New Insights into Human Mobility, Urban Computing and Planning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 2054

Special Issue Editors


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Guest Editor
Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China
Interests: human mobility patterns; urban traffic big data; urban planning; routing behavior
School of Urban Design, Wuhan University, Wuhan, China
Interests: environment analysis; urban planning; spatial analysis; sustainable development; urban climatology
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Guest Editor
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China
Interests: spatial data science; human mobility; environmental health

Special Issue Information

Dear Colleagues,

Over the last few decades, the large-scale migration of human beings to cities has brought severe challenges to urban planning and design. Environmental degradation, traffic congestion, a job–housing imbalance, and other urban problems have plagued human life and sustainable urban development. Additionally, the complexity and uncertainty of human mobilities are subtly reshaping the spatial patterns of cities. This Special Issue aims to provide a platform for global researchers to disseminate recent insights and technological developments in the areas of urban planning, urban sustainability, and human mobility.

Within this context, we would like to invite you to submit original research and review articles to disseminate and share new findings in urban planning, infrastructure management, and urban computing.

Potential topics include but are not limited to the following:

  • Human mobility;
  • Urban management;
  • Development and planning problems;
  • Routing behavior;
  • Neighborhood conservation and urban design;
  • Sustainable cities;
  • Physical planning and urban design;
  • Land use and transportation;
  • Emergency response and hazards;
  • Geocomputation;
  • Spatial statistical analysis;
  • Complex systems and artificial intelligence;
  • Space–time simulation.

Dr. Baoju Liu
Dr. Huimin Liu
Dr. Jiannan Cai
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban planning
  • spatial statistical analysis
  • urban computing
  • human mobility
  • urban transportation system
  • infrastructure management
  • urban climate
  • urban crime

Published Papers (1 paper)

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Research

13 pages, 8053 KiB  
Article
Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data
by Weijie Zhang, Jun Zhao, Wenkai Liu, Zhangzhi Tan and Hanfa Xing
Appl. Sci. 2022, 12(24), 12796; https://doi.org/10.3390/app122412796 - 13 Dec 2022
Cited by 1 | Viewed by 1349
Abstract
Network-constrained spatial flows are usually used to describe movements between two spatial places on a road network. The analysis of the spatial associations between different types of network-constrained spatial flows plays a key role in understanding the spatial relationships among different movements. However, [...] Read more.
Network-constrained spatial flows are usually used to describe movements between two spatial places on a road network. The analysis of the spatial associations between different types of network-constrained spatial flows plays a key role in understanding the spatial relationships among different movements. However, existing studies usually do not consider the effect of distance decay, which may reduce the effectiveness of the detected bivariate spatial flow patterns. Moreover, most existing studies are based on the planar space assumption, which is not suitable for network-constrained spatial flows. To overcome these problems, this study proposed a new statistical method, the Geographically Weighted Network Flow Cross K-function, which improves the Flow Cross K-Function method by taking the distance decay effect and the constraints of road networks into account. Both global and local versions are extended in this study: the global version measures the overall spatial association and the local version identifies the exact locations where a spatial association occurs. The experiments on simulated datasets show that the proposed method can identify predefined bivariate flow patterns. In a case study, the proposed method is also applied to flow data comprising Xiamen taxi and ride-hailing datasets. The results demonstrate that the proposed method effectively identifies the competitive relationships between taxi and ride-hailing services. Full article
(This article belongs to the Special Issue New Insights into Human Mobility, Urban Computing and Planning)
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