Integrating GIS, 3D-Isovist, and an NSGA-II multi-objective optimization algorithm for automation of design process in urban parks and public open spaces

Abstract

Advanced digital tools in landscape architecture are mostly limited to visualization and presentation of alternatives. However, they can potentially be used in different design stages. In this paper, we propose a method to approach a design problem as a multi-objective problem (MOP) and integrate advanced digital techniques into an automated landscape design framework to exploit their superior computational capabilities. We combined geographic information system (GIS) tools for mapping of the site, 3D Isovists for analyses, and a meta-heuristic method (constrained non-dominated sorting genetic algorithm-2 or NSGA-II), to search in the continuous solution space (fitness landscape). The case study was an urban park in Tehran, Iran, and the focus was on spatial-visual characteristics of the green space. The results showed that the NSGA-II was able to solve the complex design problem with 185 trees and 66 observers. The algorithm produced a Pareto-frontier consisting of four optimal solutions that, compared to the existing state of the park, showed more than 18% and 12 % improvement according to Tree View and Building View, respectively. These results confirmed the applicability of our proposed semi-automated design framework. This study is of interest to both professional practitioners and academics of landscape architecture since it can help bridge the gap between scientific assessment and its application in real-world design studies. The proposed method can be further developed to take other design considerations into account and also has the potential to be of use in other related design fields.

Keywords

multi-objective optimization / NSGA-II / landscape design process / 3D Isovist / spatial-visual characteristics analysis / urban parks

References

[1] N. Ban, X. Liu, C. Zhu. Landscape ecological construction and spatial pattern optimization design based on genetic algorithm optimization neural network. Wireless Communications and Mobile Computing, 2022 (2022), Article 4976361, 10.1155/2022/4976361

[2] M. Batty. Exploring isovist fields: Space and shape in architectural and urban morphology. Environment and Planning. B, Planning & Design, 28 (1) (2001), pp. 123-150, 10.1068/b2725

[3] R.H. Bhesdadiya, I.N. Trivedi, P. Jangir, N. Jangir, A. Kumar. An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem. Cogent Engineering, 3 (1) (2016), Article 1269383, 10.1080/23311916.2016.1269383

[4] A. van den Brink, D. Bruns. Strategies for enhancing landscape architecture research. Landscape Research, 39 (1) (2014), pp. 7-20, 10.1080/01426397.2012.711129

[5] M.L. Castro Pena, A. Carballal, N. Rodríguez-Fernández, I. Santos, J. Romero.Artificial intelligence applied to conceptual design. A review of its use in architecture. Automation in Construction, 124 (2021), Article 103550, 10.1016/j.autcon.2021.103550

[6] M. Chandramouli, B. Huang, L. Xue. Spatial change optimization. Photogrammetric Engineering & Remote Sensing, 75 (8) (2009), pp. 1015-1022, 10.14358/PERS.75.8.1015

[7] X. Chen, A.Y. Chang-Richards, A. Pelosi, Y. Jia, X. Shen, M.K. Siddiqui, N. Yang. Implementation of technologies in the construction industry: A systematic review. Engineering Construction and Architectural Management, 29 (8) (2022), pp. 3181-3209, 10.1108/ECAM-02-2021-0172

[8] J. Choi, D.H. Hong, S.H. Lee, H.Y. Lee, T. Hong, D.-E. Lee, H.S. Park. Multi-objective green design model for prestressed concrete slabs in long-span buildings. Architectural Engineering and Design Management, 19 (5) (2022), pp. 531-549, 10.1080/17452007.2022.2147897

[9] C.A. Coello Coello. Evolutionary multi-objective optimization: Some current research trends and topics that remain to be explored. Frontiers of Computer Science in China, 3 (1) (2009), pp. 18-30, 10.1007/s11704-009-0005-7

[10] K. Deb, H. Jain. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18 (4) (2014), pp. 577-601, 10.1109/TEVC.2013.2281535

[11] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (2) (2002), pp. 182-197, 10.1109/4235.996017

[12] A. Diener, P. Mudu.How can vegetation protect us from air pollution? A critical review on green spaces' mitigation abilities for air-borne particles from a public health perspective-with implications for urban planning. Science of the Total Environment, 796 (2021), Article 148605, 10.1016/j.scitotenv.2021.148605

[13] D.T. Do, Y. Cheng, A. Shojai, Y. Chen. Public park behaviour in Da Nang: An investigation into how open space is used. Frontiers of Architectural Research, 8 (4) (2019), pp. 454-470, 10.1016/j.foar.2019.05.006

[14] T. Du, M. Turrin, S. Jansen, A. van den Dobbelsteen, J. Fang.Gaps and requirements for automatic generation of space layouts with optimised energy performance. Automation in Construction, 116 (2020), Article 103132, 10.1016/j.autcon.2020.103132

[15] S. Gai, J. Fu, X. Rong, L. Dai.Users' views on cultural ecosystem services of urban parks: An importance-performance analysis of a case in Beijing, China. Anthropocene, 37 (2022), Article 100323, 10.1016/j.ancene.2022.100323

