Geoscience Reference
In-Depth Information
as MATLab, Excel, Python's Matplotlib, Numeric Python (Numpy); or (iv) directly
through mainstream programming languages (e.g., Java, C
CC
). The advantage
of these approaches is control and transparency, the main disadvantage is that
the development of such applications calls for a significant level of expertise and
requires ongoing maintenance.
The complexity of GIS implementations and the huge variety of applications
imply that it is not easy to develop benchmarks for testing the quality, speed and
accuracy of GIS products. As a result, it is up to the user to carefully assess their
particular current and future needs and to consider the features of each package
(cost, maintainability, transparency, flexibility, etc.) before they adopt a specific
product.
Since their appearance in the late 1960s, GIS have evolved tremendously both in
terms of the related technology and with respect to the underlying methodology.
Their ever increasing use has raised several research questions concerning the
development of theories, techniques, data and technology for interpreting the
relationships and patterns involving spatial data. In fact, this realization resulted
in the introduction of the term “Geographic Information Science” (GIScience) to
signify that the systematic study of these issues constitutes a science in its own
right (Goodchild
2010
). The need to address these issues systematically inspired
the establishment of the US University Consortium for Geographic Information
Science (
www.ucgis.org
)
which involves more than 60 institutions and defines
GIScience as “the development and use of theories, methods, technology, and data
for understanding geographic processes, relationships and patterns”. Hence, GIS are
not merely a tool for decision support but a rapidly changing domain which poses
significant challenges for academics and practitioners alike.
19.3
Generalities on Facility Location Problems
In general, the essence of Facility Location Problems (FLPs) is to determine the
position of a set of facilities in a given location space in order to provide some
service to a set of actors which are supposed to patronize some of the available
facilities. These actors correspond to the demand (actual or potential) that must be
satisfied. This definition implies the following fundamental ingredients of a FLP
(see also Eiselt and Laporte
1995
; ReVelle and Eiselt
2005
).
Location Space
It represents the space where demand points are present and
facilities are to be located. It can be a physical space (e.g., a region or a city) or
not (e.g., a market or any multi-dimensional space defined by a set of variables).
Typically, the dimension of the space is assumed to be sufficiently large to consider
facilities dimensionless in such a way that they can be represented as points.
The location space can be considered continuous, discrete or it may be repre-
sented by a network. In a continuous space facilities are allowed to be located at
any point except within potential “forbidden zones”. Continuous space models are
