After the 2008 recession, Kansas City, MO, experienced waves of unemployment and foreclosures that led many properties to fall into disrepair. Faced with this growing issue during a period of decreased funding, the city’s code enforcement officials were unable to keep up with the workload, creating an enormous backlog and doubling the workload for each inspector. Together with the JHU Center for Government Excellence (GovEx), the city developed an algorithm to predict how long a given violation will take to resolve based on internal and public data that will help inspectors proactively schedule follow-up inspections and connect more serious cases to community programs earlier. Matt is the Chief Data Scientist at the Johns Hopkins University Center for Government Excellence, where he and his team help governments apply data to performance challenges and improve the quality of life of their constituents. Prior to joining GovEx, Matt led the data, GIS, and targeting programs for national and state political campaigns, labor unions, and non-profits as they sought to register, persuade, and motivate voters. He was also the lead GIS analyst for Delaware’s State House of Representatives redistricting project in 2010.

1. Matt Pazoles
GovEx - Chief Data Scientist
pazoles@jhu.edu
Predictive Analytics & Neighborhood Health
A collaboration by GovEx and Kansas City, MO

2. Today
● Analytics in city government
● The problem in Kansas City
● Exploratory analysis
● Random forest regression
● Final scheduling tool
● Questions

3. govex.jhu.edu / @gov_ex
Quick Primer on GovEx
● Help organizations make informed
decisions with data to improve
people’s lives
● Provide targeted training and
technical assistance for dedicated
civil servants
● Team of 25 with practical
government and NGO experience

4. govex.jhu.edu / @gov_ex
Where We Work
132
US cities
2,000+
civil servants trained

5. govex.jhu.edu / @gov_ex
● Direct technical
assistance
○ Data management
○ Performance
○ Advanced analytics
● Training and education
● Applied research
How We Work

6. Analytics in City Government
...a brief history

7. 1970’S
The Abbreviated Evolution of Performance Management
Private sector
renews
emphasis on
quality and
process
improvement
(e.g. japanese auto
manufacturers)
Malcolm Baldridge
Quality Improvement
Program
Six Sigma
GPRA
COMPSTAT
STAT MANIA:
CITISTAT
CAPSTAT
SCHOOLSTAT
HUDSTAT
FEMASTAT
Moneyball
GPRA
Modernization Act
MODA
EAD
1980’S 1990’S 2000’S 2010’S

8. Results
Focused
Management
What did Performance Management give us?
A few wins:
“NYC cuts crime by 60%”
“Baltimore saves $40m in
overtime”
“DC houses 500 homeless
men”
Some
believers
Some
skeptics
Some
victims
A few losses:
Standardized Testing
Cheating Scandals
VA Wait-Time Gaming
Airline on-time departure
gaming
A lot of process:
Meetings (including STAT)
Metrics
Reports
Dashboards
Reviews
Appraisals

9. Results
Focused
Performance
Management
High
Quality
Analytics
Performance
Analytics
Without this,
analysis
does not get
used.
Without this,
opportunities
are missed.

11. Problem Definition Making Decisions Taking Action
What happened?
Why did it happen?
What will happen?
What should we do?
Insights Foresight
Insights
Insights to Action

12. Identify problem
Explore data
Create model
Adjust operations
Track results
Add dataUpdate model
Communicate
Results

13. Some examples

14. Jackson, MS
Problem:
Dwindling general fund reserves,
population decline, overstaffed?
Analysis:
Clustering algorithm to identify
similar cities, then normalize and
compare budgets
Conclusion:
Jackson was overspending on
Public Works Operations

15. San Jose, CA
Problem:
Low citywide compliance with pet
licensing
Analysis:
Identify what areas are least
compliant and most able to take
action
Conclusion:
Target veterinarians and animal
hospitals in identified geographies

16. New Orleans, LA
Problem:
Large backlog of blight abatement
cases at the review stage
Analysis:
Predict likely course of action
based on historical data
Conclusion:
Create decision-support tool to
cut down on work needed to
advance the case

17. Questions?

18. KCMO Neighborhood Preservation
Predictive Analytics Project

19. 2008-2011
2010 Foreclosure Rates by Zip Code
Source: Kansas City Star, 7/9/2017
Source: Bureau of Labor
Statistics
Unemployment Rate in Kansas City, MO

20. 2011
Source: Kansas City
• Increase in property violations
overwhelmed existing workflow
• 20% of reported violations
were not seen for 10+ weeks
• Prioritized initial inspections
• Goal: 5 to 10 days
• Result: 6 days

21. Existing Inspection Process
1. Violation is reported through 311
2. Case is opened as a priority 1 for initial
inspection (within 6 days)
3. If a violation is found, owner is sent a
notification letter
4. After 3 to 6 weeks, case is added as a priority 3
(after initial inspections and court requirements)
5. Eventually, property is reinspected; if violation
remains, another letter is issued
Legal/Policy
constraints
● City can fine owners who
live within 50 miles
○ Fines are minimal
● Owners who live more
than 50 miles from the
city are not pursued
○ Weak ability to take
possession of
blighted properties

22. 2012-2016
Source: Kansas City Open Data
201
1
201
2
201
3
201
4
201
6201
5
2011
2012
2013
2014
2016
2015

23. 2012-2016
• Inspector capacity hasn’t changed
• More open cases per inspector = higher workload
• Hurt by success of prioritizing initial inspections?
What is driving inspector workload?

