You could be
actually, there are two ways to cut rasters in ArcMap.
classical technique is the
“raster clip” (“raster
cutting”) tool in ArcToolbox.
This has been the only procedure for a long time.
since version 10, you have at your disposal the “Image
Analysis” tool in the standard toolbar. This tool
a way to cut out rasters and pictures.
main advantage of the images analysis toolbar is that you can
the final result before exporting it!
We will discuss both methods,
by cutting a part
of the orthophoto coastline of the IGN with the
municipalities’ boundaries of Pont-Aven.
Firstmethod: ArcToolbox:clip Raster tool
In ArcToolbox (Data
Management> Raster> Raster Processing> Clip), double-click the clip
Following a previous article ( Start with Postgres / Postgis ), we will discuss
an introduction to the administration of Postgres / postgis databases, the
loading of a shapefile and the connection and loading of the Postgis layer from
The most convenient way for administrating PostgreSQL databases is to
use the pgAdmin3 graphic interface.
This tool is installed automatically when
PostgreSQL is installed. It can launched from the program bar:
Decision processes are based on information from very diverse source and
type. This information is used by the decision-makers to perform choices,
i.e. to retain a certain number of entities and to exclude others.
Let’s discuss the following example:
An action must be performed on municipalities, but this action depends on: 1. the area of the communes, between 2500 and 3000 hectares
2. the number of inhabitants of the municipality, between 2500 and 5000
The purpose of the operation is to perform a classification of objects (municipalities)
according to two criteria (population and area).
Let’s reconsider our example regarding the towns ranking
for the Finistère region according to the following two criteria: population
How to rank entities according toa singlecriterion
To establish the fuzzy number allowing the
classification of the towns according a single criterion, we rely on our assessment
of two qualities: the complete satisfaction of the criterion (1) or the complete
By accomplishing this task, we have generated a complete
series of intermediate values, between 0 and 1. Working with numbers is not
easy but, above all, it is not natural. Let’s say that a town meets the 0.356
surface criterions and that the town surface is rather unsatisfactory, it does
not affect the classification process but it sure does the operator.
In a second stage (the next
article) we will discuss how to perform the
aggregation of two fuzzy criteria with another command, which we will put at
your disposal as well.
we have seen in the examples discussed in previous articles, the first step to
process the information as fuzzy is to transform the values of a classic attribute into a fuzzy number:
In this article we will discuss how to cross two fuzzy attributes, with
a command that we put at your disposal. The “Flexible Aggregation”
command allows the aggregation of two fuzzy criteria.
The interface offers three questions to the user to define the desired
aggregation formula. Then, the tool proceeds to the union or intersection of
the two criteria by producing a new data layer. This layer contains the data of
the two input layers plus one field with the result of the calculated
It is essential to create the fuzzy criteria before proceeding with their
In the case of non-numeric criteria, a manual transformation has to be executed
by the user to match numeric codes to the text attributes.
In a previous article, GIS and Decision Support (3): A tool for creating fuzzy
criteria with ArcMap , we have seen a tool for transforming numeric
attribute into a soft attribute (fuzzy number).
In this article we will discuss and put at your disposal the last one that
makes it possible to create a soft attribute (fuzzy number) starting from a
text attribute containing a classification of the entities.
The tool is composed of a command, “Text criterion”,
that makes it possible to transform a textual criterion into a fuzzy number.
The original criterion is a text field of a layer of a feature class. The
contents of this field include classified values that will be transformed
into another field, numeric, but with values within the range 0-1. This
transformation takes place to match the values of the origin field with a
degree of satisfaction of the field, criterion: 1 corresponds to a total
satisfaction, 0 to a total dissatisfaction, the other values being partial