2025/02/02 更新

写真a

カン　ジン
カン　ジン
所属
附属機関・学校 高等研究所
職名
講師（任期付）

経歴

  • 2024年04月
    -
    継続中

    早稲田大学   高等研究所   講師

  • 2021年04月
    -
    2024年03月

    広島大学   大学院先進理工学研究科   助理教授

  • 2019年07月
    -
    2021年03月

    ペンシルベニア大学   都市地域計画研究科/CM2 Project   客員研究員/研究員

  • 2017年07月
    -
    2019年12月

    清華大学   博士後研究員

  • 2017年07月
    -
    2019年07月

    CTTIC   中国交通安全緊急対応国家工程実験室   地理情報技術部門 Director（兼任）

学歴

  • 2011年09月
    -
    2017年06月

    北京師範大学   グローバル変化と地球システム科学研究科   グローバル環境変化学 (修士・博士一貫)  

所属学協会

  • 2014年
    -
    継続中

    ヨーロッパ地球科学連合(EGU)学会

  • 2012年12月
    -
    継続中

    アメリカ地球物理学会 (AGU)

  • 2024年05月
    -
    継続中

    日本GIS学会

  • 2022年04月
    -
    継続中

    アメリカ地理(AAG)学会

研究分野

  • 環境政策、環境配慮型社会   気候変動対策 / 炭素中立 / 地球人間圏科学   衛星画像解析 / 気候モデル再解析 / 地理学   GIS

受賞

  • 一等賞

    2018年10月   第5回中国高解像度地球観測会議/衛星アプリケーション競技会  

  • 英国国立海洋研究所と北京師範大学との共同で提供される博士フル奨学金

    2014年09月   中国国家留学基金管理委員会  

  • 二等賞

    2008年10月   中国全国大学生数学モデリングコンテスト  

メディア報道

 

論文

  • 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年09月  [査読有り]

    担当区分：責任著者

    DOI

    Scopus

    1
    被引用数
    (Scopus)

  • 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年04月  [査読有り]  [国際誌]

    担当区分：責任著者

    　概要を見る

    <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

    Scopus

    1
    被引用数
    (Scopus)

  • 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年04月  [査読有り]  [国内誌]

    担当区分：筆頭著者

    DOI

    Scopus

    14
    被引用数
    (Scopus)

  • 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年09月  [査読有り]

    担当区分：責任著者

    DOI

    Scopus

    5
    被引用数
    (Scopus)

  • 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年02月  [査読有り]

    担当区分：筆頭著者

    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年01月  [査読有り]

    担当区分：筆頭著者

    DOI

    Scopus

    43
    被引用数
    (Scopus)

  • 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年05月  [査読有り]

    担当区分：筆頭著者

    DOI

    Scopus

    7
    被引用数
    (Scopus)

  • 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年  [査読有り]

    担当区分：筆頭著者

    DOI

    Scopus

  • 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年  [査読有り]

    担当区分：筆頭著者

    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年01月  [査読有り]

    担当区分：筆頭著者

    DOI

  • Shadow detection in Antarctica using Landsat8 satellite images

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

       2017年  [査読有り]

    担当区分：筆頭著者

    DOI

  • 電気自動車のメガリージョン拡大への影響評価：移動データに基づく北京の大都市成長の空間分析

    Lin Zhongjie, Kang Jing

    the USDOT Tier 1 Center: CM2 Project, The University of Texas at Austin    2023年08月

  • 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年  [査読有り]

    担当区分：筆頭著者

    DOI

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

    Jing Kang, Junyi Zhang

    Transportation Amid Pandemics    2023年  [査読有り]

    担当区分：筆頭著者

    DOI

    Scopus

    2
    被引用数
    (Scopus)

  • “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年12月  [査読有り]

    　概要を見る

    <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

    Scopus

    2
    被引用数
    (Scopus)

  • 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年08月  [査読有り]

    　概要を見る

    <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

  • 都市における電気自動車

    ペンシルベニア大学    2020年07月

  • 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年12月  [査読有り]

    DOI

    Scopus

    15
    被引用数
    (Scopus)

  • 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年04月  [査読有り]

    DOI

    Scopus

    15
    被引用数
    (Scopus)

  • 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年10月  [査読有り]

    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年01月  [査読有り]

    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年  [査読有り]

