by clicking on the page. A slider will appear, allowing you to adjust your zoom level. Return to the original size by clicking on the page again.
the page around when zoomed in by dragging it.
the zoom using the slider on the top right.
by clicking on the zoomed-in page.
by entering text in the search field and click on "In This Issue" or "All Issues" to search the current issue or the archive of back issues respectively.
by clicking on thumbnails to select pages, and then press the print button.
this publication and page.
displays a table of sections with thumbnails and descriptions.
displays thumbnails of every page in the issue. Click on a page to jump.
allows you to browse through every available issue.
GCN : October 2013
GCN OCTOBER 2013 • GCN.COM 7 and used in one place, Talbot said. Prior to DataBridge, analysts could not run one agency's data to predict an outcome of another agency's regulatory area, but now they can. For example, the New York City Fire De- partment is applying data and analytics to change the way FDNY conducts daily building inspections, helping the city's 341 fire units more accurately target for inspection buildings that are potential fire risks. The Risk Based Inspection System mines information from databases across the city to help prioritize the 50,000 build- ings firefighters inspect annually. FDNY built its own data warehouse, where the department could store inspec- tion information, such as the building's oc- cupancy class, whether it has sprinklers or if it is fire-proofed. The Risk Based Inspec- tion System pulls information from the FDNY data warehouse as well as from da- tabases from the City Planning, Buildings, Environmental Protection and Finance de- partments, using the DataBridge. The system lets FDNY prioritize inspec- tions based on specified risk criteria, such as the type of building (home, storefront, manufacturing facility), the construction material, the building's fire-proof fea- tures, the height and age of the building, the last inspection date, occupancy and violation history. Meanwhile, the NYC Department of Buildings has judiciously applied data analytics to handle illegal conversion com- plaints, city officials said. The city receives 20,000 to 25,000 complaints of illegal con- versions every year. An illegal conversion is an apartment or house with residents living above maximum occupancy, often remnants of formerly legal spaces that have been divided making them unsafe for occupancy. A single-family home, for example, could be subdivided to house 30 individuals in crowded, unsafe conditions. Illegal conversions represent sig- nificant public safety hazards from fire, crime and diseases. The NYC Department of Buildings has approximately 200 in- spectors to look into those complaints, in a city of nearly a million buildings. Using data from 19 agencies, MODA built a file of all buildings in the city to help the city prioritize complaints that represent the greatest catastrophic risk. Analysts looked at a range of informa- tion, such as whether or not an owner was in arrears on property taxes, if a property was in foreclosure, the age of the struc- ture, and then cross-tabulated that data against five years of historical fire data of all of the properties that had structural fires in the city, arranged by severity. The MODA team found certain high-risk indi- cators that correlated to structures that had fires. MODA now runs new illegal conversion complaints against that file to identify those complaints that represent the top 5 percent for fire risk. The com- plaints are sent to inspectors to follow up with urgency. In the past, building inspectors re- sponding to complaints found seriously high-risk conditions 13 percent of the time. Now, they are finding these risky conditions 70 to 80 percent of the time, a five-fold return on inspection man hours, according to MODA. Working with the Building Department, MODA is now focusing on analyzing a larger set of illegal conversion complaints using SAS Analytics, Corcoran said. MODA is applying risk filters to flag high-priority complaints within the two minutes that a complaint is registered and printed out at the bureau command office. A lot of work goes on behind the scenes to put the data in a format that is ready for analysis, Talbot said. "We're work- ing with real-world data. When it comes to us, it isn't always perfect and it can be complicated to figure out," she said. Bringing data to life requires real creative processes, and technology can help. SAS' data integration and quality capabilities, to give one example, are critical to pro- ducing useful data, Talbot noted. MODA also uses a variety of other tools, ranging from basic analysis in Microsoft Excel, Microsoft SQL Server for data ac- cess, Oracle business intelligence tools to perform data look-up and Palantir for relationships and network mapping. The tools are designed to provide solutions for various classes of data analysts, from ca- sual users to sophisticated programmers. The real focus is to enhance public safety, whether that involves responding to building complaints or identifying the most critical areas to bring back online during an outage. The goal is to apply data analytics so "public safety can be [viewed] in a more intelligent way," Corcoran said.• A New York City bridge inspector surveys damage from a truck fire on the Ed Koch Queensboro Bridge in August. AP IMAGES