Urban Informatics Research Articles

Smart Materials, Future Cities: Potential for Sustainable Ambient Urban Informatics

Media façades and digital billboards are becoming an increasingly common fixture in urban landscapes. However, these contemporary signage solutions bear a number of negative social and environmental effects. In addition to obscuring historic architecture and increasing light pollution, they also compete with municipal signage, traditional advertisements, and mobile media to increase our experience of information overload. Smart (environmentally responsive) materials represent an ambient, low-energy alternative to contemporary digital signage. Although smart materials are being used as a sustainable alternative in engineering, they are less frequently used as a form of visual communication in urban informatics. This article describes three artistic experiments that reveal how smart materials can be used to convey information in urban settings.

Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks

Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multiagent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

Prospective for urban informatics

The specialization of different urban sectors, theories, and technologies and their confluence in city development have led to a greatly accelerated growth in urban informatics, the transdisciplinary field for understanding and developing the city through new information technologies. While this young and highly promising field has attracted multiple reviews of its advances and outlook for its future, it would be instructive to probe further into the research initiatives of this rapidly evolving field, to provide reference to the development of not only urban informatics, but moreover the future of cities as a whole. This article thus presents a collection of research initiatives for urban informatics, based on the reviews of the state of the art in this field. The initiatives cover three levels, namely the future of urban science; core enabling technologies including geospatial artificial intelligence, high-definition mapping, quantum computing, artificial intelligence and the internet of things (AIoT), digital twins, explainable artificial intelligence, distributed machine learning, privacy-preserving deep learning, and applications in urban design and planning, transport, location-based services, and the metaverse, together with a discussion of algorithmic and data-driven approaches. The article concludes with hopes for the future development of urban informatics and focusses on the balance between our ever-increasing reliance on technology and important societal concerns.

KD-Box: Line-segment-based KD-tree for Interactive Exploration of Large-scale Time-Series Data.

Time-series data-usually presented in the form of lines-plays an important role in many domains such as finance, meteorology, health, and urban informatics. Yet, little has been done to support interactive exploration of large-scale time-series data, which requires a clutter-free visual representation with low-latency interactions. In this paper, we contribute a novel line-segment-based KD-tree method to enable interactive analysis of many time series. Our method enables not only fast queries over time series in selected regions of interest but also a line splatting method for efficient computation of the density field and selection of representative lines. Further, we develop KD-Box, an interactive system that provides rich interactions, e.g., timebox, attribute filtering, and coordinated multiple views. We demonstrate the effectiveness of KD-Box in supporting efficient line query and density field computation through a quantitative comparison and show its usefulness for interactive visual analysis on several real-world datasets.

‘Anyway, the dashboard is dead’: On trying to build urban informatics

How do the idealised promises and purposes of urban informatics compare to the material politics and practices of their implementation? To answer this question, I ethnographically trace the development of two data dashboards by strategic planners in an Australian city over the course of 2 years. By studying this techno-political process from its origins onward, I uncovered an interesting story of obdurate institutions, bureaucratic momentum, unexpected troubles, and, ultimately, frustration and failure. These kinds of stories, which often go untold in the annals of innovation, contrast starkly with more common framings of technological triumph and transformation. They also, I argue, reveal much more about how techno-political systems are actualised in the world.

The Role of Urban Informatics in Monitoring the Physical Deteriortion of Urban Environment

The twenty-first century is witnessing a rapid growth population in urban areas; this growth needs intelligent urban planning and management. The field of urban informatics is one of the new and vital specialties to organize and analyse the urban system at all levels and areas. ICT works with interactive community participation to guide and manage the urban environment to serve, provide the residents with safety and security. The paper presents a new vision in terms of employing the field of urban informatics in mapping and monitoring the urban deterioration of the built environment in general and buildings in particular. The urban informatics system is still taking its first steps to manage and serve the city's facilities (transportation, communication, air pollution, etc.). The built environment and the deterioration through time and other factors are still far from this area. This paper aims to identify the urban information field, the situations, and types of urban deterioration and move to capture urban deterioration indicators (main and secondary), which can be measured in urban informatics. This paper recommends the adoption of such a mechanism in managing and controlling the deterioration that contributes to the reduction of material and human losses, saving time and money away from traditional methods, and the possibility of employing them in times of crisis and disasters in the urban environment.

A Joint Passenger Flow Inference and Path Recommender System for Deploying New Routes and Stations of Mass Transit Transportation

In this work, a novel decision assistant system for urban transportation, called Route Scheme Assistant (RSA), is proposed to address two crucial issues that few former researches have focused on: route-based passenger flow (PF) inference and multivariant high-PF route recommendation. First, RSA can estimate the PF of arbitrary user-designated routes effectively by utilizing Deep Neural Network (DNN) for regression based on geographical information and spatial-temporal urban informatics. Second, our proposed Bidirectional Prioritized Spanning Tree (BDPST) intelligently combines the parallel computing concept and Gaussian mixture model (GMM) for route recommendation under users’ constraints running in a timely manner. We did experiments on bus-ticket data of Tainan and Chicago and the experimental results show that the PF inference model outperforms baseline and comparative methods from 41% to 57%. Moreover, the proposed BDPST algorithm's performance is not far away from the optimal PF and outperforms other comparative methods from 39% to 71% in large-scale route recommendations.

