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The Impact of Data Science and Participated Geographic Metadata on Improving Government Service Deliveries: Prospects and Obstacles
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Author(s): Vivek Veeraiah (Sri Siddharth Institute of Technology, Sri Siddhartha Academy of Higher Education, India), Dharmesh Dhabliya (Vishwakarma Institute of Information Technology, India), Sukhvinder Singh Dari (Symbiosis Law School, Symbiosis International University, India), Jambi Ratna Raja Kumar (Genba Sopanrao Moze College of Engineering, India), Ritika Dhabliya (ResearcherConnect, India), Sabyasachi Pramanik (Haldia Institute of Technology, India)and Ankur Gupta (Vaish College of Engineering, India)
Copyright: 2024
Pages: 23
Source title:
The Ethical Frontier of AI and Data Analysis
Source Author(s)/Editor(s): Rajeev Kumar (Moradabad Institute of Technology, India), Ankush Joshi (COER University, Roorkee, India), Hari Om Sharan (Rama University, Kanpur, India), Sheng-Lung Peng (College of Innovative Design and Management, National Taipei University of Business, Taiwan)and Chetan R. Dudhagara (Anand Agricultural University, India)
DOI: 10.4018/979-8-3693-2964-1.ch020
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Abstract
This chapter examined the profound influence of data science and volunteered geographic information (VGI) on the delivery of public services. Volunteered geographic information, being material created by users, has had a substantial impact on making geographic information accessible to everybody, enabling people to actively engage in the creation and management of data. The incorporation of VGI into government operations has introduced novel prospects for enhancing service provision in diverse sectors such as education, health, transportation, and waste management. In addition, data science has enhanced VGI by using sophisticated methodologies like artificial intelligence (AI), internet of things (IoT), big data, and blockchain, thereby transforming the whole framework of government service provision. Nevertheless, in order to effectively use VGI in public sector services, it is essential to tackle significant obstacles such as data accuracy, safeguarding, inclusiveness, technical framework, and specialized expertise.
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