visualizing corruption

Posted on 02/07/2011 by

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I have been arguing that good data visualization fosters interdisciplinary cooperation, adds to the general understanding of academic topics, and serves to reveal otherwise opaque organizational features of your data.  Now we can add that network visualization can be used outside of academia as well.

Orgnet consulting used social network analysis (SNA) to assist an economic justice organization, working with a city’s attorneys office, to convict a group of slumlords accused of severe housing violations.  From the site:

The [Economnic Justice Organization]…started to notice some patterns. The EJO began to collect public data on the properties with the most violations. As the collected data grew in size, the EJO examined various ways they could visualize the data making it clear and understandable to all concerned. They tried various mind-mapping and organization-charting software but to no avail — the complex ties they were discovering just made the diagrams hopelessly unreadable.

When they visualized a social network of housing managers they realized that a number of seemingly independent LLC’s were actually connected:

The dark red lines indicate that there are two distinct family clusters, with a suspicious gap between GHI LLC and JKL LLC.  A middle man was clearly in the thick of things, avoiding responsibility and detection by way of indirect quid pro quo:

The indirect quid pro quo results in plausible deniability for the influencer because there is no direct connection to the target of influence. The red arrows show both flow of benefit and flow of influence (via the network thinkers blog).

The firm eventually realized that there was a large conspiracy involving an individual, ‘Moe’, who was fraudulently moving money around his various investments in order to strip the equity out of his properties:

Their visualization demonstrates how the conspiracy worked:

It was now obvious that properties exchanged hands not as independent and valid real estate investments but as a conspiracy to avoid fixing the building violations. The green links represent borrowed money flowing into the buildings through new mortgages. As time went on, and the buildings appreciated in value during a real estate boom — loans from the mortgage company allowed the owners to “strip mine” the equity from the buildings. This is a common slumlord modus operandi — they suck money out of a building rather than put money back in for maintenance

Using a similar graphic, their client won a series of criminal and civil cases.  As they note, social networks reveal as much about the spatial organization of actors as the relational clusters they form.  It can indicate not only social distance but power or influence (by looking at the number and direction of connections).

(Via Orgnet.com)

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