Updated on 2024/12/01

写真a

 
KANG, Jing
 
Affiliation
Affiliated organization, Waseda Institute for Advanced Study
Job title
Assistant Professor(non-tenure-track)

Research Experience

  • 2024.04
    -
    Now

    Waseda University   Institute for Advanced Study   Assistant Professor

  • 2021.04
    -
    2024.03

    Hiroshima University   Graduate School of Advanced Science and Engineering   Assistant Professor

Education Background

  • 2011.09
    -
    2017.06

    Beijing Normal University   College of Global Change and Earth System Science   Global Environmental Change  

Professional Memberships

  • 2014
    -
    Now

    European Geosciences Union (EGU)

  • 2012.12
    -
    Now

    American Geophysical Union (AGU)

  • 2024.05
    -
    Now

    Geographic Information System Association

  • 2022.04
    -
    Now

    Association of American Geographers

Research Areas

  • Environmental policy and social systems   Climate Change Adaptation / Carbon Neutral / Human geosciences   Remote Sensing / Climate Model Reanalysis / GIS / Geography   GIS

Awards

  • First Prize of China High-Resolution Remote Sensing Application Solution Competition

    2018.10   The 5th China High Resolution Earth Observation Conference  

  • National Oceanography Centre UK and Beijing Normal University Joint Scholarship Program

    2014.09   China Scholarship Council  

  • Second Prize (Province level)

    2008.10   National Undergraduate Mathematical Modeling Contest  

Media Coverage

 

Papers

  • Global disparities in CO2 emissions from mobility sectors of diverse economies: A macroscopic exploration across 188 countries/regions

    Bailing Zhang, Jing Kang, Tao Feng

    Environmental and Sustainability Indicators    2024.09  [Refereed]

    Authorship：Corresponding author

    DOI

  • A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning

    Bailing Zhang, Jing Kang, Tao Feng

    Environment and Planning B: Urban Analytics and City Science    2024.04  [Refereed]  [International journal]

    Authorship：Corresponding author

    　View Summary

    <jats:p> The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure. </jats:p>

    DOI

  • A novel geospatial machine learning approach to quantify non-linear effects of land use/land cover change (LULCC) on carbon dynamics

    Jing Kang, Bailing Zhang, Anrong Dang

    International Journal of Applied Earth Observation and Geoinformation    2024.04  [Refereed]  [Domestic journal]

    Authorship：Lead author

    DOI

  • Quantitative Attribution Framework for Urban Air Pollutant: Investigating Policy Impact on NO2 Emissions of Megacities in China and Japan

    Bailing Zhang, Jing Kang

    Sustainable Cities and Society     104965 - 104965  2023.09  [Refereed]

    Authorship：Corresponding author

    DOI

  • Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence From Remote Sensing Integrated With Ground-Station Data

    JING KANG

    Remote Sensing   15 ( 4 )  2023.02  [Refereed]

    Authorship：Lead author

    DOI

  • Mapping the Dynamics of Electric Vehicle Charging Demand within Beijing's Spatial Structure

    Jing Kang, Hui Kong, Zhongjie Lin, Anrong Dang

    Sustainable Cities and Society     103507 - 103507  2022.01  [Refereed]

    Authorship：Lead author

    DOI

  • Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

    JING KANG, Changcheng Kan, Zhongjie Lin

    ISPRS International Journal of Geo-Information   10 ( 5 )  2021.05  [Refereed]

    Authorship：Lead author

    DOI

  • An Improved Convolutional Neural Network for Monocular Depth Estimation

    Jing Kang, Anrong Dang, Bailing Zhang, Yongming Wang, Hang Su, Fei Su, Tianyu Ci, Fangping Wang

    Green, Smart and Connected Transportation Systems   617   1229 - 1237  2020  [Refereed]

    Authorship：Lead author

    DOI

  • An Automatic Method for Water Extraction From High Spatial Resolution GF-1 Imagery Based On A Deep Learning Algorithm

    Jing Kang, Tianyu Ci, Anrong Dang, Yongming Wang

    In, 2019 International Conference on Computer Intelligent Systems and Network Remote Control    2019  [Refereed]

    Authorship：Lead author

    DOI

  • An Accurate and Automated Method for Identifying and Mapping Exposed Rock Outcrop in Antarctica Using Landsat 8 Images

    Jing Kang, Xiao Cheng, Fengming Hui, Tianyu Ci

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing   11 ( 1 ) 57 - 67  2018.01  [Refereed]

