This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

Spatial econometric methods deal with the incorporation of spatial interaction and spatial structure into regression analysis. The field has seen a recent and rapid growth spurred both by theoretical concerns as well as by the need to be able to apply econometric models to emerging large geocoded data bases. The review presented in this chapter outlines the basic terminology and discusses in some detail the specification of spatial effects, estimation of spatial regression models, and specification tests for spatial effects.

This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theory-driven as well as from a data-driven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.

the company level has been dominated by what goes on inside companies. Thinking about the competitiveness of nations and states has focused on the economy as a whole, with national economic policy seen as the dominant influence. In both competition and competitiveness the role of location is all but absent. If anything, the tendency has been to see location as diminishing in importance. 1 Globalization allows companies to source capital, goods, and technology from anywhere and to locate operations wherever it is most cost effective. Governments are widely seen as losing their influence over competition to global forces. This perspective, although widespread, does not accord with competitive reality. In The Competitive

In this paper, we estimate a model of housing demand with neighborhood effects. We exploit special features of the National sample of the American Housing Survey and properties of housing markets that allow us to create “natural ” instruments and therefore identify the impact of social interactions. We find evidence of both endogenous and contextual neighborhood effects. We report two alternative sets of estimates for neighborhood effects that differ in terms of the instruments we use for estimating the model. When the endogenous neighborhood effect is large the respective contextual effects are weak, and vice versa. The elasticity of housing demand with respect to the mean of the neighbors ’ housing demands (the endogenous effect) ranges from 0.19 to 0.66 and is generally very significant. The contextual effects are also very significant. A key such effect, the elasticity with respect to the mean of neighbors ’ permanent incomes ranges from 0.17 to 0.54.

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Household and employment counts (by type) are key inputs to models of travel demand. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study performs a series of Lagrange multiplier tests to confirm the co-existence of spatial lag and error processes within individual equations (6 household types and 3 employment categories). It then provides the first application of a feasible generalized spatial 3SLS estimation procedure for a seemingly unrelated regression (SUR) model of these equations. In the resulting model of Austin, Texas data, local land use conditions offer substantial predictive power of households and jobs, and transportation access plays a role, as anticipated. The work demonstrates that SUR estimation of land use intensities from parcel-level data with two types of spatial dependence is feasible and meaningful. Coupled with an upstream model of land use type, this work offers the key inputs for travel demand analyses, with transportation system performance feedback.

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

Identifying ways to increase market participation by smallholder producers requires identifying variables that influence market access. This is usually achieved using probit estimation. An important phenomenon affecting entry decision-making is the entry decision of a ‘similar ’ household, where similarity is measured in terms of ‘location. ’ When neighborhood influences are significant, it is important to allow for them in discrete decision contexts, such as probit estimation. This paper, therefore, assesses the magnitude of neighborhood influences in smallholder decisions concerning market entry. The empirical model is based on a cross-section of (110) farms situated in northern Philippines, visited (twice) in the 2000-2001 production year (a panel of 220 observations). The vehicle for analysis is a Bayesian formulation of a standard probit model, but one that allows for spatial autoregression in the decision vector. Estimation requires a Metropolisstep addition to a basic Gibbs sampling algorithm and generates useful insights concerning quantities that are important for market-access policy. (154 words).

This research is one of the sub-research products being supported by the funds provided by the Global Development Network (GDN) under the Global Development Network Awards in the Outstanding