About
11
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Introduction
I am committed to contributing to knowledge of cognitive psychology, that investigates the processes, mechanisms, and strategies of human wayfinding in architecture and urban areas. The most important question of my research program is how the physical and social environments affect human mental representation, wayfinding behavior, and emotional appraisal of the public built environments. My research program strives to address this vexing issue in a two-pronged approach combining both basic and applied research. Of theoretical interest, one of the most important questions is how people acquire spatial and environmental knowledge to determine where they are and how to find their way. Related to application, the overall goal of my research is to provide evidence-based design intervention.
Additional affiliations
September 2010 - May 2016
July 2016 - June 2018
Future Cities Laboratory (FCL)
Position
- PhD Student
Education
September 2010 - May 2016
September 2003 - May 2006
September 1999 - May 2003
Publications
Publications (11)
The present study investigated cognitive map development in multi-level built environments. Three experiments were conducted in complex virtual buildings to examine the effects of five between-floor structural factors that may impede the accuracy of humans’ ability to build multi-level cognitive maps. Results from Experiments 1 and 2 revealed that...
There is growing interest in improving indoor navigation using 3D spatial visualizations rendered on mobile devices. However, the level of information conveyed by these visualization interfaces in order to best support indoor spatial learning has been poorly studied. This experiment investigates how learning of multi-level virtual buildings assiste...
Immersive virtual reality (VR) technology has become a popular method for fundamental and applied spatial cognition research. One challenge researchers face is emulating walking in a large-scale virtual space although the user is in fact in a small physical space. To address this, a variety of movement interfaces in VR have been proposed, from trad...
People often become disoriented and frustrated when navigating complex, multi-level buildings. We argue that the principle reason underlying these challenges is insufficient access to the requisite information needed for developing an accurate mental representation, called a multi-level cognitive map. We postulate that increasing access to global l...
People often become disoriented when navigating in complex, multi-level buildings. To efficiently find destinations located on different floors, navigators must refer to a globally coherent mental representation of the multi-level environment, which is termed a multi-level cognitive map. However, there is a surprising dearth of research into underl...
The goal of this study was to investigate how the immersion level of virtual environments (HMD vs. desktop) and rotation method (physical vs. imagined) affects wayfinding performance in multi-story virtual buildings and the development of multi-level cognitive maps. Twelve participants learned multi-level virtual buildings using three VE conditions...
It is known that people have problems when wayfinding in multi-level buildings. We propose that this challenge is largely due to development of inaccurate multi-level cognitive maps of the 3D building structure. We argue that better visualization of the layered structure of the building could facilitate multi-level cognitive map development and sig...
Location sharing in indoor environments is limited by the sparse availability of indoor positioning and lack of geographical building data. Recently, several solutions have begun to implement digital maps for use in indoor space. The map design is often a variant of floor-plan maps. Whereas massive databases and GIS exist for outdoor use, the major...
Several studies have verified that multi-level floors are an obstacle for indoor wayfinding (e.g., navigators show greater angular error when making inter-level pointing judgments and experience more disorientation when way-finding between floors). Previous literature has also suggested that a multi-level cognitive map could be a set of vertically...
Questions
Question (1)
Hello, I am using Bayesian statistics (R and brms) to model housing sales data, but I am not sure how to model time-series discrete inventory data. Could you pls give me some feedback? Thanks in advance.
The data frame contains:
1. project_name: string, a real estate development project
2. sales: integer, the number of units sold for the project (e.g., 0, 1, 2, ....20, ,,,,)
3. sale_month: date, e.g., 2019-12
4. remaining_units: integer, the number of remaining units of the project
Usually sales date are treated as binoimal or possion distributed. Notably, I may use zero_inflated_possion or zero_inflated_negbinomial, as there are quite a few zeros (no sales for certain month). For simplicity, let's say the data is possion or binomial distributed.
In brms, we can easily set the trial number (remaining_units), such as
b1 <-
brm(data = dt, family = binomial,
sales | trials(remaining_units) ~ 1 + (1 | project_name),
prior = c(prior(normal(0, 1), class = Intercept),
prior(cauchy(0, 1), class = sd)),
iter = 10000, warmup = 1000)
However, this model has a few issues:
1. it does not consider the autocorrelation of sales. brm has an argument of autocor to set the p, and q for autocorrelation, but I am not sure how to correctly set p, and q number. is there a method like auto.arima in brms. The trick part is that different projects start and end at different months, so how to deal with time-series data with different projects having different start and end date.
2. Remaining units monotonically desreases. I am not sure whether the remaining_units column is independent and whether this affect the binomial model. For example, next month remaining units = this month remaining units - sales.
Thanks,
Hengshan
