There is a small but burgeoning academic literature on managed relocation from environmental hazards related to climate change impacts (e.g., sea level rise, increased storm surges, etc.). Though much attention is currently being given to “types of buyouts” that may or may not be effective in community/resident relocation, there is a paucity of research on the destination communities of residents that relocate due to climate change impacts. Sociologists are very well-suited to better understand the complex processes involved in managed community relocations (“buyouts”), in terms of both the original and destination communities. For example, the importance of social bonds in many climate change-impacted communities, as well as the economic, racial, and cultural dimensions of a community, will play powerful roles in where and how people relocate, as well as the success of those transitions. Further, this provides ample opportunities for sociologists to help address long-standing environmental inequalities through a structured, intentional approach at undoing inequality through just managed relocation and community planning.

As a sociologist, I strongly believe in the use of geospatial analyses to teach the sociological imagination. For well over a decade, I’ve been using online mapping tools, like PolicyMap, in my introductory and upper-level courses to show how social, economic, health, and a slew of other outcomes unfold, in patterns, in geographic spaces. As PolicyMap expands in its data availability and analytical capabilities, it proves ever more useful as a teaching and learning tool for new social science students (students find it to be a relatively simple, yet powerful, interface for creating maps and writing about them).

The Environmental Protection Agency (EPA) recently released a collection of pilot health indicators that are often associated with disproportionate hazard exposure in marginalized communities. In addition to the geospatial capabilities of locating hazards relative to a community’s racial, economic, and housing characteristics with EJSCREEN 2.3, you can now use these aggregate data as another layer of empirical evidence of poorer health outcomes due to cumulative disadvantages and environmental hazard exposure. Particularly interesting, for example, are differences in blood lead levels in childhood across racial and poverty indicators. From the EPA:

“The pilot six indicators include:

• Blood Lead Levels
• Population in Monitored Counties Meeting PM2.5 National Ambient Air Quality Standards (NAAQS)
• Age-adjusted Hypertension
• Adverse Birth Outcomes
• Childhood Asthma Prevalence
• Life Expectancy”

Earlier this year, I was interviewed about the topic of environmental justice, greening, and community change for the Delaware Nature Society’s blog. Here is the article by Ken Mammarella. Take a look for an interesting discussion of racial residential segregation, environmental racism, and development through the “green orthodoxy,” with a focus on South Wilmington, DE. One pertinent question that emerges from our discussion is “how do we understand the long-term, intrinsic value of greening efforts that unfold over many years, in light of possible alternatives that may reflect contemporary community preferences?”

A joint effort between me, the Delaware Nature Society, US Fish and Wildlife Services, and the Southbridge Community Development Corporation was successful in getting funding for rain gardens designs to help mitigate flood water and mosquitoes in Southbridge. Read more about the $25,000 grant in the News Journal here.

Dr. Asia Friedman and I just published a case study on the role of brain imaging in promoting and validating new diagnoses. Certain types of sound sensitivity have often been symptoms associated with other, established psychiatric conditions, but have not necessarily been promoted as discrete clinical entities (i.e., diagnoses on their own). Recently, researchers using brain imaging to study misophonia have been instrumental in making claims about the condition and establishing its legitimacy as a separate diagnosis. Here’s the abstract from our paper:

“Misophonia has gained attention in scientific circles that utilise brain imaging to validate diagnoses. The condition is promoted as not merely a symptom of other psychiatric diagnoses but as a discrete clinical entity. We illustrate the social construction of the diagnostic category of misophonia through examining prominent claims in research studies that use brain imaging to substantiate the diagnosis. We show that brain images are insufficient to establish the ‘brain basis for misophonia’ due to both technical and logical limitations of imaging data. Often misunderstood as providing direct access to the matter of the body, brain images are mediated and manipulated numerical data (Joyce, 2005, Social Studies of Science 35(3), p. 437). Interpretations of brain scans are further shaped by social expectations and attributes considered salient to the data. Causal inferences drawn from these studies are problematic because ‘misophonics’ are clinically pre-diagnosed before participating. We argue that imaging cannot replace the social process of diagnosis in the case of misophonia, nor validate diagnostic measures or otherwise substantiate the condition. More broadly, we highlight both the cultural authority and inherent limitations of brain imaging in the social construction of contested diagnoses while also illustrating its role in the disaggregation of symptoms into new diagnoses.”