[16] P. Gao, H. Wang, S.A. Cushman, C. Cheng, C. Song, S. Ye. Sustainable land-use optimization using NSGA-II: Theoretical and experimental comparisons of improved algorithms. Landscape Ecology, 36 (7) (2021), pp. 1877-1892, 10.1007/s10980-020-01051-3

[17] R.K. Gould, P.W. Schultz. Challenges to understanding nonmaterial dimensions of human-nature connections, and how to address them. Ecology and Society, 26 (3) (2021), p. 14, 10.5751/ES-12604-260314

[18] V. Harris, D. Kendal, A.K. Hahs, C.G. Threlfall. Green space context and vegetation complexity shape people's preferences for urban public parks and residential gardens. Landscape Research, 43 (1) (2018), pp. 150-162, 10.1080/01426397.2017.1302571

[19] O.A. Hosny, E.M. Dorra, K.A. Tarabieh, K.A. Nassar, S. Zahran, M. Amer, A. Ibrahim. Development of an automated optimizer for sustainable urban landscape design. Automation in Construction, 94 (2018), pp. 93-103, 10.1016/j.autcon.2018.05.031

[20] H. Ishibuchi, R. Imada, Y. Setoguchi, Y. Nojima.Performance comparison of NSGA-II and NSGA-III on various many-objective test problems. Institute of Electrical and Electronics Engineers (IEEE) (Ed.), 2016 IEEE congress on evolutionary computation (CEC), IEEE, Vancouver (2016), 10.1109/CEC.2016.7744174

[21] H. Jain, K. Deb. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18 (4) (2014), pp. 602-622, 10.1109/TEVC.2013.2281534

[22] J.Y. Jeon, H.I. Jo.Effects of audio-visual interactions on soundscape and landscape perception and their influence on satisfaction with the urban environment. Building and Environment, 169 (2020), Article 106544, 10.1016/j.buildenv.2019.106544

[23] G. Kim, A. Kim, Y. Kim. A new 3D space syntax metric based on 3D isovist capture in urban space using remote sensing technology. Computers, Environment and Urban Systems, 74 (2019), pp. 74-87, 10.1016/j.compenvurbsys.2018.11.009

[24] R. Koenig, Y. Miao, A. Aichinger, K. Knecht, K. Konieva. Integrating urban analysis, generative design, and evolutionary optimization for solving urban design problems. Environment and Planning B: Urban Analytics and City Science, 47 (6) (2020), pp. 997-1013, 10.1177/2399808319894986

[25] S. Koma, Y. Yamabe, A. Tani. Research on urban landscape design using the interactive genetic algorithm and 3D images. Visualization in Engineering, 5 (1) (2017), p. 1, 10.1186/s40327-016-0039-5

[26] M.J. Koohsari, H. Badland, S. Mavoa, K. Villanueva, J. Francis, P. Hooper, … B. Giles-Corti. Are public open space attributes associated with walking and depression?. Cities, 74 (2018), pp. 119-125, 10.1016/j.cities.2017.11.011

[27] J. Krukar, C. Manivannan, M. Bhatt, C. Schultz. Embodied 3D isovists: A method to model the visual perception of space. Environment and Planning B: Urban Analytics and City Science, 48 (8) (2021), pp. 2307-2325, 10.1177/2399808320974533

[28] M. Liu, S. Nijhuis. The application of advanced mapping methods and tools for spatial-visual analysis in landscape design practice. Sustainability, 13 (14) (2021), p. 7952, 10.3390/su13147952

[29] R.A. Mangkuto, B. Paramita. Computation of the greenery-sky-view factor in daylit buildings. Architectural Engineering and Design Management, 18 (6) (2022), pp. 850-869, 10.1080/17452007.2022.2070123

[30] M. Manni, A. Nicolini. Multi-objective optimization models to design a responsive built environment: A synthetic review. Energies, 15 (2) (2022), p. 486, 10.3390/en15020486

[31] E. Noorzai, P. Bakmohammadi, M.A. Garmaroudi. Optimizing daylight, energy and occupant comfort performance of classrooms with photovoltaic integrated vertical shading devices. Architectural Engineering and Design Management, 19 (4) (2022), pp. 394-418, 10.1080/17452007.2022.2080173

[32] A.E. Oke, J. Aliu, S.A. Onajite. Barriers to the adoption of digital technologies for sustainable construction in a developing economy. Architectural Engineering and Design Management, 20 (3) (2023), pp. 431-447, 10.1080/17452007.2023.2187754

[33] A. Panwar, K.K. Tripathi, K.N. Jha. A qualitative framework for selection of optimization algorithm for multi-objective trade-off problem in construction projects. Engineering Construction and Architectural Management, 26 (9) (2019), pp. 1924-1945, 10.1108/ECAM-06-2018-0246

