tion, urban management, agricultural production, deforestation mapping, public health assessment, crime fighting, and socioeconomic measurements. Secondly, the authors are experts on the area, with a substantial experience on geoinformation software development, and are in a qualified position to assess the different products.
We consider the following questions: (1) What are the conditions of open-source software development? (2) Who builds geoinformation open-source software products? (3) Is there a need for innovative open-source software applications in geoinformation applications? (4) How can developing countries obtain geoinformation open-source software to meet their national needs?
Our survey indicates that the view of open-source software as a product of a team of committed individuals is not realistic, at least for the geoinformation market. Most products are built either by a very small team of individuals or by corporations, and large collaborative networked teams are responsible for a small number of products. Most projects reverse-engineer existing designs or comply with standards, and few products are innovative. Therefore, there is much scope for new ideas, especially considering recent advances in geographical information science and spatial databases and the much-increased availability of Earth observation satellites. Given the constraints in open-source software production, such advances will not happen spontaneously and will require public intervention to fund innovation.
In order to support our claims we first examine the need for innovative geoinformation tools. We consider different models of open-source software production from an intellectual property viewpoint, and then review the process of open-source geoinformation software production. Lastly, we propose a model for open-source projects in the developing world based on networks of government-financed institutions.
One of the motivations for our survey on open-source GIS software is to identify the extent of innovation in the community. There are three main drivers for innovation in geoinformation technology: (1) the evolution of database management systems to handle spatiotemporal data types; (2) the availability of a new generation of Earth observation satellites; and (3) the recent advances in geographical information science.
The complete integration of spatial data types in database management systems is bound to change completely the development of GIS technology, enabling a transition from the monolithic systems of today (that contain hundreds of functions) to a generation of spatial information appliances, small systems tailored to specific user needs (Egenhofer, 1999). Coupled with the data-handling capabilities of a new generation of database management systems, rapid application development environments will enable the construction of “vertically integrated” solutions, directly tailored to user needs. Therefore, an important challenge for the GIS community is finding ways to take advantage of the new generation of spatially enabled database systems to build “faster, cheaper, smaller” GIS technology.
A second reason for developing open-source spatial analysis tools is the need to resolve the “knowledge gap” in the process of deriving information from images and digital maps. This knowledge gap has arisen because our capacity to build sophisticated data-collecting instruments (such as remote-sensing satellites, digital cameras, and GPS) is not matched by our means of producing information from these data sources (MacDonald, 2002). To a significant extent we are failing to exploit the potential of the spatial data we collect. For example, there are very few techniques for image data mining in remote-sensing archives, and thus we are failing to use the information available in our large Earth observation data archives. Much of this knowledge gap has resulted from a substantial imbalance in public expenditure in geoinformation technology. Major Earth observation satellite programs such as ENVISAT and EOS have budgets in the billion-dollar range, where the vast majority of the money is spent on building and operating the satellites and sensors.
An additional challenge is how to incorporate recent advances from geographical information science into mainstream GIS. A number of important results have been produced in research areas such as spatiotemporal data models (Erwig et al., 1999), geographical ontologies (Fonseca et al., 2002), spatial statistics and spatial econometrics (Anselin et al., 1999), cellular automata (Batty, 2000), and environmental modeling (Burrough, 1998). These results have largely been outside of the reach of the user community because of a lack of widely available tools and systems that support them.