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With the rapid development of computer technology, urban informatics, as a new discipline in the field of urban planning, has gradually attracted academic attention. The rise of urban informatics puts new pressures on urban planning, but it also provides a new perspective of analysis. This paper is a summary of a panel discussion among scholars in urban informatics held at the 2020 International Association for China Planning(IACP).In this context, the panel outlines the definition of urban informatics, and the difference between urban informatics and urban analytic and computing, and found that urban informatics pays more attention to end user. This indicates that urban informatics has been more than a supporting role in urban planning or design, and is increasingly integrated with urban planning. The panel also discusses the connotation of urban informatics and its wide application in practice, and illustrates with examples. At the same time, the team identifies the difficulties of its development mainly reflected in the two aspects of resources and talents, and the learners of urban planning discipline have natural advantage in learning urban informatics. Finally, the panel discusses how to improve teaching, and concludes that the promotion of good cases, discipline integration, training data thinking rather than focusing too much on methods and other concepts. All in all, this panel's report contributes to the wider discussion about the role of urban informatics plays in urban planning. [ABSTRACT FROM AUTHOR]

Cycling is a sustainable form of transportation that can reduce car usage and benefit both individuals and society. Bike-sharing systems (BSSs) help to position cycling as a daily transportation option and have been widely established in many countries. Previous studies have investigated the association between urban environmental factors and BSSs' demand to promote the broader use of BSSs and determine whether demand is affected by various factors. However, research on the effects of the urban environment on BSS demand according to the trip purpose (e.g., commuting and leisure) is rare due to the difficulty in understanding users' trip purposes. In this regard, recent advancements in big data technologies make massive BSSs trip data available to the public, which is useful for in-depth analyzing BSS travel patterns and inferring the trip purposes. This study thus analyzes to what extent demand is affected by urban environmental factors for different trip purposes, focusing on Seoul Bike, through the integration of clustering users' travel patterns and analyzing geospatial data affecting demand. By observing trip data, BSS trips were clustered into short-distance travel for utilitarian purposes and longer-distance roaming for recreational purposes. The utilitarian trips were more affected by the large floating population and high land-use mix, and they were more concentrated during the rush hours in the crowded areas, while the leisure trips were more concentrated in secluded residential areas and were close to the waterfront. This study can contribute to establishing plans to increase the demand for and optimize the operation of BSSs. [ABSTRACT FROM AUTHOR]

[Display omitted] • The spatio-temporal evolution pattern of ALAN has been analyzed. • The spatial scope of night-time light control area has been recognized automatically. • The study facilitates green transformation and smart upgrading of outdoor lighting. Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction. [ABSTRACT FROM AUTHOR]

In the last decade, parallel computing techniques become more popular and offer various architectures to perform diverse processes in an individual computing node or a collection of strongly linked nodes with homogeneous hardware. In this paper, we propose a distributed secure unequal cluster-based multipath routing protocol (USCDRP) for improving the energy efficiency and also to enhance the security as well as reliability. The presented USCDRP is the enhanced work of SCMRP, which provides the security to a particular extent. The presented method offers reliability, energy efficiency and maximum lifetime. Besides, an independent clustering method is provided to eliminate the orphan nodes and cluster maintenance strategy to enhance the network lifetime. A detailed experimental analysis ensured the superiority of the USCDRP over the compared methods concerning throughput, energy efficiency and network lifetime analysis. The simulation outcome indicated that all the nodes in WSN become dead node, and the network becomes inactive in the round number of 2021 by Sec-LEACH, 2412 by SCMRP and 3245 by USCDRP. [ABSTRACT FROM AUTHOR]

Distributed wireless sensor networks (WSNs) particularly deployed in urban applications like traffic surveillance are the main source of big data, and they produce a massive quantity of data. Multipath routing favors reliable data delivery in the case of sensitive data. However, the disadvantage is that many routes might increase the number of control packets. In this paper, we present an extended dynamic address routing technique (DART) with table elimination (ET) technique named ET-DART to reduce the complexity by handling a high degree of connectedness. The new idea of "connectedness" is used to distribute data from one node as it collected from another node. This method is used for defending capture attacks, and it modifies the security in every node without modifying the packet received ratios. A set of simulations takes place to verify the superiority of the presented ET-DART in terms of different performance metrics. Under the presence of 400 nodes, the presented ET-DART method successfully decrypts 154 packets with a maximum throughput of 82.15%. The experimental outcome verified the enhanced results of the presented method over the compared ones under several aspects. [ABSTRACT FROM AUTHOR]

