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Decision Analysis and Risk Models for Land Development Affecting Infrastructure Systems

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  • Shital A. Thekdi
  • James H. Lambert

Abstract

Coordination and layering of models to identify risks in complex systems such as large‐scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk‐informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700‐mile multimodal transportation system adjacent to 60,000 tracts of potential land development.

Suggested Citation

  • Shital A. Thekdi & James H. Lambert, 2012. "Decision Analysis and Risk Models for Land Development Affecting Infrastructure Systems," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1253-1269, July.
  • Handle: RePEc:wly:riskan:v:32:y:2012:i:7:p:1253-1269
    DOI: 10.1111/j.1539-6924.2011.01719.x
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    References listed on IDEAS

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    Cited by:

    1. Hiba Baroud & Jose E. Ramirez‐Marquez & Kash Barker & Claudio M. Rocco, 2014. "Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1317-1335, July.
    2. Gregory F. Nemet & Laura Diaz Anadon & Elena Verdolini, 2017. "Quantifying the Effects of Expert Selection and Elicitation Design on Experts’ Confidence in Their Judgments About Future Energy Technologies," Risk Analysis, John Wiley & Sons, vol. 37(2), pages 315-330, February.
    3. Michelle C. Hamilton & Shital A. Thekdi & Elisabeth M. Jenicek & Russell S. Harmon & Michael E. Goodsite & Michael P. Case & Christopher W. Karvetski & James H. Lambert, 2013. "Case studies of scenario analysis for adaptive management of natural resource and infrastructure systems," Environment Systems and Decisions, Springer, vol. 33(1), pages 89-103, March.
    4. Ali Jamshidi & Shahrzad Faghih‐Roohi & Siamak Hajizadeh & Alfredo Núñez & Robert Babuska & Rolf Dollevoet & Zili Li & Bart De Schutter, 2017. "A Big Data Analysis Approach for Rail Failure Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1495-1507, August.
    5. Lam, C.Y. & Cruz, A.M., 2019. "Risk analysis for consumer-level utility gas and liquefied petroleum gas incidents using probabilistic network modeling: A case study of gas incidents in Japan," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 198-212.
    6. Vicki Bier, 2020. "The Role of Decision Analysis in Risk Analysis: A Retrospective," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2207-2217, November.

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