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GS-26 - Mapping Spatial Justice for Marginal Societies

Marginal populations are those populations that are often overlooked by government, dependent upon non-governmental aid, and lack access to basic resources such as water, food, shelter, and security.  However, these groups are increasingly included in partnerships to map their resources (or lack thereof), develop basic applications in geospatial data collection, and devise innovative approaches to participatory mapping using geospatial technologies to address local and regional problems. Rapid technological changes and increased access to mobile geospatial tools enhance data creation efforts to map marginal populations and identify their needs. However, such mapping activities reveal fundamental inequities in collecting, disseminating, and visualizing spatial data.  This chapter defines marginal populations and provides an overview of data needs, geospatial tools, and ethical obligations necessary for these partnerships.

GS-29 - GIS Participatory Modeling

Participatory research is increasingly used to better understand complex social-environmental problems and design solutions through diverse and inclusive stakeholder engagement. A growing number of approaches are helping to foster co-production of knowledge among diverse stakeholders. However, most methods don’t allow stakeholders to directly interact with the models that often drive environmental decision-making. Geospatial participatory modeling (GPM) is an approach that engages stakeholders in co-development and interpretation of models through dynamic geovisualization and simulations. GPM can be used to represent dynamic landscape processes and spatially explicit management scenarios, such as land use change or climate adaptation, enhancing opportunities for co-learning. GPM can provide multiple benefits over non-spatial approaches for participatory research processes, by (a) personalizing connections to problems and their solutions, (b) resolving abstract notions of connectivity, and (c) clarifying the scales of drivers, data, and decision-making authority. An adaptive, iterative process of model development, sharing, and revision can drive innovation of methods, improve model realism or applicability, and build capacity for stakeholders to leverage new knowledge gained from the process. This co-production of knowledge enables participants to more fully understand problems, evaluate the acceptability of trade-offs, and build buy-in for management actions in the places where they live and work.