1. Advanced Analytics Competition
Martin Sellers
Lintsen Han
Scarlett Cheng
Jeffery Ryan
Vince Rich

2. Executive Summary and
Research 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

3. Rating Model
Permit
Type
Geography
Time of
Issue
Priority
Rating

4. 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

5. 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

6. Getis-Ord Gi* Hot-Spot
Analysis of Building Violations
The 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

7. Permit Issued Cycle Time
Time period
% of the amount of issued
permits
Cumulative
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%

8. Practical Model Application
Core Measures Operational Output
Permit ID Permit_Indicator Geospatial_Signfigance Cycle_Time Prioritization_Score
1 1 1 2 0.40
2 3 2 2 0.70
3 2 2 4 0.80
4 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

9. Policy Implications For the City of Chicago
• More Efficient Resource Allocation
• Increased Public Safety
• Increased Community Awareness
• Scalability