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

VIEWS - 350 (Abstract) 151 (PDF)


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.


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

Full Text:



Alizadeh, T. (2018). Crowdsourced Smart Cities versus Corporate Smart Cities. IOP Conference Series: Earth and Environmental Science, 158, 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).

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.

D 1.3.2 Think tank transnational platform on fright mobility planning in FUAs: Vision document and setup (2017) Available online: (accessed on 12 September 2023).

Franke, J., & Gailhofer, P. (2021). Data Governance and Regulation for Sustainable Smart Cities. Frontiers in Sustainable Cities, 3.

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.

Ghermandi, A., & Sinclair, M. (2019). Passive crowdsourcing of social media in environmental research: A systematic map. Global Environmental Change, 55, 36–47.

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.

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.

Ilieva, R. T., & McPhearson, T. (2018). Social-media data for urban sustainability. Nature Sustainability, 1(10), 553–565.

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.

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.

Kousis, A., & Tjortjis, C. (2021). Data Mining Algorithms for Smart Cities: A Bibliometric Analysis. Algorithms, 14(8), 242.

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.

Mcardle, G., & Kitchin, R. (2016). Improving the Veracity of Open and Real-Time Urban Data. Built Environment, 42(3), 457–473.

Mike, C. (2023). How We Combat Scraping, Meta. Available online: (accessed on 30 August 2023).

Mohammad, S. M. (2022). Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis. Computational Linguistics, 48(2), 239–278.

Nyussupova, G., Kenespayeva, L., Tazhiyeva, D., et al. (2022). Sustainable urban development assessment: Large cities in Kazakhstan. Urbani Izziv, 33(1), 70–81.

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.

Rambocas, M., & Pacheco, B. G. (2018). Online sentiment analysis in marketing research: a review. Journal of Research in Interactive Marketing, 12(2), 146–163. (2022) Available online: (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.

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.

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.

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.

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.

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.

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.

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.

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.



  • There are currently no refbacks.

Copyright (c) 2024 Aishakhanym Zhameshova, Gulnara Sadykova, Yermek Alimzhanov, Maxim Gorodnichev, Aigerim Mansurova, Aliya Nugumanova, Vladislav Perepelkin, Danil Lebedev

License URL:

This site is licensed under a Creative Commons Attribution 4.0 International License.