A Reflection on the Ph.D. Program in Spatially Integrated Social Science at the University of Toledo

A Reflection on the Ph.D. Program in Spatially Integrated Social Science at the University of Toledo

Bhuiyan Monwar Alam (The University of Toledo, USA), Jeanette Eckert (The University of Toledo, USA) and Peter S. Lindquist (The University of Toledo, USA)
Copyright: © 2012 |Pages: 6
DOI: 10.4018/jagr.2012040105
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The use of spatial analysis tools is on the rise in many academic fields and practical applications. These tools enhance the ability to examine data from spatial perspectives. Though the study of place and space has traditionally been the domain of the field of geography, growing numbers of researchers are turning to these tools in the social sciences and beyond. The University of Toledo has established a unique Ph.D. granting program to encompass the theories, tools, and applications of spatially integrated social science. In the first couple of years of its inception the program has attracted students from different places and diverse backgrounds. It is expected that the program will continue to thrive in attracting diverse students, securing external grants, and positively impacting on the economy of Northwest Ohio. This paper is a personal reflection of the views of the authors on the Ph.D. program in Spatially Integrated Social Science at the University of Toledo two years after its inception in fall 2009. The views, by no means, are of the University of Toledo, its SISS program, or any of the participating departments and faculty members.
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Why Spatially Integrated Social Science?

As much as GIS has revolutionized many disciplines and practices, including the social sciences, GIS itself does not fully account for or measure the complexities and relationships inherent in spatial data (Páez & Scott, 2004). Páez and Scott (2004) argue that current GIS software, even with the recent inclusion of spatial modeling, do not adequately account for heterogeneity, interdependence or spatial association, and thus are not as accurate or useful as it could be if more advanced spatial statistics were used. Logan et al. (2010) argue that GIS gives way to more complex spatial analysis tools when patterns on a map lead to additional questions that simple visualization cannot answer. For example, a traditional chloropleth map made in GIS does not take into account the outliers or an uneven population. The longstanding method of collecting data in tables and translating it to maps only scratches the surface of displaying and analyzing spatial data.

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