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We commonly make decisions based on different kinds of maps, and under varying time constraints. The accuracy of these decisions often can decide even over life and death. In this study, we investigate how varying time constraints and different map types can influence people’s visuo-spatial decision making, specifically for a complex slope detection task involving three spatial dimensions. We find that participants’ response accuracy and response confidence do not decrease linearly, as hypothesized, when given less response time. Assessing collected responses within the signal detection theory framework, we find that different inference error types occur with different map types. Finally, we replicate previous findings suggesting that while people might prefer more realistic looking maps, they do not necessarily perform better with them.

This chapter presents the concept of decentralized spatial computing (DeSC) as a way to embed dynamic spatial data capture and processing capabilities within our built urban environment. The chapter illustrates the potential of DeSC for safe- guarding privacy in a dynamic location-based services scenario: Mobile service users protect their potentially sensitive location by the use of a decentralized query algorithms, solely collaborating with peers close by and thereby excluding the privacy bottleneck of an omniscient global service provider. In an extensive set of consecutive experiments several decentralized query algorithms were tested, trading the level of privacy for the quality of service. The use of a real world test bed, — a small part of Ordnance Survey’s OS MasterMap® Integrated Transport Network™ Layer for Southampton — underlines the experiments’ validity. The chapter concludes with a research and development agenda for DeSC in the urban context.

Information about dynamic spatial fields, such as temperature, windspeed, or the concentration of gas pollutant in the air, is important for many environmental applications. At the same time, the development of geosensor networks (wirelessly communicating, sensor-enabled small computing devices, distributed throughout a geographic environment) present new opportunities for monitoring of dynamic spatial fields in much more detail than ever before. This paper presents a new model for querying information about dynamic spatial fields using geosensor networks. In order to manage the inherent complexity of dynamic geographic phenomena, our approach in this paper is to focus on the qualitative representation of spatial entities, like regions, boundaries, and holes, and of events, like splitting, merging, appearance, and disappearance events. Based on combinatorial maps, we present a qualitative model as the underlying data management paradigm for geosensor networks that is capable of tracking salient changes in the network in a much more energy-efficient way. Further, our model enables reconfiguration of the communication in the geosensor network in response to changes in the environment. We present an algorithm capable of adapting sensor network granularity according to dynamic monitoring requirements. Regions of high variability can trigger increases in the geosensor network granularity, leading to more detailed information about the dynamic field. Conversely, regions of stability can trigger a coarsening of the sensor network, leading to efficiency increases in particular with respect to power consumption and longevity of the sensor nodes. Querying of this responsive geosensor network is also considered, and the paper concludes with a review of future research directions.

Spatializations are computer visualizations in which nonspatial information is depicted spatially. Spatializations of large databases commonly use distance as a metaphor to depict semantic (nonspatial) similarities among data items. By analogy to the “first law of geography”, which states that closer things tend to be more similar, we propose a “first law of cognitive geography,” which states that people believe closer things are more similar. In this paper, we present two experiments that investigate the validity of the first law of cognitive geography as applied to the interpretation of “point-display spatializations.” Point displays depict documents (or other information-bearing entities) as 2- or 3-dimensional collections of points. Our results largely support the first law of cognitive geography and enrich it by identifying different types of distance that may be metaphorically related to similarity. We also identify characteristics of point displays other than distance relationships that influence similarity judgments.

Mechanisms for overcoming semantic heterogeneity in diverse information sources are critical components of any interoperable system. In the case of diverse geographic information sources, such mechanisms present particular difficulties since the semantic structure of geographic information cannot be considered independently of its spatial structure. This paper develops an approach to the integration of semantically heterogeneous geographic information that is capable of addressing the spatial and thematic aspects of geographic information in concert. The approach is founded on an algebraic model of spatio-thematic information layers. Variations in the thematic structure of layers are explored, including the integration of layers with hierarchical thematic structure. The effects of changes in the thematic granularity of layers are also considered. Finally, a case study of the integration of heterogeneous land cover data is used to illustrate the relevance of the formal model within a practical application.