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.
DC-29 - Volunteered Geographic Information
Volunteered geographic information (VGI) refers to geo-referenced data created by citizen volunteers. VGI has proliferated in recent years due to the advancement of technologies that enable the public to contribute geographic data. VGI is not only an innovative mechanism for geographic data production and sharing, but also may greatly influence GIScience and geography and its relationship to society. Despite the advantages of VGI, VGI data quality is under constant scrutiny as quality assessment is the basis for users to evaluate its fitness for using it in applications. Several general approaches have been proposed to assure VGI data quality but only a few methods have been developed to tackle VGI biases. Analytical methods that can accommodate the imperfect representativeness and biases in VGI are much needed for inferential use where the underlying phenomena of interest are inferred from a sample of VGI observations. VGI use for inference and modeling adds much value to VGI. Therefore, addressing the issue of representativeness and VGI biases is important to fulfill VGI’s potential. Privacy and security are also important issues. Although VGI has been used in many domains, more research is desirable to address the fundamental intellectual and scholarly needs that persist in the field.