Smart Engagement in Small Cities: Exploring Minority Participation in Planning

Smart engagement approaches are now widely applied in community planning processes. However, there continues to be a lack of representation from marginalized groups such as racial/ethnic minorities in planning processes. In this study, we explore what smart community engagement methods are being applied by small cities in the U.S., and how minority communities are participating in the planning process with those engagement methods. We analyzed planning documents and public engagement data from five small cities located in different regions of the U.S. with varying levels of minority populations. We evaluated the planning processes of the study cities, specifically comprehensive planning, and what smart community engagement tools they have applied. Our study shows that smart engagements are performed primarily through community surveys and online outreach initiatives. Despite adopting these approaches, most cities received lower participation from minority populations compared to non-Hispanic Whites. Cities with higher participation rates provided more engagement opportunities and conducted targeted community events and surveys to reach out to minority and low-income communities. From this study, we conclude that cities should apply varied methods for community engagement and should not rely solely on smart approaches to engage with minority communities. For cities to increase their overall civic participation, including those underrepresented, smart engagement approaches should be supported by targeted public events and outreach activities

Moving towards disaster: examining the changing patterns of social vulnerability in a multi-hazard urban environment

Studies of social vulnerability have repeatedly emphasized the importance of identifying the drivers of vulnerability, but very few studies have focused on empirically characterizing those drivers within the domain of vulnerability science that would help in effective policymaking. This dissertation is an initial step in this direction, examining social vulnerability in the context of multiple cities and evaluating the changing patterns of vulnerability in a multi-hazard urban environment. It adopts a political-economic framing of vulnerability production (Dooling and Simon 2012) that conceptualizes vulnerability as a dynamic condition, produced through the historic interaction of economic, cultural, and social processes. It hypothesizes that the nature and distribution of social vulnerability in urban areas changes over time, and that the provision of subsidized low-income housing influences the hazard exposure of socially vulnerable populations. This is accomplished first by studying three cities in the Gulf coast region (Houston, New Orleans, and Tampa) and then by focusing on Houston, Texas as a case study city for a more detailed empirical analysis. The initial component of this research integrates neighborhood change theories and theories of social vulnerability to explain the changing patterns of social vulnerability in Houston, New Orleans, and Tampa over a 30 year time period (1980-2010). Next, the Houston case study further explores how vulnerable groups navigate the multi-hazard urban environment and how subsidized housing policies have influenced this interaction over time. The pattern of social vulnerability observed within the case study cities indicates that despite having drastically different population growth trajectories and being situated in different political and economic settings, the spatial concentration of social vulnerability has gradually decreased in the study cities in recent decades. Specific trends in vulnerability are identified for each of the cities and the potential for constraining climate adaptation efforts is discussed. After analyzing the location of subsidized housing in Houston, this study found that among the two most widespread housing subsidy programs (Housing Choice Vouchers and the Low Income Housing Tax Credit), supply based subsidies exemplified by the LIHTC significantly increases neighborhood social vulnerability when it is located in areas exposed to technological hazards. Limitations in the present administration of the subsidy programs are identified and policy alternatives are discussed that may help to reduce their contribution to vulnerability

Stark fluorescence spectroscopy on peridinin–chlorophyll–protein complex of dinoflagellate, Amphidinium carterae

Because of their peculiar but intriguing photophysical properties, peridinin–chlorophyll–protein complexes (PCPs), the peripheral light-harvesting antenna complexes of photosynthetic dinoflagellates have been unique targets of multidimensional theoretical and experimental investigations over the last few decades. The major light-harvesting chlorophyll a (Chl a) pigments of PCP are hypothesized to be spectroscopically heterogeneous. To study the spectral heterogeneity in terms of electrostatic parameters, we, in this study, implemented Stark fluorescence spectroscopy on PCP isolated from the dinoflagellate Amphidinium carterae. The comprehensive theoretical modeling of the Stark fluorescence spectrum with the help of the conventional Liptay formalism revealed the simultaneous presence of three emission bands in the fluorescence spectrum of PCP recorded upon excitation of peridinin. The three emission bands are found to possess different sets of electrostatic parameters with essentially increasing magnitude of charge-transfer character from the blue to redder ones. The different magnitudes of electrostatic parameters give good support to the earlier proposition that the spectral heterogeneity in PCP results from emissive Chl a clusters anchored at a different sites and domains within the protein network

Supplemental Material - Modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature: A machine learning approach

Supplemental Material Modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature: A machine learning approach by Bev Wilson, Shakil bin Kashem, and Lily Slonim in Environment and Planning B: Urban Analytics and City Science</p