    DOI

  • Review of the NASA IceBridge mission: Progress and prospects

    JING KANG

    遥感学报   17 ( 2 )  2013年  [査読有り]

    DOI

  • 多時相衛星リモートセンシングデータに基づく南極探検雪龍号航路の氷況緊急監視

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

    第28回中国気象学会年会——S6 氷凍圏と極地気象学    2011年12月  [査読有り]

▼全件表示

書籍等出版物

  • 実用リモートセンシング技術：学際的アプローチと応用

    Jing Kang, Ming Zhang, Tanaka Takahiro( 担当： 共著)

    CRC Press | Taylor & Francis  2024年

  • 高解像度リモートセンシングによる南極大陸のマッピング

    Fengming Hui, Xiao Cheng, Yan Liu, Jing Kang, Xinqing Li( 担当： 共著)

    中国海洋出版社  2022年

  • COVID-19とビッグデータ技術：中国での経験

    ( 担当： 分担執筆)

    Elsevier  2022年

講演・口頭発表等

  • 気候と地球観測データにおけるAI技術の応用

    KANG JING

    国際シンポジウム: 人工知能AI技術の実用化と教育  

    発表年月： 2024年12月

  • カーボンニュートラルへの道： 気候変動、影響、および緩和の道筋

    Kang Jing  [招待有り]

    早稲田大学 高等研究所月例研究会  

    発表年月： 2024年10月

  • カーボンシンクの計画：自然ベースの緩和策における土地利用と土地被覆の潜在的役割

    Jing Kang

    Nature Conferences: Air Pollution and Climate Change  

    発表年月： 2024年05月

  • 新エネルギー充電インフラと都市空間の調整：空間機械学習手法

    Jing Kang  [招待有り]

    広島大学・テキサス大学オースティン校 国際交流セミナー  

    発表年月： 2023年06月

  • 電気自動車の導入が都市エネルギー構造の転換に与える影響の評価：北京における地理空間機械学習研究

    Jing Kang, Hui Kong, Zhongjie Lin

    EGU General Assembly 2023  

    発表年月： 2023年04月

  • 大流行の文脈における電気自動車ユーザーの公共インフラ需求の空間分析：複数ソースのビッグデータ統合に基づく

    Applied Urban Modelling (AUM) 2022, University of Cambridge  

    発表年月： 2022年07月

  • 電気自動車の利用が都市を越えた生活を促進するのか？スマートフォンデータ収集とマイニングによる証拠

    the 14th IACP Annual Conference  

    発表年月： 2020年12月

  • 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  [招待有り]

    Advanced Transportation Technology Seminar in Arctic Research Center  

    発表年月： 2018年09月

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

     [招待有り]

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

    発表年月： 2018年08月

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

    Jing Kang

    International Workshop on Ice in Motion  

    発表年月： 2014年08月

  • 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  

    発表年月： 2014年04月

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

    Jing Kang

    AGU Fall Meeting  

    発表年月： 2012年12月

▼全件表示

共同研究・競争的資金等の研究課題

  • 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

    研究期間:

    2024年04月
    -
    2026年03月
     

産業財産権

  • 都市機能収縮分析方法、装置及び記憶媒体

    特許ZL 2020 1 0809164.4

    マ・チーウェイ,ゴン・チャオヤ, カン・ジン,リュー・シー

    特許権

 

担当経験のある科目(授業)

  • 理工学融合共通科目１：スマートな発展

    広島大学  

    2023年04月
    -
    2024年03月
     

  • Geographic Information System (GIS) Technology

    広島大学  

    2021年04月
    -
    2024年03月
     

  • 自然災害と社会 II

    広島大学  

    2021年04月
    -
    2024年03月
     

  • 自然災害と社会 I

    広島大学  

    2021年04月
    -
    2024年03月
     

 

学術貢献活動

  • Energy

    査読等

    Energy  

  • Land Use Policy

    査読等

    Land Use Policy  

  • Transportation Research Interdisciplinary Perspectives

    査読等

  • Journal of Transport Geography

    査読等

  • Sustainable Cities and Society

    査読等

  • Environment and Planning B: Urban Analytics and City Science

    査読等

  • Remote Sensing Applications: Society and Environment

    査読等

  • Applied Optics

    査読等

  • Environmental and Sustainable Indicators

    査読等

▼全件表示