Spatial Analysis of Privacy Measured Through Individual Uniqueness Based on Simple U.S. Demographics Data

University of Minnesota M.A. thesis. May 2015. Major: Geography. Advisor: Francis Harvey. 1 computer file (PDF); vii, 52 pages.Previous studies reveal that, using U.S. census data, over 60% population of the U.S. could be uniquely identied with a combination of gender, zip code, date of birth attributes in 1990 and 2000. This thesis extends these studies to examine spatial variation of individual uniqueness in 2010 at dierent scales and regions in the U.S. In this thesis, I use spatial and non-spatial statistics to study the spatial patterns on both global and local scales. Specically, I provide 1) the comparison of national level uniqueness between 2000 and 2010, 2) the investigation of spatial variation of uniqueness in different regions and at dierent scales, 3) the identication of local uniqueness clusters outliers and 4) the evaluation of urban-rural divides on individual uniqueness segregation. On the global scale, the comparison between 2000 and 2010 reveals that, although overall individual uniqueness changes little, the individual uniqueness of middle-age group members has signicantly decreased. The study of regional differences finds that low individual uniqueness for college-age population are spatially homogeneous despite that the overall uniqueness are spatially heterogeneous. The analysis at different scales discloses that overall uniqueness decreases, and the dierences between age-group uniqueness reduce, when geographical scales focus on the cores of urban area. On the local scale, the results indicate an urban-rural divides of individual uniqueness segregation. The Clusters and Outliers Analysis nd that places where low individual uniqueness cluster the most are also very urbanized area. The average individual uniqueness of urban area is computed as 58.02% whereas that of rural area is computed as 88.43%. This means, if a person is from an urban area, given the zip code, gender and date of birth information, he/she is much less likely to be identied uniquely. This study offers contributions to geographic information privacy, particularly relevant to reverse geocoding and related spatial aggregation techniques used in census data

Another R&D Anomaly?

In this paper, we investigate the relation between stock returns and R&D spending under different market conditions. Our empirical evidence suggests that investors’ response to R&D activities varies according to stock market status. Following the conventional definitions of markets, we first categorize the market into four different states: slightly up (up by 0-20%), bull (up by more than 20%), slightly down (down by 0-20%), and bear (down by more than 20%). Using firms in high-tech industries from 1992 to 2009 as our sample, we show that investors value R&D spending consistently positively only when the market (proxied by the S&P 500) is up. R&D is valued less in the downward market and R&D response coefficients even turn negative during bear markets. However, earnings response coefficients are consistently positive regardless of market status. The results remain unchanged after we control for beta, bankruptcy risk, size, and different measuring windows. Our findings cannot be explained by risk-based hypothesis. The study advances our understanding of the relation between stock returns and R&D activities by empirically documenting its variations in market valuation across different market states; particularly, we found empirical evidence that R&D response coefficients in the down markets are negative. The study also provides additional input to the ongoing debate on finding the appropriate accounting treatment for intangible assets

Building Transportation Foundation Model via Generative Graph Transformer

Efficient traffic management is crucial for maintaining urban mobility, especially in densely populated areas where congestion, accidents, and delays can lead to frustrating and expensive commutes. However, existing prediction methods face challenges in terms of optimizing a single objective and understanding the complex composition of the transportation system. Moreover, they lack the ability to understand the macroscopic system and cannot efficiently utilize big data. In this paper, we propose a novel approach, Transportation Foundation Model (TFM), which integrates the principles of traffic simulation into traffic prediction. TFM uses graph structures and dynamic graph generation algorithms to capture the participatory behavior and interaction of transportation system actors. This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy and provides a foundation for solving complex transportation problems with real data. The proposed approach shows promising results in accurately predicting traffic outcomes in an urban transportation setting