{"title":"智能城市大数据分析","authors":"Cassie Davies","doi":"10.47941/ijce.2057","DOIUrl":null,"url":null,"abstract":"Purpose: This study sought to explore big data analytics for smart cities. \nMethodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. \nFindings: The findings reveal that there exists a contextual and methodological gap relating to exploring big data analytics for smart cities. The integration of big data analytics into smart city operations significantly improved urban management efficiency, sustainability, and residents' quality of life. By leveraging advanced analytics, cities optimized traffic flow, reduced energy consumption, enhanced public safety, improved healthcare delivery, and monitored environmental conditions in real-time. These advancements led to smoother services, economic sustainability, better public safety, effective disaster management, and proactive environmental and health interventions, making cities more responsive, resilient, and sustainable. \nUnique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Socio-Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on big data analytics for smart cities. The study made significant contributions to theory, practice, and policy by extending the Diffusion of Innovations and Socio-Technical Systems theories with empirical evidence, recommending robust data governance frameworks and skilled analytics units for practical implementation, and advocating for comprehensive policies to ensure data privacy and security. It highlighted the importance of stakeholder collaboration, investment in technological infrastructure, and future research on long-term impacts, ethical considerations, and emerging technologies to enhance the efficiency and sustainability of smart cities.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":"8 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Big Data Analytics for Smart Cities\",\"authors\":\"Cassie Davies\",\"doi\":\"10.47941/ijce.2057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose: This study sought to explore big data analytics for smart cities. \\nMethodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. \\nFindings: The findings reveal that there exists a contextual and methodological gap relating to exploring big data analytics for smart cities. The integration of big data analytics into smart city operations significantly improved urban management efficiency, sustainability, and residents' quality of life. By leveraging advanced analytics, cities optimized traffic flow, reduced energy consumption, enhanced public safety, improved healthcare delivery, and monitored environmental conditions in real-time. These advancements led to smoother services, economic sustainability, better public safety, effective disaster management, and proactive environmental and health interventions, making cities more responsive, resilient, and sustainable. \\nUnique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Socio-Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on big data analytics for smart cities. The study made significant contributions to theory, practice, and policy by extending the Diffusion of Innovations and Socio-Technical Systems theories with empirical evidence, recommending robust data governance frameworks and skilled analytics units for practical implementation, and advocating for comprehensive policies to ensure data privacy and security. It highlighted the importance of stakeholder collaboration, investment in technological infrastructure, and future research on long-term impacts, ethical considerations, and emerging technologies to enhance the efficiency and sustainability of smart cities.\",\"PeriodicalId\":198033,\"journal\":{\"name\":\"International Journal of Computing and Engineering\",\"volume\":\"8 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computing and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47941/ijce.2057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computing and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47941/ijce.2057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Purpose: This study sought to explore big data analytics for smart cities.
Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
Findings: The findings reveal that there exists a contextual and methodological gap relating to exploring big data analytics for smart cities. The integration of big data analytics into smart city operations significantly improved urban management efficiency, sustainability, and residents' quality of life. By leveraging advanced analytics, cities optimized traffic flow, reduced energy consumption, enhanced public safety, improved healthcare delivery, and monitored environmental conditions in real-time. These advancements led to smoother services, economic sustainability, better public safety, effective disaster management, and proactive environmental and health interventions, making cities more responsive, resilient, and sustainable.
Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Socio-Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on big data analytics for smart cities. The study made significant contributions to theory, practice, and policy by extending the Diffusion of Innovations and Socio-Technical Systems theories with empirical evidence, recommending robust data governance frameworks and skilled analytics units for practical implementation, and advocating for comprehensive policies to ensure data privacy and security. It highlighted the importance of stakeholder collaboration, investment in technological infrastructure, and future research on long-term impacts, ethical considerations, and emerging technologies to enhance the efficiency and sustainability of smart cities.