Catalyst: Architecture for Change and Social Justice

In major cities today, there are neighborhoods that have been continually underserved and as a result are in decay. Private investors and developers turn to these particular neighborhoods, propose large developments that gentrify these areas, displacing communities and with them their social, political, and economic issues. The purpose of this thesis is to analyze South West, Baltimore, a community composed of 8 neighborhoods on the verge of being gentrified, by incoming development. Through investigating the key issues present in this community for many years, this thesis will attempt to develop a catalytic environment, which will facilitate change within the community by providing a place for its members to help tackle these issues, improving their circumstances, and the circumstances of the neighborhoods they form part of.

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.

Mathematical Programming Models for Influence Maximization on Social Networks

In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.