24. Can we reduce the amount of
work to get a property into
compliance?

25. Identify problem
Explore data
Create model
Adjust operations
Track results
Add dataUpdate model
Communicate
Results

26. Questions
● Is the backlog still growing?

27. Is the backlog of inspections growing?
Year Net Violations
2012 5,204
2013 13,703
2014 1,398
2015 1,402
2016 -4,431

28. Questions
● Is the backlog still growing?
● Are more violations being
issued?

29. 2012-2016
Source: Kansas City Open Data Portal

30. Are there more violations per month?

31. Questions
● Is the backlog still growing?
● Are more violations being
issued?
● Are a few properties
responsible?

32. Are more properties becoming noncompliant?

33. Are repeat offenders a growing issue?
Violations by
Property
Most Recent Violation Opened
2010 2011 2012 2013 2014 2015 2016
Grand
Total
Oldest
Violation
Opened
2010 3,783 1,010 1,020 1,130 1,241 1,603 3,541 13,406
2011 3,433 1,038 972 1,101 1,266 2,873 10,749
2012 3,183 837 778 852 1,911 7,600
2013 3,658 1,129 943 1,896 7,667
2014 3,530 1,192 1,771 6,548
2015 3,589 1,934 5,560
2016 4,656 4,690
Grand
Total 3,794 4,452 5,252 6,605 7,790 9,462 18,617 56,220
Years After
Initial
% Repeat
1 14%
2 25%
3 34%
4 43%
5 52%
6 59%

34. Questions
● Is the backlog still growing?
● Are more violations being
issued?
● Are a few properties
responsible?
● Do more inspections
decrease resolution time?

35. 2012-2016
Source: Kansas City Open Data Portal
60% of
cases
70% of
reinspections

36. Do inspections reduce resolution times?

37. Questions ● Are inspections themselves
part of the issue?

38. Are inspections closing violations efficiently?
Average number of inspections per year:
32,291
+/-3,000
“Inefficient Reinspection”
Reinspections where no violations are closed
Nearly doubled between 2012 and 2016

40. Insights
● Reinspection of open cases
accounts for most work
○ Majority of reinspections
on a minority of cases
● Timing of inspections is
more important than
quantity
● Many reinspections create
more work for inspectors

41. Improve inspection efficiency by modeling
how long a violation will take to resolve.
Reduce the number of inspections
that close 0 violations
At the address/case level

42. Identify problem
Explore data
Create model
Adjust operations
Track results
Add dataUpdate model
Communicate
Results

43. PostgreSQL
database
+
PostGIS
spatial
extension
Data Infrastructure
Property violation, 311,
property ownership, and
neighborhood related data
from OpenData KC
Demographic data by census
block from the US Census
Bureau
Property values and building
features from county data
Data Infrastructure

44. Data Infrastructure
PostgreSQL
database
+
PostGIS
Schedule of Inspections
• Tier 1: Initial Inspections
• Tier 2: Court-ordered
• Tier 3: Model Prioritized
• Tier 4: Low Priority
• AWS hosted server for analysis
• Python + Jupyter, R + R Studio
• Scikit-Learn
• Random Forest Regression
• 70% of data as training set
■ Corrected for class imbalance
• Mutual regression feature selection
• Grid Search parameter optimization
• Target: Violation Days Open
• Grouped violations by property to produce
recommendations for inspectors
• How many days has it been open?
• What are the other violations present?

45. Correctly sorts
violations from
shortest to longest.
(Bars rise from left to right)

46. Underestimated
time to resolution
Overestimated time to
resolution
Red line represents a
perfect prediction
Blue dots represent
violations from the
“test” data
MSE = 45.5 days

47. Current Status
Cases predicted as ready to be closed:
3,870 properties (62% of open cases)
(38% of open cases can be deferred)

48. Model-informed
inspection cycle
INITIAL INSPECTION
New violations come in
ACTION
Enhance inspection
schedule with
model-informed
targets
DATA WRANGLING
Add other relevant data and
run model
AGGREGATION
Compare prediction to
current violation age;
group by address

49. Anticipated outcomes
•More time helping residents
•More timely inspections
•Fewer follow-up visits documenting existing issues
•Less frustration all around!

50. Future opportunities for analysis
• “Inspector Effect”
• The inspection area is a predictive feature, but no data linking inspectors to specific inspections
• Weather impacts
• More detailed analyses could find relations between ground saturation, wind speed, and specific
violation types
• Utility Analysis
• Can water bills be used to proactively identify severe violations?
• Court Data
• How effective are current penalties in preventing reviolation?
• Equity in Outcomes
• Testing for bias by examining outcomes of inspection processes

51. Next Steps
(this project)
● Technical infrastructure
● Fold into prioritization
● Field testing
● Future analyses

52. Questions?

53. Thank you!
Matt Pazoles
pazoles@jhu.edu