Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Practical guidelines for modelling post-entry spread in invasion ecology

In this article we review a variety of methods to enable understanding and modelling the spread of a pest or pathogen post-entry. Building upon our experience of multidisciplinary research in this area, we propose practical guidelines and a framework for model development, to help with the application of mathematical modelling in the field of invasion ecology for post-entry spread. We evaluate the pros and cons of a range of methods, including references to examples of the methods in practice. We also show how issues of data deficiency and uncertainty can be addressed. The aim is to provide guidance to the reader on the most suitable elements to include in a model of post-entry dispersal in a risk assessment, under differing circumstances. We identify both the strengths and weaknesses of different methods and their application as part of a holistic, multidisciplinary approach to biosecurity research

Potential applications of geospatial information systems for planning and managing aged care services in Australia

[Abstract]: This paper discusses the potential applications of Geospatial Information Technology (GITs) to assist in planning and managing aged care programs in Australia. Aged care is complex due to the numbers of participants at all levels of including planning of services, investing in capacity, funding, providing services, auditing, monitoring quality, and in accessing and using facilities and services. There is a vast array of data spread across the entities that are joined to aged care. The decision-making process for investment in capacity and service provision might be aided by technology including GIT. This is also expected to assist in managing and analysing the vast amount of demographic, geographic, socio-economic and behavioral data that might indicate current and future demand for services the aged and frail-aged population. Mapping spatio-temporal changes in near real time can assist in the successful planning and management of aged care programs. Accurate information on the location of aged care services centres and mapping the special needs of clients and their service needs may assist in monitoring access to services and assist in identifying areas where there are logistic challenges for accessing services to meet needs. GIT can also identifying migrations of aged people and of the cohorts of the population who are likely to be the next wave of clients for aged care services. GITs include remote sensing, geographic information systems (GIS) and global positioning systems (GPS) technologies, which can be used to develop a user friendly digital system for monitoring, evaluating and planning aged care and community care in Australia. Whilst remote sensing data can provide current spatiotemporal inventory of features such as locations of carer services, infrastructure, on a consistent and continuous coordinate system, a GIS can assist in storing, cross analysing, modeling and mapping of spatial data pertaining to the needs of the older people. GITs can assist in the development of a single one-stop digital database which will prove a better model for managing aged care in Australia. GIT will also be a component of technologies such as activity monitors to provide tracking functionality. This will assist in tracking dementia sufferers who may be prone to wandering and be exposed to risk

Stochastic urban pluvial flood hazard maps based upon a spatial-temporal rainfall generator

It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially—and temporally—variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km2) located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions

A Spatial Agent-based Model for Volcanic Evacuation of Mt. Merapi

Natural disasters, especially volcanic eruptions, are hazardous events that frequently happen in Indonesia. As a country within the “Ring of Fire”, Indonesia has hundreds of volcanoes and Mount Merapi is the most active. Historical studies of this volcano have revealed that there is potential for a major eruption in the future. Therefore, long-term disaster management is needed. To support the disaster management, physical and socially-based research has been carried out, but there is still a gap in the development of evacuation models. This modelling is necessary to evaluate the possibility of unexpected problems in the evacuation process since the hazard occurrences and the population behaviour are uncertain. The aim of this research was to develop an agent-based model (ABM) of volcanic evacuation to improve the effectiveness of evacuation management in Merapi. Besides the potential use of the results locally in Merapi, the development process of this evacuation model contributes by advancing the knowledge of ABM development for large-scale evacuation simulation in other contexts. Its novelty lies in (1) integrating a hazard model derived from historical records of the spatial impact of eruptions, (2) formulating and validating an individual evacuation decision model in ABM based on various interrelated factors revealed from literature reviews and surveys that enable the modelling of reluctant people, (3) formulating the integration of multi-criteria evaluation (MCE) in ABM to model a spatio-temporal dynamic model of risk (STDMR) that enables representation of the changing of risk as a consequence of changing hazard level, hazard extent and movement of people, and (4) formulating an evacuation staging method based on MCE using geographic and demographic criteria. The volcanic evacuation model represents the relationships between physical and human agents, consisting of the volcano, stakeholders, the population at risk and the environment. The experimentation of several evacuation scenarios in Merapi using the developed ABM of evacuation shows that simultaneous strategy is superior in reducing the risk, but the staged scenario is the most effective in minimising the potential of road traffic problems during evacuation events in Merapi. Staged evacuation can be a good option when there is enough time to evacuate. However, if the evacuation time is limited, the simultaneous strategy is better to be implemented. Appropriate traffic management should be prepared to avoid traffic problems when the second option is chosen

