Daniela-Luminita Constantin

English

Research Papers in Economics

 
 
 
 
BOOK REVIEW 
 
Manfred M. Fischer, Spatial Analysis and Geocomputation, Springer Verlag, 2006 
by Daniela-Luminita Constantin, Academy of Economic Studies of Bucharest 
 
 
In  2007  Manfred  M.  Fischer,  Professor  of  Economic  Geography  at  Vienna  University  of 
Economics and Business and one the top contemporary regional scientists, with a tremendous 
contribution to the development of Regional Science, celebrated his 60
th anniversary. On this 
occasion he received a long series of honours: among them, the Fellow Award for the outstanding 
contribution and dedicated leadership to the field of Regional Science from the Regional Science 
Association International and a recognition in his honour by a special issue of the Review of 
Regional Studies. The Romanian Regional Science Association also bestowed upon Manfred M. 
Fischer a certificate of appreciation in recognition of his invaluable support to the development of 
the Romanian RSA and its integration in the international Regional Science community. 
 
In another register, Springer Verlag launched on the same occasion two books of selected essays 
of Manfred M. Fischer. The Romanian Journal of Regional Science has the privilege to publish in 
this issue the review of both books. 
 
The  book  Spatial  Analysis  and  Geocomputation  highlights  a  new  computational-intensive 
paradigm, that of geocomputation, which is able to contribute to a radical change of the current 
research practice in spatial analysis. It is based on the latest developments in estimation theory, 
model selection and testing: from the very beginning is underlined the aim of providing new 
insights  into  neural  networks,  as  advanced  tools  for  non-parametrical  modelling  and  spatial 
interaction modelling.  
 
Accordingly, the book is structured into four parts, which address the proposed topic gradually, 
from  the  spatial  analysis  and  Geographic  Information  Systems  (GIS)  to  computational 
intelligence in spatial data analysis, geocomputation in remote sensing environments and, finally, 
to new frontiers in neural spatial interaction modelling. 
 
After Introduction, in Part I - Spatial Analysis and GIS, Chapter 2 focuses on spatial analysis in 
geography, discussing the crucial role of spatial data analysis in the detection of patterns and 
exploration and modelling of the relationships between these patterns and the understanding  of 
the processes behind them. 
 
Chapter 3 approaches spatial interaction models and their role in GIS, explaining the usefulness 
of  GIS  functionalities  in  three  steps  of  the  modelling  process,  namely  zone  design  matrix 
building and visualisation. 
 
Chapter 4 envisages data model and design issues that are specifically oriented to the application 
of  GIS  to  research,  planning  and  management  in  transportation,  identifying  various 
improvements  of  the  traditional  network  data  model  that  are  required  to  support  advanced Manfred M. Fischer, Spatial Analysis and Geocomputation, Springer Verlag, 2006 by 
Daniela Luminita Constantin 
 
121 
 
network  analysis  in  a  ground  transportation  context.  They  refer  to  turn-tables,  dynamic 
segmentation, linear referencing, traffic lines and non-planar networks. 
 
In Part II - Computational Intelligence in Spatial data Analysis, Chapter 5 addresses the expert 
systems  and  artificial  neural  networks  for  spatial  data  analysis  and  modelling  as  essential 
components  for  knowledge-based  GIS.  It  highlights  the  differences  between  spatial  expert 
systems  and  artificial  neural  networks,  stressing  the  idea  that  neurocomputing  shows  greater 
flexibility than  expert systems to deal with situations typical for the GIS and expressing the 
opinion  that  “the  future  might  bring  shared  geoprocessing  with  expert  systems  and  artificial 
networks, genetic algorithms and fuzzy logic, especially when GIS are implemented on large 
multi-processor systems” (p.75). 
 
Chapter 6 refers to computational neural networks as tools for spatial data analysis, offering 
valuable topics for both students and professional researchers, which point out the properties of 
the processing elements, the network topology and learning in the network. 
 
Chapter 7, written with Sucharita Gopal, proposes a new approach to modelling interregional 
telecommunication  flows  via  artificial  networks.  It  represents  a  clear  break  with  traditional 
methods that explain spatial interactions, being based on a general nested sigmoid neural network 
model. The feasibility of this model is demonstrated in the context of modelling interregional 
telecommunication traffic in Austria, its performance being evaluated in comparison with the 
classical regression  approach and gravity type. 
 
