of 15 agricultural research centers that receive principal funding from governments, private foundations, and international and regional organizations, most of which are members of the

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doi:10.1111/j.1467-9493.2006.00241.x Driving forces of tropical deforestation: The role of remote sensing and spatial models

Research Goals and Objectives: The main goal of this project was to develop an analytical approach that will allow researchers to incorporate spatial error structures in models of rare crimes. In order to examine the causes of violence, researchers are frequently confronted with the need to apply spatial econometric methods to models with discrete outcomes. Appropriate methods for doing so when the outcomes are measured at intra-city areal units are lacking. The aim of this research was to fill that gap. This research effort developed and applied the framework to a real-world empirical problem. It examined the socio-economic and demographic determinants of disaggregate homicide rates at two different intra-city levels of areal aggregation and compared inferences derived from several sets of models. The analysis was conducted on disaggregated homicide counts (1989-91) recorded in Chicago’s census tracts and neighborhood clusters using explanatory factors obtained from census sources. Research Design and Methodology: An extension of the Generalized Cross Entropy (GCE) method was applied to these data in an attempt to utilize their flexibility in

Spatial regression deals with the specification, estimation and diagnostic checking of regression models that incorporate spatial effects. Two broad classes of spatial effects may be distinguished, referred to as spatial depen-dence and spatial heterogeneity (Anselin 1988b). In this chapter, attention

Understanding determinants of land use in developing countries has become a priority for researchers and policy makers with a wide range of interests. For the vast majority of these land use issues, the location of change is as important as its magnitude. This overview paper highlights new economic approaches to modeling land use determinants that combine non-traditional data sources with novel economic models and econometric techniques. A key feature is that location is central to the analysis. All data elements include an explicit location attribute, estimation techniques include the potential for complications from spatial effects, and results are location-specific. The paper reviews the theory underlying these models. Since this paper is intended to provide the potential new researcher with an introduction to the challenges of this analysis, we present an overview of how remotely-sensed data are collected and processed, describe key GIS concepts and identify sources of data for this type of econometric analysis. Finally, selected papers using these techniques are reviewed.

The following paper outlines a new econometric model designed to capture both the temporal and spatial dynamics of housing prices. The paper combines existing spatial econometric techniques with a model that allows parameters to evolve over time. In addition, we provide an empirical application to the price effects of confined animal feeding operations to a data set of residential real estate in Tippecanoe County, Indiana from 1993 through 2006. ∗ Copyright 2008 by T.H. Kuethe, K.A. Foster, and R.J.G.M. Florax. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Hedonic price analysis is a popular tool in applied economic research. The technique has been applied to various goods to capture the marginal contribution of a number of attributes to its total sales price (Lancaster, 1966; Rose, 1974). Hedonic price models are attractive because the results are simple to interpret, are almost always consistent with intuition, and appeal to Lancaster’s theory of choice and revealed preference

We show that for any sample size, any size of the test, and any weights matrix outside a small class of exceptions, there exists a positive measure set of regression spaces such that the power of the Cliff-Ord test vanishes as the autocorrelation increases in a spatial error model. This result extends to the tests that define the Gaussian power envelope of all invariant tests for residual spatial autocorrelation. In most cases, the regression spaces such that the problem occurs depend on the size of the test, but there also exist regression spaces such that the power vanishes regardless of the size. A characterization of such particularly hostile regression spaces is provided. Keywords: Cliff-Ord test; point optimal tests; power; spatial error model; spatial lag model; spatial unit root.

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Was local job growth a significant determinant of poverty reduction between 1990 and 2000? This research takes advantage of newly available data and techniques to explore the job growth on tract-level poverty reduction. Spatial corrections to the model allow for more accurate identification of the significant determinants of poverty reduction across the United States. Results indicate that job growth is a highly significant predictor of poverty reduction, though its effect is modest. While spatial models didn't materially affect the regression coefficients, significant gains in model explanatory power were seen when using a spatial model as compared to OLS.