City Data: Big, Open and Linked

Working Paper by Mark S. Fox (University of Toronto): “Cities are moving towards policymaking based on data. They are publishing data using Open Data standards, linking data from disparate sources, allowing the crowd to update their data with Smart Phone Apps that use Open APIs, and applying “Big Data” Techniques to discover relationships that lead to greater efficiencies.
One Big City Data example is from New York City (Schönberger & Cukier, 2013). Building owners were illegally converting their buildings into rooming houses that contained 10 times the number people they were designed for. These buildings posed a number of problems, including fire hazards, drugs, crime, disease and pest infestations. There are over 900,000 properties in New York City and only 200 inspectors who received over 25,000 illegal conversion complaints per year. The challenge was to distinguish nuisance complaints from those worth investigating where current methods were resulting in only 13% of the inspections resulting in vacate orders.
New York’s Analytics team created a dataset that combined data from 19 agencies including buildings, preservation, police, fire, tax, and building permits. By combining data analysis with expertise gleaned from inspectors (e.g., buildings that recently received a building permit were less likely to be a problem as they were being well maintained), the team was able to develop a rating system for complaints. Based on their analysis of this data, they were able to rate complaints such that in 70% of their visits, inspectors issued vacate orders; a fivefold increase in efficiency…
This paper provides an introduction to the concepts that underlie Big City Data. It explains the concepts of Open, Unified, Linked and Grounded data that lie at the heart of the Semantic Web. It then builds on this by discussing Data Analytics, which includes Statistics, Pattern Recognition and Machine Learning. Finally we discuss Big Data as the extension of Data Analytics to the Cloud where massive amounts of computing power and storage are available for processing large data sets. We use city data to illustrate each.”