Temple University Department of Criminal Justice Spatial Clustering of Illegal Drug Dealers:...

Temple UniversityDepartment of Criminal Justice

Spatial Clustering of Illegal Drug Dealers:

Swarming for Safety or Agglomeration for Profit

Dr. George F. Rengert

Department of Criminal Justice

Temple University

Philadelphia, PA. 19122

[email protected]


Where can illegal drug markets locate? Folk wisdom: any where they want to. Scientific knowledge: any where they want to as

long as: Safe from neighbors and detection by police. Profits can be made.

Safest areas to sell drugs generally thought to be in socially disorganized areas. If not socially disorganized, may experience

resistance from neighbors. Example from North Philadelphia:


But most socially disorganized areas may be least profitable areas. Lack local demand—abandoned houses.

Drug dealing can lead to abandoned houses as more people sell than buy houses in this community.

Would you buy one of these houses located in a drug sales area of North Philadelphia?


The following is an example of housing abandonment measured by tax delinquency around a drug sales area.


Which is it, profit or social disorganization?

Critical issues concerning profits in retail operations: location, location, location. Good locations allow:

ready access attract large numbers of customers increase the potential sales of retail outlets.


Retail market analysts commonly use demographic variables to predict market share = Demographic Profile. Who is likely to purchase illegal drugs?

Young adults aged 15 to 29. High school drop-outs. Unemployed.

Marketing geographers have developed several strategies for determining optimal locations of retail firms.

Location-allocation model most often used. Includes: the objective function, demand points,

feasible sites, a distance matrix, and an allocation rule.


We use the objective function of maximizing sales volume by minimizing distance to potential customers identified by the Demographic Profile.

Data from Wilmington, Delaware. Demand points = centroids of census tracts. Distance matrix = distance between centroids of

. census tracts.

Allocation rule = customers assigned to the census tract that minimizes total distance traveled by potential customers for illegal drugs.

Assumption = all users in city purchase drugs at

this census tract


Would have to travel the fewest person-miles if illegal drug market was located in census tract 1600.

This tract was fourth from the highest in reality.

Limitations of the simple form of location-allocation model: Planar model = any location in city is a potential

site. Residents of expensive housing areas not likely.

Masked out areas where medium housing values above average.

Local addicts will travel any distance for drugs. Assigned zero to distance if beyond a mile, 1 if less.


Distance matrix is replaced with zeros and ones. Two clusters of census tracts identified:

2200, 2300, and 1400. 602 and 601. Surprise: not in the center of the city.


The analysis yielded two clusters of census tracts. The census tracts that ranked first, second and

third formed the first cluster. The census tracts that ranked fifth and sixth the

second. The preceding map portrayed this analysis.

It is not census tract 600 or 1600 that are in the center of the city.

Rather it is a group of census tracts that are in the center of a population of potential drug users.

Rather large areas. We need specific sites for our drug market. Requires more refined analysis possible with GIS.


What can Geographic Information Systems do for us? Compare traditional analysis with what possible

with GIS:

Traditional analysis assigns features to census space. Census tracts. Block groups. Block faces.

Census boundaries are set and determine the spatial nature of the analysis.


Refined method: Create GIS buffers about features and allocate

proportion of area of tract that is within buffer. Advantages:

Feature does not have to be in tract to impact it.

Impact is proportional to size of tract.

Disadvantages:

Assumes impact uniformly distributed across entire tract.

Proportion not site specific.


GIS method: Create new geographies with buffers around

features. Create ‘interaction effects’ with overlays of

buffers. Advantages:

Does not assume effect is uniform over census tract. Buffers can be sized to reflect spatial reach of a feature.

Disadvantages: New geographies vary markedly in size. Small slivers created that lack geographic meaning. Zero counts overrepresented.


Drug Market Analysis of Wilmington, Delaware, GIS. Initially start with census data at block group level.

Local Demand: 1. Percent of population age 14 to 29. 2. Unemployed males. 3. Percent of population over age 18 with less than a high school education. 4. Median Income. 5. Number of children under age 5 living in poverty. R2 = .467


Identify features that attract potential drug users.

