A policy knowledge- and reasoning-based method for data-analytic city policymaking

ABSTRACT

Efforts are being directed towards the implementation of data analysis in various areas of policymaking. In many studies, data analysis has been conducted by applying scientific methods on objective data. However, very few studies have dealt with this aspect pragmatically, starting from the data collection stage. This paper presents knowledge and reasoning systems for establishing city policies based on data analysis. First, city policy-related data are collected, and a clustering method is used for analysis. Next, Shapley value theory is used to determine the levels of inter-variable influence, and machine learning techniques, such as the decision tree, Bayesian analysis, and regression analysis, are implemented using the major variables to determine policies. Finally, a system dynamics model is designed to review the policy reasoning and assess its practicality.

KEYWORDS:

• Foresight
• policy knowledge model
• policy reasoning
• machine learning

Acknowledgements

Conceptualization done by Sun-Young Ihm and Young-Ho Park; methodology by Sun-Young Ihm and Young-Ho Park; software by Hye-Jin Lee and Eun-Ji Lee; investigation by Hye-Jin Lee and Eun-Ji Lee; writing – original draft preparation by Sun-Young Ihm; writing – review and editing by Sun-Young Ihm, Hye-Jin Lee, Eun-Ji Lee; supervision by Young-Ho Park; project administration by Sun-Young Ihm; and funding acquisition by Young-Ho Park.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology [NRF-2018R1D1A1B07046550].