Information and Knowledge Management using ArcGIS ModelBuilder

João Agrela et al.
ETRS 89 projection was used as the INSPIRE European Directive states. Several changes had to be
made in the thematic layers of information.
Every variable was stored within a geoDataset, and every dataset in a geoDatabase. This is needed
for it allows better operational flow within the model. It was necessary to create some layers of
information from other sources, using, for instance, PDF imagery from municipal services (for
example, the information on Geology did not exist on shapefile format and had to be recreated
through vectorization from a PDF municipal file).
Another important step was to limit all the information to the institutional municipal limits, so as to limit
any error that might occur during the map algebra stage of this process.
All the above steps were needed just to create a viable geographical information database, with
correctly georeferenced information, limiting redundant information and allowing quicker processing of
all Model Builder tools. One aspect has to be pointed out by the authors: these steps are vital not only
for a Model Builder based project. It is necessary to take all these steps in any GIS based project to
achieve maximum efficiency. All the data normalization may be, at times, a time consuming and
troublesome stage but it is essential to create a steady basis for the project. It does not do well to
build a castle on sandy foundations.
The theoretical starting point is a simple assumption: some geographical variables have in their
nature a prohibitive aspect that does not allow the installation of the above mentioned public
installation. Geographical variables have between themselves relations of neutrality, repulsion and
attractiveness. Giving a quick example, the geographical nature of the variable “Hospital” repels the
geographical nature of the variable “Sewage Treatment Plant”. The intensity of repulsion or
attractiveness is directly related to the direct or partial incompatibity of natures of the geographical
variables.
After all the pre-project stages of data formatting and preparation it is thus necessary to identify the
nature of all the variables and the variables that are absolutely opposite, or only partially opposing (for
instance, there might not be any geologically suitable land parcel in the municipality, which means we
would have to choose the “lesser evil”, which is to say, a less suitable land parcel).This method
implies that there will be intermediate cartography created. This is a useful fact, because the users of
the models can always go back to the intermediate cartography to check why the final choice of
location was made. It is also a very valuable stage of direct validation of the model, since the users
can check which branch of the model is returning more accurate outputs. In this model we may point
out three Intermediate outputs.
These intermediate outputs have meaning and an ethical and value related approach to it. The first of
the three branches is named Favorable Placing Cartography (FPC) and every pixel has a positive
numeric value.
Figure 3: Favorable placing cartography branch
5