bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introduction to Urban Systems and Applications
Authors: Kwan Mei-Po 
Published: April 2021

AbstractAs new information technologies and large amounts of data from a wide range of sources become available to government agencies and the public, urban researchers have started to investigate how these data can be used to enhance the planning and management of various urban systems. As a result, new methods for collecting and analyzing complex space–time data about urban systems have been developed to address various urban issues. These urban systems include transportation systems, energy systems, and health systems. In recent years, considerable new work has been conducted to examine how new information technologies and data can enhance our understanding of and ability to address urban issues. The eight chapters in this section present various applications of urban informatics to specific urban systems or phenomena, including human mobility and travel, urban freight systems, urban resilience and disaster response, urban crime, urban governance, the use of remote sensing for environmental monitoring, health and wellbeing, and urban energy systems. All of them emphasize how new, big, or open data are useful for helping us to better understand and manage specific urban systems. They also highlight significant challenges in such applications of urban informatics, which would be particularly helpful to urban researchers and planners.
Laboratories for Research on Freight Systems and Planning
Authors: Alho André Romano , Sakai Takanori , Zhao Fang , You Linlin , Jing Peiyu , Cheah Lynette , Zegras Christopher , Ben-Akiva Moshe 
Published: April 2021

AbstractAdvancements in information and communication technologies (ICT) and the advent of novel mobility solutions have brought about drastic changes in the urban mobility environment. Pervasive ICT devices acquire new sources of data that can inform detailed transportation simulation models, and are useful in analyzing new policies and technologies. In this context, we developed software laboratories that leverage the latest technological developments and enhance freight research. Future mobility sensing (FMS) is a data-collection platform that integrates tracking devices and mobile apps, a backend with machine-learning technologies and user interfaces to deliver highly accurate and detailed mobility data. The second platform, SimMobility, is an open-source, agent-based urban simulation platform which replicates urban passenger and goods movements in a fully disaggregated manner. The two platforms have been used jointly to advance the state of the art in behavioral modeling for passenger and goods movements. In this chapter, we review recent developments in freight-transportation data-collection techniques, including contributions to transportation modeling, and state-of-the-art transportation models. We then introduce FMS and SimMobility and demonstrate a coordinated application using three examples. Lastly, we highlight potential innovations and future challenges in these research domains.
Urban Risks and Resilience
Authors: Cutter Susan L. 
Published: April 2021

AbstractThe resilience concept has become more significant in the past decade as a means for understanding how cities prepare and plan for, absorb, recover from, and more successfully adapt to adverse events. Definitional differences—resilience as an outcome or end-point versus resilience as a process of building capacity—dominate the literature. Lagging behind are efforts to systematically measure resilience to produce a baseline and subsequent monitoring, in order to gauge what, where, and how intervention or mitigation strategies would strengthen or weaken urban resilience. The chapter reviews research and practitioner attempts to develop urban informatics for resilience and provides selected case studies of cities as exemplars.
Urban Crime and Security
Authors: Cheng Tao , Chen Tongxin 
Published: April 2021

AbstractScientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.
Urban Energy Systems: Research at Oak Ridge National Laboratory
Authors: Bhaduri Budhendra , McManamay Ryan , Omitaomu Olufemi , Sanyal Jibo , Rose Amy 
Published: April 2021

AbstractIn the coming decades, our planet will witness unprecedented urban population growth in both established and emerging communities. The development and maintenance of urban infrastructures are highly energy-intensive. Urban areas are dictated by complex intersections among physical, engineered, and human dimensions that have significant implications for traffic congestion, emissions, and energy usage. In this chapter, we highlight recent research and development efforts at Oak Ridge National Laboratory (ORNL), the largest multipurpose science laboratory within the U.S. Department of Energy’s (DOE) national laboratory system, that characterizes the interactions between the human dynamics and critical infrastructures in conjunction with the integration of four distinct components: data, critical infrastructure models, and scalable computation and visualization, all within the context of physical and social systems. Discussions focus on four key topical themes: population and land use, sustainable mobility, the energy-water nexus, and urban resiliency, that are mutually aligned with DOE’s mission and ORNL’s signature science and technology capabilities. Using scalable computing, data visualization, and unique datasets from a variety of sources, the institute fosters innovative interdisciplinary research that integrates ORNL expertise in critical infrastructures including energy, water, transportation, and cyber, and their interactions with the human population.
Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics
Authors: Yao Wei , Wu Jianwei 
Published: April 2021

AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.
What Urban Cameras Reveal About the City: The Work of the Senseable City Lab
Authors: Duarte Fábio , Ratti Carlo 
Published: April 2021

