Spatial Analysis & Data Visualization @ SFSU
     

Project Proposal

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.

Please direct any questions or comments about this project to Richard LeGates or Ayse Pamuk at San Francisco State University.

Project Overview

Problems Project Seeks to Address

Project Goals and Objectives

Project Description

Pedagogy

Substantive Material

Relationship to Survey Research

Data Sets

Answering Spatial Questions and Doing Spatial Analysis with Statistics

Project Overview

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).

Problems Project Seeks to Address

  • The failure of social science research methods and data analysis courses other than in geography to adequately address spatial dimensions of social and public policy phenomena using available GIS software
  • Undergraduate social science students' fear of rigorous social science research methods and data analysis courses
  • The high dropout rate of social science majors before beginning, during, or just after completing research methods and data analysis courses
  • The inability of textbook publishers to keep pace with computer software changes
  • The failure of many instructors to strike an appropriate balance between conventional and hi-tech teaching methods

Project Goals and Objectives

  • Create two instructional modules for undergraduates in the social sciences, urban planning and public policy
  • Develop an exciting new mode of instruction using spatial analysis and data visualization to motivate students to learn quantitative methods (Deadman et. al, 2000; Tufte, 1983, 1990, 1997)
  • Teach students technological skills to access, analyze, and communicate spatial information
  • Teach students to test normative values against empirical evidence. The instructional modules will require students to operationalize their own values with respect to important topics that touch their own lives and to test them interactively against empirical evidence as others have found effective (Stocks and Freddolino, 2000)
  • Implement an innovative way of delivering educational material combining published instructional modules, CD-ROM and web based exercises and data sets, and e-text updates that will keep pace with rapidly changing software
  • Increase the retention rate of social science majors by overcoming their fear of computer and research methodology, building their confidence, and exciting them

Project Description

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.

Pedagogy

San Jose State professor of Urban and Regional Planning Earl G. Bossard will take the lead in developing pedagogy to teach data visualization.

Substantive Material

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.

Relationship to Survey Research

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.

Data Sets

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.

Answering Spatial Questions and Doing Spatial Analysis with Statistics

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.