• An integrated urban multi-source dataset was developed to examine the effect of the new progressive tariff (PT) system. • Nearly half of the sample apartments had a significant and moderate increase in electricity use. • Factors in energy use and billing, household, apartment, and neighborhood significantly influenced electricity use change. • The new PT benefited higher-income groups more, raising concern over its negative impact on energy poverty. A new electricity progressive tariff (PT) system was introduced to cities in South Korea at the end of 2016 to replace the previous one as a policy response to increased energy demand and costs in hotter summers. Public concerns have been raised over its effects on annual energy use and energy poverty. However, studies on this topic are very few due to the lack of relevant data. In the broader literature on the effect of the PT system, studies are limited in that they mostly focus on household characteristics. This study contributes to the literature and practical discussions by examining how the new PT system influences residential electricity use in 243 sample apartments in Seoul, considering a wide range of factors that impact energy use decisions. An integrated urban dataset was developed based on urban big data from multiple sources, and a combination of two models was used for analysis. The interrupted time series analysis was adopted to find whether the change in household electricity use was significant for each apartment using the dataset. A logistic regression model was then used to examine the relationships between the significance of change and influential factors in energy use and billing, household, apartment, and neighborhood. Results suggest that nearly half of the sample apartments significantly increased electricity usage, and energy and built environment factors significantly influenced the change. Furthermore, higher-income groups were found to benefit more from the new PT system. The findings provide a reference for policymakers to evaluate the new policy and develop strategies toward broad sustainability goals. Given the wicked nature of urban energy policy, suggestions were made on policy implementation design and urban data integration for more rigorous evaluation. [ABSTRACT FROM AUTHOR]

As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD. [ABSTRACT FROM AUTHOR]

With the explosion of digital data, the need for advanced visual analytics, including coordinated multiple views (CMV), is rapidly increasing. CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets. CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network (AURIN) project. AURIN offers a platform providing seamless and secure access to an extensive range of distributed urban datasets across Australia. Visual exploration of these datasets is essential to support research endeavors. This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV. We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations. Using the concept of data cubes and hierarchical dimensions, we present strategies to automatically build render groups. This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion. The CMV approach is demonstrated using aggregate-level data, which is provided through federated data services. The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration. [ABSTRACT FROM AUTHOR]

The notable increase in artificial night light (ANL) induced by the rapid urbanization process has been widely studied, but a deep understanding of the supply and demand status of ANL is still lacking. This paper attempts to map the supply and demand of ANL from the human perspective by using advanced Loujia1-01 nighttime imagery and social media derived population density (PD) data, which provides a new tool for light regulation in urban management. The bivariate clustering based k-means algorithm and template matching technique are integrated to delineate mismatch regions at the block scale to further analyze the underlying reason for unbalanced status. The results showed that the high supply but low demand (HSLD) ANL status was the leading component in the mismatch regions, occupying more than 650,000 ha and mainly occurring in the city center. The HSLD proportion was considerable in terms of public services (44%), commercial (40%), industrial (39%), transportation (56%), and green space areas (53%). Moreover, the HSLD area notably increased 946 ha over time from 18:00 to 22:00. The measurements for validation obtained by field investigation showed highly linear relationship with ANL (R2 = 0.75) and PD (R2 = 0.62), and the mapping results were consistent with the actual conditions. This study reveals the highly unbalanced ANL status, and appeals to planners for the establishment of optimal lighting regulations to alleviate disruptive effects. Image 1 • The supply and demand status of ANL in open areas is mapped. • The relationship between ANL and PD has been quantified. • The significant HSLD status of ANL calls for high attention. • Unbalanced status of ANL is highly related to housing price. • Light regulation is necessary to alleviate the disruptive effects. [ABSTRACT FROM AUTHOR]