skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Is Time on Our Side? A Longitudinal Analysis of the Quality of Mandated Plans
Do plans get better over time? This deceptively simple question goes to the heart of our profession’s effectiveness. We use a multi-state, longitudinal analysis of eighty-four local plans to add to our knowledge base about changes in plan quality over time. Our longitudinal findings reinforce and extend the conclusions of previous cross-sectional studies of hazard mitigation plan quality. Simply put, local planning to meet federal Disaster Mitigation Act requirements grossly underperforms compared with its potential. Our findings raise cautionary lessons beyond the realm of risk reduction, as well as federal and state policymakers who seek to incentivize local planning.  more » « less
Award ID(s):
1760183
PAR ID:
10464068
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
SAGE Publications
Date Published:
Journal Name:
Journal of Planning Education and Research
Volume:
45
Issue:
1
ISSN:
0739-456X
Format(s):
Medium: X Size: p. 218-232
Size(s):
p. 218-232
Sponsoring Org:
National Science Foundation
More Like this
  1. U.S. state, territorial, and tribal government officials develop State Hazard Mitigation Plans (SHMPs) to assist in reducing the risk of disaster impacts on people, physical infrastructure, and the natural environment. The Federal Emergency Management Agency (FEMA) approves SHMPs every five years as a requirement to be eligible to receive funding for FEMA disaster relief grants and disaster mitigation projects. As of April 2023, updated FEMA policy guidance for SHMPs is in effect that calls for greater community engagement in the planning process and stipulates that plans consider equity and climate change. In response to these changes, this project takes the position that more robust conceptualizations of socially vulnerable populations and inclusive use of social vulnerability data can help states in the development of multi-hazard risk assessments. Social vulnerability emerges from systemic inequities, resulting in populations facing disproportionate impacts in disasters. It is a helpful framework for identifying underserved and marginalized populations. Given the crucial importance of considering social vulnerability in mitigation planning, our research team developed two novel datasets with descriptive data of the populations, definitions, and different measures of social vulnerability included in SHMPs for all 50 states and 5 inhabited U.S. territories. Specifically, this project includes two datasets: (1) a quantitative dataset where mentions of socially vulnerable populations and concepts are marked with a binary indicator of inclusion or exclusion in the State Hazard Mitigation Plan and (2) a qualitative dataset that contains quotes and locations of populations and concepts throughout each SHMP. The corresponding mission for each dataset includes: (1) the State Hazard Mitigation Plan dataset; (2) a data dictionary with description of each variable output; (3) variable definitions for the population groups included in State Hazard Mitigation Plans; and (4) a READ ME file with important information. These datasets and associated materials can help State Hazard Mitigation Officers and their technical partners identify gaps in addressing social vulnerability as they update the SHMPs for the areas they serve. These resources are available to researchers, practitioners, policy makers, and others who are interested in addressing social vulnerability in hazard mitigation planning. 
    more » « less
  2. The inspection-planning problem calls for computing motions for a robot that allow it to inspect a set of points of interest (POIs) while considering plan quality (e.g., plan length). This problem has applications across many domains where robots can help with inspection, including infrastructure maintenance, construction, and surgery. Incremental Random Inspection-roadmap Search (IRIS) is an asymptotically-optimal inspection planner that was shown to compute higher-quality inspection plans orders of magnitudes faster than the prior state-of-the-art method. In this paper, we significantly accelerate the performance of IRIS to broaden its applicability to more challenging real-world applications. A key computational challenge that IRIS faces is effectively searching roadmaps for inspection plans—a procedure that dominates its running time. In this work, we show how to incorporate lazy edge-evaluation techniques into IRIS’s search algorithm and how to reuse search efforts when a roadmap undergoes local changes. These enhancements, which do not compromise IRIS’s asymptotic optimality, enable us to compute inspection plans much faster than the original IRIS. We apply IRIS with the enhancements to simulated bridge inspection and surgical inspection tasks and show that our new algorithm for some scenarios can compute similar-quality inspection plans 570× faster than prior work. 
    more » « less
  3. ABSTRACT Objective: This study investigated how the effectiveness of household emergency plans during tornadoes was associated with family discussions, and the attributes of the plan for different age groups. Methods: A telephone survey was conducted in 2014, one year after two 2013 Enhanced Fujita 4/5 tornadoes. The working sample included 223 respondents who reported having a household emergency plan before the tornadoes. The latent class analysis was used to identify the patterns of the plans and develop a typology based on their content. Logistic regression was used to examine predictors for plan effectiveness. Results: Two classes of plans were identified: quality plans that were rich in content and limited plans that had lower levels of content richness. Older adults were less likely to have quality plans and less likely to have family discussions. Quality of the plan and discussions with family members increased plan effectiveness among older adults, but not younger adults. Conclusions: Better emergency planning could be more important for older than for younger adults. The findings were discussed from a gerontological perspective that focuses on older adults’ unique needs, vulnerabilities, and resilience factors. 
    more » « less
  4. State and federal governments use governance platforms to achieve central policy goals through distributed action at the local level. For example, California’s 2014 Sustainable Groundwater Management Act (SGMA) mandates local policy actors to work together to create new groundwater management institutions and plans. We argue that governance platforms entail a principal-agent problem where local decisions may deviate from central goals. We apply this argument to SGMA implementation, where local plans may respond more to local political economic conditions rather than address the groundwater problems prioritized by the state. Using a Structured Topic Model (STM) to analyze the content of 117 basin management plans, we regress each plan’s focus on core management reform priorities on local socio-economic and social-ecological indicators expected to shape how different communities respond to state requirements. Our results suggest that the focus of local plans diverges from problem conditions on issues like environmental justice and drinking water quality. This highlights how principal-agent logics of divergent preferences and information asymmetry can affect the design and implementation of governance platforms. 
    more » « less
  5. Abstract PurposeAs a challenging but important optimization problem, the inverse planning for volumetric modulated arc therapy (VMAT) has attracted much research attention. The column generation (CG) type method is so far one of the most effective solution schemes. However, it often relies on simplifications leading to significant gaps between the output and the actual feasible plan. This paper presents a novel column generation (NCG) approach to push the planning results substantially closer to practice. MethodsThe proposed NCG algorithm is equipped with multiple new quality‐enhancing and computation‐facilitating modules as below: (1) Flexible constraints are enabled on both dose rates and treatment time to adapt to machine capabilities as well as planner's preferences, respectively; (2) a cross‐control‐point intermediate aperture simulation is incorporated to better conform to the underlying physics; (3) new pricing and pruning subroutines are adopted to achieve better optimization outputs. To evaluate the effectiveness of this NCG, five VMAT plans, that is, three prostate cases and two head‐and‐neck cases, were computed using proposed NCG. The planning results were compared with those yielded by a historical benchmark planning scheme. ResultsThe NCG generated plans of significantly better quality than the benchmark planning algorithm. For prostate cases, NCG plans satisfied all planning target volume (PTV) criteria whereas CG plans failed on D10% criteria of PTVs for over 9 Gy or more on all cases. For head‐and‐neck cases, again, NCG plans satisfied all PTVs criteria while CG plans failed on D10% criteria of PTVs for over 3 Gy or more on all cases as well as the max dose criteria of both cord and brain stem for over 13 Gy on one case. Moreover, the pruning scheme was found to be effective in enhancing the optimization quality. ConclusionsThe proposed NCG inherits the computational advantages of the traditional CG, while capturing a more realistic characterization of the machine capability and underlying physics. The output solutions of the NCG are substantially closer to practical implementation. 
    more » « less