Raymond Wong
Dept of Sociology
University of California, Santa Barbara

POSITION STATEMENT

What are your research interests in the areas of inequality and equity, and what spatial dimensions do you currently or potentially see in them?

I am interested to understand the underlying patterns and changes of inequality over time and across different contexts (national and sub-group variations). One of my new areas of research that explicitly utilizes the spatial dimension is a study of the changes of urban neighborhoods in the United States in the past few decades. In general, past studies by demographers and urban sociologists have not considered how spatial configurations may aid our understanding of urban neighborhood patterns and their changes over time. Although there are some recent interests in spatial arrangements, for example, the relationship between poverty and place and the geographic concentration of urban poverty, the concept of space is still only vaguely incorporated into its theoretical and empirical framework. My plan is to situate the study of changes in poor neighborhoods and the concentration of urban poverty in a broader context of the ecological transformations and realignments of urban neighborhoods, which are affected by changes in social, economic, demographic, and political forces over time. I believe that a mapping of which neighborhoods become gentrified or downgraded to slums over time in major urban areas, and an analysis of the characteristics of these neighborhoods and factors that contribute to such ecological transformations promise a new and fruitful way of understanding urban inequality.

What kinds of spatial data, models, techniques, software, etc. do you use or have considered using in your research. Which of these work well for you? Where do you see problems and/or shortcomings?

A wide variety of statistical techniques can be utilized, including log-linear models, multinomial models, poisson regression models, and spatial regression models. Each is useful for particular types of data, but only the last incorporates the spatial dimension explicitly in statistical modeling.

Can you point out any "best practice examples" of spatially-oriented research in your field? Do you have any suggestions for Learning Resources CSISS might provide? Workshops we might offer?

I am not aware of any empirical work in inequality and stratification that incorporates the spatial dimension in statistical modeling. I strongly believe that a wider dissemination of the appropriate spatial statistical techniques would be extremely helpful. I would like the CSISS to consider offering a one-day or two-days workshop that would teach beginners the necessary skills to handle and analyze spatial data.