    Authorship：Lead author

    DOI

  • Shadow detection in Antarctica using Landsat8 satellite images

    Jing Kang, Xiao CHENG, Yan LIU, Fengming HUI, Tian Li, Lunxi OUYANG

    Journal of Beijing Normal University (Natural Science)    2017  [Refereed]

    Authorship：Lead author

    DOI

  • Assessing the Effects of Electric Vehicle Adoption on Urban Energy Structure Transition: A Geospatial Machine Learning Study in Beijing

    Jing Kang, Hui Kong, Zhongjie Lin

    EGU    2023  [Refereed]

    Authorship：Lead author

    DOI

  • COVID-19 and big data technologies: Experience in China

    Jing Kang, Junyi Zhang

    Transportation Amid Pandemics    2023  [Refereed]

    Authorship：Lead author

    DOI

  • “What should be computed” for supporting post-pandemic recovery policymaking? A life-oriented perspective

    Junyi Zhang, Tao Feng, Jing Kang, Shuangjin Li, Rui Liu, Shuang Ma, Baoxin Zhai, Runsen Zhang, Hongxiang Ding, Taoxing Zhu

    Computational Urban Science   1 ( 1 )  2021.12  [Refereed]

    　View Summary

    <jats:title>Abstract</jats:title><jats:p>The COVID-19 pandemic has caused various impacts on people’s lives, while changes in people’s lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people’s lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people’s lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about “what should be computed?” in <jats:italic>Computational Urban Science</jats:italic> with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.</jats:p>

    DOI

  • Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion

    Qiwei Ma, Zhaoya Gong, Jing Kang, Ran Tao, Anrong Dang

    Remote Sensing   12 ( 16 ) 2513 - 2513  2020.08  [Refereed]

    　View Summary

    <jats:p>Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.</jats:p>

    DOI

  • Electric Vechicles in the City

    Zhongjie Lin, Jing Kang

    University of Pennsylvania    2020.07

  • Aerial photography based census of Adélie Penguin and its application in CH4 and N2O budget estimation in Victoria Land, Antarctic

    Hong He, Xiao Cheng, Xianglan Li, Renbin Zhu, Fengming Hui, Wenhui Wu, Tiancheng Zhao, Jing Kang, Jianwu Tang

    Scientific Reports   7 ( 1 )  2017.12  [Refereed]

    DOI

  • AntarcticaLC2000: The new Antarctic land cover database for the year 2000

    FengMing Hui, Jing Kang, Yan Liu, Xiao Cheng, Peng Gong, Fang Wang, Zhan Li, YuFang Ye, ZiQi Guo

    Science China Earth Sciences   60 ( 4 ) 686 - 696  2017.04  [Refereed]

    DOI

  • Aerial photography based estimation of greenhouse gas emissions from penguins in Victoria Land, Antarctica

    Hong HE, XiangLan LI, Xiao CHENG, RenBin ZHU, JianWu TANG, FengMing HUI, WenHui WU, TianCheng ZHAO, Yan LIU, Jing KANG

    Chinese Science Bulletin   61 ( 30 ) 3268 - 3277  2016.10  [Refereed]

    DOI

  • Monitoring the Amery Ice Shelf front during 2004-2012 using ENVISAT ASAR data

    Chen ZHAO, Xiao CHENG, Fengming HUI, Jing KANG, Yan LIU, Xianwei WANG, Fang WANG, Cheng CHENG, Zhunzhun FENG, Tianyu CI, Tiancheng ZHAO, Mengxi ZHAI

    ADVANCES IN POLAR SCIENCE   24 ( 2 ) 133 - 137  2014.01  [Refereed]

    DOI

  • The slow-growing tooth of the Amery Ice Shelf from 2004 to 2012

    Chen Zhao, Xiao Cheng, Yan Liu, Fengming Hui, Jing Kang, Xianwei Wang, Fang Wang, Cheng Cheng

    Journal of Glaciology   59 ( 215 ) 592 - 596  2013  [Refereed]

    DOI

  • Review of the NASA IceBridge mission: Progress and prospects

    JING KANG

    遥感学报   17 ( 2 )  2013  [Refereed]

    DOI

  • Emergency Monitoring of Ice Conditions Along the Xuelong Expedition Route in Antarctica Based on Multi-Temporal Satellite Remote Sensing Data

    Fengming Hui, Xiao Cheng, Shuo Zhao, Tingbiao Chen, Zhixin Wang, Fang Wang, Jing Kang, Yan Liu, Kun Wang