AbstractCameras are part of the urban landscape and a testimony to our social interactions with city. Deployed on buildings and street lights as surveillance tools, carried by billions of people daily, or as an assistive technology in vehicles, we rely on this abundance of images to interact with the city. Making sense of such large visual datasets is the key to understanding and managing contemporary cities. In this chapter, we focus on techniques such as computer vision and machine learning to understand different aspects of the city. Here, we discuss how these visual data can help us to measure legibility of space, quantify different aspects of urban life, and design responsive environments. The chapter is based on the work of the Senseable City Lab, including the use of Google Street View images to measure green canopy in urban areas, the use of thermal images to actively measure heat leaks in buildings, and the use of computer vision and machine learning techniques to analyze urban imagery in order to understand how people move in and use public spaces.
User-Generated Content and Its Applications in Urban Studies
Authors: Tu Wei , Li Qingquan , Zhang Yatao , Yue Yang 
Published: April 2021

AbstractThe emergence of Web 2.0 and mobile Internet produces massive user-generated content (UGC), including geo-tagged photos, social network posts, street view images, and crowdsourced GPS trajectories. UGC creates unprecedented opportunities to sense what was previously hidden in the physical surfaces of cities and to portray the interactions of infrastructures, geo-information, and people; therefore, it is not only a new lens for urban space but also leads to innovative applications. In this chapter, we will introduce several typical types of UGC, such as geo-tagged photos, social media data, crowdsourcing GPS trajectories, and videos. We showcase ways in which user-generated big data can be harvested and analyzed to generate invisible and impressionistic landscapes of urban dynamics and to stimulate innovative applications. We discuss typical UGC-driven applications to demonstrate the potential of UGC in revealing how urban spaces are perceived by the public, establishing links between tangible artifacts and physical-cyber-social spaces. This fosters alternative approaches to urban informatics that better capture the intricate nature of urban space and its dynamics.
Introduction to Urban Computing
Authors: Shi Wenzhong , Zhang Anshu 
Published: April 2021

AbstractThis chapter overviews Part V of this book themed urban computing. This part of the book covers the topics of visual analytics, cloud, edge, and mobile computing, data mining and knowledge discovery, AI and deep learning for urban computing, and a range of mainstream urban models and simulation methods. It provides a systematic review of computing technologies for urban governance and urban services, together with the examples of their usage, in the context of urban computing.
Street View Imaging for Automated Assessments of Urban Infrastructure and Services
Authors: Zünd Daniel , Bettencourt Luís M. A. 
Published: April 2021

AbstractMany forms of ambient data in cities are starting to become available that allows tracking of short-term urban operations, such as traffic management, trash collections, inspections, or non-emergency maintenance requests. However, arguably the greatest promise of urban analytics is to set up measurable objectives and track progress toward systemic development goals connected to human development and sustainability over the longer term. The challenge for such an approach is the connection between new technological capabilities, such as sensing and machine learning and local knowledge, and operations of residents and city governments. Here, we describe an emerging project for the long-term monitoring of sustainable development in fast-growing towns in the Galapagos Islands through the convergence of these methods. We demonstrate how collaborative mapping and the capture of 360-degree street views can produce a general basis for a broad set of quantitative analytics, when such actions are coupled to mapping and deep-learning characterizations of urban environments. We map and assess the precision of urban assets via automatic object classification and characterize their abundance and spatial heterogeneity. We also discuss how these methods, as they continue to improve, can provide the means to perform an ambient census of urban assets (buildings, vehicles, services) and environmental conditions.
Cloud, Edge, and Mobile Computing for Smart Cities
Authors: Liu Qian , Gu Juan , Yang Jingchao , Li Yun , Sha Dexuan , Xu Mengchao , Shams Ishan , Yu Manzhu , Yang Chaowei 
Published: April 2021

AbstractSmart cities evolve rapidly along with the technical advances in wireless and sensor networks, information science, and human–computer interactions. Urban computing provides the processing power to enable the integration of such technologies to improve the living quality of urban citizens, including health care, urban planning, energy, and other aspects. This chapter uses different computing capabilities, such as cloud computing, mobile computing, and edge computing, to support smart cities using the urban heat island of the greater Washington DC area as an example. We discuss the benefits of leveraging cloud, mobile, and edge computing to address the challenges brought by the spatiotemporal dynamics of the urban heat island, including elevated emissions of air pollutants and greenhouse gases, compromised human health and comfort, and impaired water quality. Cloud computing brings scalability and on-demand computing capacity to urban system simulations for timely prediction. Mobile computing brings portability and social interactivity for citizens to report instantaneous information for better knowledge integration. Edge computing allows data produced by in-situ devices to be processed and analyzed at the edge of the network, reducing the data traffic to the central repository and processing engine (data center or cloud). Challenges and future directions are discussed for integrating the three computing technologies to achieve an overall better computing infrastructure supporting smart cities. The integration is discussed in aspects of bandwidth issue, network access optimization, service quality and convergence, and data integrity and security.
Data Mining and Knowledge Discovery
Authors: Zhang Chao , Han Jiawei 
Published: April 2021

