A couple weeks ago, I pulled into my neighborhood and was surprised to see an Everest-size trash pile in my neighbor’s driveway.
It looked like they’d taken the entire contents of their house and thrown it out the fourth-floor window. And since I had out-of-town guests arriving the next day, I wasn’t thrilled. It made the street look, well, trashy.
Turns out they were cleaning their house to put it on the market, and a garbage collection crew came the next morning to haul away the junk.
But what if they hadn’t removed it? Would the trash pile have sat there indefinitely?
If left untended, a situation like that would have eventually fallen to the local government to manage. And in larger cities, it’s something they deal with on a daily basis.
A single property can impact the state of the entire neighborhood. If an apartment building is owned by a neglectful landlord, the value of the property goes down, tenants are unhappy, and a steady decline begins that impacts the entire neighborhood.
Isolating properties where code violations most frequently occur helps inspectors identify where to begin their efforts.
For emergency responders, understanding when and where they’ll be needed is critical. For example, if the police know that the majority of police incidents within a particular neighborhood occur at midnight, they can more effectively and efficiently staff that area. And if a fire department sees a surge in 911 calls from a particular neighborhood, they can pre-position a firetruck to reduce response time.
The effort to keep our cities clean and safe involves a cross-section of government, from police and fire fighters, to city inspectors, to administrators. Historically, each data set lives within its respective departments: police incidents and 911 calls are managed by the police department, 311 calls by the city services department, etc.
Pulling the city’s data together into one view provides the full picture. Now officials can quickly identify when and where resources are needed, and make changes that’ll improve the city and decrease their costs.
Take a look at one example using Qlik Sense here, a mashup of neighborhood data from the areas surrounding Kansas City and Independence, Missouri. Thanks to our friends at Qlarion and Idevio for helping us put this together!
Photo credit: GHR2009 via Foter.com / CC BY-SA