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Antonio Páez Associate Professor Centre for Spatial Analysis and McMaster Institute for Transport and Logistics School of Geography and Earth Sciences McMaster University, Hamilton, Ontario paezha@mcmaster.ca Editor-in-Chief, Journal of Geographical Systems 
Do adequate facilities (courseware, tutorials, sample data, self-learning modules, etc.) exist in the geospatial educational streams? What according to you is required by the education and research community in the geospatial domain? Taking the above two questions as starting point, I thought that providing a Canadian perspective would be useful and interesting. To this end, I contacted a number of colleagues at other Canadian institutions that have active Geographical Information Systems and Science, and Spatial Analysis educational and research programs, to solicit their views. Below, I transcribe their opinions (lightly edited for presentation) before providing my own perspective based on our experience at McMaster University. These opinions, I believe, reflect some commonalities, but also some divergences in terms of perceived needs and directions. These may stem from a variety of disciplinary perspectives, career stages (all contributors are relatively junior academics), as well as the institutional context that couches each perspective. Fortunately, I think the points of convergence and divergence are sufficiently evident as to require little further additional commentary on my part. Trisalyn Nelson Assistant Professor Spatial Pattern Analysis and Research Laboratory, Department of Geography, University of Victoria, Victoria, B.C. I have not had my students use pre-existing modules or tutorials. Each course and program has a different goal which often requires customization of material. That said, I do encourage students to look for supplemental information, including tutorials, on the web. When writing lectures and lab exercises accessing example data sets for GIS labs and demonstrations is always a challenge. I end up using a lot of my own research data to provide examples, which may limit the range of applications to which students are exposed. Where I think the community could develop training expertise is in teaching non-GIS experts. The general population is exposed to spatial data through popular culture and this has convinced a number of communities of the benefits of incorporating GIS when addressing research questions. However, teaching spatial science to biologists, epidemiologist, or emergency planners will require a new approach. We should be developing short tutorials and classes that teach user groups key concepts essential for accurate use of GIS. As well, we should be providing guidelines on when and how GIS users should consult with spatial experts. I am finding that the best spatial analysis graduate students are coming from technical programs. These students have an undergraduate degree (often in geography), but then go on to take technical classes such as programming and GIS development. University- based GIS programs should be integrating mathematics and computer science. If we hope to move spatial science forward in Canada, we need to provide undergraduate students with technical expertise. Often, technical competence leads to creative spatial thinking which is essential to succeed at the graduate level and beyond. In an era of increased pressure to maintain or grow student enrollments, it is difficult to balance curriculum that is good for geomatics students with curriculum that is desirable to them. I believe that students are most willing to take on the challenge of learning technical material if they can see the practical questions they will be able to address with acquired skills. As such, there is a need for problem-based learning in GIS classrooms. Claus Rinner Assistant Professor Program Director, Master of Spatial Analysis Department of Geography, Ryerson University, Toronto, Ontario In my view, basic GIS courses depend very much on the textbook selected and there are many available with different focus, some of them providing a broad introduction to GIScience and Systems, a focus on techniques and specific software packages, or others that emphasize the decision-making capabilities of GIS. At higher levels, I would expect more individual course materials. However, not many people have the time to set up and maintain tutorials that tend to be outdated ever more quickly. In terms of training materials, sample data are often taken from projects. This begs the question of how much exchange between programs is possible or even desirable. Finally, for self-learning, there are corporate initiatives (e.g. ESRI's virtual campus seems popular), but we at Ryerson think that learners will have a better experience with continuing education that motivates them throughout a term and provides a frame to achieve a recognizable degree. Ron Buliung Assistant Professor Department of Geography and Program in Planning University of Toronto at Mississauga, Mississauga, Ontario Clearly there is going to be some "spatial heterogeneity" in the provision of adequate resources. Different programs emphasize different sub-disciplines. In the Canadian system there is no gold standard to aim for across universities, and it is my experience that there is even a heterogeneous implementation of analytical software across programs in the face of the delivery of "hopefully" a uniform set of concepts. GIS education has been a mainstay of Canadian geographical education for nearly 20 years now. While several GIS texts are in circulation, there are only a few that truly stand out, balancing analytical rigour with a sufficiently comprehensive treatment of subject areas. Courseware, tutorials are largely the domain of the individual. Sample data abounds as Canadian governments at the Provincial, Municipal, and Federal levels and the private sector, have increasingly acknowledged the value in liberating their data and rolling it out into institutional environments. This sort of practice encourages data dependencies that track into the next phase of professional life for the students who use these data in their studies. Self learning occurs in two ways - GIS vendors provide opportunities, and individuals create opportunities through a more organic construction of knowledge surrounding the application of GIS to specific scientific, human, or social research questions. I suppose there is also the intersection of these approaches, and the growing interest in "participatory" GIS. Moreover, I'm not sure that we should ignore, entirely, the diffusion of geospatial innovation (e.g., Google) that now places basic GIS concepts and applications into the public domain. GIS is no longer solely the domain of the geographer, with educational programmes emerging outside of the discipline, and the "tools" of GIS becoming implicitly and/or explicitly part of everyday life. For the second part of the question (What according to you is the required