The Humanitarian Situation Risk Index (HSRI) is a tool created by UN OCHA in Colombia. The objective of HSRI is to determine the probability that a humanitarian situation occurs in each of the country’s municipalities in relation to the ongoing complex emergency. HSRI’s overall purpose is to serve as a “complementary analytical tool in decision-making allowing for humanitarian assistance prioritization in different regions as needed.”
UPDATE: I actually got in touch with the HSRI group back in February 2009 to let them know about Ushahidi and they have since “been running some beta-testing on Ushahidi, and may as of next week start up a pilot effort to organize a large number of actors in northeastern Colombia to feed data into [their] on-line information system.” In addition, they “plan to move from a logit model calculating probability of a displacement situation for each of the 1,120 Colombian municipalities, to cluster analysis, and have been running the identical model on data [they] have for confined communities.”
HSRI uses statistical tools (principal component analysis and the Logit model) to estimate the risk indexes. The indexes range from 0 to 1, where 0 is no risk and 1 is maximum risk. The team behind the project clearly state that the tool does not indicate the current situation in each municipality given that the data is not collected in real-time. Nor does the tool quantify the precise number of persons at risk.
The data used to estimate the Humanitarian Situation Risk Index “mostly comes from official sources, due to the fact that the vast majority of data collected and processed are from State entities, and in the remaining cases the data is from non-governmental or multilateral institutions.” The following table depicts the data collected.
I’d be interested to know whether the project will move towards doing any temporal analysis of the data over time. This would enable trends analysis which could more directly inform decision-making than a static map representing static data. One other thought might be to complement this “baseline” type data with event-data by using mobile phones and a “bounded crowdsourcing” approach a la Ushahidi.
iRevolutions 