A Study on the Usability of Agent Based Modeling from Space-Structural Perspectives by Comparison of Space Syntax with Pedestrian Simulation


  • Suk Tae Kim
  • . .






Agent based model, Space syntax, Convex map, Pedestrian Simulation, Density


Background/Objectives: It is very difficult to analyze space as a physical object. As an alternative, space syntax is a solution to codify space and analyze the topological relation between each spatial unit. In the architecture field, the study of space syntax has shown considerable progress. However, due to the value-neutral limitation, there has not been any progress toward an empirical study. This drove us to conduct a base study using an agent-based model to overcome some limitations of space syntax.

Methods/Statistical analysis: We prepared 12 sample models, which consist of spatial units with the same size and the same distance between them. We used convex map based space syntax and pedestrian-based discrete event simulation to compare indexes and analyze their relations. For 12 models, 3 characteristics (structural symmetry, depth, and structural form) were cross-applied and each was used to generate 4 alternatives. Based on this, we determined what kind of effects structural characteristics have and the relation between space syntax and the characteristics of each index.

Findings: After 1000 seconds of pedestrian-based discrete event simulation, it is found that global integration and local integration are very highly correlated to ABM density, whereas connectivity and control value have a relatively low relation with it. Therefore, the analysis range of each spatial unit has a high relation with density prediction. From the perspective of the structural symmetry of space, symmetry shows a higher relation than asymmetry. As for depth, a shallow structure is higher than a deep structure. From the perspective of form, a ring structure shows a higher relation with ABM density than a tree structure. This is considered to be related to the intelligibility of space syntax.

Improvements/Applications: It is difficult to come to any conclusion based only on the experiment of applying 12 simplified sample models. However, as long as space syntax shows a clear limitation in its use for empirical analysis of space, the Agent-Based Model, which can be expanded above this limitation has meaning as a base study for empirical research. Therefore, it is necessary to have subsequent studies, which can be expanded in terms of diversifying sample space, adding variables, etc.



[1] Kiwan., L., (2001) Interpretation of crime-prone locations through spatial analysis theory (master's dissertation), Seoul National University, Republic of Korea. Retrieved from http://s-space.snu.ac.kr/handle/10371/32121

[2] Archea J., Visual access and exposure: An architectural basis for interpersonal behavior, the Pennsylvania State University (Doctoral Dissertation), 1984

[3] Benedikt M., “Isovist and Isovist fieldsâ€, Paper presented at the meeting of the Environment design research association, Arizona, 1978

[4] Hillier, B. & Hanson J., The social logic of space, Cambridge University Press, 1984

[5] Space Syntax Network. (2018). Retrieved from http://www.spacesyntax.net

[6] van der Hoeven, F., & van Nes, A. (2014). Improving the design of urban underground space in metro stations using the space syntax methodology. Tunnelling and Underground Space Technology, 40, 64-74.

[7] Bafna, S. (2003). Space syntax: A brief introduction to its logic and analytical techniques. Environment and Behavior, 35(1), 17-29.

[8] Space Syntax Network. (2018). Retrieved from http://www.spacesyntax.net

[9] al-Sayed, K., Turner, A., Hillier, B., Iida, S., & Penn, A. (2014). Space syntax methodology. UCL (draft version). Retrieved from http://discovery.ucl.ac.uk/1415080/1/spacesyntax-ulltextbook_higherre.pdf

[10] Blumer, H, Symbolic interactionism: Perspective and method, Englewood Cliffs, NJ: Prentice-Hall

[11] Antonini, G., Bierlaire, M., & Weber, M. (2006). Discrete choice models of pedestrian walking behavior. Transportation Research Part B: Methodological, 40(8), 667-687.

[12] Davidsson, P. (2002). Agent based social simulation: A computer science view. Journal of Artificial Societies and Social Simulation, 5(1).

[13] Borshchev, A. & Filippov, A. (2004, July). From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In: Proceedings of the 22nd international conference of the system dynamics society (Vol. 22). System Dynamics Society Oxford.

[14] Choi, J., Choi, H., Cho Y., Cho H., & Kim M.. A study development of spatial analysis computer application.Journal of the Architectural Institute of Korea (Planning & Design),9(1), 15-27. retrieved from https://scholar.google.co.kr/scholar?output=instlink&q=info:pameuvhckj:scholar.google.com/&hl=ko&as_sdt=2005&sciodt=0,5&scillfp=4971892306286328605&oi=lle

[15] Lab. for architecture & urban space. Retrieved from http://laus.snu.ac.kr/_pages/s3.php

[16] Teknomo, K. (2006). Application of microscopic pedestrian simulation model. Transportation Research Part F: Traffic Psychology and Behaviour, 9(1), 15-27.

[17] AnyLogic official homepage. Retrieved from http://www.anylogic.com

View Full Article:

How to Cite

Tae Kim, S., & ., . (2018). A Study on the Usability of Agent Based Modeling from Space-Structural Perspectives by Comparison of Space Syntax with Pedestrian Simulation. International Journal of Engineering & Technology, 7(2.33), 1255–1260. https://doi.org/10.14419/ijet.v7i2.33.18113
Received 2018-08-23
Accepted 2018-08-23
Published 2018-06-08