«Fung Ki LAM A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Department of Architecture ...»
Simulating the Effect of Microclimate on Human Behavior in
Small Urban Spaces
Fung Ki LAM
A dissertation submitted in partial satisfaction of the
requirements for the degree of
Doctor of Philosophy
Department of Architecture
University of California, Berkeley
Committee in charge:
Prof. Yehuda Kalay, Chair
Prof. Ed Arens
Prof. Peter Bosselmann
Simulating the Effect of Microclimate on Human Behavior in Small Urban Spaces, by Fung Ki Lam Doctor of Philosophy in Architecture University of California, Berkeley Prof. Yehuda Kalay, Chair This dissertation describes a behavior simulation model which could reveal spatial usage pattern of small urban places in association with the tangible physical built environment and the intangible microclimatic conditions. The simulation model extends the existing environmental assessments which do not include human interactions and human behavior simulations which do not take into account microclimatic conditions. It addresses the problem of predicting and evaluating the impacts of microclimatic conditions on the spatial usage of its human inhabitants, which has been studied, but not successfully implemented in the past.
The concept is to carry out a virtual post occupancy evaluation which virtual users would exhibit their spatial usage pattern according to the environmental conditions and physical environment so experienced. Evaluators then could see how virtual users perform under certain conditions and assess if the design intention is achieved.
The simulation model is developed using Wei Yan’s (Yan, 2005) urban simulation model as a base. In addition Yan’s usability-based building model and agent-based behavior model, an environmental model is included. It manages environmental data from user input, database and other simulation programs. It records the shading and air speed profile onto the building model to affect individual user behavior, develops outdoor activity comfort maps which virtual users would use to develop activity option maps, and control collective usage pattern in terms of level of usage, activity type distribution and spatial distribution.
To ensure quality results, the environmental model handles only relatively simple computation, like generating outdoor activity comfort maps. It does not handle assessments which require specialized algorithms and processes like air flow simulation and comfort evaluation. Rather, these assessments are handled by certified programs outside the system. The environmental model processes the result and distributes the 1 information to the building model and the agent model to control the behavior of the virtual users at both collective and individual levels.
At the collective level, the behavioral pattern is mainly governed by statistics obtained from a year round field study. At individual level, the behavior is based on well studied and defined behavioral rules which are derived from theoretical and practical environment-behavior studies.
The simulation generates both graphical and textual outputs. The two dimensional graphical output offers views of paths of virtual users; the three dimensional output offers shows spatial usage as a whole; and textual output provide information of usage and thermal states.
It is expected the result of this research to change how architects and environmental behavior experts will approach the design and evaluation of built environments.
List of Figures
List of Tables
Chapter 1 Introduction
1.2 Previous methods and a new solution - Virtual Users
1.3.1 Usability-based building model
1.3.2 Agent-based virtual user model
1.3.3 The environmental model
1.4.1 Collecting and quantifying microclimate related behavioral data
1.4.2 Simulating the microclimatic environment and comfort
1.4.3 Establishing the comfort mapping
Chapter 2 Background
2.1 Microclimate, outdoor thermal comfort and human behavioral studies
2.1.1 Qualitative studies
2.1.2 Quantitative studies
2.3 Assessment of outdoor thermal comfort
2.4 Design evaluation of and environment - behavior
2.4.1 General design evaluation methods for environmental behavior
2.4.2 Simulation of environmental behavior
2.5 Computer simulation of environmental behavior
2.5.1 Pedestrian simulation
2.5.2 Crowd and fire egress simulation
2.5.3 Urban microclimate simulation
2.5.4 Urban space simulation
2.6 Influences of the studies on the research
2.6.1 Criteria employed – independent variables
2.6.2 Criteria employed – dependent variables
2.6.3 Criteria employed - behavior
2.6.4 Simulation approach
Chapter 3 Urban Spatial Behavior
3.1 Urban setting – the Sproul Plaza
3.1.1 The physical environment
3.1.2 The microclimatic environment
3.2 Field study
3.2.2 Video recording
3.2.3 Surveyed dates
3.3 The usage pattern
3.3.1 Number of users and time of usage
3.3.3 Distribution of users
3.3.4 Duration of usage
3.4 Effects of microclimate
3.4.1 Influence of individual microclimatic factor
18.104.22.168 Influence of individual microclimatic factor from questionnaire
22.214.171.124 Influence of individual microclimatic factor from video record
3.4.2 Collective effects of microclimatic factors
3.5 Prediction of spatial behavior
Chapter 4 The Simulation model Structure
4.1 The usability-based building model
4.1.1 Geometrical model
4.1.2 Usability-based model
4.2 The agent-based virtual user model
4.2.1 Geometrical modeling and motion control
4.2.2 Perception modeling
4.2.3 Behavioral modeling
4.3 The environmental model
4.3.1 The structure
4.3.2 The intermediate information
4.3.3 The terminal information
4.4 The life of an agent
Chapter 5 Simulation and Visualization
5.1 The simulation process
Chapter 6 Conclusion
6.2 Potential applications
6.2.1 Design evaluation
6.2.2 Application domain
6.3 Contribution to architectural design practice and education
6.4 Future research directions
Appendix: Sample survey
Figure 1.1 Bamboo garden at 560 Mission Street, San Francisco, an enjoyable privately owned public space after the 1985 Downtown Plan
Figure 1.2 Modeling components of the simulation.
