A S A B R I D G E T O B U I L D A D ATA E C O S Y S T E M T O D E L I V E R S E R V I C E T O R E S I D E N T S O P E N D ATA A S A B R I D G E T O B U I L D A D ATA E C O S Y S T E M T O D E L I V E R S E R V I C E T O R E S I D E N T S @CHICAGOCDO @CHICAGOCDO
led by Chicago residents interested in technology and society. Smart Chicago Collaborative and non-profits provide assistance and city officials regularly engage in meetups and other activities. This group has produced several helpful apps. Community
local, temporal data on using a variety of sensors: §Sensors measuring sound and vibration §Low-resolution infrared cameras measuring sidewalk temperature §Climate and environmental data, such as air-quality and temperature
use data. Likewise, people wanted to sometimes correct our data. Data posted on GitHub can be edited by others and comes with a business-friendly MIT license. Open-source data github.com/Chicago/osd-street-center-line
which we organize all of our work: Access to the Internet & technology, Skills to use technology once you've got access, and Data, which we construe as something meaningful to look at once you have access and skills.
civic developers. However, these events rarely lead to “Learnathons” Weekend events dedicated to providing free workshops on introductory data analysis and advanced analysis. Using the data portal and open-source software tools.
where rodent complaints are most likely in the next week. We used spatial- temporal relationships to create these predictions, which started as an investigation of over 350 different factors. Spatial Correlation Temporal Correlation
Alliance and Allstate Insurance Company’s data science team to help develop the predictive model. Data from the open data portal was used to develop the model. While other data were considered, almost all of the useful data was publicly available.
risk level Location of restaurant Nearby garbage and sanitation complaints Type of facility Nearby burglaries Whether the establishment has a tobacco or has an incidental alcohol consumption license. Length of time since last inspection. Length of time the restaurant has been inspecting. The model predicts the likelihood of a food establishment having a critical violation, a violation most likely to lead to food borne illnesses. Over a dozen data sources were used to help define the model. Ultimately, ten different variables proved to be useful predictors of critical violations. Significant Predictors:
70% The research revealed an opportunity to find deliver results faster. Within the first half of work, 69% of critical violations would have been found by inspectors using a data-driven approach. During the same period, only 55% of violations were found using the status quo method. Critical violations
rate of finding violations was accelerated by an average of 7.4 days in the 60 day pilot. That means more violations would be found sooner by CDPH’s inspectors. 7 days IMPROVEMENT The food inspection model is able to deliver results faster.
Officer City of Chicago @ChicagoCDO firstname.lastname@example.org data.cityofchicago.org github.com/Chicago techplan.cityofchicago.org report.cityofchicago.org opengovhacknight.org arrayofthings.github.io datadictionary.cityofchicago.org digital.cityofchicago.org