BARCELONA, Spain, Nov. 13, 2012 /PRNewswire/ -- During Smart City Expo and World Congress, IBM (NYSE: IBM) today announced that the City of Almere, the seventh largest city in the Netherlands and part of the Amsterdam Metropolitan Area, will use predictive analytics software from IBM to support improved service provision to its citizens. The City of Almere is the first local government organization in the Netherlands to infuse analytical insights based on existing, publicly available data into municipal operations to better manage resources.
Almere was established as a new town in 1975 and has since then been one of the fastest growing cities in Europe. Being the youngest city in the Netherlands with approximately 200,000 residents and 80,000 jobs, the city has been tasked by the central government to double in size by approximately 2030. To support this rapid growth, IBM analytics software will help the city make sense of the available data to make smarter decisions, quickly resolve problems and improve operational efficiency.
The City of Almere will use the analytics solution to improve the municipality's ability to provide services to citizens. By making it easier to import data and prepare and automate analysis and reporting, IBM SPSS predictive analytics software makes it possible to create an accurate data set using the city's available records to improve decision-making. This insight is used to help Almere improve the allocation and administration of citizen benefits, allowances and subsidies. By using IBM analytics software, the city is able to dramatically improve what used to be a tedious, largely manual process.The city can now, for example, gain insight into the large volumes of public data, which helps it to more effectively define and identify which citizens are eligible for benefits. It can also better determine if current benefits are up to date and provided on the basis of correct data, or if further investigations are needed. Furthermore, the analytics software will be used to develop methods for predicting socio-economic anomalies in the city so that preventative measures can be taken.
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