July August September. October November December

Lighting Crime: An empirical investigation of the impact of an LED streetlight conversion project on crime rates in Oakland, California

Monica Testa University of California, Berkeley | College of Environmental Design CP204c | Fundamentals of GIS in City Planning | Spring 2014 Prof. John Radke | GSIs: Lauren Heumann & David Von Stroh

Motivation: In June 2013, Oakland, California began the rollout of a citywide streetlight retrofit project intended to

enhance public safety by providing a better lighting quality. The city’s 30,333 cobra head streetlights will be

converted from High Pressure Sodium (HPS) fixtures to Light Emitting Diodes (LEDs). The new LED

fixtures provide a directional, broad spectrum white-light which renders color more accurately than the

yellow spectrum light emitted by the HPS lamps they are replacing. The improved visibility associated with

the LED technology is anticipated to deter crime by increasing the likelihood that the perpetrator may be

seen and subsequently caught. By comparing Oakland’s LED rollout to changes in crime rates, this study

tests the hypothesis that LEDs reduce crime.

Modeling Techniques: Processing, modeling and integrating raw data: geo-referencing and spatial joins

• Geo-reference LED installations and reported crime for 2012 and 2013

• Spatial join: LED installation points by month and reported crime by month to census blocks

Vector-based Overlay and Neighborhood Modeling of Discrete Objects: erase, spatial joins, & unions

• Erase water polygons from census block shapefile, join monthly LED installation point data to census

blocks, creating a count of LEDs on each block each month, union monthly LED installation polygons,

generate map of LED rollout over time. Repeat steps using Crime data. Union LED & Crime datasets.

Quantifying Relationships & Analyzing Patterns: spatial statistics

• Ordinary Least Square (OLS) Regression: Is there a relationship between the change in opportunistic

crimes and LED installations areas?

• Spatial Autocorrelation (Global Moran’s I): Are the under- and over- predictions randomly distributed?

• Hot Spot Analysis (Getis-Ord Gi*): Is there a spatial pattern associated with the under- and over-

predictions?

Modeling the 3rd Dimension

• Use ArcGlobe to represent both spatial and temporal dimensions of crimes.

Implications and Conclusions: This investigation found no correlation between the installation of LED Fixtures and variation in crime.

This is surprising considering that the City of Oakland, California explicitly stated the “primary goal [of the

LED Streetlight Conversion Project] is to support public safety with brighter and better lighting.”

Furthermore, there was very little change in monthly opportunistic crimes in between 2012 and 2013 and

no change whatsoever when normalized by population. This is perhaps even more surprising in the context

that Oakland had one of the highest crime rates in the country in 2012 and was on track to top the charts

again in 2013, until the Oakland police department re-launched Operation Ceasefire to target violent crime

East of High Street, hired more officers, and began making raids as of mid-2013. Although Ceasefire

focused on violent crime, the increase in police activity appears not to have had any impact or spillover

effect on opportunistic nighttime crime, despite being concentrated in the same general area as the LED

installations.

Limitations: There are a number of limitations to this study which warrant further investigation. These include the

limited timeframe of the project and the limited number of crime records which were successfully geo-

coded. Including a greater number of records reflecting a longer period after the LED Street Conversion

Project has completed would increase the reliability of the results.

LED Fixture HPS Fixture

Directional white light Dispersed yellow light

LED Installations Completed in 2013 Assumptions, Data Selection, and Modeling Decisions: To model the installations over time and

create LED treatment areas, each lamp was first geo-referenced based on recorded street address.

28,207 of the original 30,333 records were successfully geo-referenced. The LED point shapefile was

then joined to a block shapefile, generating a count of the total lampposts on each block. Next, using

the select by attributes feature, LED points were selected by installation date and new shapefiles were

created from each selection representing LED installations completed by the end of each month. I

then added a field to the attributes table and calculated the percentage of converted streetlights by

dividing the number of LEDs installed by the total number of lamp posts successfully geo-referenced.

Using the total number of LED installations as a proxy for total streetlights was a reasonable

assumption, as all cobrahead streetlights in Oakland will ultimately be converted from HPS to LED

as part of the conversion project. I created a binary variable indicating LED treatment areas if blocks

had ≥ 75% LED installations and repeated the process for each month. This was based on the

assumption that the LED streetlights would only produce an effect on crime once each block was

close to completely lit, whereas partial conversion or intermittent lighting hot spots may produce a

different result. Finally, the 6 months of LED installation were unioned and LED indicator fields

summed to produce a map illustrating the rollout of LEDs overtime.

June July August September October November

Ordinary Least Square (OLS) Regression & Spatial Autocorrelation (Global Moran’s I) of Regression Residuals. This brings us to the moment of truth: is there a relationship between the change in opportunistic crime between 2012 and 2013 and the LED installations? Are the over- and under-predictions of changes in crime randomly distributed? The following maps model the relationship between the year-over-year difference in

crime counts. The percent of LEDs installed by the end of the previous month is treated as the independent variable and the opportunistic crime count of each block is treated as the dependent variable. The red and blue areas represent under- and over- predictions of the model respectively. Stated another way, red

represents blocks where more crimes occurred than the OLS regression model predicted and blue represents blocks where less crime occurred than predicted.

