Risk and Vulnerability Assessment

Risk and Vulnerability Analysis

Our exposure to hazards is increasing at alarming rates. Without proper tools, planners are unable to develop appropriate response plans. GIS offers the ability to integrate data of different types and sources and analyze the risks associated with changing events. GIS also provides the environment to actually simulate hazards and make predictions about future scenarios and their impacts.

Clark Labs' technologies offer a full suite of tools for risk and vulnerability analysis, including distinctive decision support and uncertainty management techniques. Vulnerability mapping tools allow you to create hard predictions as well as maps of vulnerability to risk.

Application areas include:
- Drought prediction
- Landslide vulnerability
- Earthquake assessment
- Flood analysis
- Surface and groundwater contamination evaluation
- Hazardous material transportation planning
- Emergency planning
- Impact analysis

Analytical Examples: [click images to enlarge]


NDVI data are often used for monitoring change. Here long-term average NDVI for July is compared to July 1994 NDVI to develop an image of NDVI anomalies for July 1994. Monthly NDVI data for Africa, 1982-1999, as well as other data sets are available on CD from Clark Labs.


Results of a fire danger study are shown in the small image at the lower left. To make the image more dramatic and interpretable, an elevation model was used to calculate a hillshaded relief image for the area (upper left image). This was enhanced and merged with the classified image to produce the final relief-shaded fire danger image.


This screen shot illustrates some of the data products developed for a study on the effects of sea level rise on a coastal rice-producing area in Vietnam. Part of the study included prediction of future sea level and inundated areas. This portion of the study explicitly incorporated uncertainty in the elevation data as well as the projected sea level rise.


IDRISI includes an unparalleled suite of tools for multi-objective/multi-criteria decision support. Multiple suitability problems are defined in terms of factors, constraints, factor weights and risk-taking strategy. Then the composite suitability images for each objective are used together to best allocate areas to each objective. Rather than one-shot black-box solutions, this approach provides decision makers with understandable and defensible methods that can be iteratively improved. Here, industrial and commercial suitability are modeled.