An Approach to Developing a Spatio-Temporal Composite Measure of Climate Change-Related Human Health Impacts in Urban Environments

Introduction: Rapid population growth along with an increase in the frequency and intensity of climate change-related impacts in costal urban environments emphasize the need for the development of new tools to help disaster planners and policy makers select and prioritize mitigation and adaptation measures. Using the concept of the resilience of a community, which is a measure of how rapidly the community can recover to its previous level of functionality following a disruptive event is still a relatively new concept for many engineers, planners and policy makers, but is becoming recognized as an increasingly important and some would argue, essential component for the development and subsequent assessment of adaptation plans being considered for communities at risk of climate change-related events. The holistic approach which is the cornerstone of resilience is designed to integrate physical, economic, health, social and organizational impacts of climate change in urban environments. This research presents a methodology for the development of a quantitative spatial and temporal composite measure for assessing climate change-related health impacts in urban environments. Methods: The proposed method is capable of considering spatial and temporal data from multiple inputs, relating to both physical and social parameters. This approach uses inputs such as the total population density and densities of various demographics, burden of diseases conditions, flood inundation mapping, and land use change for both historical and current conditions. The research has demonstrated that the methodology presented generates sufficiently accurate information to be useful for planning adaptive strategies. To assemble all inputs into a single measure of health impacts, a weighting system was assigned to apply various priorities to the spatio-temporal data sources. Weights may be varied to assess how they impact the final results. Finally, using spatio-temporal extrapolation methods the future behavior of the same key spatial variables can be projected. Although this method was developed for application to any coastal mega-city, this thesis demonstrates the results obtained for Metro Vancouver, British Columbia, Canada. The data was collected for the years 1981, 1986, 1991, 1996, 2001, 2006 and 2011, as information was readily available for these years. Fine resolution spatial data for these years was used in order to give a dynamic simulation of possible health impacts for future projections. Linear and auto-regressive spatio-temporal extrapolations were used for projecting a 2050’s Metro Vancouver health impact map (HIM). Conclusion: Results of this work show that the approach provides a more fully integrated view of the resilience of the city which incorporates aspects of population health. The approach would be useful in the development of more targeted adaptation and risk reduction strategies at a local level. In addition, this methodology can be used to generate inputs for further resilience simulations. The overall value of this approach is that it allows for a more integrated assessment of the city vulnerability and could lead to more effective adaptive strategies

A Methodology for Assessing Dynamic Resilience of Coastal Cities to Climate Change Influenced Hydrometeorological Disasters

Confronted with rapid urbanization, intensified tourism, population densification, increased migration, and climate change impacts, coastal cities are facing more challenges now than ever before. Traditional disaster management approaches are no longer sufficient to address the increased pressures facing urban areas. A paradigm shift from disaster risk reduction to disaster resilience building strategies is required to provide holistic, integrated, and sustainable disaster management looking forward. To address some of the shortcomings in current disaster resilience assessment research, a mathematical and computational framework was developed to help quantify, compare, and visualize dynamic disaster resilience. The proposed methodological framework for disaster resilience combines physical, economic, engineering, health, and social spatio-temporal impacts and capacities of urban systems in order to provide a more holistic representation of disaster resilience. To capture the dynamic spatio-temporal characteristics of resilience and gauge the effectiveness of potential climate change adaptation options, a disaster resilience simulator tool (DRST) was developed to employ the mathematical framework. The DRST is applied to a case study in Metro Vancouver, British Columbia, Canada. The simulation model focuses on the impacts of climate change-influenced riverine flooding and sea level rise for three future climates based on the results of the CGCM3 global climate model and two (2) future emissions scenarios. The output of the analyses includes a dynamic set of resilience maps and graphs to demonstrate changes in disaster resilience in both space and time. The DRST demonstrates the value of a quantitative resilience assessment approach to disaster management. Simulation results suggest that various adaptation options such as access to emergency funding, provision of mobile hospital services, and managed retreat can all help to increase disaster resilience. Results also suggest that, at a regional scale, Metro Vancouver is relatively resilient to climate change influenced-hydrometeorological hazards, however it is not distributed proportionately across the region. Although a pioneering effort by nature, the methodological and computational framework behind the DRST could ultimately provide decision support to disaster management professionals, policy makers, and urban planners