Routine activities create ‘crime generators.’ Schools, taverns, homeless shelters, etc.

Create buffers around these features to determine their areal reach if any.

Use location quotients to determine if feature associated with spatial aggregation of drug dealers.


LQ = CR / CN

CR = Number of drug arrests per square mile in

GIS identified area.

CN = Number of drug arrests per square mile in

entire city.


Accessibility Buffers Location Quotients

400-ft Major Roads 1.49 600-ft Major Roads 1.30 800-ft Major Roads 1.20

400-ft I-95 Exits 0.59 800-ft I-95 Exits 1.10 1200-ft I-95 Exits 1.41 1600-ft I-95 Exits 1.76 2000-ft I-95 Exits 1.65

400-ft Transit Locations 1.66 600-ft Transit Locations 1.13 800-ft Transit Locations 0.98


Major Roads Buffer (mask)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft600 ft800 ft


#Y#Y

#Y#Y

#Y

#Y#Y

#Y

#Y Train Station

Rodney Square (Bus)

N

EW

S

1 0 1 Miles

I 95 BuffersWilmington, DE

Masked Area2000 ft. Buffer1600 ft. Buffer1200 ft. Buffer800 ft. Buffer400 ft. BufferMajor RoadsI 95

#Y I 95 Exits


I 95 Exits Buffer (mask)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft800 ft1200 ft1600 ft2000 ft


#Y#Y

#Y#Y

#Y

#Y#Y

#Y

#Y Train Station

Rodney Square (Bus)

N

EW

S

1 0 1 Miles

Major Transit Location BuffersWilmington, DE

Masked AreaTransit buff 800.shpTransit buff 600.shpTransit buff 400.shpMajor RoadsI 95

#Y I 95 Exits


Transit Locatons Buffer (mask)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft600 ft800 ft


Local Accessibility

Routine activity nodes. Anchor points of daily activities.


Opportunity Buffers Location Quotient 400-ft Schools 1.19 800-ft Schools 1.04 1000-ft Schools 0.92 1200-ft Schools 0.58 400-ft Taverns 2.30 800-ft Taverns 1.94 1200-ft Taverns 1.71 400-ft Liquor Stores 2.42 800-ft Liquor Stores 1.66 1200-ft Liquor Stores 1.40 400-ft Check Cashing 3.00 800-ft Check Cashing 2.48 1200-ft Check Cashing 2.06 400-ft Pawn Shop 0.10 800-ft Pawn Shop 1.29 1200-ft Pawn Shop 1.54 400-ft Police Station 1.61 800-ft Police Station 1.49 1200-ft Police Station 1.31 400-ft Court 0.48 800-ft Court 0.65 1200-ft Court 0.95 400-ft Fire Station 0.86 800-ft Fire Station 0.87 1200-ft Fire Station 1.03 400-ft Shelter 2.93 800-ft Shelter 3.17 1200-ft Shelter 2.75


School Buffer (Masked)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft800 ft1000 ft1200 ft


Tavern Buffer (Masked)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft800 ft1200 ft


Liquor Store Buffer (Masked)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft800 ft1200 ft


Check Cashing Buffer (Masked)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft800 ft1200 ft


Pawn Shops Buffer (Masked)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

LQ

400 ft800 ft1200 ft


Police Buffer (Masked)Location Quotient by Year

-0.50

0.50

1.50

2.50

3.50

4.50

5.50

6.50

7.50

8.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997Year

LQ

400 ft800 ft1200 ft


Fire Stations Buffer (Masked)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997Year

LQ

400 ft800 ft1200 ft


Shelters Buffer (Masked)Location Quotient by Year

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997Year

LQ

400 ft800 ft1200 ft


#

#

#

#

#

#

##

##

#

##

#

#

#

#

#

#

#

##

#

#

#

#

#

#

#

#

##

#

##

#

#

#

#

#

#

#

#

#

##

#

#

#

##

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

## #

#

##

#

#

#

#

#

##

#

#

#

#

#

#

#

#

#

#

#

#

#

###

#

##

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

0 1 2 Miles

N

EW

S

Homeless Shelters

Social Service

Centers

Check Cashing

Stores

Taverns

Liquor Stores

Wilmington, DE

Crime Generators andCriminal Attractors


0 1 2 Miles

N

EW

S

Homeless Shelters 800 feet

Social Service Centers 800 feet

Check Cashing Stores 400 feet

Taverns 400 feet

Liquor Stores 400 feet

Wilmington, DE



The Analysis

Create buffers around point and line features. Assign the buffer areas to census block groups.

