Following are highlights of the project proposal submitted to NSF. Please refer to the Research Methods and Data Analysis modules on this website and the final report on the project (which will filed when the project is completed) for a precise description of the project.
The team will develop two instructional modules and accompanying CD-ROM and web based data sets and exercises. The modules will be targeted to upper division undergraduate students in introductory social science research methods and data analysis courses with no prior experience with social science research methods, data analysis, or Geographic Information Systems (GIS).
Underlying the modules is team members' shared belief that teaching students to envision spatial data is both a pedagogically powerful teaching device and provides them an essential approach in social science. An important part of the modules is to give students a workable set of real analytical skills.
Geography departments teach undergraduate students well-developed principles for understanding and representing physical reality on maps that is valuable in all of the social sciences (Robinson, 1995; Dent, 1996). Except for a few urban planning courses, this material is not yet part of the methods and data analysis curricula in other of the social sciences.
Almost all geography departments now teach undergraduate students GIS concepts and operational GIS skills. Undergraduate urban planning programs are also beginning to teach GIS. But GIS has hardly penetrated undergraduate research methods and data analysis courses in other of the social sciences.
Material on visualizing data developed by Yale Professor Emeritus Edward Tufte and others has become a mainstream part of graduate social science research methods and statistical analysis courses, and scholars have developed a body of knowledge about visualizing information (Card, Mackinlay, & Schneiderman, 1999, Jacobson, 1999, Davenport, 1997). Even though undergraduate students can easily understand material on visualizing information, like it, and put it to good use, visualizing information is not usually taught in undergraduate social science research methods or data analysis classes today.
San Jose State professor of Urban and Regional Planning Earl G. Bossard will take the lead in developing pedagogy to teach data visualization.
The substantive content of the material will be related to space, culture, and urban policy, specifically: managing urban development at the global and regional scale, understanding human settlements, immigrant housing, and urban political cultural conflicts.
In addition to demographic variables and variables related to attributes of cities and regions we will include some opinion data collected through survey research such as the General Social Survey (GSS) and the Roper Social Capital survey. US Census data and international household survey data will be used to show the connection between spatial analysis with GIS and data gathered using standard survey research methods.
The modules will draw upon a number of datasets about cities, culture, and urban policy: the 2000 U.S. Census of Housing and Population, UN Commission on Human Settlements data on cities, World Bank World Development Indicators data, and State of the Nation's Cities (SONC) data.
Many spatial questions can be answered without training in statistics. Others require or are better answered using descriptive and inferential statistics. The research methods module will teach students how to answer spatial questions that do not involve training in statistics; the data analysis module will teach students questions that require statistics at the level taught in introductory social science statistics courses.