Designing a smart city sustainability assessment information system based on the adapted SULPiTER methodology

Aishakhanym Zhameshova, Gulnara Sadykova, Yermek Alimzhanov, Maxim Gorodnichev, Aigerim Mansurova, Aliya Nugumanova, Vladislav Perepelkin, Danil Lebedev

Article ID: 3106
Vol 8, Issue 4, 2024

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Abstract


Increasing number of smart cities, the rise of technology and urban population engagement in urban management, and the scarcity of open data for evaluating sustainable urban development determines the necessity of developing new sustainability assessment approaches. This study uses passive crowdsourcing together with the adapted SULPiTER (Sustainable Urban Logistics Planning to Enhance Regional freight transport) methodology to assess the sustainable development of smart cities. The proposed methodology considers economic, environmental, social, transport, communication factors and residents’ satisfaction with the urban environment. The SULPiTER relies on experts in selection of relevant factors and determining their contribution to the value of a sustainability indicator. We propose an alternative approach based on automated data gathering and processing. To implement it, we build an information service around a formal knowledge base that accumulates alternative workflows for estimation of indicators and allows for automatic comparison of alternatives and aggregation of their results. A system architecture was proposed and implemented with the Astana Opinion Mining service as its part that can be adjusted to collect opinions in various impact areas. The findings hold value for early identification of problems, and increasing planning and policies efficiency in sustainable urban development.


Keywords


smart city; sustainable development; SULPiTER; passive crowdsourcing; opinion mining; intelligent system

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References


Alizadeh, T. (2018). Crowdsourced Smart Cities versus Corporate Smart Cities. IOP Conference Series: Earth and Environmental Science, 158, 012046. https://doi.org/10.1088/1755-1315/158/1/012046

Alotaibi, S., Mehmood, R., & Katib, I. (2019). Sentiment Analysis of Arabic Tweets in Smart Cities: A Review of Saudi Dialect. 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). https://doi.org/10.1109/fmec.2019.8795331

Altinok, D. (2021). Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem. Packt Publishing Ltd.

Arku, R. N., Buttazzoni, A., Agyapon-Ntra, K., et al. (2022). Highlighting smart city mirages in public perceptions: A Twitter sentiment analysis of four African smart city projects. Cities, 130, 103857. https://doi.org/10.1016/j.cities.2022.103857

D 1.3.2 Think tank transnational platform on fright mobility planning in FUAs: Vision document and setup (2017) Available online: https://programme2014-20.interreg-central.eu/Content.Node/SULPiTER/D.T1.3.2-and-D.T1.3.5-Vision-document-and-Educational-Model (accessed on 12 September 2023).

Franke, J., & Gailhofer, P. (2021). Data Governance and Regulation for Sustainable Smart Cities. Frontiers in Sustainable Cities, 3. https://doi.org/10.3389/frsc.2021.763788

Garg, N., Sharma, K. (2022). Text pre-processing of multilingual for sentiment analysis based on social network data. International Journal of Electrical & Computer Engineering, 12(1), 776–784. https://doi.org/10.11591/ijece.v12i1

Ghermandi, A., & Sinclair, M. (2019). Passive crowdsourcing of social media in environmental research: A systematic map. Global Environmental Change, 55, 36–47. https://doi.org/10.1016/j.gloenvcha.2019.02.003

Gorodnichev M, Lebedev D (2021). Semantic tools for development of high-level interactive applications for supercomputers. Journal of Supercomputing. 2021 Oct;77(10):11866–11880. https://doi.org/10.1007/s11227-021-03731-6

Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550

Ilieva, R. T., & McPhearson, T. (2018). Social-media data for urban sustainability. Nature Sustainability, 1(10), 553–565. https://doi.org/10.1038/s41893-018-0153-6

Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019). Smart cities: Advances in research—An information systems perspective. International Journal of Information Management, 47, 88–100. https://doi.org/10.1016/j.ijinfomgt.2019.01.004

Jain, P. K., Quamer, W., Saravanan, V., & Pamula, R. (2022). Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10417–10429. https://doi.org/10.1007/s12652-022-03698-z

Kousis, A., & Tjortjis, C. (2021). Data Mining Algorithms for Smart Cities: A Bibliometric Analysis. Algorithms, 14(8), 242. https://doi.org/10.3390/a14080242

Loukides, M., Mason, H., Patil, D. J. (2018). Ethics and Data Science. O’Reilly Media.

