Authors: Barnali Dixon*, University Of South Florida, Johns A Rebecca, University of South Florida, Initiatve on Coastal Adaptation and Resileince, Rachelle Pontes, University of South Florida
Topics: Hazards, Risks, and Disasters, Geographic Information Science and Systems, Qualitative Methods
Keywords: Resiliency, geovisualization, qualiative method and GIS
Session Type: Paper
Presentation File: No File Uploaded
Climate change and consequent extreme weather events are key topics of our time in the context of resiliency, which require deeper and nuanced understanding for effective policy-making and resource allocation. Qualitative data provide one of the pillars for such analysis; however, usually qualitative data are not integrated with GIS. Studies have shown variables such as attitude, perceived levels of preparedness, actual preparedness, access to information, impact/experience, response to extreme events and strength of social networks, play a key role in preparedness and resiliency of communities. These variables (‘resiliency variables’) are often analyzed using qualitative methods. This study hypothesized that incorporation of qualitative data into a GIS to analyse resiliency variables at a neighbourhood scale will provide alternative ways to visualize spatial distribution of variables and consequently will facilitate nuanced analysis of qualitative factors over space for policy formulation and resource allocation. This project aims to examine i) spatial distribution of resiliency variables obtained via qualitative data from diverse communities in the context of socio-economic and biophysical data integrated with GIS and ii) explore limitations and opportunities for incorporation of qualitative data with GIS for spatial analysis. Principle Component Analysis and directional distribution (1st standard deviations) were used for this analysis. This research shows that incorporation of qualitative data can provide nuanced analysis over space and can advance our understanding of the conditions that shape the vulnerability of places and resiliency of people; nonetheless, issues with small sample size of survey data impacts depth of spatial analysis that can be conducted.
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