The Geography of Loss: Mapping Airport Baggage Theft Risk and the Role of Visual Deterrence as a Tamper Signal

Abstract

Baggage theft and tampering remain persistent yet unevenly distributed risks in U.S. air travel. While Transportation Security Administration (TSA) screening has focused primarily on threat interdiction, passenger property loss continues to generate large numbers of claims annually. This study synthesizes TSA claims guidance, secondary analyses of TSA claims data, Federal Aviation Administration (FAA) enplanement statistics, and behavioral criminology theory to examine two questions: (1) whether smaller, non-hub airports plausibly exhibit higher theft risk per passenger than major hubs, and (2) whether luggage locks function less as physical security devices and more as visual deterrents and tamper signals. We find strong conceptual and empirical support for a “risky facilities” model in which lower automation, reduced surveillance density, and fragmented responsibility increase opportunity-based theft risk at smaller airports. Further, evidence suggests that reimbursement for broken locks or missing items is rare, shifting the traveler’s optimal strategy from reliance on compensation to early detection and rapid reporting. The findings reframe luggage locks as informational devices that reduce downstream loss by signaling unauthorized access at the point of arrival.

1. Introduction

Air travelers commonly assume that large, crowded airports pose the greatest risk for baggage theft. This intuition aligns with traditional crime theories emphasizing anonymity and volume. However, aviation security operates within a highly structured, segmented system in which access, surveillance, and accountability vary significantly by airport size and infrastructure. This paper challenges the “crowds equal crime” assumption and advances an alternative framework: that theft risk is better explained by opportunity gradients embedded in airport operations.

Using TSA claims processes, FAA passenger volume normalization, and opportunity-based criminology, this study analyzes how theft risk may concentrate in unexpected locations and why common consumer countermeasures—specifically luggage locks—retain value even when they do not prevent forced entry.

2. Literature Review

2.1 Opportunity-Based Crime and “Risky Facilities”

Crime science literature finds that a small number of facilities often account for a disproportionate share of crime, a phenomenon described as “risky facilities” (Eck, Clarke, & Guerette, 2007). Theft is particularly sensitive to reduced guardianship, ambiguous responsibility, and low probability of immediate detection. Airports function as complex facilities with layered responsibility across TSA screeners, airline baggage handlers, and third-party contractors. These conditions may be amplified at smaller airports where automation and surveillance infrastructure are limited.

2.2 TSA Screening, Claims, and Attribution Complexity

The TSA distinguishes between damage or loss caused by TSA screening and that caused by airline baggage handling. Travelers who believe TSA is responsible must file a tort claim (Standard Form 95) and demonstrate circumstances and damages; claims are commonly denied when investigators conclude TSA did not open the bag for physical inspection or when responsibility lies elsewhere (TSA, n.d.-a; TSA, n.d.-b).

A large secondary analysis of TSA claims from 2010–2017 found that approximately 41% of claims were denied, underscoring the difficulty of obtaining reimbursement even when loss occurs (McCartney, 2019).

2.3 Airport Size, Automation, and Theft Risk

FAA enplanement statistics demonstrate large differences in passenger volume between hub and non-hub airports (FAA, n.d.-a; FAA, n.d.-b). Large hubs commonly employ more automated baggage handling systems, while smaller airports may rely more heavily on manual handling and physical transfer points. From an opportunity perspective, manual systems increase access points, handling time per bag, and unobserved custody intervals.

3. Data and Methods

3.1 Data Sources

Because TSA microdata are not continuously published in machine-readable form, this study synthesizes:

• TSA claims process documentation and public guidance
• Secondary analyses of TSA claims datasets (2010–2017)
• FAA airport enplanement statistics
• Public airline baggage reporting requirements

3.2 Normalization: Claims per Million Enplanements

Prior analyses of TSA claims emphasize that raw claim counts strongly correlate with passenger volume. To assess relative risk, claims are normalized by passenger enplanements:

Claims per Million Enplanements (CPME) = (Claims / Enplanements) × 1,000,000

3.3 Airport Size Categories

Airports can be categorized into large hubs, medium hubs, small hubs, and non-hub (regional) airports. Comparative analysis focuses on rate ratios between hub classes rather than absolute counts.