Malik, T., Tahir, A., Bilal, A., et al. (2022). Social Sensing for Sentiment Analysis of Policing Authority Performance in Smart Cities. Frontiers in Communications and Networks, 2. https://doi.org/10.3389/frcmn.2021.821090

Mcardle, G., & Kitchin, R. (2016). Improving the Veracity of Open and Real-Time Urban Data. Built Environment, 42(3), 457–473. https://doi.org/10.2148/benv.42.3.457

Mike, C. (2023). How We Combat Scraping, Meta. Available online: https://about.fb.com/news/2021/04/how-we-combat-scraping/ (accessed on 30 August 2023).

Mohammad, S. M. (2022). Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis. Computational Linguistics, 48(2), 239–278. https://doi.org/10.1162/coli_a_00433

Nyussupova, G., Kenespayeva, L., Tazhiyeva, D., et al. (2022). Sustainable urban development assessment: Large cities in Kazakhstan. Urbani Izziv, 33(1), 70–81. https://doi.org/10.5379/urbani-izziv-en-2022-33-01-01

Omuya, E. O., Okeyo, G., & Kimwele, M. (2022). Sentiment analysis on social media tweets using dimensionality reduction and natural language processing. Engineering Reports, 5(3). Portico. https://doi.org/10.1002/eng2.12579

Rambocas, M., & Pacheco, B. G. (2018). Online sentiment analysis in marketing research: a review. Journal of Research in Interactive Marketing, 12(2), 146–163. https://doi.org/10.1108/jrim-05-2017-0030

Ranking.kz. (2022) Available online: https://ranking.kz/?s=Statcounter+Global+Stats (accessed on 1 October 2023).

Rubini, L., & Lucia, L. D. (2018). Governance and the stakeholders’ engagement in city logistics: The SULPiTER methodology and the Bologna application. Transportation Research Procedia, 30, 255–264. https://doi.org/10.1016/j.trpro.2018.09.028

Sadykova, G., Dzhakupov, N., Yelshibekov, A (2021). Analysis of indicators for assessing sustainable development of Nur-Sultan city (Russian). Vestnik KazATK 119 (4): 34–47. https://doi.org/10.52167/1609-1817-2021-119-4-34-47

Sadykova, G., Zhameshova, A., Tyulubaeva, D., & Dzhakupov, N. (2022). Content and Structure of Indicators for Assesing Sustainable Urban Development: Prospects for Application in Kazakhstan. Bulletin of KazATK, 121(2), 114–125. https://doi.org/10.52167/1609-1817-2022-121-2-114-125

Saeed, T., Kiong Loo, C., & Shahreeza Safiruz Kassim, M. (2022). Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities. Computers, Materials & Continua, 71(1), 143–157. https://doi.org/10.32604/cmc.2022.021502

Schrammeijer, E. A., van Zanten, B. T., & Verburg, P. H. (2021). Whose park? Crowdsourcing citizen’s urban green space preferences to inform needs-based management decisions. Sustainable Cities and Society, 74, 103249. https://doi.org/10.1016/j.scs.2021.103249

Steils, N., Hanine, S., Rochdane, H., & Hamdani, S. (2021). Urban crowdsourcing: Stakeholder selection and dynamic knowledge flows in high and low complexity projects. Industrial Marketing Management, 94, 164–173. https://doi.org/10.1016/j.indmarman.2021.02.011

Sundararajan, M., Taly, A., Yan, Q. (2017). Axiomatic attribution for deep networks. IN: Proceedings of the 34th International Conference on Machine Learning. PMLR. pp. 3319–3328.

Toli, A. M., & Murtagh, N. (2020). The Concept of Sustainability in Smart City Definitions. Frontiers in Built Environment, 6. https://doi.org/10.3389/fbuil.2020.00077

Vaidya, H., Chatterji, T. (2020). SDG 11 Sustainable Cities and Communities. In: Franco I, Chatterji, T., Derbyshire, E., Tracey, J. (editors). Actioning the Global Goals for Local Impact. Science for Sustainable Societies. Springer, Singapore.

Xing, Y., Horner, R. M. W., El-Haram, M. A., & Bebbington, J. (2009). A framework model for assessing sustainability impacts of urban development. Accounting Forum, 33(3), 209–224. https://doi.org/10.1016/j.accfor.2008.09.003

Yue, A., Mao, C., Chen, L., et al. (2022). Detecting Changes in Perceptions towards Smart City on Chinese Social Media: A Text Mining and Sentiment Analysis. Buildings, 12(8), 1182. https://doi.org/10.3390/buildings12081182




DOI: https://doi.org/10.24294/jipd.v8i4.3106

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