Spatial Clustering of Illegal Drug Dealers: Dr. George F. Rengert

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 grengert@temple.edu

Temple University






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:

Temple University


Temple University


 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?

Temple University


Temple University


Temple University


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

Temple University


Temple University


Temple University


 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.

Temple University


 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.

Temple University






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

Temple University






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.

Temple University


 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.

Temple University








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.

Temple University


 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.

Temple University


Temple University


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.

Temple University


Temple University


 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.

Temple University


Temple University


 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.

R 2 = .467

Temple University


 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.

Temple University


LQ = C

R

/ C

N

C

R

= Number of drug arrests per square mile in

GIS identified area.

C

N

= Number of drug arrests per square mile in entire city.

Temple University


Accessibility Buffers Location Quotients

400-ft Major Roads 1.49

600-ft Major Roads

800-ft Major Roads

1.30

1.20

400-ft I-95 Exits

800-ft I-95 Exits

1200-ft I-95 Exits

1600-ft I-95 Exits

2000-ft I-95 Exits

400-ft Transit Locations



0.59

1.10

1.41

1.76

1.65

1.66

1.13

0.98

Temple University


Temple University


Major Roads Buffer (mask)

Location Quotient by Year

2.50

2.00

1.50

1.00

0.50

0.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

600 ft

800 ft

Temple University


I-

9

5

C

Lan cas ter

Pe n ns ylv ania

B ay n ar d

W ash ing ton

M a rke t

U ni on

L in co ln

4th

2nd

Ma ryla nd

B ro o m

F ra nk lin

Y

Y

J ac k so n

Y

A da m s

W as h in gt on

W es t

12th

11th

10th

K in g

W al nu t

MLK

T ra in S t a tio n

S pr u ce

C hu rc h

R o d n e y Sq u a re (Bu s )

N or th ea st

12th

C hr is tia n a

H e al d

N e w

C a st le

I 95 Buffers

Wilmington, DE W

N

S

E

1 Mi le s


I 95 Exits

I 95

Major R oads

400 ft. Buffer

800 ft. Buffer

1200 ft. Buffer

1600 ft. Buffer

2000 ft. Buffer

Mask ed Area

Temple University


I 95 Exits Buffer (mask)


2.50

2.00

1.50

1.00

0.50

0.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

1600 ft

2000 ft

Temple University


I-

9

5

C

Lan cas ter

Pe n ns ylv ania

B ay n ar d

W ash ing ton

M a rke t

U ni on

L in co ln

4th

2nd

Ma ryla nd

B ro o m

F ra nk lin

Y

Y

J ac k so n

Y m s

A da

W as h in gt on

W es t

12th

11th

10th

K in g

W al nu t

MLK

T ra in S t a tio n

S pr u ce

C hu rc h

R o d n e y Sq u a re (Bu s )

N or th ea st

12th

H e al d

N e w

C a s e

C hr is tia n a

Major Transit Location Buffers

Wilmington, DE

W

N

S

E

1 Mi le s


I 95 Exits

I 95

Major R oads

Transit buff 400.shp



Mask ed Area

Transit Locatons Buffer (mask)


3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

600 ft

800 ft

Temple University


Local Accessibility





Routine activity nodes.

Anchor points of daily activities.

Temple University


Opportunity Buffers

400-ft Schools

800-ft Schools

1000-ft Schools

1200-ft Schools

400-ft Taverns

800-ft Taverns

1200-ft Taverns

400-ft Liquor Stores



400-ft Check Cashing



400-ft Pawn Shop

800-ft Pawn Shop

1200-ft Pawn Shop

400-ft Police Station



400-ft Court

800-ft Court

1200-ft Court

400-ft Fire Station

800-ft Fire Station

1200-ft Fire Station

400-ft Shelter

800-ft Shelter

1200-ft Shelter

Temple University


Location Quotient

1.19

1.04

0.92

0.58

2.30

1.94

1.71

2.42

1.66

1.40

3.00

2.48

2.06

0.10

1.29

1.54

1.61

1.49

1.31

0.48

0.65

0.95

0.86

0.87

1.03

2.93

3.17

2.75

School Buffer (Masked)


2.00

1.50

1.00

0.50

0.00

4.00

3.50

3.00

2.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1000 ft

1200 ft

Temple University


Tavern Buffer (Masked)


2.00

1.50

1.00

0.50

0.00

4.00

3.50

3.00

2.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

Temple University


Liquor Store Buffer (Masked)


2.00

1.50

1.00

0.50

0.00

4.00

3.50

3.00

2.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

Temple University


Temple University


Check Cashing Buffer (Masked)


4.50

4.00

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

Temple University


Pawn Shops Buffer (Masked)


4.00

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

Temple University


Police Buffer (Masked)


2.50

1.50

0.50

-0.50

8.50

7.50

6.50

5.50

4.50

3.50

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

Temple University


Fire Stations Buffer (Masked)


4.00

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

Temple University


Shelters Buffer (Masked)


2.50

2.00

1.50

1.00

0.50

0.00

5.00

4.50

4.00

3.50

3.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

400 ft

800 ft

1200 ft

Temple University


Crime Generators and

Criminal Attractors

#

#

#

#

#

#

#

#

#

#

#

##

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

## #

#

# #

#

#

#

#

##

#

#

#

#

# #

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

Homeless Shelters

Social Service Centers

Check Cashing Stores

Taverns

Liquor Stores

0 1 2 Miles W

Temple University


S

N

E

#

#

#

##

#

Wilmington, DE



Homeless Shelters 800 feet

Social Service Centers 800 feet

Check Cashing Stores 400 feet

Taverns 400 feet

Liquor Stores 400 feet

N

0

Temple University

1 2 Miles


W

S

E 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.

Temple University


Temple University


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.

Temple University


 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

Temple University


 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

Temple University


 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?”

Temple University


 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.

Temple University






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:

Temple University


Census Block Groups

N

0

Temple University

1 2 Miles


W

S




Homeless Shelters 800 feet

Social Service Centers 800 feet

Check Cashing Stores 400 feet

Taverns 400 feet

Liquor Stores 400 feet

N

0 1 2 Miles

Temple University


W

S

E

Wilmington, DE



Single Coverage combined polygons

N

0

Temple University

1 2 Miles


W

S


Regional

Accessibility,



Single Coverage combined polygons

N

0

Temple University

1 2 Miles


W

S


Census Block Groups and Built Environment

N

0

Temple University

1 2 Miles


W

S


Areas within

Buffers of:

Liquor Store or Tavern and I95 Exits and Major Roads

N

0

Temple University

1


2 Miles W

S


Census Block Groups and Built Environment

Single Coverage

N

0

Temple University

1 2 Miles


W

S






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


Spatial Clustering of Illegal Drug Dealers: Dr. George F. Rengert

Local Accessibility

The Analysis

Zero Inflated Poisson Model

Products

Support

Spatial Clustering of Illegal Drug Dealers: Dr. George F. Rengert

Local Accessibility

The Analysis

Zero Inflated Poisson Model

Add this document to collection(s)

Add this document to saved

Suggest us how to improve StudyLib