual paper discusses different probing techniques through their fundamental characteristics: informing, inspiring, observational, experiential, static, dynamic etc. We further argue that 1) it is logic that probes with different characteristics fit different phases of the design process 2) dynamic, inspiring, Experiential Probes are more desired when initiating innovation for societal transformation

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

The Smart Mobile Application Framework (SMAF) - Exploratory Evaluation in the Smart City Contex

What makes mobile apps "smart"? This paper challenges this question by seeking to identify the inherent characteristics of smartness. Starting with the etymological foundations of the term, elements of smart behavior in software applications are extracted from the literature, elaborated and contrasted. Based on these findings we propose a Smart Mobile Application Framework incorporating a set of activities and qualities associated with smart mobile software. The framework is applied to analyze a specific mobile application in the context of Smart Cities and proves its applicability for uncovering the implementation of smart concepts in real-world settings. Hence, this work contributes to research by conceptualizing a new type of application and provides useful insights to practitioners who want to design, implement or evaluate smart mobile applications