    The 28th Annual Meeting of the Chinese Meteorological Society——S6 Cryosphere and Polar Meteorology    2011.12  [Refereed]

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Books and Other Publications

  • Accessible Remote Sensing: Interdisciplinary Approach and Applications

    Jing Kang, Ming Zhang, Tanaka Takahiro( Part： Joint author)

    CRC Press | Taylor & Francis  2024

  • High-Resolution Remote Sensing Mapping of Antarctica

    Fengming Hui, Xiao Cheng, Yan Liu, Jing Kang, Xinqing Li( Part： Joint author)

    China Ocean Press  2022

  • COVID-19 and big data technologies: Experience in China”. In Transportation Amid Pandemics: Lessons Learned from COVID-19

    Jing Kang, Junyi Zhang( Part： Contributor)

    Elsevier  2022

Presentations

  • The Road to Carbon Neutral: Climate Change, Impacts and Mitigation Pathways

    Jing Kang  [Invited]

    Waseda University WIAS Monthly Workshop 

    Presentation date： 2024.10

  • Planning of carbon sinks: the potential role of Land Use and Land Cover in nature-based mitigation

    Jing Kang

    Nature Conferences: Air Pollution and Climate Change 

    Presentation date： 2024.05

  • Spatial Coordination Between New Energy Charging Infrastructure and Urban Space Planning with Machine Learning

    Jing Kang  [Invited]

    International Seminar for Hiroshima University-The University of Texas at Austin Exchange 

    Presentation date： 2023.06

  • Assessing the Effects of Electric Vehicle Adoption on Urban Energy Structure Transition: A Geospatial Machine Learning Study in Beijing

    Jing Kang, Hui Kong, Zhongjie Lin

    EGU General Assembly 2023 

    Presentation date： 2023.04

  • Spatial Analysis of EV Users' Demands for Public Infrastructure in the Context Of COVID-19, Based on Multi-Source Big Data Integration

    Jing Kang

    Applied Urban Modelling (AUM) 2022, University of Cambridge 

    Presentation date： 2022.07

  • Will EV Mobility Drive People Live Beyond the City? Evidence from Individual Smartphone Data Collection and Mining

    Jing Kang, Zhongjie Lin

    the 14th IACP Annual Conference 

    Presentation date： 2020.12

  • Advanced Driver Assistance Systems (ADAS) embedded in GNSS location for safety and early warning service, based on Transportation Operation Vehicles Networking Control data platform in China

    Jing Kang  [Invited]

    Advanced Transportation Technology Seminar in Arctic Research Center 

    Presentation date： 2018.09

  • China Transportation Information System: Platform and Big Data Application, Integrated with Remote Sensing

    Jing Kang  [Invited]

    Marine Transport Seminar in Chalmers University of Technology, Göteborg, Sweden 

    Presentation date： 2018.08

  • Sea Level Rise and Its Impacts on Coastal Cities of China

    Jing Kang

    International Workshop on Ice in Motion 

    Presentation date： 2014.08

  • Increasing Risks to China’s Coastal Cities with its Expansion to Low-lying seaward to Rising Sea Level

    Jing Kang

    EGU General Assembly 2015, Vienna, Austria 

    Presentation date： 2014.04

  • Vulnerability of Islands to Increasing Sea Level Rise in Zhoushan, China

    Jing Kang

    AGU Fall Meeting 

    Presentation date： 2012.12

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Research Projects

  • Assessing the Coordination of Electric Vehicle Adoption on Urban Energy Transition: A Geospatial Machine Learning Framework

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    2024.04
    -
    2026.03
     

 

Teaching Experience

  • Smart Urban Development

    Hiroshima University  

    2023.04
    -
    2024.03
     

  • Geographic Information System (GIS) Technology

    Hiroshima University  

    2021.04
    -
    2024.03
     

  • Natural Disasters and Society

    Hiroshima University  

    2021.04
    -
    2024.03
     

  • Natural Disasters and Society

    Hiroshima University  

    2021.04
    -
    2024.03
     

 

Academic Activities

  • Energy

    Peer review

    Energy  

  • Land Use Policy

    Peer review

    Land Use Policy  

  • Transportation Research Interdisciplinary Perspectives

    Peer review

  • Journal of Transport Geography

    Peer review

  • Sustainable Cities and Society

    Peer review

  • Environment and Planning B: Urban Analytics and City Science

    Peer review

  • Remote Sensing Applications: Society and Environment

    Peer review

  • Applied Optics

    Peer review

  • Environmental and Sustainable Indicators

    Peer review

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