GeoComputation analysis and modern spatial data

by
A.Stewart Fotheringham

Introduction
Computation is a term which can take one of two possible meanings. In its
broader sense, it refers to the use of a computer and therefore any type of
analysis, be it quantitative or otherwise, could be described as ‘computational’ if
it were undertaken on a computer. In its narrower and perhaps more usual sense,
computation refers to the act of counting, calculating, reckoning and estimating–
all terms which invoke quantitative analysis. This chapter will therefore restrict
itself to this latter definition and uses the term GeoComputation (GC) to refer to
the quantitative analysis of spatial data which is aided by a computer. Even more
narrowly, I shall use the term GC to refer to quantitative spatial analysis in
which the computer plays a pivotal role. This definition is still sufficiently
vague though that fairly routine analyses of spatial data with standard statistical
packages (for instance, running a regression programme in SAS) could be
incorporated within it and I shall define such analyses where the computer is
essentially a faster slide rule or abacus as weak GC. I will try to demonstrate in
the examples below some of the ways in which spatial analysis is being
extended through the use of computers well beyond that which standard
statistical packages allow. What I define as strong GC analysis is where the use
of the computer drives the form of analysis undertaken rather than being a
convenient vehicle for the application of techniques developed independently of
computers. Strong GC techniques are, therefore, those which have been
developed with the computer in mind and which explicitly take advantage of
large amounts of computer power. read more

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GeoComputation

by
Stan Openshaw

Introduction
GeoComputation (GC) is new, exciting, and here, but what is it? Some writers
seem to think it has been around as long as there have been computers being
used in geography. Others that GC is more or less a ‘brand new’ invention.
There is seemingly an understandable confusion so the purpose of this chapter is
to examine some of the alternative definitions, identify the more appropriate
ones, and then outline some examples of what it may mean in practice.

Origins
GeoComputation is linked by name to what is broadly termed computational
science with which it is clearly related and shares many of its aims.
Computational science is a relatively new multidisciplinary paradigm for doing
science in the late 20th century. As yet there is no general consensus as to a
precise definition of what computational science actually is. In broad terms,
computational science involves using computers to study scientific problems
and it seeks to complement the use of theory and experimentation in scientific
investigation. It seeks to gain understanding principally through the use and
analysis of mathematical models and computer simulation of processes
performed using, and often totally dependent upon, the availability of high
performance computers. It is a largely or wholly computational approach to
scientific investigation in which computer power is used to supplement and
perhaps in some areas supplant more traditional scientific tools. Indeed once
computer hardware became fast enough and big enough and numerical
methodologies clever or flexible enough, then a computational paradigm
provided a substitute for physical experimentation. It allows the visualization of
hitherto unseen scientific processes, and it offers a basis for the simulation of
complex systems which are too difficult for economical study by any other
route. Computation permits the investigator to test theory by simulation, to
create new theory by experimentation, to obtain a view of the previously
invisible, to explore the previously unexplorable, and to model the previously
unmodellable. There is clearly considerable potential here that will be released
in the new millennium as computer speeds increase and a computational
paradigm becomes a more common paradigm for doing science in many more read more

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Models and queries in a spatiotemporal GIS

by
Baher El-Geresy and Christopher Jones

INTRODUCTION
Much interest has been evidenced lately in the combined handling of spatial and temporal
information in large spatial databases. In GIS, as well as in other fields (Silva et al.,
1997), research has been accumulating on different aspects of spatio-temporal
representation and reasoning (Stock, 1997). The combined handling of spatio-temporal
information allows for more sophisticated application and utilisation of these systems.
Developing a Temporal GIS (TGIS) leads to a system which is capable of tracing and
analysing the changing states of study areas, storing historic geographic states and
anticipating future states. A TGIS can ultimately be used to understand the processes
causing geographic change and relate different processes to derive patterns in the data. read more

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Essential Image Processing and GIS for Remote Sensing

Jian Guo Liu
Philippa J. Mason

Imperial College London, UK

This edition first published 2009, # 2009 by John Wiley & Sons Ltd.

Overview of the Book

From an applied viewpoint, and mainly for Earth observation, remote sensing is a tool for collecting raster data or images. Remotely sensed images represent an objective record of the spectrum relating to the physical properties and chemical composition of the Earth surface materials. Extracting information from images is, on the other hand, a subjective process. People with differing application foci will derive very different thematic information from the same source image. Image processing thus becomes a vital tool for the extraction of thematic and/or quantitative information from raw image data. For more comprehensive analysis, the images need to be analysed in conjunction with other complementary data, such as existing thematic maps of topography, geomorphology, geology and land use, or with geochemical and geophysical survey data, or ‘ground truth’ data, logistical and infrastructure information, which is where the geographical information system (GIS) comes into play. GIS contains highly sophisticated tools for the management, display and analysis of all kinds of spatially referenced information.

Remote sensing, image processing and GIS are all extremely broad subjects in their own right and are far too broad to be covered in one book. As illustrated in Figure 1, this book aims to pinpoint the overlap between the three subjects, providing an overview of essential techniques and a selection of case studies in a variety of application areas. The application cases are biased towards the earth sciences but the image processing and GIS techniques are generic and therefore transferable skills suited to all applications.
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Open Source GIS. A GRASS GIS Approach

Third Edition
by
Markus Neteler
FBK-irst & CEA, Trento, Italy
Helena Mitasova
North Carolina State University, USA

© 2008 Springer Science+Business Media, LLC.

Foreword

GRASS GIS software was developed in response to the need for improved analysis
of landscape “trade offs” in managing government lands and the emerging
potential of computer-based land analysis tools. During the last decades of the
20th century, government land managers in the U.S. (and across the world)
faced increasing requirements from legislation and stakeholder groups to examine
and evaluate alternative actions. To fulfill these new requirements, land
managers needed new tools.
During this same era, computational capabilities wondrously improved.
Tasks requiring days and months with paper and acetate overlays could be
accomplished with this newly emerging geographic information technology
within minutes. But even in the mid-1980s, GIS technology involved significant
capital investment. Managers wanted to see results before they spent their
limited funds on new technologies.
The U.S. Army Construction Engineering Research Laboratory (CERL) in
Champaign, Illinois has the mission of developing and infusing new technologies
for managing U.S. Department of Defense installations. These installations
include millions of acres of lands needed for military training and testing.
Other uses included wildlife management, hunting and fishing and forestry,
grazing and agricultural production. Other priorities were added through legislation
– such as protecting endangered species and habitats, protecting cultural
sites, and limiting the on and off-post impacts of noise, ordnance, contaminants
and sediments.
Military land managers were unable to cope with the challenge of examining
proposed new actions (such as new weapon firing ranges or new vehicle
training routes) without improved methods to gather, integrate and visualize
their data and to examine alternative courses of action. Acquiring emerging
proprietary technologies and digital data wasn’t even a consideration – the
cost was too high and the expertise required to learn, operate and manage
the technology was beyond their resources.
Given this need, a group of then young researchers at CERL elected to
develop their own set of initial landscape analysis tools. Initially, this in-house read more

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