4. Findings

4.1 The Plausibility of the “Spoke Vulnerability” Effect

While large hub airports generate the highest absolute number of claims, normalization by enplanements suggests that per-passenger risk plausibly increases as airport size decreases, consistent with risky facility theory. Contributing mechanisms include fewer automated custody controls, lower surveillance density, greater reliance on contractor labor, and reduced redundancy in chain-of-custody verification.

4.2 Lock-Related Claims and Reimbursement Outcomes

TSA guidance indicates that locks may be cut during screening and that compensation depends on proof of TSA responsibility (TSA, n.d.-a; TSA, n.d.-b). Public claims analyses suggest many property damage claims are denied, reinforcing the limited protective value of reimbursement mechanisms (McCartney, 2019).

4.3 Locks as Visual Deterrents and Tamper Signals

Although locks do not reliably prevent access, they serve two behavioral functions:

1. Visual deterrence: opportunity theft is sensitive to effort and visibility; locked bags impose marginally higher effort and risk.
2. Tamper detection: a missing or broken lock provides immediate evidence of unauthorized access, prompting travelers to inspect contents and report issues before leaving the airport.

Airlines commonly require prompt reporting for baggage damage, often before leaving the airport, making early detection valuable (American Airlines, n.d.).

5. Discussion

5.1 Rethinking “Security Theater”

Consumer security products are sometimes framed as “security theater.” However, this framing can overlook the informational value of tamper-evident devices. In an environment where compensation is uncertain and attribution is contested, early detection becomes a primary loss-mitigation strategy.

5.2 Implications for Travelers

• Theft and tampering risk is not evenly distributed across airports.
• Smaller airports may pose higher per-passenger risk under opportunity-based models.
• Locks are best evaluated as signals and deterrents rather than as vaults.

5.3 Implications for Policy and Design

Improving passenger outcomes may require clearer chain-of-custody signaling, improved passenger education on immediate inspection, and facility-level risk transparency.

6. Limitations

This study relies on secondary analyses rather than direct TSA microdata and cannot produce definitive airport-level rankings or causal estimates. Future research should incorporate full claims datasets and regression controls for airport layout, tourism mix, and screening model.

7. Conclusion

Baggage theft in U.S. air travel is best understood as an opportunity-driven phenomenon shaped by facility design and operational scale. Smaller airports, despite their benign appearance, may expose travelers to higher per-passenger risk. In this context, luggage locks function most effectively not as physical barriers but as tamper signals that enable rapid detection, reporting, and loss mitigation.

References

1. Transportation Security Administration. (n.d.-a). Claims for damage, delay, or loss. U.S. Department of Homeland Security.
2. Transportation Security Administration. (n.d.-b). Standard Form 95 and claims process guidance. U.S. Department of Homeland Security.
3. Transportation Security Administration. (n.d.-c). Not all airports are staffed by TSA. U.S. Department of Homeland Security.
4. Federal Aviation Administration. (n.d.-a). Passenger boarding (enplanement) statistics. U.S. Department of Transportation.
5. Federal Aviation Administration. (n.d.-b). Airport passenger data resources. U.S. Department of Transportation.
6. McCartney, S. (2019). Why the TSA rarely pays passengers’ claims. Wall Street Journal.
7. American Airlines. (n.d.). Delayed or damaged baggage policy.
8. Eck, J. E., Clarke, R. V., & Guerette, R. T. (2007). Risky facilities: Crime concentration in homogeneous sets of establishments and facilities. Crime Prevention Studies, 21, 225–264.

Note: This article synthesizes publicly available guidance and secondary analyses. Specific airport-level rankings require access to TSA claims microdata and year-matched FAA enplanement files.