Spatial Modelling in Forest Ecology and Management: A Case Study. Edited by M. JANSEN,M. JUDAS and J. SABOROWSKI (Berlin: Springer Verlag, 2002). [Pp. xiii+225]. ISBN 3-540-43357-0. Price €89.95. Hardback

Urban segregation has received increasing attention in the literature due to the negative impacts that it has on urban populations. Indices of urban segregation are useful instruments for understanding the problem as well as for setting up public policies. The usefulness of spatial segregation indices depends on their ability to account for the spatial arrangement of population and to show how segregation varies across the city. This paper proposes global spatial indices of segregation that capture interaction among population groups at different scales. We also decompose the global indices to obtain local spatial indices of segregation, which enable visualization and exploration of segregation patterns. We propose the use of statistical tests to determine the significance of the indices. The proposed indices are illustrated using an artificial dataset and a case study of socio‐economic segregation in São José dos Campos (SP, Brazil).     

GIS for Water Resources and Watershed Management. Edited by JOHN G. LYON (London: Taylor & Francis, 2002). [Pp. vii+266]. ISBN: 0-415-28607-7. Price £45. Hardback

Nowadays, a huge quantity of information is stored in digital format. A great portion of this information is constituted by textual and unstructured documents, where geographical references are usually given by means of place names. A common problem with textual information retrieval is represented by polysemous words, that is, words can have more than one sense. This problem is present also in the geographical domain: place names may refer to different locations in the world. In this paper we investigate the use of our word sense disambiguation technique in the geographical domain, with the aim of resolving ambiguous place names. Our technique is based on WordNet conceptual density. Due to the lack of a reference corpus tagged with WordNet senses, we carried out the experiments over a set of 1,210 place names extracted from the SemCor corpus that we named GeoSemCor and made publicly available. We compared our method with the most‐frequent baseline and the enhanced‐Lesk method, which previously has not been tested in large contexts. The results show that a better precision can be achieved by using a small context (phrase level), whereas a greater coverage can be obtained by using large contexts (document level). The proposed method should be tested with other corpora, due to the fact that our experiments evidenced the excessive bias towards the most‐frequent sense of the GeoSemCor.     

A Review of: “Applications of Spatial Data Structures: Computer Graphics,Image Processing,and GIS”. By H. SAMET. (Addison-Wesley Publishing Company,Inc, 1989.) [Pp. xv + 507; figures,tables, appendix,references, name and credit index,subject index.] Price US $49.95. ISBN 020150300 X (hardback).

The introduction of automated generalisation procedures in map production systems requires that generalisation systems are capable of processing large amounts of map data in acceptable time and that cartographic quality is similar to traditional map products. With respect to these requirements, we examine two complementary approaches that should improve generalisation systems currently in use by national topographic mapping agencies. Our focus is particularly on self‐evaluating systems, taking as an example those systems that build on the multi‐agent paradigm. The first approach aims to improve the cartographic quality by utilising cartographic expert knowledge relating to spatial context. More specifically, we introduce expert rules for the selection of generalisation operations based on a classification of buildings into five urban structure types, including inner city, urban, suburban, rural, and industrial and commercial areas. The second approach aims to utilise machine learning techniques to extract heuristics that allow us to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are tested individually and in combination for the generalisation of buildings from map scale 1:5000 to the target map scale of 1:25 000. Our experiments show improvements in terms of efficiency and effectiveness. We provide evidence that both approaches complement each other and that a combination of expert and machine learnt rules give better results than the individual approaches. Both approaches are sufficiently general to be applicable to other forms of self‐evaluating, constraint‐based systems than multi‐agent systems, and to other feature classes than buildings. Problems have been identified resulting from difficulties to formalise cartographic quality by means of constraints for the control of the generalisation process.