Integrating Spatial and Ethnographic Methods for Resilience Research: A Thick Mapping Approach for Hurricane Maria in Puerto Rico

Abstract

Hurricane Maria left unprecedented impacts on Puerto Rican communities, leaving some without infrastructure services and unable to communicate with family for several months. To understand the forms of community-level resilience that emerged while hard infrastructure systems took time recover, this article (1) abductively explores resilience as an emergent phenomenon of complex adaptive systems; (2) identifies subsequent forms of social capital, local adaptive capacities, and manifestations of quantifiable variables, such as infrastructure performance, in community experiences; and (3) demonstrates a framework to integrate disparate methodologies for resilience assessments via a multiplicity of mappings of space and place. We combine ethnographic and geospatial methods into an interactive GeoApp for analysis using participant-coded narratives and a series of geospatial indicators as a thick map. Thick mapping facilitates quantitative and qualitative data analysis at several scales, while enabling qualitative query of collected narratives. Results highlight local innovation, community bonding and bridging, and nuances in the role of public institutions as emergent elements of resilience. The thick map shows how top-down assessments can be augmented by thick data and how multiple framings can be anchored in the same system or place. These findings are important to inform and integrate community-oriented and technocentric solutions toward resilience-enhancing measures.

玛丽亚飓风对波多黎各的社区造成了前所未有的影响, 部分社区丧失了基础设施服务, 数月内都无法与家人联络。为了理解在基础设施硬件系统恢复期间的社区恢复力, 本文（1）将恢复力视为复杂适应系统中的新现象, 追溯性地探讨了恢复力；（2）确定了社区经历中的社会资本形式、本地适应能力和可量化变量的表达（例如, 基础设施的作用）；（3）展示了一个基于时空制图多元性的在恢复力评估中融合各种方法的框架。我们将人种学和空间方法集成到一个交互式地理分析App中, 将参与者的叙述和一系列空间指标作为厚地图。厚制图促进了多尺度的定量和定性数据分析, 支持对叙述的定性查询。结果突出了地方创新、社区纽带和联系、作为恢复力新要素的公共机构所起的作用。厚地图显示了自上而下的评估如何与厚数据相结合, 如何在同一系统或地点支持多个框架。这些发现, 有助于提供和融合以社区为导向的、以技术为中心的解决方案, 采取增强恢复力的措施。

El Huracán María dejó tras de sí impactos sin precedentes en las comunidades portorriqueñas, algunas de las cuales quedaron sin los servicios de infraestructura y sin la capacidad de comunicarse con sus familias durante varios meses. Para entender las formas de resiliencia a nivel comunitario que aparecieron mientras los sistemas de infraestructura dura se recuperaban, este artículo, (1) explora de manera abductiva la resiliencia como un fenómeno emergente de sistemas de adaptación complejos; (2) identifica las formas subsiguientes de capital social, las capacidades locales de adaptación y las manifestaciones de variables cuantificables en las experiencias comunitarias, tales como el desempeño de la infraestructura, y (3) hace la demostración de un marco que integra metodologías dispares para evaluar la resiliencia a través de una multiplicidad de mapeos de espacio y lugar. Combinamos los métodos etnográficos y geoespaciales en una GeoApp interactiva para el análisis, usando narrativas codificadas por los participantes y una serie de indicadores geoespaciales como un mapa grueso. El mapeo grueso facilita los análisis cuantitativo y cualitativo de los datos a varias escalas, permitiendo al mismo tiempo la consulta cualitativa de las narrativas recogidas. Los resultados destacan la innovación local, los vínculos y puentes comunitarios y el matizado de los roles de las instituciones públicas como elementos emergentes de la resiliencia. El mapa grueso muestra cómo las evaluaciones de arriba a abajo pueden aumentarse por los datos gruesos, y cómo se pueden anclar múltiples marcos en el mismo sistema o lugar. Estos hallazgos son importantes para informar e integrar las soluciones tecnocéntricas y de orientación comunitaria hacia medidas que fortalezcan la resiliencia.

Key Words:

• complexity, Hurricane Maria, mixed methods, resilience, thick mapping

关键词：:

• 复杂性
• 玛丽亚飓风
• 恢复力
• 厚制图。

Palabras clave::

• complejidad
• Huracán María
• mapeo grueso
• resiliencia

Acknowledgments

The authors acknowledge and extend our appreciation to Amy Chester, Eve Marenghi, Kate Bryson, Verónica Franco Londoño, Alicia Tyson, Katia Lucuy, and Beverly Collazo for their assistance in designing the language for web-capture tools and facilitating participation of research subjects, and to the data collection team: Alexander Lorenzo Ramos, Ariana Espinosa Santiago, and Dabnerys Yariz Sanchez-Milian. The authors are very thankful to the University of Puerto Rico–Mayaguez for hosting project events, and to the communities of Puerto Rico for hosting and participating in this research.

Supplemental Material

Supplemental data for this article can be accessed on the publisher’s site at: http://dx.doi.org/10.1080/24694452.2022.2071200

Notes

1 GeoApp code is available on the Github repository: https://github.com/varinaldi/ThickMapMaria.

Additional information

Funding

This work was funded by National Science Foundation grant number CRISP-1832678. New York University has applied and received IRB exemption for this work.

Notes on contributors

Thomaz Carvalhaes

THOMAZ CARVALHAES is an R&D Associate for Data-Driven Risk Analysis and Resilience Modeling, Oak Ridge National Laboratory, Oak Ridge, TN 37830. E-mail: [email protected]. His research focuses on linking social science and engineering approaches around a variety of applications involving infrastructure resilience in the face of climate change, extreme weather, and complexity in the Anthropocene.

Vivaldi Rinaldi

VIVALDI RINALDI is a Doctoral Student in Urban Systems, Tandon School of Engineering, New York University, Brooklyn, NY 11201. E-mail: [email protected]. His research focuses on urban observation and modeling through geoinformatics and remote sensing.

Zhen Goh

ZHEN GOH is currently Co-Director of The Emerginarium, Singapore, an organization that employs multimethod approaches to facilitate emergence. E-mail: [email protected]. She is an anthropologist, and works at the nexus of data, policy, and human systems. She is interested in research that uses transdisciplinary principles.

Shams Azad

SHAMS AZAD is a PhD Candidate in the Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201. E-mail: [email protected]. His interests include performing innovative research to solve the emerging challenges in cities and promote the transformation of existing cities into more livable, resilient, and sustainable environments.

Juanita Uribe

JUANITA URIBE is a Senior Consultant and Representative for the Latin America Region for Cognitive Edge, Singapore. E-mail: [email protected]. She has dedicated her professional life to finding ways to understand the world and communicate complex things in a way that most people can understand.

Masoud Ghandehari

MASOUD GHANDEHARI serves on the Faculty in Civil and Urban Engineering, Tandon School of Engineering, New York University, Tandon School of Engineering, Brooklyn, NY 11201. E-mail: [email protected]. He is also Associate Faculty at the NYU Center for Urban Science and Progress. His research focus is on urban systems engineering and the application of advanced instrumentation and data analysis targeting the aging, health, and performance of infrastructure systems.