Using NLP to Compare 

Resilience, Sustainability, and 

Adaptation Planning

Juan Fung, Emily Walpole, Chris Clavin

NIST Community Resilience Program

TLP-COI 

April 21, 2021
Background and motivation

“Resilience” is about capacity to absorb shocks

● Planning for resilience occurs alongside planning for other 

goals (e.g., sustainability; climate adaptation)

● Where does resilience fit in with other goals?

○ Similarities in planning process?

○ Similarities in actions?
Why NLP?

Similarities/differences between planning for X and Y (setting objectives)

● Streamline/standardize planning process for X and Y

● Assess adherence to guidelines

Similarities/differences between actions for X and Y (achieving objectives)

● Implementation: what actions available for X and Y?

● Investment: what is the cost?
The need for custom corpora

Planning documents (e.g., capital improvement plans, hazard mitigation 

plans, budgets) are long, messy, not standardized

● Domain expertise

○ Identifying objectives

○ Identifying actions and attaching to objectives

● Other challenges

○ Definitions (standard definitions vs “emergent” based on use)

○ Linking planning and budget documents
The value of those corpora

Assist communities with planning

● Assess existing planning documents

● Assist in developing/revising planning documents

● Streamlining or combining processes where possible

● Adhere to guidelines required for funding

● Query available actions (what works for X and Y; what is the cost)
Case Study: Resilience, Climate Adaptation, and Sustainability 

(“RAS”) Planning Guidance (1/2)

Goal: identify similarities and differences between community focused 

RAS planning processes - help improve communication, identify trade 

offs, and reduce redundancies

Published guidance documents assessed for methods described (data 

needs, analysis, and strategies suggested), goals, and outputs expected 

using qualitative content analysis (i.e. annotations). 
Case Study: Resilience, Climate Adaptation, and Sustainability 

(“RAS”) Planning Guidance (2/2)

● Annotated database of guidance documents 

serves as corpus for subsequent analysis

○ Two-independent coders, 26 documents = 2,681 

coded segments ~ 3 mo

○ Existing planning document studies have similar 

annotated corpora

● Corpus developed for RAS planning 

guidance documents provides convenient 

demonstration case for NLP

○ May be unique application for examining corpora 

at intersection of urban planning, design, 

environmental management, & administration.

○ Explores feasibility to extend NLP methods to 

reexamine prior, similar efforts or new domains

*Figure prepared by Samantha Wong, Binghamton University
Case study: (Cherry-picked) Example

“One common zoning incentive is an increased floor-to-area ratio (FAR), which 

regulates the density of development on a site. The City of Portland, Oregon offers 

increased FAR as an incentive for installing green roofs.” 

- Building Community Resilience with Nature-Based Solutions (FEMA 2020)

“Incentives can be financial, such as tax credits for protecting natural resources, or 

procedural, such as height or floor area bonuses for designs that are more 

resilient to hazards.”

- Planning Framework for a Climate-Resilient Economy (EPA 2016) Adaptation
Next steps

● Complete data cleaning and analysis of case study documents

● Use case study data to test some NLP tasks (e.g., embeddings, 

classification, topic modeling, ...)

● Assess strengths/weaknesses of “off-the-shelf” methods

Thank you!

juan.fung@nist.gov