What accounts for the differences in the kinds of communities within the metropolis in which members of different racial and ethnic groups live? Do socioeconomic advancement and acculturation provide greater integration with whites or access to more desirable locations for minority-group members? Are these effects the same for Asians or Hispanics as for blacks? Does suburbanization offer a step toward greater equality in the housing market, or do minorities find greater discrimination in the suburban housing market? Data from 1980 for five large metropolitan regions are used to estimate "locational-attainment models," which evaluate the effects of group members' individual attributes on two measures of the character of their living environment: the socioeconomic standing (median household income) and racial composition (proportion non-Hispanic white) of the census tract where they reside. Separate models predict these outcomes for whites, blacks, Hispanics, and Asians. Net of the effects of individuals' background characteristics, whites live in census tracts with the highest average proportion of white residents and the highest median household income. They are followed by Asians and Hispanics, and-at substantially lower levels-blacks. Large overall differences exist between city and suburban locations; yet the gap between whites and others is consistently lower in the suburbs than in the cities of these five metropolitan regions.
Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a "black box" operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT's Interactive Data Exploration. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants' accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.
We report preliminary results of our ongoing field study of IT professionals who are involved in security management. We interviewed a dozen practitioners from five organizations to understand their workplace and tools. We analyzed the interviews using a variation of Grounded Theory and predesigned themes. Our results suggest that the job of IT security management is distributed across multiple employees, often affiliated with different organizational units or groups within a unit and responsible for different aspects of it. The workplace of our participants can be characterized by their responsibilities, goals, tasks, and skills. Three skills stand out as significant in the IT security management workplace: inferential analysis, pattern recognition, and bricolage.
This study tested whether multiple-object tracking-the ability to visually index objects on the basis of their spatiotemporal history-is scene based or image based. Initial experiments showed equivalent tracking accuracy for objects in 2-D and 3-D motion. Subsequent experiments manipulated the speeds of objects independent of the speed of the scene as a whole. Results showed that tracking accuracy was influenced by object speed but not by scene speed. This held true whether the scene underwent translation, zoom, rotation, or even combinations of all 3 motions. A final series of experiments interfered with observers' ability to see a coherent scene by moving objects at different speeds from one another and by distorting the perception of 3-D space. These reductions in scene coherence led to reduced tracking accuracy, confirming that tracking is accomplished using a scene-based, or allocentric, frame of reference.
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