The team analyzed DC crime and neighborhood data from 2011-2014 to explore crime patterns and their relationship to property values. They cleaned and joined the datasets, then used Tableau, R and regression analysis to visualize crime incidents by neighborhood and examine the correlation between violent crime rates and property values. The analysis found negative correlations between violent crime types and property values, indicating violent crime impacts safety and wealth accumulation. A web app was created to map and track crime trends in real-time to provide residents and businesses insights into neighborhood crime risks.

1. TEAM DC: Georgetown Data Analytics:
Exploring DC crime with neighborhood cluster
analysis, interactive mapping tool, and regression
analysis.

2. Problem
DC property owners and metro area residents are at greater risk of
becoming victims of crime if they are unaware of the frequency
and types of crime in their neighborhoods of interest.
Imperfect information - A person visiting or buying a home in an
unfamiliar neighborhood faces imperfect information regarding
crime and its impact on safety and property values.

3. Data Science Pipeline

4. Began with:
-DC Crime Data - 2011-2014
-DC Neighborhood Data (US Census Data) - 2000-2012
Based on given data, we decided to go with the most up to date property values
and population numbers (2012) from DC Neighborhood data to be most
representative of current state.
Data Munging/Exploration

5. DC Crime Data:
1. Did not have neighborhood names, but rather cluster, block number,
and latitude/longitude coordinates as far as geographic location.
2. Through visualization (Tableau), we discovered that the
latitude/longitude coordinates were out of date and needed to be
updated to be read by our mapping tool.
3. Only provided a long date (MM/DD/YYYY TT:TT:TT).
Data Munging/Exploration:
Problems

6. Data Munging/Exploration:
Solutions to problems
Long Date problem:
• MonthName function (Excel or Access) to populate month field
• DayName function (Excel or Access) to populate day field
• Right function (Excel or Access) to parse out time of day
• Updated Year field by individual year conversion (SQL)
SQL Example to update year field from long date:
UPDATE [CrimeData_2011-2014]
SET [CrimeData_2011-2014].REPORT_YEAR = "2014"
WHERE ((([CrimeData_2011-2014].REPORT_DAT) Like "*2014*"));

7. Data Munging/Exploration -
Solutions to problems
Neighborhood Names problem:
• Neighborhood Data contained cluster number and neighborhood
name, while Crime data only included cluster. An inner join on the
cluster fields brought in Neighborhood Names.
SQL Code for INNER JOIN:
UPDATE [CrimeData_2011-2014]
INNER JOIN [CensusbyNeighborhood-CSV1]
ON [CrimeData_2011-2014].NEIGHBORHOOD_CLUSTER = [CensusbyNeighborhood-
CSV1].NeighClst
SET [CrimeData_2011-2014].NEIGHBORHOOD = [CensusbyNeighborhood-
CSV1].[Neighborhood];

8. Data Munging/Exploration -
Solutions to problems
Latitude/Longitude problem:
• Latitude/Longitude coordinates were outdated, and Tableau couldn’t
read them for mapping.
• We used Python (pyproj and proj.4 libraries) to bring in up-to-date
latitude/longitude coordinates.
Code can be viewed on Github.

9. Data Analysis & Exploration
Methods/Tools
• Using Tableau, R and Excel explored the data visually and with regression.
• Tableau mapping and charts – getting an understanding of the data, the
neighborhood clusters and crime incidence.
• Linear/multiple regressions on current 39 Cluster data (2012 median
property value, total per crime category).
• Exploring time series median home price data on violent crime per 1000
residents 2000-2011 in R.
• Exploring regression coefficients between crime areas to determine
impact of violent crime events on property value and if there is marginal
negative return to violent crime – as hypothesized.

10. Data exploration of raw counts of crime by neighborhood cluster. Shows
heavy crime incidence in commercial areas (dominated by theft). Needs
further exploration.

