Academic Analytic Competition Presentation

Advanced Analytics Competition

Martin SellersLintsen Han

Scarlett ChengJeffery RyanVince Rich

Executive Summary andResearch Question

Research Problem: Scarcity of Public Resources for City Building Permitting and Violations

Goal: To create a operational model to identify which buildings are most likely to have violations upon inspection

Model Input Variables:

• Permit Type Risk Rating

• Geospatial Significance

• Permit Issue Date Cycle Time

Rating Model

Permit Type Geography Time of

IssuePriority Rating

Permit Types Risk Rating

• Each permit type was given a priority for inspection of High, Medium or Low.

• Priority was determined by correlating inspection failures with issued permit types.

• High Priority: at least 5% more failures than average• Medium Priority: Within 5% of the overall percent failed• Low Priority: at least 5% less failures than average

Kernal Density Analysis of Building

Violations

Graphical representation of building violations within Chicago utilizing the density study to illustrate the magnitude of these spatial relationships• The findings of this density analysis is consistent with

the findings of Getis-Ord Gi*

• Wards Most at Risk• 28• 24• 17• 48• 42

• Wards Least at Risk• 1• 27• 47

Getis-Ord Gi* Hot-Spot Analysis of

Building ViolationsThe measure identifies the statistical significance of spatial clusters. A cluster with a high value can be interesting but may not be a statistically significant hot spot. To be a statistically significant hot spot, a cluster will have a high value and be surrounded by other features with high values as well.

• Several distinct geospatial clusters indicating violation hot spots

• Priority should be placed geographically on hot spots, these areas are indicative of more violations occurring

• Cold spots indicate areas where low priority should be placed as a result of a distinct lack of significance

• Areas of Responsibility should be standardized to align with other city functions, however more analysis is needed to determine geographic constraints incorporating resource allocation optimization, e.g. human resources

Permit Issued Cycle Time

Time period% of the amount of issued

permitsCumulative Percentage

0-3 Years 20.9% 20.9%

3-7 Years 30.7% 51.6%

7-10 Years 35.5% 87.1%

10+ Years 12.9% 100%

Practical Model Application

Core Measures Operational Output

Permit ID Permit_Indicator Geospatial_Signfigance Cycle_Time Prioritization_Score

1 1 1 2 0.402 3 2 2 0.703 2 2 4 0.804 1 3 1 0.50

5 3 1 1 0.50

Measure Key Index Score

1 = High 1 = High Signfigance 1 = 0 - 3 yrs 0 - .3 = High

2= Medium 2 = No Significagance 2 = 3 -7 yrs .3 - .7 = Medium

3 = Low 3 = Negative Signficgance 3 = 7 - 10 yrs .7 - 1 = Low

4 = 10+ yrs

Policy Implications For the City of Chicago

• More Efficient Resource Allocation

• Increased Public Safety

• Increased Community Awareness

• Scalability