[34] C.Y. Park, E.J. Yoon, D.K. Lee, J.H. Thorne.Integrating four radiant heat load mitigation strategies is an efficient intervention to improve human health in urban environments. Science of the Total Environment, 698 (2020), Article 134259, 10.1016/j.scitotenv.2019.134259

[35] T. Qi, G. Zhang, Y. Wang, C. Liu, X. Li. Research on landscape quality of country parks in Beijing as based on visual and audible senses. Urban Forestry & Urban Greening, 26 (2017), pp. 124-138, 10.1016/j.ufug.2016.12.007

[36] H. Rahimbakhsh, M.E. Kohansal, A. Tarkashvand, M. Faizi, M. Rahbar.Multi-objective optimization of natural surveillance and privacy in early design stages utilizing NSGA-II. Automation in Construction, 143 (2022), Article 104547, 10.1016/j.autcon.2022.104547

[37] M.M. Rahman, G. Szabó.Multi-objective urban land use optimization using spatial data: A systematic review. Sustainable Cities and Society, 74 (2021), Article 103214, 10.1016/j.scs.2021.103214

[38] S. Rana. Isovist analyst: An Arcview extension for planning visual surveillance (2006) Retrieved from http://eprints.ucl.ac.uk/2104

[39] H. Seada, K. Deb.Effect of selection operator on NSGA-III in single, multi, and many-objective optimization. Institute of Electrical and Electronics Engineers (IEEE) (Ed.), 2015 IEEE congress on evolutionary computation (CEC), IEEE, Sendai (2015), pp. 2915-2922, 10.1109/CEC.2015.7257251

[40] S. Sharma, V. Kumar. A comprehensive review on multi-objective optimization techniques: Past, present and future. Archives of Computational Methods in Engineering, 29 (7) (2022), pp. 5605-5633, 10.1007/s11831-022-09778-9

[41] R. Snowden, P. Thompson, T. Troscianko. Basic vision: An introduction to visual perception (2012) Retrieved from https://books.google.com/books?id=SLDLw4gNdyYC

[42] M. Song, D. Chen. A comparison of three heuristic optimization algorithms for solving the multi-objective land allocation (MOLA) problem. Annals of GIS, 24 (1) (2018), pp. 19-31, 10.1080/19475683.2018.1424736

[43] D. Sun, Q. Li, W. Gao, G. Huang, N. Tang, M. Lyu, Y. Yu. On the relation between visual quality and landscape characteristics: A case study application to the waterfront linear parks in Shenyang, China. Environmental Research Communications, 3 (11) (2021), Article 115013, 10.1088/2515-7620/ac34c7

[44] Y. Sun, T. Dogan. Generative methods for urban design and rapid solution space exploration. Environment and Planning B: Urban Analytics and City Science, 50 (6) (2022), pp. 1577-1590, 10.1177/23998083221142191

[45] S. Verma, M. Pant, V. Snasel. A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems. IEEE Access, 9 (2021), pp. 57757-57791, 10.1109/ACCESS.2021.3070634

[46] J. Wang, J. Liu, H. Wang, C. Mei. Approaches to multi-objective optimization and assessment of green infrastructure and their multi-functional effectiveness: A review. Water, 12 (10) (2020), p. 2714, 10.3390/w12102714

[47] Y. Wu, Q. Zhan, S.J. Quan, Y. Fan, Y. Yang.A surrogate-assisted optimization framework for microclimate-sensitive urban design practice. Building and Environment, 195 (2021), Article 107661, 10.1016/j.buildenv.2021.107661

[48] L. Xiang, M. Cai, C. Ren, E. Ng.Modeling pedestrian emotion in high-density cities using visual exposure and machine learning: Tracking real-time physiology and psychology in Hong Kong. Building and Environment, 205 (2021), Article 108273, 10.1016/j.buildenv.2021.108273

[49] B. Yang, S. Li, C. Binder. A research frontier in landscape architecture: Landscape performance and assessment of social benefits. Landscape Research, 41 (3) (2016), pp. 314-329, 10.1080/01426397.2015.1077944

[50] P.P.J. Yang, S. Chang, N. Saha, H.W. Chen. Data-driven planning support system for a campus design. Environment and Planning B: Urban Analytics and City Science, 47 (8) (2020), pp. 1474-1489, 10.1177/2399808320910164

[51] E.J. Yoon, B. Kim, D.K. Lee. Multi-objective planning model for urban greening based on optimization algorithms. Urban Forestry & Urban Greening, 40 (2019), pp. 183-194, 10.1016/j.ufug.2019.01.004

[52] R. Zhang. Integrating ergonomics data and emotional scale to analyze people's emotional attachment to different landscape features in the Wudaokou Urban Park. Frontiers of Architectural Research, 12 (1) (2023), pp. 175-187, 10.1016/j.foar.2022.06.007

[53] W. Zhong, T. Schröder, J. Bekkering. Biophilic design in architecture and its contributions to health, well-being, and sustainability: A critical review. Frontiers of Architectural Research, 11 (1) (2022), pp. 114-141, 10.1016/j.foar.2021.07.006