Evolving research method in three-dimensional and volumetric urban morphology of a highly dense city: assessing public and quasi-public space typologies
Abstract
An appropriate urban density is a vital part of a sustainable urban fabric. However, when it comes to measuring the built urban fabric and how people walk through it and use, a difficulty has been observed in defining applicable measurement tools. With the intention of identifying the variables that will allow the best characterization of this fabric and movement, a multi-variable analysis methodology from the field of artificial intelligence (AI) is proposed. The main objective of this paper is to prove the capacity of AI as an evolving research method in urban morphology and specifically to evaluate the capacity of such a methodology to measure the way in which people travel through defined multi-levels of typologies of public urban space. The research uses the case of Hong Kong as a dense city that is three-dimensionally activated in terms of its public realm, not just at street level, but also via below ground subways and upper-level walkways, public and quasi-public spaces. This includes the three-dimensional volumetric assessment of public and quasi-public space typologies within a highly dense city. For the purpose of the study, a characterization and term definition of these spaces has been further developed: “Junctions”, “Landmarks”, “Intersections”, “Districts”, “Passages” and “Lobbies” (both outdoor and indoor) based on Lynch’s 5 main key elements (District, landmark, path, edges, node). The results obtained using AI prove to be more robust and rational than those based on a more limited range of tools, evidencing that using AI can offer operational opportunities for better understanding of morphological and typological evolution within the vertical and volumetric built urban fabric.
Keyword : urban shape, built density, urban fabric, artificial intelligence, measurement of urban morphology
How to Cite
Choi, H. S. (Sunny), Bruyns, G., Cheng, T., & Xie, J. (2024). Evolving research method in three-dimensional and volumetric urban morphology of a highly dense city: assessing public and quasi-public space typologies. Journal of Architecture and Urbanism, 48(1), 25–38. https://doi.org/10.3846/jau.2024.18841
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817–820. https://doi.org/10.1177/2399808318796416
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Chettiparamb, A. (2006). Metaphors in complexity theory and planning. Planning Theory, 5(1), 71–91. https://doi.org/10.1177/1473095206061022
Choi, H. S., Bruyns, G., Reeve, A., & Cui, M. (2023). The negotiated public realm in the contemporary city: Hybrid walkable urban networks of Hong Kong. City, Territory and Architecture, 10(1), Article 10. https://doi.org/10.1186/s40410-023-00194-5
Choi, H. S., & Yang, X. (2022). The impact of Hi-Technology navigation systems on the reading of the five elements of urban design, using the case of the Central District of Hong Kong. Current Urban Studies, 10(03), 451–466. https://doi.org/10.4236/cus.2022.103027
Conzen, M. (1960). Alnwick, Northumberland: A study in town-plan analysis. Transactions and Papers (Institute of British Geographers), 27, iii–122. https://doi.org/10.2307/621094
Cooper, C. (2021). Spatial Design Network Analysis (sDNA) Open version 4.2 manual. Cardiff University.
Cooper, C., & Chiaradia, A. J. (2020). sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python. SoftwareX, 12, Article 100525. https://doi.org/10.1016/j.softx.2020.100525
Cullen, G. (1961). Townscape. Architectural Press.
Cummer, K., & DiStefano, L. D. (2021). Asian revitalization: Adaptive reuse in Hong Kong, Shanghai, and Singapore. Hong Kong University Press.
Ding, C., Knaap, G. J., & Hopkins, L. D. (1999). Managing urban growth with urban growth boundaries: A theoretical analysis. Journal of Urban Economics, 46(1), 53–68. https://doi.org/10.1006/juec.1998.2111
Dovey, K. (2010). Place as assemblage. In Becoming places (pp. 25–42). Routledge. https://doi.org/10.4324/9780203875001
Dovey, K., & Pafka, E. (2015). The science of urban design? Urban Design International, 21(1), 1–10. https://doi.org/10.1057/udi.2015.28
Ferrara, A. R., Nisticò, R., & Lombardo, R. (2019). Subjective and objective well-being: Bridging the gap. Scienze Regionali, 18, 575–610.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schäfer, B., Valcke, P., & Vayena, E. (2018). AI4People – An ethical framework for a Good AI Society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for Social Good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771–1796. https://doi.org/10.1007/s11948-020-00213-5
Foth, M., & Sanders, P. S. (2008). Impacts of social computing on the architecture of urban space. In A. Aurigi, F. De Cindio, & M. Carmona (Eds.), Augmented urban spaces: Articulating the physical and electronic city. Ashgate.
Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. https://doi.org/10.1080/24694452.2017.1352480
Fotheringham, A. S., Yu, H., Wolf, L. J., Oshan, T. M., & Li, Z. (2022). On the notion of “bandwidth” in geographically weighted regression models of spatially varying processes. International Journal of Geographical Information Science, 36(8), 1485–1502. https://doi.org/10.1080/13658816.2022.2034829
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
Gilby, C. (2022). AI skeptic or enthusiast? Why not both? https://blogs.juniper.net/en-us/enterprise-cloud-and-transformation/ai-skeptic-or-enthusiast-why-not-both
Goodchild, M. F. (2010). Twenty years of progress: GIScience in 2010. Journal of Spatial Information Science, 1, 3–20. https://doi.org/10.5311/JOSIS.2010.1.2
Generalova, E. M., Generalov, V. P., & Potienko, N. D. (2016). Affordable housing under shaping dense vertical urbanism: Cities to megacities: Shaping dense vertical urbanism. In Proceedings of the CTBUH (pp. 650–659). Council on Tall Buildings and Urban Habitat.
