Optimizing Public Services through Spatial Data Analysis (SDA) and Machine Learning Towards an Inclusive Smart City in Denpasar

Kadek Jemmy Waciko ()
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Kadek Jemmy Waciko: Politeknik Negeri Bali, Business Administration Department

A chapter in Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Social Applied Science 2024 (ICoSTAS-SAS 2024), 2024, pp 166-175 from Springer

Abstract: Abstract The rapid population growth and urbanization in Denpasar present significant challenges for local governments in delivering quality and inclusive public services. This study explores the optimization of public service delivery through the integration of spatial data analysis (SDA) and machine learning within the Smart City framework. Using a mixed-methods approach, the study combines quantitative data collected from 98 Welfare Service Recipients (WSR) through a structured questionnaire with qualitative insights gathered from interviews with key stakeholders across various government agencies. Data were analyzed using statistical techniques for the quantitative portion and thematic analysis for the qualitative portion. The findings indicate moderate awareness of challenges in public service optimization, with technological infrastructure and community engagement highlighted as critical areas needing improvement. Although the benefits of SDA and machine learning are acknowledged, challenges in implementation emphasize the need for improved training and stronger collaboration among stakeholders. This research contributes to the ongoing discourse on Smart City development by identifying key challenges and opportunities for leveraging advanced technologies to create a more efficient, inclusive, and sustainable urban environment in Denpasar.

Keywords: Machine Learning; Smart City; Spatial Data Analysis (SDA) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-622-2_19

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DOI: 10.2991/978-94-6463-622-2_19

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