Urban Informatics in Sustainable Waste Management: A Spatial Analysis of Korea’s Informal Recycling Networks

Large-scale informal recycling networks often emerge among developing economies in response to the challenges of modern urban waste accumulation. South Korea, despite its highly industrialized, developed economy, still maintains an extensive informal recycling sector made up of networks of local junk shops and individual waste pickers. As cities’ large data sources have become more widely available, the use of urban informatics in sustainable smart waste management has become more widespread. In this paper, we use geographic information system (GIS) analysis in order to uncover patterns within Korea’s informal recycling system, looking at the relationship between population demographics, waste levels, and urban planning with the prevalence of junk shops across Korea. We then interviewed junk shop owners, urban planning researchers, and government officials in order to better understand the factors that led to the coexistence of the country’s informal and formal systems of waste management and how junk shops have changed their operations over time in response to recent developments in cities’ urban fabrics. We conclude by giving suggestions for how the usage of urban informatics could increase the efficiency and sustainability of the country’s waste management systems, while also discussing the possible pitfalls of using such existing datasets for future policy decisions.

URBAN INFORMATICS A Tool for Shaping the Future

This research paper is an attempt to bring light on the vital application of technological advancement that can help city managers, urban planners and designers for effective planning and management of our cities. The “Urban Informatics” could be the answer to create social inclusion between the city and its citizens, between the user and the service provider. Through this research paper, various real-time applications and use of informatics are discussed. The major developments around the world in the field of informatics have been briefed while analysing the development of urban informatics. With examples of the informatics led solutions to urban issues, the prospective change is discussed in consideration with Indian cities.

Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

What does urban informatics add to planning support technology?

What does urban informatics add to planning support technology?

Serendipity-based Points-of-Interest Navigation

Traditional venue and tour recommendation systems do not necessarily provide a diverse set of recommendations and leave little room for serendipity . In this article, we design MPG, a Mobile Personal Guide that recommends: (i) a set of diverse yet surprisingly interesting venues that are aligned to user preferences and (ii) a set of routes, constructed from the recommended venues. We also introduce EPUI, an Experimental Platform for Urban Informatics. Our comparison with the state-of-the-art schemes indicates that MPG is capable of providing high-quality venues and route recommendations while incorporating seamlessly both the notion of diversity and that of serendipity.

Exploring research trends in big data across disciplines: A text mining analysis

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several topics associated with the storage, collection and analysis of large datasets; the publications were predominantly published in computational fields. Other identified research topics show the influence of big data methods and techniques in areas beyond computer science, such as education, urban informatics, business, health and medical sciences. In fact, the prevalence of these topics has increased over time. In contrast, some themes like parallel computing, network modeling and big data analytic techniques have lost their popularity in recent years. These results probably reflect the maturity of big data core topics and highlight flourishing new research trends pertinent to big data in new domains, especially in social sciences, health and medicine. Findings of this article can be beneficial for researchers and science policymakers to understand the scope and structure of big data in different academic disciplines.

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively available traffic data collected from various sensors in transportation cyber-physical systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, in this article, we propose a wide-attention and deep-composite (WADC) model, consisting of a wide-attention module and a deep-composite module, in this article. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with self-attention mechanism. The deep-composite module can generalize local key features via convolutional neural network component and long short-term memory network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

An urban informatics approach to understanding residential mobility in Metro Chicago

This paper proposes that urban informatics can represent a flow of information from diverse and voluminous data into and back from the planning process. We present a proof-of-concept of how an urban informatics approach can improve public understanding of essential information in the planning process, and we propose a theoretical construct of how it can make planning processes more democratic and participatory, especially for disadvantaged groups. Specifically, this study uses a multi-dimensional spatial scan technique to discover household movement patterns in Chicago from millions of household address records between 2006 and 2015. The results locate the types of movement and displacement associated with specific places at a highly detailed spatial resolution. A user-interface is developed and proposed to enhance sharing of the information to the general public and involves diverse stakeholders in policy-making processes.

Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.

Curriculum design for urban informatics

Curriculum design for urban informatics

Deep Reinforcement Learning for Social-Aware Edge Computing and Caching in Urban Informatics

Empowered with urban informatics, transportation industry has witnessed a paradigm shift. These developments lead to the need of content processing and sharing between vehicles under strict delay constraints. Mobile edge services can help meet these demands through computation offloading and edge caching empowered transmission, while cache-enabled smart vehicles may also work as carriers for content dispatch. However, diverse capacities of edge servers and smart vehicles, as well as unpredictable vehicle routes, make efficient content distribution a challenge. To cope with this challenge, in this article we develop a social-aware nobile edge computing and caching mechanism by exploiting the relation between vehicles and roadside units. By leveraging a deep reinforcement learning approach, we propose optimal content processing and caching schemes that maximize the dispatch utility in an urban environment with diverse vehicular social characteristics. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics

Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy.

A Survey on Spatial Prediction Methods

With the advancement of GPS and remote sensing technologies, large amounts of geospatial data are being collected from various domains, driving the need for effective and efficient prediction methods. Given spatial data samples with explanatory features and targeted responses (categorical or continuous) at a set of locations, the spatial prediction problem aims to learn a model that can predict the response variable based on explanatory features. The problem is important with broad applications in earth science, urban informatics, geosocial media analytics, and public health, but is challenging due to the unique characteristics of spatial data, including spatial autocorrelation, heterogeneity, limited ground truth, and multiple scales and resolutions. This paper provides a systematic review on principles and methods in spatial prediction. We provide a taxonomy of methods categorized by the key challenge they address. For each method, we introduce its underlying assumption, theoretical foundation, and discuss its advantages and disadvantages. We also discuss spatiotemporal extensions of methods. Our goal is to help interdisciplinary domain scientists choose techniques to solve their problems, and more importantly, to help data mining researchers to understand the main principles and methods in spatial prediction and identify future research opportunities.