Details of a Researcher

写真a

KANG, Jing

Google Scholar Information (Citations per year)

Citation Count: 257 h-index: 10 i10-index: 10

Click to view the Google Scholar page.

Affiliation

Affiliated organization, Waseda Institute for Advanced Study

Job title

Assistant Professor(non-tenure-track)

Degree

Doctor of Science ( Beijing Normal University )

Mail Address

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
2020.03

-

2021.03

University of Pennsylvania University Transportation Center--CM2 Project Research Fellow
2019.07

-

2020.02

University of Pennsylvania Institute for Urban Research Visiting Scholar
2017.07

-

2019.12

Tsinghua University Postdoc Researcher
2017.07

-

2019.07

China Transport Telecommunications & Information Center(CTTIC) National Engineering Laboratory for Traffic Safety and Emergency Response Director

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Education Background

2011.09

-

2017.06

Beijing Normal University College of Global Change and Earth System Science Global Environmental Change (Quantitative Remote Sensing)
2014.10

-

2016.04

National Oceanography Centre & Beijing Normal University

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 / Spatial Data Integration / GIS / Geography Spatial Econometrics

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

Here’s How Slowly New York City Is Moving on Electric Vehicles

Newspaper, magazine

The New York Times

USA

2022.04
How Will Electric Vehicles Influence the Changing Urban Infrastructure and Energy Network?

Internet

University of Pennsylvania

Kleinman Center for Energy Policy

2021
Electric Vehicles in the City: The Relationship of EV Infrastructure and Spatial Development in Beijing

Internet

University of Pennsylvania

Kleinman Center for Energy Policy

2020.06
The Architecture School Team Achieved Outstanding Results in the High-resolution Remote Sensing Application Competition

Internet

Tsinghua University News

2018.10

Papers

Spatial Coordination of Electric Vehicle Adoption within the Urban Built Environment Using Machine Learning

Jing Kang

Sustainable Futures 2025.06 [Refereed] [International journal]

Authorship：Lead author, Corresponding author

DOI
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] [International 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 and Ground-Station Data

Jing Kang, Bailing Zhang, Junyi Zhang, Anrong Dang

Remote Sensing 15 ( 4 ) 1068 - 1068 2023.02

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

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
Profiling user segments and spatial clusters of EV uptake through multi-method modeling

Bailing Zhang, Jing Kang

Sustainable Environment 11 ( 1 ) 2025.08 [Refereed]

Authorship：Corresponding author

DOI
Assessing Electric Vehicle’s Impact on Megaregion Expansion: Spatial Analysis of Beijing’s Metropolitan Growth Based on Mobility Data

Lin Zhongjie, Kang Jing

the USDOT Tier 1 Center: CM2 Project, The University of Texas at Austin 2023.08
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 Vehicles 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( Part： Sole author)

CRC Press | Taylor & Francis 2025
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

Mapping Land Use/Land Cover Changes and Estimating Carbon Storage/Loss using Remote Sensing

JING KANG [Invited]

Waseda University Research Institute of Business Administration Seminar

Presentation date： 2025.05
Application of AI Technologies in Climate and Earth Observation Data

JING KANG

International Workshop: Practical Applications and Education of AI Technologies, Waseda University

Presentation date： 2024.12
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
Towards Effective Climate-Land Governance: Evidence-Based Approaches to Sustainable Land Use Policies

Waseda University Grant for Special Research Projects (Tokutei Kadai)

Project Year :

2024

-

2025

Industrial Property Rights

A Method, Device, and Storage Medium for Analyzing Urban Functional Shrinkage

特許ZL 2020 1 0809164.4

Ma Qiwei, Gong Zhaoya, Kang Jing,Liu Xi

Patent

Syllabus

Spatial Econometrics and Data 1

School of Commerce

2025 spring semester
Spatial Econometrics and Data

Graduate School of Commerce

2025 spring semester

Teaching Experience

Spatial Econometrics and Data

Waseda University
2025.04

-

Now
Smart Urban Development

Hiroshima University
2023.04

-

2024.03
Geographic Information System (GIS) Technology

Hiroshima University
2021.04

-

2024.03
Natural Disasters and Society II

Hiroshima University
2021.04

-

2024.03
Natural Disasters and Society I

Hiroshima University
2021.04

-

2024.03

Academic Activities

Nature Scientific Data

Scientific advice/Review
Energy

Peer review

Energy
Land Use Policy

Peer review

Land Use Policy
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
Transportation Research Interdisciplinary Perspectives

Peer review
Environmental and Sustainable Indicators

Peer review
Journal of Transport Geography

Peer review

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Sub-affiliation

Faculty of Commerce School of Commerce
Faculty of Social Sciences School of Social Sciences
Faculty of Commerce Graduate School of Commerce

Internal Special Research Projects

Towards Effective Climate-Land Governance: Evidence-Based Approaches to Sustainable Land Use Policies

2024

　View Summary

Nature-based Solutions (NbS), afforestation efforts, and technological strategies such as renewable energy initiatives have been put forward as crucial measures for implementing carbon reduction. However, there remains a lack of effective quantitative assessment methods to guide the transition from technology to policy implications, particularly regarding how these processes are closely intertwined with land use planning and policies, including various land-use transformations. This study first evaluates the nonlinear relationships between nine different land use changes and carbon emissions, based on top-down satellite carbon dioxide (CO2) and land use datasets. Furthermore, we leverage the latest CMIP6 climate models from the IPCC, integrating the predictions for the year 2100 under different Shared Socioeconomic Pathways (SSPs)—SSP126 for sustainability, SSP245 for middle-of-the-road, SSP370 for regional rivalry, and SSP585 for fossil-fueled development—to analyze the effects of land use changes on carbon emissions across different socioeconomic development scenarios. By utilizing satellite remote sensing observation data for regional bias correction, we enhanced the spatial accuracy of our findings. The results are expected to inform actionable land use policies that support effective and equitable climate-land mitigation strategies within the Earth System governance framework.