Crisis Mapping is by definition a cross-disciplinary field. Crises can be financial, ecological, humanitarian, etc., but these crises all happen in time and space, and necessarily interact with social networks. We may thus want to learn how different fields such as health, environment, biology, etc., visualize and analyze large complex sets of data to detect and amplify or dampen specific patterns.
We can’t all become specialists in each others’ areas of expertise but we can learn from each other, especially if we share a common language. Like the field of complexity science, Crisis Mapping can provide a common but malleable language, taxonomy and conceptual framework to facilitate the exchange of insights driven by innovative thinking in diverse fields.
This explains why I was excited to come across the International Journal of Health Geographics a few days ago. The Journal is an online and open-access resource. This means new ideas can be shared openly, which is conducive to innovation, just like arXiv.
Two of the Journal’s latest articles caught my interest:
1) An Agent-Based Approach for Modeling Dynamics of Contagious Disease Spread
This study developed a spatially explicit epidemiological model of infectious disease to better understand how contagious diseases spatially diffuse through a network of human contacts. To do this, the authors developed an agent-based model (ABM) that integrates geographic information systems (GIS) to simulate the spatial diffusion. (See my previous post on ABM and crisis mapping).
What is very neat about the authors’ approach is that they chose to draw on georeferenced land use data and census data. In other words, they combined the fomalistic rules of ABM with empirical GIS data. This means that the model can actually be tested and different scenarios can be played out by adding or changing some of the parameters. Could we use this model for conflict contageon?
2) Combining Google Earth and GIS Mapping Technologies in a Dengue Surveillance System
This study overlayed georeferenced epidemiological data on a town in Nicaragua with satellite imagery from Google Earth to enable dengue control specialists to prioritize specific neighborhoods for targetted interventions. The authors used ArcGIS to “accurately identify areas with high indices of mosquito infestation and interpret the spatial relationship of these areas with potential larval development sites such as garbage piles and large pools of standing water.”
It’s worth noting that the above Google Earth imagery was not particularly high resolution but the authors were still able to make full use of the imagery.
This approach to mapping for decision-support is particularly relevant for resource-limited settings since. As the authors note, the surveillance project “utilizes readily available technologies that do not rely on Internet access for daily use and can easily be implemented in many developing countries for very little cost.”
While the team had a free copy of ArcGIS thanks to the Global Fund, they plan to consider free and low-cost alternatives such as SaTScan, MapServer and Quantum GIS in the future. (See this post for additional alternatives like GeoCommons). I hope the authors also know about Walking Papers. I’ll email them just in case. Here’s to cross-disciplinary collaboration!