The Influence of Place Attachment, Aspirations, and Rapidly Changing Environments on Resettlement Decisions

Resettlement associated with development projects results in a variety of negative impacts. This dissertation uses the resettlement context to frame the dynamic relationships formed between peoples and places experiencing development. Two case studies contribute: (a) the border zone of Mozambique’s Limpopo National Park where residents contend with changes to land access and use; and (b) Bairro Chipanga in Moatize, Mozambique where a resettled population struggles to form place attachment and transform the post-resettlement site into a “good” place. Through analysis of data collected at these sites between 2009 and 2015, this dissertation investigates how changing environments impact person-place relationships before and after resettlement occurs. Changing environments create conditions leading to disemplacement—feeling like one no longer belongs—that reduces the environment’s ability to foster place attachment. Research findings indicate that responses taken by individuals living in the changing environment depend heavily upon whether resettlement has already occurred. In a pre-resettlement context, residents adjust their daily lives to diminish the effects of a changing environment and re-create the conditions to which they initially formed an attachment. They accept impoverishing conditions, including a narrowing of the spaces in which they live their daily lives, because it is preferred to the anxiety that accompanies being forced to resettle. In a post-resettlement context, resettlement disrupts the formation of place attachment and resettled peoples become a placeless population. When the resettlement has not resulted in anticipated outcomes, the aspiration for social justice—seeking conditions residents had reason to expect—negatively influences residents’ perspectives about the place. The post-resettlement site becomes a bad place with a future unchanged from the present. At best, this results in a population in which more members are willing to move away from the post-resettlement site, and, at worse, complete disengagement of other members from trying to improve the community. Resettlement thus has the potential to launch a cycle of movement- displacement-movement that prevents an entire generation from establishing place attachment and realizing its benefits. At the very least, resettlement impedes the formation of place attachment to new places. Thus, this dissertation draws attention to the unseen and uncompensated losses of resettlement.

POSITIVE FILTERED P_N METHOD FOR LINEAR TRANSPORT EQUATIONS AND THE ASSOCIATED OPTIMIZATION ALGORITHM

We propose a positive, accurate moment closure for linear kinetic transport equations based on a filtered spherical harmonic (FP_N) expansion in the angular variable. The FP_N moment equations are accurate approximations to linear kinetic equations, but they are known to suffer from the occurrence of unphysical, negative particle concentrations. The new positive filtered P_N (FP_N+) closure is developed to address this issue. The FP_N+ closure approximates the kinetic distribution by a spherical harmonic expansion that is non-negative on a finite, predetermined set of quadrature points. With an appropriate numerical PDE solver, the FP_N+ closure generates particle concentrations that are guaranteed to be non-negative. Under an additional, mild regularity assumption, we prove that as the moment order tends to infinity, the FP_N+ approximation converges, in the L2 sense, at the same rate as the FP_N approximation; numerical tests suggest that this assumption may not be necessary. By numerical experiments on the challenging line source benchmark problem, we confirm that the FP_N+ method indeed produces accurate and non-negative solutions. To apply the FP_N+ closure on problems at large temporal-spatial scales, we develop a positive asymptotic preserving (AP) numerical PDE solver. We prove that the propose AP scheme maintains stability and accuracy with standard mesh sizes at large temporal-spatial scales, while, for generic numerical schemes, excessive refinements on temporal-spatial meshes are required. We also show that the proposed scheme preserves positivity of the particle concentration, under some time step restriction. Numerical results confirm that the proposed AP scheme is capable for solving linear transport equations at large temporal-spatial scales, for which a generic scheme could fail. Constrained optimization problems are involved in the formulation of the FP_N+ closure to enforce non-negativity of the FP_N+ approximation on the set of quadrature points. These optimization problems can be written as strictly convex quadratic programs (CQPs) with a large number of inequality constraints. To efficiently solve the CQPs, we propose a constraint-reduced variant of a Mehrotra-predictor-corrector algorithm, with a novel constraint selection rule. We prove that, under appropriate assumptions, the proposed optimization algorithm converges globally to the solution at a locally q-quadratic rate. We test the algorithm on randomly generated problems, and the numerical results indicate that the combination of the proposed algorithm and the constraint selection rule outperforms other compared constraint-reduced algorithms, especially for problems with many more inequality constraints than variables.