Las esferas del reconocimiento

Short-term residents who had moved in since Hurricane Katrina identified proportionally more features that stood out as recovery failures (21%) than did medium or long-term residents who had pre-Katrina experience (14-15%) (Figure 5.15). The higher rate of failure assessments could be due to new residents’ lack of pre-storm memory to serve as a measuring stick for success; instead, these newer residents could be comparing local landmarks with equivalent features from former home areas. Long- time residents of over 20 years identified twice as many slow features (27%) as did

short-term residents (14%) who had moved in since about the time of Hurricane Katrina.

Figure 5.15 Proportion of participant-assigned recovery feature labels stratified by the participant’s time living on the Mississippi Coast

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Once again, this could be a function of greater place attachments and the loss of familiar routines among long-time residents, while post-Katrina arrivals lack such attachments and pre-storm memories to assess the speed. Long-time residents may also compare to previous storm experiences in their speed and outcome assessments, which did come out in several interviews (Interviews: Ellen, Fred, Ruth).

Comparing proportions of feature use types, groups who had pre-Katrina knowledge of the area (medium and long-time groups) identified greater proportions of community features (Figure 5.16) than did those without pre-Katrina knowledge. Business landmarks dominated in all groups, comprising a third or better of recovery features. Long-time coast residents identified the highest proportion of businesses,

Figure 5.16 Recovery feature use types stratified by the participant’s time living on the Mississippi Coast

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which were mostly iconic landmarks or establishments that had gone out of business (Interviews: Brad, Mary, Wanda). Medium and longer-term residents also identified fewer mixed use features than did those who had moved in during the last 10 years.

5.5 Findings

This second research question asked: how do local residents assess recovery progress and recovery outcomes? Once again a diverse sample of people were needed to determine the criteria for assessment (i.e., recovery to what?) and whether these criteria were different based on one’s geographic and social locations (i.e., recovery for whom?). Systematic analysis of residents’ labeled maps of the Mississippi Coast

supplemented with their interview remarks showed that personal activity space and its determinants (i.e., life stage, physical mobility, income, place attachment) more strongly influenced where residents saw community recovery and how they judged success than did axes of stratification themselves (e.g., race, ethnicity, gender, age).

Considering participant assessments in the aggregate, several trends were evident. Businesses were the most common spatial indicators for recovery speed, and they overwhelmingly exemplified fast recovery along the coast. Casinos and big box stores were commonly identified. Residential features were next most important, though housing recovery was slow and residents were largely dissatisfied with the results of residential reconstruction either due to the long time frame over which it occurred, elevation requirements, or inability to rebuild in their original location. Public, community, and mixed-use features, when assessed together, were most prominent on residents’ maps of recovery—more so than businesses or residences alone. Harbors,

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churches, new bridges, shopping and entertainment districts, and the status of public beaches themselves served as litmus tests for the progression of recovery. Eight to nine years post-Katrina, these features were largely assessed as successes, and in hindsight, deemed quick to recover.

Several pertinent differences emerged in terms of how residents assessed recovery based on facets of their own storm experience or positionality. Residents who received damage to their home more often identified residential features as criteria for recovery assessment, including their own home and homes of friends or neighbors, as well as public features; residents receiving no damage identified a larger proportion of businesses which were deemed quick to recover. Higher income residents pointed more often to mixed-use features as indicators for recovery, while lower income

residents highlighted businesses. Lower income groups also focused more on the speed with which these features recovered, whereas residents with higher incomes more frequently assessed the success or failure of outcomes. Older residents were less likely to emphasize recovery failures, but more often spoke of public features as indicators for assessing the recovery process. Younger residents identified more mixed-use places typically associated with entertainment. Residents with dependent children during recovery identified more features based on failure outcomes than did residents without who focused on slow to recover businesses. As length of time in residence on the coast increased, residents identified fewer failure outcomes but more slow features as

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equated businesses with recovery, while medium and long-time residents tended to identify more community features.

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The third research question asks whether there are differences between participant recovery assessments and recovery indicators based on quantitatively derived secondary data. Acknowledging that both approaches hold value and

recognizing that the best recovery measurements, whether qualitative or quantitative, should validate one another, this chapter compares results from these two disparate forms of analysis. To do this, I co-construct two types of landscapes for understanding recovery—one based on the bottom-up summation of residents’ spatial perceptions and the other on indicators that depict singular changes in facets of the physical landscape, as seen from the top-down vantage point of a policy maker, local decision maker, or planner. This chapter first explains how I aggregate participant assessments of recovery derived from the participatory mapping exercise and transform these qualitative data into two, census tract level quantitative indicators of recovery speed and recovery outcome. Spatial patterns of these qualitatively derived indicators are discussed here as well. Next, I detail data sources and aggregation procedures for four quantitative

indicators: 1) reconstruction, 2) repopulation, 3) home improvement, and 4) home purchase. These quantitative indicators measure the recovery concepts of rebuilding

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(and demolition), return (and vacancy), rehabilitation, and residential turnover,

respectively. Since these data were longitudinal and collected throughout the recovery period, I discuss how self-organizing maps were used to group census tracts with similar recovery trends. I then describe the clusters produced by using the self-organizing map algorithm. The final subsections of this chapter assess the comparability between the participant assessment indicators and the four quantitative recovery indicators using difference of means tests (i.e., ANOVA and/or Welch’s ANOVA).