Statistically analyze the importance of each variable. Begin with drug sales figures and the plotting of

each feature on a map of Wilmington, Delaware.


Zero Inflated Poisson Model

Two phase analysis. Like analysis of number of children a couple

chooses to have: Choice to have children Choice of how many children to have once decide to

have them.


Factors Associated with the Establishment of a Drug Market Positively Associated:

Percentage of nonwhite residents. As the percentage of nonwhite residents increases, the

chance that the area will never have a drug-market arrest decreases.

The spatial lag term. As the number of arrests in the surrounding area increases,

the chance of the parcel never having a drug-sale arrest diminishes.

Negatively Associated: I-95 exits.

Being located near to an access ramp for I-95 increases the chance that an area will not have drug-market arrests.

Rest not statistically significant


Factors associated with the size of drug markets given that a drug market exists: Positively associated:

I-95 exits. Female headed households with children. Vacant homes. Non-white residents. Check-cashing stores. Liquor stores. Homeless shelters. Spatial lag term.

Negatively associated: Renter occupied units. Social service programs. Taverns.

Rest not statistically significant


Implications of the Study. Significant difference between taverns and Liquor

stores. Place managers of tavern owners?

Negative association between rental housing and drug sales arrests.

Interaction between population density and neighborhood control?

Significance of “spatial lag term.” Is it “Agglomeration economies? “ Is it “social networks?” Is it a result of “spatial diffusion?”


Association between Black population and drug sales arrests. Is it “environmental racism?

Noxious facilities are put in vulnerable neighborhoods

Is it a lack of “social efficacy.” Do not use all the tools available including the police.

Not police “crackdowns.” Rather, prioritize calls for service—create social efficacy.


Clearly what is needed at this point is contextual analysis to determine interaction effects. We especially see this in the I-95 access.

Not all are bad. But if is bad, is very bad as size of market illustrates.

We also see this in the difference between taverns and liquor stores.

Notice that the difference between location quotients is not great for taverns.

Indicates they might locate in bad areas rather than attracting drug sales.

Liquor stores have greater difference in LQ. In order to obtain contextual variables, can use GIS

to visualize:


0 1 2 Miles

N

EW

S

Census Block Groups

Wilmington, DE


0 1 2 Miles

N

EW

S

Homeless Shelters 800 feet

Social Service Centers 800 feet

Check Cashing Stores 400 feet

Taverns 400 feet

Liquor Stores 400 feet

Wilmington, DE



0 1 2 Miles

N

EW

S

Single Coverage combined polygons

Wilmington, DE



0 1 2 Miles

N

EW

S

Single Coverage combined polygons

Wilmington, DE

Regional Accessibility, Crime Generators andCriminal Attractors


0 1 2 Miles

N

EW

S

Census Block Groupsand Built Environment

Wilmington, DE


0 1 2 Miles

N

EW

S

Areas within Buffers of:

Liquor Store or Tavernand I95 Exits and Major Roads

Wilmington, DE


0 1 2 Miles

N

EW

S

Single Coverage

Census Block Groupsand Built Environment

Wilmington, DE


How do you know which interaction effects are significant? Which should you choose?

Answer Tree analysis in SPSS-- interaction trees. Can force first split. Drug Sales Arrests

Low income High income

Temple University Department of Criminal Justice Spatial Clustering of Illegal Drug Dealers:...

Documents

Transcript of Temple University Department of Criminal Justice Spatial Clustering of Illegal Drug Dealers:...