In a multitude of nations, the provision of education is predominantly facilitated through public schooling systems. These systems are structured in accordance with school districts, which are geographical territories where educational institutions share identical administrative frameworks and frequently coincide with the confines of a city or county. To enhance the operational efficiency of these schooling systems, the demarcations of public schools undergo periodic modifications. This procedure, also known as school redistricting, invariably engenders a myriad of tensions within the associated communities. This dissertation addresses the potential and necessity to integrate geographically-enabled crowd-sourced input into the redistricting process, and concurrently presents and evaluates a feasible solution. The pivotal contributions of this dissertation encompass: i) the delineation of the interdisciplinary sub-field at the nexus of HCI, CSCW, and education policy, ii) the identification of requirements from participants proficient in traditional, face-to-face deliberations, representing a diverse array of stakeholder groups, iii) the conception of a self-serve interactive boundary optimization system, and iv) a comprehensive user study conducted during a live public school rezoning deliberation utilizing the newly proposed hybrid approach. The live study specifically elucidates the efficacy of key design choices and the representation and rationalization of intricate user constraints in civic deliberations and educational policy architecture. My research looks into four primary areas of exploration: (i) the application of computer science usability-design principles to augment and expedite the visual deconstruction of intricate multi-domain data, thereby enhancing comprehension for novice users, (ii) the identification of salient elements of experiential learning within the milieu of visual scaffolding, (iii) the development of a preliminary platform designed to expand the capacity for crowd-sourcing novice users in the act of reconciling geo-spatial constraints, and finally, (iv) the utilization of Human-Computer Interaction (HCI) and data-driven analysis to discern, consolidate, and inaugurate novel communication channels that foster the restoration of trust within communities. To do so, I analyzed the previous work that was done in the domain, proposed a new direction, and created a web-application, called Redistrict. This an on-line platform allows the user to generate and explore "what if" scenarios, express opinions, and participate asynchronously in proximity-based public school boundary deliberations. I first evaluated the perceived value added by Redistrict through a user study with 12 participants experienced in traditional in-person deliberations, representing multiple stakeholder groups. Subsequently, I expanded the testing to an online rezoning. As a result of all interactions and the use of the web application, the participants reported a better understanding of geographically enabled projections, proposals from public officials, and increased consideration of how difficult it is to balance multidisciplinary constraints. Here, I present the design possibilities used and the effective online aid for the issue of public school rezoning deliberations and redistricting. This data-driven approach aids the school board and decision makers by offering automated strategies, a straightforward, visual, and intuitive method to comprehend intricate geographical limitations. The users demonstrated the ability to navigate the interface without iii any previous training or explanation. In this work, I propose the following three new concepts: (i) A new interdisciplinary subfield for Human Computing Interaction -Computer Supported Cooperative Work that combines Computer Science, Geography, and Education Policy. We explain and demonstrate how single domain approach failed in supporting this field and how complex geo-spatial problems require intensive technology to simultaneously balance all education policy constraints. This sits only at the intersection of a multi-domain approach. (ii) A sophisticated deconstruction of intricate data sets is presented through this methodology. It enables users to assimilate, comprehend, and formulate decisions predicated on the information delineated on a geospatial representation, leveraging preexisting knowledge of geographical proximity, and engaging in scenario analysis. Each iterative attempt facilitates incremental understanding, epitomizing the concept of information scaffolding. The efficacy of this process is demonstrated by its ability to foster independent thought and comprehension, obviating the need for explicit instructions. This technique is henceforth referred to as 'visual scaffolding'. (iii) In our most recent investigation, we engage in an introspective analysis of the observed input in civic decision making. We present the proposition of integrating digital civic engagement with user geolocation data. We advocate for the balance of this input, as certain geographical areas may be disproportionately represented in civic deliberations. The introduction of a weighting mechanism could facilitate a deeper understanding of the foundational premises on which civic decisions are based. We coin the term 'digital geo-civics' to characterize this pioneering approach. / Doctor of Philosophy / Public deliberations are often the main ingredient in community decisions. However, traditional, time-constrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for the zoning of public schools. This dissertation provides ways to increase the cohesiveness of a community through technology support that can help to clarify complex data and multidisciplinary constraints.

Computer hardware and algorithm design have seen significant progress over the years. It is also seen that there are several domains in which humans are more efficient than computers. For example in image recognition, image tagging, natural language understanding and processing, humans often find complicated algorithms quite easy to grasp. This thesis presents the different case studies to learn human mapping strategy to solve the mapping problem in the area of coarse-grained reconfigurable architectures (CGRAs). To achieve optimum level performance and consume less energy in CGRAs, place and route problem has always been a major concern. Making use of human characteristics can be helpful in problems as such, through pattern recognition and experience. Therefore to conduct the case studies a computer mapping game called UNTANGLED was analyzed as a medium to convey insights of human mapping strategies in a variety of architectures. The purpose of this research was to learn from humans so that we can come up with better algorithms to outperform the existing algorithms. We observed how human strategies vary as we present them with different architectures, different architectures with constraints, different visualization as well as how the quality of solution changes with experience. In this work all the case studies obtained from exploiting human strategies provide useful feedback that can improve upon existing algorithms. These insights can be adapted to find the best architectural solution for a particular domain and for future research directions for mapping onto mesh-and- stripe based CGRAs.