Virtual geographic environments in socio-environmental modeling: a fancy distraction or a key to communication?

Modeling and simulation are recognized as effective tools for management and decision support across various disciplines; however, poor communication of results to the end users is a major obstacle for properly using and understanding model output. Visualizations can play an essential role in making modeling results accessible for management and decision-making. Virtual reality (VR) and virtual geographic environments (VGEs) are popular and potentially very rewarding ways to visualize socio-environmental models. However, there is a fundamental conflict between abstraction and realism: models are goal-driven, and created to simplify reality and to focus on certain crucial aspects of the system; VR, in the meanwhile, by definition, attempts to replicate reality as closely as possible. This elevated realism may add to the complexity curse in modeling, and the message might be diluted by too many (background) details. This is also connected to information overload and cognitive load. Moreover, modeling is always associated with the treatment of uncertainty–something difficult to present in VR. In this paper, we examine the use of VR and, specifically, VGEs in socio-environmental modeling, and discuss how VGEs and simulation modeling can be married in a mutually beneficial way that makes VGEs more effective for users, while enhancing simulation models

Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities

Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent‐based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual‐level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state‐of‐the‐art, and the outlook for the field over the next decade. We argue that although agent‐based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems

Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective

Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic perspective, certain challenges remain, including the modelling of geographic-featured environments with GIS data model, the enhancement of GIS’s analysis functions for comprehensive geographic analysis and achieving human-oriented geographic information presentation. Several basic theoretical and technical ideas that follow the workflow and processes of geographic information induction, geographic scenario modelling, geographic process analysis and geographic environment representation are proposed to fill the gaps between GIS and geography. We also call for designing methods for big geographic data-oriented analysis, making best use of videos and developing virtual geographic scenario-based GIS for further evolution

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit

A computational sandbox with human automata for exploring perceived egress safety in urban damage scenarios

In earthquakes and building collapse situations, volumes of people may need to move, suddenly, through spaces that have been destroyed and seem unfamiliar in configuration and appearance. Perception is significant in these cases, determining individual movement, collective egress, and phenomena in between. Alas, exploring how perception shapes critical egress is tricky because perception is both physical and cerebral in genesis and because critical scenarios are often hazardous. In this paper, we describe a computational sandbox for studying urban damage scenarios. The model is built as automata, specialized as human automata and rigid body automata, with interactivity provided by slipstreaming. Our sandbox supports parameterization of synthetic built settings and synthetic humans in fine detail for large interactive collections, allowing flexible analyses of damage scenarios and their determining processes, from micro-perspectives through to the macrocosm of the phenomena that might result. While we have much work to do to improve the model relative to real-world fidelity, our work thus far has produced some meaningful results, supporting practical questions of how urban design and parking scenarios shape egress, and pointing to potential phenomena of perceptual shadowing as a translation mechanism for processes at the built-human interface

WSN based sensing model for smart crowd movement with identification: a conceptual model

With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way