Hillier, B. (2008). Space and spatiality: What the built environment needs from social theory. Building Research & Information, 36(3), 216–230. https://doi.org/10.1080/09613210801928073
HKSAR Land Department. (2020). Open data, topographic map. http://www.landsd.gov.hk
Hopkins, L. D. (1999). Structure of a planning support system for urban development. Environment and Planning B: Planning and Design, 26(3), 333–343. https://doi.org/10.1068/b260333
Hospers, G.-J. (2010). Lynch’s the image of the city after 50 years: City marketing lessons from an urban planning classic. European Planning Studies, 18(12), 2073–2081. https://doi.org/10.1080/09654313.2010.525369
Ireson, A., & Barley, N. (2000). City levels. Princeton Architectural Press.
Kim, D., Jeong, J., Ko, Y., Kwon, Y., & Kim, Y. (2018). The construction of database of community health outcomes and health determinants in the Republic of Korea. Public Health Weekly Report, KCDC, 11(30), 979–983.
Li, M. L., Chen, M. S., & Sato, K. (2020). Digital map design elements for local tourism: Comparing user cognition between age of 20s and above 60. In Proceedings of the 8th International Conference on Kansei Engineering and Emotion Research (pp. 55–65). Springer. https://doi.org/10.1007/978-981-15-7801-4_6
Lin, Z., & Gámez, J. L. S. (2018). Vertical urbanism: China Studio 2012-2014. UNC Charlotte School of Architecture. https://doi.org/10.4324/9781351206839-1
Lynch, K. (1960). Image of the city. M.I.T. Press.
Makki, M., Showkatbakhsh, M., Tabony, A., & Weinstock, M. (2019). Evolutionary algorithms for generating urban morphology: Variations and multiple objectives. International Journal of Architectural Computing, 17(1), 5–35. https://doi.org/10.1177/1478077118777236
Marshall, S. (2004). Streets and patterns. Spon Press. https://doi.org/10.4324/9780203589397
Marshall, S. (2012). E-learning and higher education: Understanding and supporting organizational change in New Zealand. Journal of Open, Flexible and Distance Learning, 16(1), 141–155. https://doi.org/10.61468/jofdl.v16i1.96
Nethercote, M., & Horne, R. (2016). Ordinary vertical urbanisms: City apartments and the everyday geographies of high-rise families. Environment and Planning A: Economy and Space, 48(8), 1581–1598. https://doi.org/10.1177/0308518x16645104
Oliveira, V., & Medeiros, V. (2016). Morpho: Combining morphological measures. Environment and Planning B: Planning and Design, 43(5), 805–825. https://doi.org/10.1177/0265813515596529
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), Article 269. https://doi.org/10.3390/ijgi8060269
Oshan, T. M., Smith, J. P., & Fotheringham, A. S. (2020). Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. International Journal Health Geographics, 19, Article 11. https://doi.org/10.1186/s12942-020-00204-6
Rao, F., Dovey, K., & Pafka, E. (2018). Toward a genealogy of urban shopping: Types, adaptations and resilience. Journal of Urban Design, 23(4), 544–557. https://doi.org/10.1080/13574809.2017.1405726
Rauber, A., & Krafta, R. (2018). Alexander’s theories applied to urban design. Urban Science, 2(3), Article 86. https://doi.org/10.3390/urbansci2030086
Sanchez, T. W., Shumway, H., Gordner, T., & Lim, T. (2022). The prospects of artificial intelligence in urban planning. International Journal of Urban Sciences, 27(2), 179–194. https://doi.org/10.1080/12265934.2022.2102538
Shabrina, Z., Buyuklieva, B., & Ng, M. K. M. (2021). Short‐term rental platform in the urban tourism context: A Geographically Weighted Regression (GWR) and a Multiscale GWR (MGWR) approaches. Geographical Analysis, 53(4), 686–707. https://doi.org/10.1111/gean.12259
Shea, K., Aish, R., & Gourtovaia, M. (2005). Towards integrated performance-driven generative design tools. Automation in Construction, 14(2), 253–264. https://doi.org/10.1016/j.autcon.2004.07.002
Shelton, B., Karakiewicz, J., & Kvan, T. (2011). The making of Hong Kong: From vertical to volumetric. Routledge.
Stevens, Q. (2006). The shape of urban experience: A reevaluation of Lynch’s five elements. Environment and Planning B: Planning and Design, 33(6), 803–823. https://doi.org/10.1068/b32043
Topcu, K. D., & Topcu, M. (2012). Visual presentation of mental images in urban design education: Cognitive maps. Procedia - Social and Behavioral Sciences, 51, 573–582. https://doi.org/10.1016/j.sbspro.2012.08.208
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), Article 233. https://doi.org/10.1038/s41467-019-14108-y
Ye, Y., Yeh, A., Zhuang, Y., van Nes, A., & Liu, J. (2017). “Form syntax” as a contribution to geodesign: A morphological tool for urbanity-making in urban design. Urban Design International, 22(1), 73–90. https://doi.org/10.1057/s41289-016-0035-3
Yigitcanlar, T., & Cugurullo, F. (2020). The sustainability of artificial intelligence: An urbanistic viewpoint from the lens of smart and sustainable city. Sustainability, 12(2), Article 8548. https://doi.org/10.3390/su12208548