Exploratory GIS: Modelling Past Land Use and Occupancy with Functional Connectivity, Willandra Lakes Region World Heritage Area, NSW, Australia

Exploratory GIS models present multiple different conceptual versions of space. This article focusses on the landscape level pathways between areas defined as suitable for land use and occupancy within the Willandra Lakes Region World Heritage Area (WLRWHA), New South Wales (NSW), Australia. Models of the potential connections between ecologically significant land use patches and key hydrology provide iterative networks of functional connectivity, highlighting salient pathways of past land use and occupancy of Country. The shape of the connections between places is important to understanding Country from the inside. Outputs from these network models are a powerful visualisation tool because they display areas where contact with the 19thc Europeans, particularly through fence construction and ground water appropriation, caused greater levels of exploitation and damage than currently recognised. Concomitantly, the benefit of situating these network techniques within an exploratory framework cannot be understated. The iterative nature of the exploratory design allows for multiple presentations of the connectivity between the spaces within the WLRWHA and therefore multiple ways of knowing and seeing space. Modelling the potential pathways between suitable patches opens the door to discussions about the diverse possible corridors of activity within pre-European settlement of Country and the corollary discussion of how European settlement substantially impacted upon these connections and continues to impact on a living Country

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

On the relation between body and movement space representation: an experimental investigation on spinal cord injured people

Body Representation (BR) and Movement Space Perception (MSP) are fundamental for human beings in order to move in space and interact with object s and other people. Both BR and space representation change after spinal cord injuries in complete paraplegic individuals (CPP), who suffer from lower limbs paralysis and anesthesia. To date, the interaction between BR and MSP in paraplegic individuals rem ains unexplored. In two consecutive experiments, we tested I ) if the individual\u2019s wheelchair is embodied in BR; and ii) if the embodied wheelchair modifies the MSP. For the first question a speeded detection task was used. Participants had to respond to v isual stimuli flashing on their trunk, legs or wheelchair. In three counterbalanced conditions across participant, they took part to the experiment while: 1) sitting in their wheelchair, 2) in another wheelchair, or 3) with the LEDs on a wooden bar. To in dicate the embodiment, there was no difference in the CPP\u2019s responses for LEDs on the body and personal wheelchair while these were slower in other conditions After this, while sitting in their or another wheelchair, CPPs were asked to judge the slope of a ramp rendered in immersive virtual reality and to estimate the distance of a flag positioned over the ramp. When on their own wheelchair, CPPs perceived the flag closer than in the other wheelchair. These results indicate that the continuous use of a too l induces embodiment and that this i mpact on the perception of MSP

The relationship between wayfinding performance, spatial layout and landmarks in virtual environments

Environmental factors, including landmarks that affect people’s wayfinding performance in unfamiliar environments have been discussed in a great number of studies. However, there is still no consensus on the factors that shape people’s performance or what makes a landmark preferable during wayfinding. Hence, this study aims to understand the impact of different spatial layouts, environmental conditions and landmarks on people’s wayfinding performance, and the factors that make landmarks salient. Sea Hero Quest (SHQ), an online game that has been played by more than 4.3 million people from 2016 to date, is selected as a case study to investigate the impact of different environments and other factors, in particular landmarks. Forty-five wayfinding levels of SHQ are analysed and compared using Geographic Information System (GIS) and Space syntax axial, segment and visibility graph analyses. A cluster analysis is conducted to examine the relationship between levels. Varying conditions associated with landmarks, weather and maps were taken into consideration. In order to investigate the process of selecting landmarks, visual, structural (whether landmarks are global or local) and cognitive saliency are analysed using web-based surveys, saliency algorithms and the visibility of landmarks. Results of this study show that the complexity of layouts plays a major role in wayfinding; as the complexity of layout increases, so does the time taken to complete the wayfinding task. Similarly, the weather condition has an effect; as the weather becomes foggy and visibility decreases, the time taken to complete the wayfinding task increases. It is discovered that landmarks that are visible for more than 25% of a journey can be defined as global landmarks whereas the rest can be defined as local landmarks. Findings also show that landmarks that are visually salient (objects with a unique colour and size) and structurally salient (objects that are closer to people) are registered more by people in unfamiliar environments. This study contributes to the existing literature by exploring the factors that affect people’s wayfinding performance by using the largest dataset in the field (so providing more accurate results), focusing on 45 different layouts (while current research studies mostly focus on one or two different layouts), by proposing a threshold to distinguish global and local landmarks, and analysing visual, structural and cognitive saliency through various measures