Background: Disease data sharing is important for the collaborative preparation, response, and recovery stages of disease control. Disease phenomena are strongly associated with spatial and temporal factors. Web-based Geographical Information Systems provide a real-time and dynamic way to represent disease information on maps. However, data heterogeneities, integration, interoperability, and cartographical representation are still major challenges in the health geographic fields. These challenges cause barriers in extensively sharing health data and restrain the effectiveness in understanding and responding to disease outbreaks. To overcome these challenges in disease data mapping and sharing, the senior authors have designed an interoperable service oriented architecture based on Open Geospatial Consortium specifications to share the spatiotemporal disease information.

Background: There is great concern within health surveillance, on how to grapple with environmental degradation, rapid urbanization, population mobility and growth. The Internet has emerged as an efficient way to share health information, enabling users to access and understand data at their fingertips. Increasingly complex problems in the health field require increasingly sophisticated computer software, distributed computing power, and standardized data sharing. To address this need, Web-based mapping is now emerging as an important tool to enable health practitioners, policy makers, and the public to understand spatial health risks, population health trends and vulnerabilities. Today several webbased health applications generate dynamic maps; however, for people to fully interpret the maps they need data source description and the method used in the data analysis or statistical modeling. For the representation of health information through Web-mapping applications, there still lacks a standard format to accommodate all fixed (such as location) and variable (such as age, gender, health outcome, etc) indicators in the representation of health information. Furthermore, net-centric computing has not been adequately applied to support flexible health data processing and mapping online.

Natural disasters normally find people unprepared and emergency planners are faced with a big task of evacuating people. The response time needed to evacuate people, especially those that are socially vulnerable is very important in saving lives. There exist a number of important factors when planning for an evacuation, e.g. the number of people to be evacuated, time available for the evacuations, the distance to travel and also the available routes for evacuation. The recent flooding in Fredericton has identified that the socially vulnerable population require more resources and emergency planning than the evacuation of the rest of population. The socially vulnerable population needs to be identified before the disaster occurs and their special needs need to be documented. The provisions of medicaments, special food and any additional resources have to be planned and prepared in advance. This paper presents the approach to identify, map and assist the evacuation of the population that is socially vulnerable during floodings in Fredericton while taking care of their special needs. The main result of our research is a web based GIS system that provides appropriate information to the relevant authorities and general public in a timely manner and easy to understand.

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With global warming and extensive infrastructure development close to rivers, the impacts of flooding events have greatly increased over recent years. To support flood management, early prediction is very useful. In this research, we have developed a decision support system for flood prediction and monitoring that integrates GIS and hydrological modelling with additional bridge sensors and users' observations. Hydrological modelling considers a wide range of information that affect flooding such as snow conditions, temperatures, precipitation patterns, water levels and stream to generate flood predictions. The predicted water levels for the next 24 and 48 hours can be displayed via dynamic web pages, and overlaid with maps of the transportation network, property boundaries, municipal infrastructure and water depth contour lines. In conclusion, this research can provide good flood prediction precision and strong support to the public evacuation if flood events happen.

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