The Environmental Model column is the major additional components to Yan's model
Figure 1.3 The generation of comfort mappings (partial)
Figure 1.4 Survey dates on the sun path coverage.
Shaded area covers the academic months and the lines represents the survey dates.Error! Bookmark not defined.
Figure 2.1 Overshadowing image from the center of the Plaza
Figure 2.2 Left: Centrifugal effect.
Adjacent tenants are isolated by the stair locations.
Right: Centripetal effect. Adjacent tenants are brought into contact by the stair locations.
Figure 3.1 The upper Sproul Plaza in the morning
Figure 3.2 Berkeley temperature.
Figure 3.3 Berkeley precipitation.
Figure 3.4 Berkeley relative humidity.
Figure 3.5 Overshadowing at Jun 21 (Summer Solstice)
Figure 3.6 Overshadowing at Dec 21 (Winter Solstice)
Figure 3.7 Wind rose at SFO, prepared by Pacific Environmental Services, Inc.
Figure 3.8 Wind speed and wind direction at Berkeley. (Source:
Figure 3.10 Sun paths of the survey days (lines) in the academic period (shaded area).
. 40 Figure 3.11 Purpose of usage from questionnaire.
Figure 3.12 Frequency of usage of interviewees.
Figure 3.13 Time of usage from questionnaire
Figure 3.14 Number of users on the surveyed days
Figure 3.15 Number of walkers on the surveyed days
Figure 3.16 Number of sitters on the surveyed days.
Figure 3.17 Number of non-walkers-sitters on the surveyed days.
Figure 3.18 Number of users of different types of activity in the Plaza
Figure 3.19 Distribution of sitters
Figure 3.20 Distribution of standers.
Figure 3.21 Distribution of duration of users, excluding walkers.
Figure 3.22 Duration of usage from questionnaire.
Figure 3.23 Difference between the asked sensation vote and the calculated thermal sensation
Figure 3.24 Previous activities of interviewees before entering the Plaza.
Figure 3.25 Temperature with thermal sensation from questionnaire.
Figure 3.26 Thermal Sensation against wind speed from questionnaire.
Figure 3.27 Thermal Sensation against relative humidity from questionnaire.
................ 51 Figure 3.28 Warmth, sun, shade and wind preferences against temperature
Figure 3.29 Warmth, sun, shade and wind preferences against wind speed
Figure 3.30 Warmth, sun, shade and wind preferences against relative humidity.
Figure 3.32 Number of sitters against the surveyed days’ temperatures.
Figure 3.33 Number of sitters against the normalized temperature
Figure 3.34 Number of sitters against wind speed
Figure 3.35 Number of sitters against relative humidity.
Figure 3.36 Number of sitters against the proportion of exposed area
Figure 3.37 Number of sitters against normalized sitting comfort.
Figure 3.38 Probability density function.
Figure 4.1 The modeling structure of the simulation
Figure 4.2 Graphical view of the SVG model displayed in a SVG viewer (Yan 2005).
.. 70 Figure 4.3 Graphical view of the discrete space model - the fountain side, fountain water, benches, steps are all marked with different colors (Yan 2005).................. 71 Figure 4.4 The VRML model of the Sproul Plaza
Figure 4.5 Two-dimensional model of an agent.
Figure 4.6 Three-dimensional model of an agent.
Figure 4.7 Behavioral control hierarchy.
Figure 4.8 Poisson distribution for discrete probability process like arrival rate (Source:
Figure 4.9 Normal distribution for distribution like duration (Source:
http://www.climateaudit.org/wp-content/uploads/2007/01/linsay7.jpg)..... 76 Figure 4.10 Uniform distribution curves for distribution like meeting acquaintance (Source: http://en.wikipedia.org/wiki/Uniform_distribution)
Figure 4.11 Virtual users' social spaces (Yan 2005)
Figure 4.12 User interface
Figure 4.14 Outdoor comfort determination procedure
Figure 4.15 The process of generating thermal comfort map
Figure 4.16 The process of generating wind comfort map.
Figure 4.18 The process of generating wind chill map
Figure 4.19 The process of generating outdoor thermal comfort.
Figure 4.20 The process of generating activity option map
Figure 4.21 Model flow of an agent intended to sit
Figure 5.1 The simulation window.
Figure 5.2 Two dimensional simulation with textual output.
Figure 5.3 Two dimensional display of the path list
Figure 5.4 Photo of field survey show majority of sitters at the fountain were in shade.
94 Figure 5.5 Plaza usage simulation using the same parameters as field record.
................ 94 Figure 5.6 Simulation of the scenario with majority of fountain sitters at shade.
............ 95 Figure 5.7 Field record showing limited people using the Plaza
Figure 5.8 Simulation also shows low level of usage
Figure 5.9 Comparison of a low usage scenario in a cool and windy morning.
............... 96 Figure 5.10 Left: simulation with 26.3 °C; right: simulation with a lower temperature,
Table 2.1 Mechanical effects of standard equivalent mean wind speed
Table 2.2 Wind speed ranges for basic outdoor activities, based on 20% probability of exceedance.
Table 3.1 Correlation of various factors with total number of users.
Table 3.2 Correlation of various factors with different types of activities.
Table 3.3 Correlation of various factors with distribution
Table 3.4 Correlation coefficients of usage at the foutain, ASUC, Sproul stairs and the benches
Table 3.5 Correlations of various factors with duration of usage
Table 3.6 R-squared for numbers of walkers at different class peaks.
Table 4.1 Thermal sensation (TSENS) scale