Abstract. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models. Key words: Object-orientation, Agent-based models, Spatial-temporal modeling, Topology

The authors thank William Greene and seminar participants at the 2000 Atlantic Economic

When payoffs from different actions are unknown, agents use their own past experience as well as the experience of their neighbors to guide their current decision making. This paper develops a general framework to study the relationship between the structure of information flows and the process of social learning. We show that in a connected society, local learning ensures that all agents obtain the same utility, in the long run. We develop conditions under which this utility is the maximal attainable, i.e. optimal actions are adopted. This analysis identifies a structural property of information structures -- local independence -- which greatly facilitates social learning. Our analysis also suggests that there exists a negative relationship between the degree of social integration and the likelihood of diversity. Simulations of the model generate spatial and temporal patterns of adoption that are consistent with empirical work. Key Words: Connected societies, conformism, social integ...

Because knowledge plays an important role in the creation of wealth, economic actors often wish to skew the flow of knowledge in their favor. Managers seek to spread knowledge widely within their organization but prevent its diffusion to rivals. Regional planners promote knowledge diffusion within a local economy but not beyond it. We ask, when will knowledge developed in one area of dense social connections – such as a firm, a geographic locale, or a technological community – tend to diffuse to the edge of that area but not beyond it? Marrying social network theory with a view of knowledge transfer as a search process, we argue that the degree of knowledge inequality across social boundaries depends crucially on the nature

and suggestions that improved upon later versions. This paper uses a computational model and real-world data to examine the relationship between the residential choices of individuals and aggregate patterns of neighborhood change. Our goal is to determine what assumptions about individuals ’ preferences for the race/ethnic composition of their neighborhoods produce residential tipping. We then use classic vignette data from the 1976 and 1992 Detroit Area Studies, and the Boston and Los Angeles modules of the Multi-City Study of Urban Inequality, to evaluate the plausibility of these assumptions. We find that high levels of segregation occur only when we assume that individuals’ preferences follow a threshold function. But the vignette data do not support the threshold assumption. Assuming that these data accurately measure individuals ’ race/ethnic preferences, our findings suggest that race preferences alone are insufficient to account for the high levels of segregation observed in American cities.

In this paper we investigate the relationship between location patterns, innovation processes and industrial clusters. In order to do this we extend a transactions costsbased classification of industrial clusters into a knowledge-based taxonomy of clusters, along the lines suggested by a critical revision of the main assumptions underlying most of the existing literature on spatially defined clusters. Our arguments show that the transactions costs approach and the innovation and technological change framework are broadly consistent, and that real insights into the microfoundations, nature, and evolution of clusters can be provided by these classification systems.

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ABSTRACT: This paper examines the degree to which the constraints imposed by various social contexts influence social interaction. We draw on two data sets. In each, we compare the patterning of interaction of the same individuals across different contexts. If minimal constraints are imposed, then the interaction patterns among the individuals in the two contexts should be similar. But if one of the contexts involves major constraints, then interaction patterns in the two should differ. The results suggest further that the constraints found in any context are not unlimited in their impact. Moreover, individuals who can, apparently do manipulate the context to minimize the constraint imposed by the context.

Fig. 1. Presto enables maritime-domain experts to insert artificial vessel trajectories in tracked AIS data. An artificial track, in orange, is added in the opposite direction of a Dutch sea lane that is visible by a trail of green triangles, which indicate tracked vessels. Abstract—We present an integrated and multi-disciplinary approach for analyzing behavior of moving objects. The results are ongoing research of four different partners in the Dutch Poseidon project [3] where we aim for new developments for Maritime Safety and Security (MSS) systems to monitor vessels. We focus on the following requirements for an MSS system: abstraction of large amounts of trajectory sensor data, fusion of multiple heterogeneous data sources, and visual analysis of the combined data sources. We start by extracting segments of consistent movement from trajectory data, which we store as instances of the Simple Event Model (SEM), an event ontology represented in the Resource Description Framework (RDF). Then we add data from the web about vessels to enrich the sensor data. This additional information is integrated with the representation of the vessels (actors) in SEM. The enriched trajectory data is stored in a knowledge base, which is queried by a visual analytics tool to search for spatio-temporal patterns. Although our approach is dedicated to MSS systems, we expect it to be useful in other domains.

The authors would like to thank Noa Golan and two anonymous reviewers for a number of helpful comments and suggestions. 1