Statistics for Spatial Data.

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Twitter mood predicts the stock market.

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-tech...

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Critical questions for big data

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

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Computational Social Science

Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them.

Geometric Deep Learning: Going beyond Euclidean data

Over a mere ve or six human generations, agriculture has all but disappeared from rural landscapes in the eastern half of the United States. During an equally brief period, agriculture has transformed forests, valleys, prairies, and plains in the interior of the continent. Economic models have explained the shift in terms of the lower costs of land, the larger scale of farming, and better connections to export markets, via river transport at rst and then by rail (Fitzgerald, 2003; Gardner, 2002; Hart, 2003). Enormous gains in agricultural productivity since the green revolution have accelerated these trends, ensuring that the physical extent of agricultural land peaked in the United States in 1950, and the pace of abandonment has quickened (Theobald, 2001; Theobald et al., 2005). In the information age, distance to nearby population centers is an increasingly unimportant factor in predicting the prevalence of agricultural land use. With a nationally integrated market and export-driven demand shaping landuse patterns, the production locations have shifted to landscapes with fertile soils; 2 at, open terrain; and favorable climates. Still, we know that landscapes in the six areas examined have not escaped the legacies of prior patterns of development. Each was managed in different and path-dependent ways during the past 130 years. Choices framed by the timing of settlement, cultural inheritances, and institutional arrangements continue to shape the overall sustainability of ecosystems long after the initial transformation of landscapes. The legacies of these distinct agricultural systems are explored in this chapter primarily through the lens of the agricultural census. Scientists have expressed growing interest in understanding the effect of historical land use on ecosystem dynamics. Land-use data have been used to drive ecosystem models capable of

Changing Agrarian Landscapes Across America: A Comparative Perspective

*Myron P. Gutmann is Assistant Professor in the Department of History and Population Associate in the Population Research Center at the University of Texas at Austin. Record linkage is the bringing together of a number of bits of information, all pertaining to the same person, event, moment, or even thought or document. All of us do it at some time or another, and all history can be seen to be based on it. What

Review Essay : THE FUTURE OF RECORD LINKAGE IN HISTORY Reviewed by Myron P. Gutmann* Identifying People in the Past Edited by E. A. Wrigley London: Edward Arnold, 1973, 159 pages

Using a representative sample of the Hispanic population of the United States based on the manuscripts of the 1910 census, we estimate childhood mortality for the period from approximately 1890 to 1910. We find high child mortality in the Hispanic population, higher than for non-Hispanic whites but not significantly different than among nonwhite non-Hispanics (mostly African Americans). Hispanic rural farm populations in California, Texas, and Arizona experienced high mortality, but not as high as other Hispanic populations. Child mortality was very high among Hispanic residents of New Mexico and those in Florida outside Tampa; it was especially low in the Hispanic population in Tampa.

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Intra-Ethnic Diversity in Hispanic Child Mortality, 1890–1910 *

In the next decades, aging farmers in the United States will make decisions that affect almost 1 billion acres of land. The future of this land will become more uncertain as farm transfer becomes more difficult, potentially changing the structure of agriculture through farm consolidation, changes in farm ownership and management, or taking land out of production. The Great Plains Population and Environment Project interviewed farmers and their spouses between 1997 and 1999. Farm Family Survey participants were ambiguous about their plans to leave farming, transfer land to others, and even long-term land use, largely due to concerns about the continued economic viability of farming. Participants living far from metropolitan areas expected to sell or rent to other farmers, while those near residential real-estate markets expected to sell to developers. Delays in planning for retirement and succession were common, further threatening the success of intergenerational transitions.

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Myron P. Gutmann

Papers

Changing Agrarian Landscapes Across America: A Comparative Perspective

Review Essay : THE FUTURE OF RECORD LINKAGE IN HISTORY Reviewed by Myron P. Gutmann* Identifying People in the Past Edited by E. A. Wrigley London: Edward Arnold, 1973, 159 pages

Intra-Ethnic Diversity in Hispanic Child Mortality, 1890–1910 *

Land Use and Transfer Plans in the U.S. Great Plains

Reconstituting Wandre: an approach to semi-automatic family reconstitution