11. Total Crime per Capita (000)

12. Map of Clusters: Tableau (hyperlink)

16. Comparing Neighborhoods - Crime Types
AdMo/Kalorama- Prop: $1.1M - Crime: 2863 Foxhall/Palisades- Prop: $1.0M - Crime: 602
Fairlawn- Prop: $243K - Crime: 2763Union St/E. Market- Prop: $549K - Crime: 6178

17. Adams Morgan/Kalorama
Comparing Neighborhoods - Day of Week
Foxhall/Palisades
Union Station/Eastern Market Fairlawn

18. Products for Residents/Business Owners
http://danieljwood.github.io/DC-crime/
https://www.google.com/maps/d/edit?mid=zUgxKwpr9tdQ.kygX4j2r7scg

21. Property Value plotted against Violent
Crime Rate, by neighborhood cluster,
2000-2011

24. Total crimes (count) show little relationship to property value –
explained mainly due to the slightly positive correlation (and
aggregate number) of non-violent crime events.

25. Correlations: Negative correlations for violent crime indicate impact on
property values – flat/positive correlations for non-violent theft
ARSON
ASSAUL
T
W/DAN
GEROUS
WEAPO
N
BURGL
ARY
HOMICI
DE
MOTOR
VEHICLE
THEFT
ROBBER
Y
SEX
ABUSE
THEFT
F/AUTO
THEFT/
OTHER
Correlation:
Property Value -0.34 -0.5981 -0.41 -0.494 -0.615527 -0.3761 -0.3893 0.11122 0.191

26. Analysis of Correlation and Map
Clustering
• Maps are excellent for quickly looking through categories in geographic
proximity, but don’t provide much insight without neighborhood specifics
such as commercial activity(JP will elaborate). Of particularly interest are
population density and # liquor licenses (as a proxy for commercial
activity)
• Correlations show that the only two crime groups that are weakly
positively correlated with property value are Theft and Theft from Motor
Vehicle (not Motor Vehicle Theft). This conforms to our expectations
about violent crime impact property value. The correlation itself is fairly
weak, but its sign is interesting.
• Other inferences from the map: trees commit very few crimes ☺

27. Model1=lm(formula =
Median2012 ~ VCrime2010,
data = dccrimereg)
Coefficients:
(Intercept) VCrime2010
940372 -31296

28. lm(formula = Prop09 ~ VC07 * VC08, data = dccrimereg2)
Coefficients:
(Intercept) VC07 VC08 VC07:VC08
1065111 -22563 -38566 1290

29. Average % violent crime per 1000 plotted against % change in housing
asset value over time – not what I expected!
Or is it! Perhaps what we’re observing is the decline in overall violent
crime is showing a massive rebound for the higher crime (asset price
depressed) neighborhood clusters. Gentrification is occurring, but what
is also observed is that housing prices seem to be playing catch up in
those high crime neighborhoods.

30. Regression Discussion
• Non-violent crime is neutral to weakly associated with increasing property values
(unsurprising).
• Asset value accumulation is an important part of breaking cycle of poverty and
violent crime thus has a detrimental effect on the ability to accumulate
wealth….HOWEVER, high violent crime areas are experiencing much growth in
asset values which indicates that falling crime rates in otherwise desirable areas
are an instrumental factor. Decay rate seems fast. The perception of falling
crime rates may be key factor.
• Further thoughts on where the data might go….
– Drilling down to one number for the impact of violent crime on property value
– Decay rate of violent crime negative impact on property value growth rate.
– Quantifying negative wealth accumulation effect over time of violent crime.

31. Conclusion
Original Problem: DC property owners and metro area residents are at greater risk of
becoming victims of crime if they are unaware of the frequency and types of crime in
their neighborhoods of interest.
Solution: By implementing effective data analytic techniques, we were able to expose
important trends and relationships regarding crime activity in relation to the different
neighborhoods and associated property values throughout the DC metro area. This
information can provide important insight for business owners, potential
homebuyers, as well as the DC Metro Police Department.
Additional Development: Create a web-based application which can graphically
represent trends, patterns, and crime frequencies in near-real time.
https://www.google.com/maps/d/edit?mid=zUgxKwpr9tdQ.kygX4j2r7scg

32. Appendix

33. Crime Type Percentage per Capita

34. Data Analysis
• Neighborhoods which have more retail businesses, restaurants
and bars are more affected by certain types of crime (e.g.
theft) than residential neighborhoods.
• Residential neighborhoods with higher average incomes and
property values generally have a lower occurrence of violent
crimes vs residential neighborhoods with lower average
incomes and property values
• Day of the week appears to impact certain types of crime
(robbery/theft) in high-traffic neighborhoods but it is not a
direct correlation in every case.