Visualizing Spatial & Temporal Dimensions of Crime Using 3D Analysis

Results & Findings LED Rollout

One of the key takeaway from the map of the LED rollout is

that the fixtures appear to have been rollout out somewhat

randomly. The spatial pattern may have been influenced by the

circuitry or another technological consideration, however, the

variation Although a general South to North trend emerges, the

time at which each block reaches 75% or greater LED

installations complete varies widely. This is important to

consider when using the installation of LEDs as an experimental

treatment.

Monthly Variation in Crime

Average monthly variation in nighttime opportunistic crimes

ranged from a decrease of 2 crimes per block per month up to

an increase of 2 crimes per block per month. Three outlier

observations (values of 4, 4, and -6) were cleaned from the

dataset. Normalizing by population removed much of the

noise from the total count and revealed that there is virtually no

change in average monthly crimes per block per person, with

only a few exceptions.

OLS Regression and Spatial Autocorrelation

For all months, regression residuals are clustered. Given the Z-

scores far above 2.58 and p-values of essentially 0 for each

month, there is a less than 1% likelihood that spatial

distributions of over- and under- predictions of change in crime

are due to random chance. Furthermore, the adjusted R-

squared values from the OLS regressions fell well below 1% for

all months, indicating that the percent of LEDs installed on

each block explain less than 1% of the variation in crime for

that block. These results indicate that LEDs cannot be expected

to predict to influence crime in any measurable way. Instead,

the highly clustered pattern of the over- and under- predictions

of crime suggest that there are spatial influences other than

lighting responsible for driving the changing pattern of

opportunistic crimes committed.

Hot Spot Analysis (Getis-Ord Gi*)

The hot spot analysis revealed interesting results regarding the

spatial pattern of the under- and over- predictions in crime. For

every month, areas were identified where high and low crime

values cluster spatially. Particularly striking is that the over-

predicted areas where crime was significantly lower than

expected (represented in blue) stay relatively constant over time

and are consistently concentrated to the North-West of Lake

Merritt. This is in contrast to the under-predicted areas where

crime was higher than expected (represented in red) which vary

significantly from month to month and display drastically

different patterns. These patterns open many more

opportunities for analysis. One potential hypothesis for the

different patterns of high and low crime rates might be that the

areas with lower than anticipated crime may be most influenced

by changing demographic patterns which tend to evolve slowly

whereas the high crime areas may be driven primarily by

changing police beats which adapt quickly.

3D Visualization of both Space and Time

The intention of this graphic was to demonstrate that crimes

occur at different points in space, but also at different points in

time. Unfortunately, with so many points, this map may have

just added more confusion than clarity. One insight that can be

gleaned, however, is that a disproportionate number of auto-

burglaries appear to dominate the very end of the study period

in December, which likely reflects a problem with the data.

July August September. October November December

Quantifying Relationships & Analyzing Spatial Patterns

Monthly Crime Variation by Block (2012 – 2013)

Data Sources Crime Data: Oakland Police Department ftp:///crimewatchdata.oaklandnet.com; LED Data: Oakland City Administration Public Records Request records.oaklandnet.com; Census Data: Professor John Radke.

Assumptions, Data Selection, and Modeling Decisions: The main goal for this part of the model

was to create a visual illustration of the change in crime rates from year to year by month and

block. In this case, LEDs are assumed only to effect nighttime crimes when the lights are on

and only opportunistic crimes or crimes otherwise likely to be effected by enhanced visibility on

the street and sidewalks. Daily sunrise and sunset times were appended to the crime report

(using STATA), from which a night variable was created indicating whether each particular

crime occurred at night. Opportunistic crimes were selected from the total crime report dataset

including robbery, auto burglary, grand theft auto, petty theft, and vandalism.

117, 647 out of 252,367 crimes were successfully geo-referenced for 2012 and 2013, of which

8023 were opportunistic crimes which occurred at night. Individual point shapefiles of

nighttime opportunistic crime were created for each month of the study (July through

December) and joined to the census block shapefile with the LED installations by month,

generating a count of crimes on each block every month. To calculate the change in crime

counts, 2012 monthly crime by block was subtracted from 2013 monthly crime counts by block,

resulting in a year-over-year difference in total crimes by month. Each month was modeled

separately to account for correlations with weather and temperature.

For every block, the average monthly year-over-year change in crime following 75% or greater

LED installations for that particular block was calculated and then normalized by population.

Assumptions, Data Selection, and Modeling Decisions: The main goal for the 3D Analysis of crime was to illustrate both spatial and temporal dimensions of crime. This map was generated using 3D analyst, where a time lapse variable was

created for each crime point and treated as the z-coordinate in the 3D analysis. In other words, the height of each crime reflects the time passed since the beginning of the study period on July 1st, 2013.

Hot Spot Analysis (Getis-Ord G*): Spatial patterns of over- and under-predictions of crime. Hot Spot Analysis maps were developed from the regression residuals (the under- and over- predictions of crime) identified in the OLS regression. Hot spots represent statistically significant spatial clusters of high and low values, where higher than expected crime rates are red and lower than expected crime rates are blue.