Abstract
This paper concerns the challenge to evaluate and predict a district vitality index (VI) over the years. There is no standard method to do it, and it is even more complicated to do it retroactively in the last decades. Although, it is essential to evaluate and learn features of the past to predict a VI in the future. This paper proposes a method to evaluate such a VI, based on a k-mean clustering algorithm. The meta parameters of this unsupervised machine learning technique are optimized by a genetic algorithm method. Based on the resulting clusters and VI, a linear regression is applied to predict the VI of each district of a city. The weights of each feature used in the clustering are calculated using a random forest regressor algorithm. The results are applied to the city of Trois-Rivières. Each VI is defined using a magnitude of vitality and a cluster that can be used to compare districts. The consistency of the clusters are presented using a Silhouette index (SI). The results show the VI and a clustering membership for each district. Many tables and graphics display different analysis of the data, drawing the conclusion that this method can be a powerful insight for urbanists and inspire the redaction of a city plan in the smart city context.
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References
Soltani, A., Karimzadeh, D.: The spatio-temporal modeling of urban growth using remote sensing and intelligent algorithms, case of Mahabad, Iran. TEMA J. Land Use Mobil. Environ. 6(2), 189–200 (2013). https://doi.org/10.6092/1970-9870/1547
Li, X., Gar-On Yeh, A.: Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int. J. Geogr. Inf. Sci. 6(4), 323–343 (2002). https://doi.org/10.1080/13658810210137004
Tayyebi, A., Pijanowski, B., Tayyebi, A.H.: An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Landsc. Urban Plan. 100, 35–44 (2011). https://doi.org/10.1016/j.landurbplan.2010.10.007
Guan, Q., Wang, L., Clarke, K.C.: An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartogr. Geogr. Inf. Sci. 32(4), 369–380 (2005). https://doi.org/10.1559/152304005775194746
Benninger, C.C.: Principles of intelligent urbanism: the case of the new capital plan for Bhutan. Ekistics 69(412), 60–80 (2002)
Santoso, S., Kuehn, A.: Intelligent urbanism: convivial living in smart cities, p. 5 (2013)
Murgante, B., Borruso, G., Lapucci, A.: Geocomputation and urban planning, pp. 1–17 (2009). https://doi.org/10.1007/978-3-540-89930-3_1
Wu, N., Silva, E.A.: Artificial intelligence solutions for urban land dynamics: a review. J. Plan. Lit. 24(3), 246–265 (2010)
Grekousis, G., Manetos, P., Photis, Y.N.: Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities 30, 193–203 (2013)
Wang, L.X., Mendel, J.M.: Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Netw. 3(5), 807–814 (1992)
White, G., Zink, A., Codecá, L., Clarke, S.: A digital twin smart city for citizen feedback. Cities 110, 103064 (2021)
Kaur, M.J., Mishra, V.P., Maheshwari, P.: The convergence of digital twin, IoT, and machine learning: transforming data into action. In: Farsi, M., Daneshkhah, A., Hosseinian-Far, A., Jahankhani, H. (eds.) Digital Twin Technologies and Smart Cities. IT, pp. 3–17. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-18732-3_1
Ida vitality index
Drewes, J.E., van Aswegen, M.: Determining the vitality of urban centres, pp. 15–25 (2011)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis (2009)
Gueorguieva, N., Valova, I., Georgiev, G.: M&MFCM: fuzzy Cmeans clustering with Mahalanobis and Minkowski distance metrics. Procedia Comput. Sci. 114, 224–233 (2017)
Cannon, A.J.: Classification of 1477 stars by means of their photographic spectra. Ann. Harvard Coll. Observ. 56, 65–114 (1912)
Acknowledgment
This work has been supported by the City of Trois-Rivières, The “Cellule d’expertise en robotique et intelligence artificielle” of the Cégep de Trois-Rivières and IDE Trois-Rivières.
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Dessureault, JS., Simard, J., Massicotte, D. (2022). Unsupervised Machine Learning Methods for City Vitality Index. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_15
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DOI: https://doi.org/10.1007/978-3-031-10464-0_15
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