Definitions within a community of practice have multiple benefits. Definitions reduce differences in semantics, and focus a community of practice towards goals that reinforce individual and collective efforts, make knowledge accessible to those at the edges of the community, and expand a study area by integrating related external concepts (Sager, 2000). Moreover, clearly defined concepts in a knowledge domain can better facilitate theory building. There are five types of definitions, and we have chosen to specify a theoretical definition for collaborative GIS since this type of definition aims to capture a commonality in the research area, and to relate that commonality to a broader intellectual framework (Sager, 2000). This chapter is organized as follows: firstly, a theoretical definition of collaborative GIS is presented; secondly, a historical footprint is established to reinforce the theoretical definition; and thirdly, the linkages between collaborative GIS and its broader conceptual framework are outlined.
What is Collaborative GIS?
There is a mutual influence between geographic information science and collaborative geographic information systems. GIScience is the rationale or science (axioms, theories, methods) that justifies the design and application of geographic information systems (Goodchild, 1992). Geographic information systems on the other hand are the physical designs and processes that integrate people and computer technology to manage, transform, and analyze spatially referenced data to solve ill-defined problems (Wright, Goodchild, & Proctor, 1997). Collaborative GIS are influenced by both GIS and GIScience. Hence, the name collaborative GIS will be used as systems, science, or both, depending on the context.
Collaborative GIS can be defined as an eclectic integration of theories, tools, and technologies focusing on, but not limited to structuring human participation in group spatial decision processes. In particular, the aim is to probe at the participant-technology-data nexus, and to describe, model, and simulate effects on the consensual process outcomes. The participants are typically a mixture of technical experts and the public, the technological tools being computers that are networked or distributed, and the data being spatially referenced maps and attributes. The outcomes do not result from implementing a task-oriented approach, but rather they emerge from a joint and structured exploration of ill-defined problems to benefit planning, problem solving, and decision making. In planning, the intention is to develop steps to achieve a desired outcome, while problem solving deals with the formulation of plans in new contexts. Decision making is the process of choosing among a set of alternatives.
Structuring is defined in the Webster Online Dictionary (http://www.m-w.com) as “the act of building, arrangement of parts, or relationship between parts of a construction.” In this regard, structuring in collaborative GIS deals with the creation of process designs, how those designs enable the participant-technology-data interactions, and the relationships between the component parts of the designs. Hence, collaborative GIS is situated within the enhanced adaptive structuration theory 2 (EAST2) framework (Jankowski & Nyerges, 2001a). The framework outlines a detailed set of concepts and relationships linking the content, process, and outcome of collaborative spatial decision making. The content constructs of EAST2 examine the socioinstitutional, group participant, and GIS technology influences. The process constructs examine the social interactions between humans and computers. The outcome constructs address societal impacts of the decisions. Constructs five (group processes) and six (emergent influence) are important for collaborative GIS because they deal with “idea exchange as social interaction” and “emergence of socio-technical information influence” respectively. The interactions that occur in these constructs are more collaboration rather than cooperation.
Questions that engage collaborative GIS research activities include “What collaborative spatial decision making structures can generate meaningful outcomes? How can the attitudes and needs of participants be integrated into the group process? What are the effects of spatial data and cognitive overload on participation quality? How can prior solutions be integrated into the designs of collaborative spatial decision making systems? How can the outcomes of the process be evaluated and assessed for quality?”