Social Networks and Water Purification in Buenos Aires

In a previous post (Ultimate Networks, Jan. 8, 2008) I wrote about the role of social networks in B2B environments. Today I’d like to comment on a fascinating Clean Tech application developed by Rebeca Hwang, a friend and Stanford PhD student, as well as the Judging Chair for the California Clean Tech Open (Social Networks, Pizza and Clean Tech, Dec. 14, 2007).

Rebeca’s objective is to use her technical know-how in water treatment technology and her research in quantitative theory of social networks to improve water quality in the slums of Buenos Aires. The water in Buenos Aires’ sprawling slums carries birth-defect-causing nitrates and arsenic in amounts up to 10 times what is considered safe. Because of the lack of government resources, communities have formed water cooperatives to help provide clean water to their constituents. Unfortunately, many of these co-ops are small and not always well managed so water treatment and delivery services lack funding. Moreover, while it is cost-efficient to treat water for 100,000 households, these co-ops have only an average of 2,000 houses each and are therefore rarely profitable.

Rebeca Hwang believes that this considerable challenge can be addressed by applying social networks analysis to map out the relationships between decision-makers at water cooperatives. The idea is that the identification of patterns can pinpoint an organization’s strengths and weaknesses, and in turn, may help that organization to improve its efficiency and effectiveness.
The characterization of the structure of these 480 water cooperatives - servicing 700,000 households - is a massive undertaking. One that requires, through extensive surveys, the tagging of the co-ops by service type and managerial style, both formal and informal. Rebeca is using open-source software programs like Pajek and Ucinet to analyze and convert the raw data into meaningful information. Also, she anticipates that using another program like NetDraw to visualize the intensity of the relationships between the different constituents will reveal some key problem areas. Interviews will complement the study and bring qualitative color to the motivations and approaches of decision makers that may be influencing the various co-op operations but that are not formally represented in their structures.

The expected outcome of this “social network analysis” project is to formulate specific ways to improve the profitability of the cooperatives while increasing the volume of clean water produced.

I believe that innovative approaches, like this one, that leverage existing technologies and apply social network analysis theory can provide a significant boost toward improving people’s daily life while maximizing the use of natural resources. Such project does not require huge investment or new technology. It empowers local communities and enables greater sustainability. Please drop me a line if you know of other interesting clean tech social applications.