KEISUKE TAKANO | FACULTY | Faculty and Graduate School of Social Data Science, Hitotsubashi University

FACULTY

KEISUKE TAKANO

Graduate School of Social Data Science/Assistant Professor

Field of Study

Geographic Information Science

Hitotsubashi Researchers
Information

INTERVIEW

Unique Appeal of the Social Data Science Program

SDS has a remarkable advantage in that the students can gain expertise in both data analysis and information science, as well as a broad understanding of the social sciences, including economics and business administration. In recent years, we have entered an era in which even advanced and complex statistical analyses can be easily conducted using widely available and often free statistical software. However, without academic or practical knowledge of the subject matter being analyzed, it is difficult to assess the validity of the obtained results and derive useful academic or practical insights. In this sense, the SDS program's emphasis on cultivating individuals capable of interpreting data based on background knowledge of socio-economic contexts is a significant strength. Another distinctive feature is its focus on practical implementation in society. In particular, the "Project-Based Learning (PBL) Seminar" course allows students to engage with people from various companies and policy-making fields, providing an opportunity to learn how the knowledge and techniques acquired in lectures and exercises are applied in real-world practice.

Innovative education and research encouraged in the Social Data Science Program

What I consider most important in education is the concept of "Shu-Ha-Ri" (守破離). Data science is evolving rapidly, and the number of statistical and machine learning models available continues to increase. On the other hand, unless we consistently practice the fundamentals—such as clarifying the research question, identifying the added value of your analysis based on existing academic and practical knowledge, selecting the most appropriate method for achieving the goal, conducting the analysis, and clearly and concisely communicating the results—the value of the analysis will be significantly diminished, no matter how advanced the methods or how valuable the data used. Driven by this motivation, my lectures focus not only on developing a solid foundation in statistical analysis but also on cultivating the fundamental mindset and practical skills essential for engaging in academic research. In addition, I work on various economic analyses grounded in geographic information science, aiming to produce academic research outcomes while also feeding the knowledge gained back into educational practice.

MESSAGE

In recent years, the nature of learning at universities has shifted toward more active learning, emphasizing discussion and collaboration among students, as well as between students and faculty members (and occasionally, professionals from industry). The SDS program provides an ideal environment for fully engaging in such active learning through interactions with those who share a common foundation in data science. By bringing together their areas of expertise and working with peers to produce a major outcome, students will not only face challenges but also engage in many opportunities for enjoyment and learning. These experiences are extremely valuable in laying the foundation for how one works and engages with others as a professional in the future. Moreover, peers, seniors, and juniors whom students meet through group work and lab activities will become lifelong colleagues—valuable connections who will continue to support and encourage one another. I hope students will cherish both the development of technical and specialized knowledge as well as the opportunities to connect with various people and make the most of their student life.

CLASS

(U)Data Analysis for Property Market
(U)Multivariate Analysis
(G)Advanced Statistical Analysis (practice)

RESEARCH Research Website

My research field is Geographic Information Science (GIS). GIS is a domain that analyzes and interprets various phenomena occurring in geographic space by utilizing data and mathematical models. Within this field, my focus is particularly on understanding the mechanisms behind the agglomeration of economic activities—such as population and industries—and the flow of these activities across locations. For instance, I have conducted empirical analyses on the spatial distribution of firms and inter-firm transactions. More recently, I have begun a research project regarding student commuting behavior within the city. In recent years, spatial data has attracted attention as part of the growing trend of utilizing alternative data in economic analysis. However, handling spatial data requires specialized knowledge and techniques that differ from those used with conventional tabular data. Therefore, I also work on methods to facilitate the smooth analysis of spatial data. Statistical analysis that accounts for the tendency that near things are more related than distant things, known as the first law of geography, can offer highly valuable insights. They help elucidate the underlying mechanisms behind the data and improve the accuracy of various predictions related to economic activities. Specifically, I engage in research that addresses a wide range of questions about the spatial distribution of economic activity, employing approaches such as spatial statistics and spatial interaction models like gravity models.

Keywords

GIS (Geographic Information Science)
urban and spatial economics
microeconometric analysis

FACULTY

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