AbstractOur physical world is being projected into online cyberspace at an unprecedented rate. People nowadays visit different places and leave behind them million-scale digital traces such as tweets, check-ins, Yelp reviews, and Uber trajectories. Such digital data are a result of social sensing: namely people act as human sensors that probe different places in the physical world and share their activities online. The availability of massive social-sensing data provides a unique opportunity for understanding urban space in a data-driven manner and improving many urban computing applications, ranging from urban planning and traffic scheduling to disaster control and trip planning. In this chapter, we present recent developments in data-mining techniques for urban activity modeling, a fundamental task for extracting useful urban knowledge from social-sensing data. We first describe traditional approaches to urban activity modeling, including pattern discovery methods and statistical models. Then, we present the latest developments in multimodal embedding techniques for this task, which learns vector representations for different modalities to model people's spatiotemporal activities. We study the empirical performance of these methods and demonstrate how data-mining techniques can be successfully applied to social-sensing data to extract actionable knowledge and facilitate downstream applications.
Transportation Modeling
Authors: Miller Eric J. 
Published: April 2021

AbstractInformatics are rapidly and radically transforming urban transportation in ways not seen since the introduction of the automobile over a hundred years ago. Near-ubiquitous smartphone usage, pervasive cellular and Wi-Fi connectivity, powerful and cost-effective computing capabilities, advanced GIS software and databases, advanced platforms for managing and scheduling service operations, etc., are combining to enable the introduction of new mobility services and technologies that are increasingly disrupting conventional trip-making behavior and the “rules of the game” in terms of transportation network operations and the regulation of system performance. The implications of these major informatics-driven changes for transportation modeling are equally disruptive and major. These include changes in: travel behavior; transportation system performance; the data available for model development and application; and modeling methods. Each of these broad areas of impact are discussed in this chapter.
Urban Human Dynamics
Authors: Shaw Shih-Lung 
Published: April 2021

AbstractUrban areas are places where people concentrate in a relatively high density built environment to carry out a wide range of activities. Each urban area should provide adequate infrastructure and services to support the needs of its population. Since various resources, services, and facilities are at different locations, urban areas manifest a complex system of flows of people, goods, and information to support the economic, social, cultural, and political systems in human society. These activities, flows, and systems are driven by various processes and exhibit various spatiotemporal patterns that are the outcomes of human dynamics. However, how we investigate the various dynamic processes and complex systems in urban areas has been and continues to be a challenging research topic. Urban human dynamics cover multiple aspects and can be studied from different perspectives. This chapter discusses urban dynamics and human dynamics in terms of their respective approaches and methods, along with some selected examples. It then connects urban human dynamics research with urban informatics to highlight their relationships and how together they could lead to urban areas that can better serve human needs and improve the quality of life.
Geosmartness for Personalized and Sustainable Future Urban Mobility
Authors: Raubal Martin , Bucher Dominik , Martin Henry 
Published: April 2021

AbstractUrban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.
Urban Metabolism
Authors: Derrible Sybil , Cheah Lynette , Arora Mohit , Yeow Lih Wei 
Published: April 2021

AbstractUrban metabolism (UM) is fundamentally an accounting framework whose goal is to quantify the inflows, outflows, and accumulation of resources (such as materials and energy) in a city. The main goal of this chapter is to offer an introduction to UM. First, a brief history of UM is provided. Three different methods to perform an UM are then introduced: the first method takes a bottom-up approach by collecting/estimating individual flows; the second method takes a top-down approach by using nation-wide input–output data; and the third method takes a hybrid approach. Subsequently, to illustrate the process of applying UM, a practical case study is offered using the city-state of Singapore as an exemplar. Finally, current and future opportunities and challenges of UM are discussed. Overall, by the early twenty-first century, the development and application of UM have been relatively slow, but this might change as more and better data sources become available and as the world strives to become more sustainable and resilient.
Conceptualizing the City of the Information Age
Authors: Couclelis Helen 
Published: April 2021

AbstractCities are among humanity’s most important and most complex creations, and they have been steadily increasing in complexity since the advent of the digital age. Informatics, the science of information, has by now advanced to a point where high expectations of improved understanding and evidence-based actionable knowledge for urban researchers, managers, and planners appear justified. But while there is more information than ever before, many kinds of theories, models, approaches, and tools that we have relied on thus far may no longer be of much use in the city of the information age. This chapter provides an overview of the state of affairs in urban science and planning, pointing out the limitations of formerly reliable methods and tools in the face of dramatic developments in the life and function of cities in the developed world. The chapter closes with suggestions for data-oriented strategies that might replace the ways we have used urban data up until recently.