Chapter 8, written with Yee Lung, concentrates on evolutionary computational neural network 
(CNN)  based  on  genetic  algorithms  for  modelling  spatial  interaction  data.  It  considers  this 
problem  as  a  global  optimisation  problem  proposing  a  new  approach  that  “embeds  back-
propagation learning into the evolutionary paradigm of genetic algorithms” (p.128). In order to 
illustrate its performances and to evaluate its robustness the approach has been applied to the 
family  of  three  inputs,  single  hidden  layer,  single  output  feedforward  CNN  models  using 
interregional telecommunication traffic data in Austria. 
 
Part III offers a spotlight on Geocomputation inRemote Sensing Environments, more specifically 
on  adaptive  spectral  classifiers  as  implemented  with  backpropagation  networks,  radial  basis 
function networks and fuzzy ARTMAP. 
 
Chapter 9, witten with Sucharita Gopal, Petra Staufer and Klaus Steinnocher, focuses on the 
evaluation of neural patterns classifiers for remote sensing application. The main task of the 
multispectral pattern classification is to assign pixels to one of eight prespecified urban land use 
categories on a pixel-by-pixel basis. The research has been based on four classes of simulations 
so as to illustrate the properties of the classifier in general as well as the stability of results with 
respect to control parameters. 
 
Chapter  10,  written  with  Petra  Staufer,  discusses  the  question  of  optimisation  in  an  error 
backpropagation  neural  network  environment  with  a  performance  test  on  a  spectral  pattern 
classification problem. It aims to develop a mathematically rigid framework for the minimisation 
of the cross-entropy function in an error backpropagation framework. To this end it investigates 
different techniques for the optimisation of the multiple-class cross-entropy error function to train Manfred M. Fischer, Spatial Analysis and Geocomputation, Springer Verlag, 2006 by 
Daniela Luminita Constantin 
 
122 
 
single  hidden  layer  neural  network  classifiers  with  softmax  output  transfer  functions  in  the 
realworld of the multispectral pixel-by-pixel classification problem. 
 
Chapter 11, written with Sucharita Gopal, is placed in the area of the Adaptiv Resonance Theory 
of Carpenter and Grossberg (1987), which provides a large family of models and algorithms in 
spite of limited analysis of their properties in the realworld. Manfred Fischer and Sucharita Gopal 
make a step forward in this respect, the chapter describing design features system dynamics and 
simulation algorithms for the corresponding learning system, which is trained and tested for the 
classification of the multispectral image of the Landsat-5 Thematic Mapper scene from the city of 
Vienna on a pixel-by-pixel basis. 
 
Part IV has selected three papers in order to highlight the advances towards  ew Frontiers in 
 eural Spatial Interaction Modelling. 
 
Chapter  12,  written  with  Martin  Reissmann  and  Katerina  Hlavackova-Schindler,  proposes  a 
novel neural approach for the case of origin or destination -  constrained spatial interaction flows. 
The  authors  demonstrate  the  efficiency  of  this  approach  using  Autrian  interregional 
telecommunication traffic data. 
 
Chapter 13 addresses the learning process in neural spatial interaction models from a statistical 
perspective. The Alopex based global search is employed – as opposed to local search based 
upon backpropagation of gradient descents, each in combination with the bootstrapping pairs 
approach, this study demonstrating the superiority of the Alopex based global search, measured 
in terms of Kullbach and Leibler’s information criterion. 
 
Chapter  14,  written  with  Martin  Reissmann,  develops  a  methodology  for  neural  spatial 
interaction modelling in a mathematically rigid and unified framework. In comparison with the 
current practice in neural network modelling, which seems to suffer from the least squares and 
normality  assumptions,  this  study  suggests  a  more  suitable  estimation  approach,  namely 
maximum  likelihood  estimation  under  more  realistic  distributional  assumptions  of  Poisson 
processes  and  employs  a  global  search  procedure  –  Alopex  in  order  to  solve  the  maximum 
likelihood estimation problem. 
 
The whole book appears as an impressive collection of essays which proposes not only a wide 
range of approaches to spatial analysis and geocomputation, but also a novelty of solutions to the 
problems identified, trained and tested in the case of Austria. Therefore the book is strongly 
recommended to all experts who aim at using geocomputation models and techniques for solving 
real world problems.  
 
At the same time with sincere congratulations for this great achievement, I would like to express 
Professor Manfred M. Fischer my entire gratitude for the opportunity to pay a couple of visits to 
the Institute of Economic Geography and GIScience, whose director he is, which offered me the 
chance of a wide, deep view, “from inside”, of the laboratory where the very challenging ideas 
expressed in the book have been developed.  
 
 
 

BOOK REVIEW: Manfred M. Fischer, Spatial Analysis and Geocomputation, Springer Verlag, 2006

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