Obtained and geocoded prostitution data from Oakland CrimeWatch.

These were the “experimental” points.

Created a group of random control points

Decomposed census block group data into points and interpolated 13 rasters of

demographic characteristics (by kriging)

Used “extract value to points” to add spot

estimates of demographic characteristics from

rasters to experimental and control points

Exported table to excel, ran means-comparison tests on demographics

particular to prostitution areas vs. control.

For demographic characteristics particular to prostitution areas, set

opportunity and constraint boundaries

Classified major streets as thoroughfares and created a buffer.

Reclassified floating point rasters to integers based

on opportunity and constraint boundaries; decomposed integer rasters to polygons

Unioned demographic polygons and buffered

thoroughfares to create a final suitability layer.

QUESTION: Given where street prostitution occurs now, what areas of Oakland are most at risk of becoming future prostitution centers?

SOLUTION: First identify the particular characteristics of existing prostitution hotspots, then look for Oakland neighborhoods with similar characteristics.

FINDINGS: On average, areas with street prostitution have lower income, a

lower rate of owner occupancy, and a higher vacancy rate, proportionally fewer whites and more minorities, and a greater proportion of young people than the average area in Oakland.

99% of reported prostitution incidents occur within 500 feet of a thoroughfare.

Future hotspots may include deeper East Oakland along Bancroft or Seminary.

Conditions in West Oakland may facilitate an increase in street prostitution activity there.

Ryan Hunter, May 2011

Means comparison Opportunities and constraints

LIMITATIONS OF THE ANALYSIS: Due to time and logistical constraints, I could not include many

relevant variables. Ideally, I would have liked to examine street lighting, land use, police assignments, the level of engagement in local Neighborhood Crime Prevention Councils, and more.

I cannot claim that any of the statistically significant factors in the analysis cause street prostitution, only that they are correlated with it.

OPD police report data may not accurately represent street prostitution activity. For example, if both International Blvd. and San Pablo Ave. have equal prostitution activity, but OPD conducts more operations on International, San Pablo will (falsely) appear in the data to have relatively little street prostitution.