by the education and research community in the geospatial domain?) I assume the objective is to elucidate what is required to implement research and teaching programs in GIS. I would argue that the following attributes provide a useful starting point: recognition and incorporation of the interdisciplinarity of GIS applications; dedicated personnel (professors, lab technicians, and adequately trained teaching assistants, administrative champions); inclusive epistemologies; a consistent and reliable revenue stream to sustain facilities, technologies, and personnel; outlets for GIS pedagogy and research where graduate students and faculty can mix and mingle; student and faculty networks centered around GISc to encourage the development of the discipline. For example, going back to the times of the quantitative revolution - the notable folks were passing students around the network - building shared intellectual capital, and stimulating scientific innovation (e.g., Geomatics for Informed Decisions - GEOIDE is a more recent Canadian example of this sort). AUTHOR’S PERSPECTIVE Geography at McMaster University has a long history as a center for quantitative analysis of geographical phenomena. In part thanks to this tradition, GIS was adopted early on in education and research at McMaster. Strong institutional support has provided the physical infrastructure required for teaching (including a GIS lab with 30+ workstations for undergraduate teaching and training) and research (with the Centre for Spatial Analysis and satellite labs, and corporate agreements with software vendors that facilitate access to tools required for research). Over the years, GIS has become a central element in our program, spanning human geography and earth sciences subjects. There are currently three GIS courses, one remote sensing course, 2 spatial statistics courses, and a transportation course that make use of different forms of geospatial technologies. From the vantage point of having high quality facilities for teaching and research, a major emphasis in recent years has been to obtain and develop the human resources required to deliver high quality contents. Recent hires in the School include specialists in transportation and GIS, spatial statistics, and spatial analysis of environment and health, in addition to a Canada Research Chair-holder in Spatial Analysis. There is a full-time dedicated Instructional Assistant who has played a central role in the development of the program. Currently, courseware is in place, and is revised on an ongoing basis, to support a majority of courses that employ GIS technology. Such is the case of the spatial statistics courses, and transportation systems analysis. Having these resources has allowed us to concentrate on the development of other "facilities", such as sample data and tutorials. We have over the past few years amassed an extensive collection of spatial datasets that can be used for assignments and projects. Some of these datasets we use to illustrate specific aspects of a course (for example area data for our assignment on spatial regression analysis). Routinely as well, we ask students to find their own sources of data to support larger, groupbased term projects, or encourage them to use selfcollected data or data available from supervisors for undergraduate- or graduate-level thesis. Students have been very successful at identifying suitable datasets that typically align closely with their academic or professional interests, thus promoting self learning. Very recent examples of such projects include a study of usage levels of parent-and-children development centers in the City of Hamilton (one group member is professionally associated with the Social Development and Early Childhood Services in the city), a study of the spatial pattern of places of worship in the city of Hamilton (one group member is doing a minor in religious studies), and an investigation of crime data in the state of Massachusetts (data was available and the subject was interesting!). These datasets were available through professional affiliations, the local government, or a website at a remote location. While identifying and obtaining an interesting and useful dataset sometimes represents a bit of a challenge, we also believe that it is an important aspect of training for our students. Other subject-specific courses have required more purposeful data collection efforts on the part of our teaching staff, in order to provide the students with the materials needed for training, when the focus in not on data collection. Such is the case of our senior-level course Transport Systems Analysis (a course open to geography and civil engineering students), where the term projects are based on the Census Transportation Planning Package produced by the US Bureau of Transportation Statistics. The files publicly available have proved to be an invaluable resource to link GIS to the analysis of transportation processes, while allowing the students to become immersed in a realistic analysis environment for the duration of the course.Of the different areas of growth in the GIS field (including open-source GIS, new programming languages, next generation technologies, etc.) one that I think deserves special emphasis is quantitative spatial data analysis (statistics and econometrics). In introducing my students to our spatial analysis stream, I discuss the significant progress achieved in the past three decades in developing ever more powerful hardware and software, and identify the next frontier for the geospatial scientist and analyst as (using a term borrowed from a GIS conference a few years ago) the development of "brainware". Brainware, as a complement to technology, is the ability of an adequately trained individual to transform spatial data into spatial information, and to think of this information in terms of spatial processes. The emphasis is not only in (geo-)graphical representation, or data retrieval, but in testing ideas (in a statistical sense) and creating new knowledge. Currently there is a feeling that much progress has been made in developing techniques useful to boost this brainware - most of it, however, confined to the world of academia, and still relatively rare in applied research. What is needed, I think, is forming a generation of students with formal spatial analysis training, who will go on to become industry or government analysts and researchers, and who will be able to transplant some of the methods and techniques absorbed during their education, into whatever field occupies them as professionals, thus bringing these ideas into the mainstream of professional practice. In other words, there is a need to create a demand for a kind of education that emphasizes brainware as an essential complement to hardware and software. This development in my view is indispensable to improve the ability of individuals, firms, and organizations to make intelligent spatial decisions. Page 1 of 1
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