Official Title Associate Professor
Affiliation Department of Urban and Civil Engineering, Faculty of Architecture and Urban Design
Profile My research is characterized by the collection, analysis, and visualization of various spatio-temporal information to understand phenomena in real space, and I am involved in many research projects related to analysis and evaluation of cities and regions and planning support. The spatial information we handle is diverse, including various open data, big data, micro-geodata, statistical information, and public data held by local governments. Processing of these data involves programming and database techniques, knowledge of various statistical analyses (clustering, AI, machine learning, deep learning, etc.), and techniques to visualize the analysis results by GIS (Geographic Information System). All of these researches are realized by analyzing various statistics and data based on the spatial statistics approach to specific issues related to cities and regions, and by developing and analyzing new data as necessary. In addition, by integrating the various data accumulated through these researches, we aim to realize a data world that can reproduce real space in digital space with as much precision as possible. Furthermore, by implementing these results for society in collaboration with private companies and local governments, we aim to quickly apply and solve various social problems in real space.
Research Field(Keyword & Summary)
  1. (1) Development of dynamic geodemographics using mobile big data

    By integrating mobile big data (big data of cell phone movement trajectory) and existing statistics in a spatio-temporal manner, we will realize new dynamic (time-series) demographics that can estimate the dynamic spatial population, its distribution and movement, and people's attributes.

  2. (2) Realization of micro population census by deep learning using satellite images in developing countries

    Using satellite images from multiple time points and existing statistics, we will establish a method for understanding the spatial distribution of the population of a city and its surrounding areas from the past to the present, including the income level of its people, in a time-series and ultra-high resolution (building-by-building) for Asian mega-cities. In the future, this method will be applied worldwide to realize global micro population census.

  3. (3) Dynamic understanding of consumption trends before and after COVID-19 pandemic using mobile big data

    By integrating mobile big data with existing statistics and estimating the income and consumption location of each phone users, we will realize a database capable of monitoring the time-series evolution of consumption in consumption locations. In addition, we will use the results to establish a method for understanding consumption trends before and after the COVID-19 pandemic spatio-temporally and for monitoring their spatio-temporal evolution.

  4. (4) Development of a method for estimating the spatial distribution of vacant houses using public data

    In order to understand the spatial distribution of vacant houses, which has become an increasing problem in Japan in recent years, we are developing a method to quickly and inexpensively understand the distribution of vacant houses in a wide area by utilizing various public data held by local governments. We are developing a highly accurate method for estimating the distribution of vacant houses by learning the characteristics of vacant houses from multiple data sources through machine learning.

  5. (5) Ultra-high resolution spatial analysis of urban heat island phenomena (UHI) using thermal infrared images collected by drones

    We will establish an ultra-high definition heat exhaust monitoring technology in urban space using a device that combines a drone and a thermal infrared camera. We will also combine high-definition urban spatial information with observation results to clarify the characteristics of exhaust heat sources that cause the urban heat island phenomenon (UHI). We will also develop this method overseas and study its applicability overseas.

Representative Papers
  1. (1) Seasonal variations of park visitor volume and park service area in Tokyo: A mixed-method approach combining big data and field observations, Urban Forestry & Urban Greening, 126973, 2021.
  2. (2) Improving the 3D Model Accuracy with a Post-Processing Kinematic (PPK) method for UAS surveys, Geocarto International, DOI: 10.1080/10106049.2021.1882004, 2021.
  3. (3) Delineating urban park catchment areas using mobile phone data: A case study of Tokyo, Computers, Environment and Urban Systems, 81, 101474, 2020.
  4. (4) The Size Distribution of ‘Cities’ Delineated with a Network Theory‐based Method and Mobile Phone GPS Data, International Journal of Economic Theory, 2020, 1-13, 2020.
  5. (5) A Detailed Method to Estimate Inter-regional Capital Flows Using Inter-firm Transaction and Person Flow Big Data, Asia-Pacific Journal of Regional Science, 4, 219–239, 2020.
  6. (6) Estimating the Spatial Distribution of Vacant Houses using Public Municipal Data, Geospatial Technologies for Local and Regional Development, 165-183, 2020.
  7. (7) Spatial Distribution and Relocation Potential of Isolated Dwellings in Japan Using Developed Micro Geodata,Asia-Pacific Journal of Regional Science, 3(5), 1-17, 2019
  8. (8) Development of Micro Population Data for Each Building: Case Study in Tokyo and Bangkok, 2019 First International Conference on Smart Technology and Urban Development (STUD), 1-6, 2019.
  9. (9) Development of Building Micro Geodata for Earthquake Damage Estimation, IGARSS 2019 Proceedings (ISBN 978-1-5386-9154-0),5528-5531, 2019.
  10. (10) Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification by DMSP/OLS Night light Image, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84, 2016.
Award (1) Geospatial Society of Japan Award (Encouragement Award)(2020)
(2) MIC ICT Regional Revitalization Awards 2020 (Encouragement Award) (2020)
(3) STUD2019 Outstanding Paper Award (2019)
(4) GISA (GIS Association of Japan) Award (2019, 2015, and 2012)
(5) CSIS DAYS 2018 Best Research Presentation Award (2018)
(6) JAS(Japan Association of Surveyors) Surveying and Geospatial Information Technology Encouragement Award (2018)
Received 23 other awards.
Grant-in-Aid for Scientific Research Support: Japan Society for Promotion of Science (JSPS)
Research Grants/Projects including subsidies, donations, grants, etc.
(1) Chubu University International GIS Center Joint Research Project (2015~2021)
(2) Sumitomo Foundation Environmental Research Grant (2018~2022)
(3) Jusoken (Japan Housing Research Institute) Research Grant (Honorable Mentions) (2018~2019)
(4) Jusoken (Japan Housing Research Institute) Research Grant (2016~2017)
(5) MLIT national land policy research support (2012)

Zenrin Co. Ltd., NTT TownPage Corporation, SOMPO Risk Management, LocationMind Inc., TORUS.CO.,LTD. etc.

LocationMind Inc.
Recruitment of research assistant(s) Employable
Affiliated academic society (Membership type) (1) GIS Association of Japan (member)
(2) Japan Society of Civil Enginnering (member)
(3) Architectural Institute of Japan (member)
(4) The City Planning Institute of Japan (member)
(5) Japan Society of Photogrammetry and Remote Sensing (member)
(6) Japan Society of Traffic Engineers (member)
(7) The Association of Japanese Geographers (member)
(8) Japan Association on Geographical Space (member)
Education Field (Undergraduate level) Transportation Planning and Traffic Engineering, Urban and Regional Analysis, Surveying, Surveying Practice, Basic Drawing of Architecture, Introduction to Urban and Civil Engineering
Education Field (Graduate level) Traffic Engineering, Adv.