A Review of: “Three Dimensional Applications in Geographical Information Systems”. Edited by J. RAPER. (London: Taylor & Francis, 1989.) [Pp. 189.] Price £30·00. ISBN 0850667763.

Studies on the structural properties of road network and its close relationship with the traffic flow distribution have received intensive interdisciplinary attention. However, most of these attempts were theoretical. It is also a challenge to understand the relationship between the structure and morphology of a road network and peoples' movement. We developed a new methodology to deal with this challenge in this study. The first attempt was to apply the ego network analysis (which is rooted in social science) to the formation of hierarchical road networks. Then, the ego network was improved to become weighted ego network by assigning a weight to each of the links in a network. A measure called weighted average centrality rank is developed to define the order of links in a complex network. The ego network and the weighted ego network are both evaluated with a notional network and two sets of real-life road networks. Traffic flow data were used as a benchmark for the evaluation of the two approaches. The results show that they both perform well. But the hierarchies formed by weighted ego network analysis are more consistent with the real-life traffic flow, and the improvement is clearly observable.     

A Review of: “Accuracy of Spatial Databases”. Edited by M. Goodchild and S. Gopal. (London: Taylor & Francis, 1989.) [Pp. 290.] Price £35.00.

The integration of multisource heterogeneous spatial data is one of the major challenges for many spatial data users. To facilitate multisource spatial data integration, many initiatives including federated databases, feature manipulation engines (FMEs), ontology-driven data integration and spatial mediators have been proposed. The major aim of these initiatives is to harmonize data sets and establish interoperability between different data sources.

On the contrary, spatial data integration and interoperability is not a pure technical exercise, and there are other nontechnical issues including institutional, policy, legal and social issues involved. Spatial Data Infrastructure (SDI) framework aims to better address the technical and nontechnical issues and facilitate data integration. The SDIs aim to provide a holistic platform for users to interact with spatial data through technical and nontechnical tools.

This article aims to discuss the complexity of the challenges associated with data integration and propose a tool that facilitates data harmonization through the assessment of multisource spatial data sets against many measures. The measures represent harmonization criteria and are defined based on the requirement of the respective jurisdiction. Information on technical and nontechnical characteristics of spatial data sets is extracted to form metadata and actual data. Then the tool evaluates the characteristics against measures and identifies the items of inconsistency. The tool also proposes available manipulation tools or guidelines to overcome inconsistencies among data sets. The tool can assist practitioners and organizations to avoid the time-consuming and costly process of validating data sets for effective data integration.     

A Review of: “Remote Sensing of Soils and Vegetation in the USSR”. By P. J. CURRAN,G. M. FOODY,K. YA. KONDRATYEV,V. V. KOZODEROV and P. P. FEDCHENKO. (London: Taylor & Francis, 1990.) [Pp. 200.] Price £30·00. ISBN 085066 4020.

As they increase in popularity, social media are regarded as important sources of information on geographical phenomena. Studies have also shown that people rely on social media to communicate during disasters and emergency situation, and that the exchanged messages can be used to get an insight into the situation. Spatial data mining techniques are one way to extract relevant information from social media. In this article, our aim is to contribute to this field by investigating how graph clustering can be applied to support the detection of geo-located communities in Twitter in disaster situations. For this purpose, we have enhanced the fast-greedy optimization of modularity (FGM) clustering algorithm with semantic similarity so that it can deal with the complex social graphs extracted from Twitter. Then, we have coupled the enhanced FGM with the varied density-based spatial clustering of applications with noise spatial clustering algorithm to obtain spatial clusters at different temporal snapshots. The method was experimented with a case study on typhoon Haiyan in the Philippines, and Twitter’s different interaction modes were compared to create the graph of users and to detect communities. The experiments show that communities that are relevant to identify areas where disaster-related incidents were reported can be extracted, and that the enhanced algorithm outperforms the generic one in this task.     

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.