geographic patterns and profiling of illegal land border crossings d. kim rossmo quint c. thurman

Geographic Patterns and Profiling of Illegal Land Border Crossings

D. Kim RossmoQuint C. Thurman

Border Control

Geographic range Porous nature US national security Significant challenge

Rugged terrain of El Paso border

The Illegal Border Crossing “Arms Race”

Ground sensor and UAV

Fencing and electronic gate

Trucks in Rio Grande canyon

Inner tubes along the Rio Grande

Berlin Wall

10,315 days August 1961 to November 1989 East / West Berlin border 27 miles West Berlin border 96 miles 12-foot wall, 302 watch towers, 20 bunkers 192 deaths, 200 shooting injuries 5,000 escapes (3.4 per week)

US Border Patrol Sectors

Texas-MexicoBorder 1,000 miles 52% of

southern border

1,933 miles 50% federal

land

Theoretical Perspective Criminal behavior

Bounded rationality Shaped by personal and environmental factors

Migration theory Origin (push) factors Destination (pull) factors) Distance (effort, time, money)

Cost/benefit (risk/reward) analysis framework

Illegal Border Crossing Requirements

Trip origin Border Staging area Crossing Landing Trip destination

Premise Identify border crossing factors

Facilitating Inhibiting

Determine features related to probability of illegal border movement Environmental Physical

US Border Patrol Data

2001 to 2004 (4 years) 1 to 1.6 million cases annually in the US 20 to 25% of these cases are in Texas Laredo, McAllen, Del Rio, Marfa,

and El Paso Sectors

Dependent Illegal Texas-Mexico land border crossings

Independent Physical geography Human geography

Research Variables

Preliminary Focus Area

Del Rio Border Patrol Sector

Val Verde, Kinney, & Maverick Counties

111 Landmark Mile Markers (170 miles)

Physical Geography

Hydrography Terrain Vegetation Temperature (day and night)

Hydrography Types of features

Streams Rivers Lakes Reservoirs

Data sources US Census (USA) INEGI (Mexico)

Terrain (USGS DEM) Extent

All counties Mexico overlap

Primary Output Elevation Slope Aspect Hillshade

Identification Ravines Gullies

Vegetation Types of features

Brush Crops Parks Woods

Data sources TPWD (US) INEGI (Mexico)

Temperature

Monthly normals Minimum Maximum

Annual normals Mean

Human Geography Variables

Major transportation Population density Urban developments Political region Additional features

Major Transportation Types

Airports Railroads Roads Bridges

US data sources US Census TCEQ

Mexico data sources GeoCommunity

Population Density & Urban Development

US data 2000 US Census All counties

Mexico Data 1990 Census Mexico

October 1, 2000 - August 17, 2004All Entries

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000V

-1

V-5

V-9

V-1

3

V-1

7

V-2

1

V-2

5

V-2

9

V-3

2

V-3

6

V-3

9

V-4

1

V-4

3

V-4

7

K-3

K-7

M-2

.5

M-6

M-0

9 A

M-1

3

M-1

7

M-2

1

M-2

5

M-2

9

M-3

3

M-3

7

M-4

1

M-4

5

Landmark Mile Marker(North to South Order)

En

trie

s

October 1, 2000 - August 17, 2004Criminal Disposition Entries

0

10

20

30

40

50

60

70

80

90

100

V-1

V-5

V-9

V-1

3

V-1

7

V-2

1

V-2

5

V-2

9

V-3

2

V-3

6

V-3

9

V-4

1

V-4

3

V-4

7

K-3

K-7

M-2

.5

M-6

M-0

9 A

M-1

3

M-1

7

M-2

1

M-2

5

M-2

9

M-3

3

M-3

7

M-4

1

M-4

5

Landmark Mile Marker(North to South Order)

En

trie

s

Del Rio Illegal Border Crossings

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

1 21 41 61 81 101

Landmarks

Cro

ss

ing

sDel Rio Illegal Border Crossings

(Cumulative %)

G = 0.64ID = 0.49

Del Rio Sector – All Illegal Entries

2000 14,520

2001 86,974

2002 61,974

2003 53,744

2004 37,534

2000-04254,717

All Entries

Del Rio Sector – Criminal Entries

2000 -

2001 5

2002 129

2003 289

2004 162

2000-04 585

Criminal Entries

Density of Del Rio Sector Illegal Entries

Entries by MonthAll Entries

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

Month

En

trie

s

Entries by MonthCriminal Disposition Entries

0

10

20

30

40

50

60

70

80

Month

En

trie

s

Entries by Day of MonthAll Entries

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Day of Month

En

trie

s

Entries by Day of WeekAll Entries

30,000

32,000

34,000

36,000

38,000

40,000Sunday

Monday

Tuesday

WednesdayThursday

Friday

Saturday

Entries by Day of WeekCriminal Disposition Entries

40

50

60

70

80

90

100

110Sunday

Monday

Tuesday

WednesdayThursday

Friday

Saturday

Time of Entry - All Entries

4000

8000

12000

16000

0:00

1:00

2:00

3:00

4:00

5:00

6:00

7:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

23:00

Time of Entry - Criminal Disposition Entries

0

5

10

15

20

25

30

35

40

45

500:00

1:00

2:00

3:00

4:00

5:00

6:00

7:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

23:00

Residence Country (Excluding Mexico)

US or Mexico State of Birth

Gender

Apprehension Age

Distance to United States Urban Areas

Distance to Mexico Urban Areas

Slope Least Cost Path

Cost-weighted distance (higher slope = greater cost)

Cost-weighted direction (least cost direction to source)

Least cost path (to rural highway segments)

Combined Slope Cost-Weighted Distance

Top 10 criminal disposition entry Landmark Mile Markers

Weighted by number of criminal disposition entries

323 total entries

Slope Cost Distance Analysis

Travel across terrain values is more difficult (higher “cost”)

Border areas with low slope cost values are attractive to illegal border crossers

Elevation Change Analysis

Border areas with higher elevation on the Mexico side are attractive to illegal border crossers

Elevation differentials allows for surveillance of US Border Patrol vehicles

Viewshed Analysis

The viewshed (line of sight) identifies all locations visible from a given point

Areas with low viewshed values are attractive to illegal border crossers due to low detection risk

Illegal Border Crosser Population Estimates

Official estimates: +10 to 50% Line officer estimates: +100%

Capture-RecaptureAnalysis P

S1

S2O

P = S1S2

O

?

Scholarly Outcomes

Illegal movements International migration Spatial displacement Natural and built factors Microlevel environmental influences

Practical Products

US Border Patrol operations Unidentified crossing points Displacement anticipation Criminal and drug interdiction Counter-terrorism applications Training