IBM's Smarter Cities Challenge helps 100 cities around the globe improve education, infrastructure, public safety and economic development. Look how 10 winning cities are tackling tough problems.
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Syracuse, N.Y., a 2011 winner of a Smarter Cities Challenge Grant, sought IBM's help with the problem of foreclosures. Exacerbated by an exodus of jobs and population to surrounding suburbs, foreclosures lead to properties moving off the tax rolls and ending up in the city's hands. Stephanie Miner, elected mayor in 2009, wanted a data system that could predict the properties most likely to go off the tax rolls before the city faced the problem.
A prototype "Vacant Property Predictive System of Systems" designed by IBM used available housing and tax-roll data and turned up some interesting correlations, according to IBM.
"The smaller the lot size, the more likely it was to be abandoned, and we also found that male heads of household in the city were more likely to have a problem with poverty," Ari Fishkind, an IBM spokesperson told InformationWeek.
The predictive system performs "situational analysis" whereby recommendations are prioritized based on the state of the surrounding neighborhood and the likelihood that individual vacancies might tip neighborhoods into a distressed state, according to IBM.
"We're moving from having a sense of what's important for neighborhoods and housing to putting in a system in place where we actually have objective data so we can prevent vacancies and bring properties back online," said Mayor Miner in a video about the city's Challenge Grant project.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
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