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SAMPLING AND VARIANCE ANALYSIS IN REGGIO CALABRIA 
Vincenzo Barrile* and Rossella Nocera** 
* DIMET University of Reggio Calabria - Italy 
Phone: ++39 965 875200, Fax:++39 965 875247, e-mail: barrile@ns . inq. unirc . it 
** IGP ETH Zurich - Switzerland 
Phone: ++41 1 6333049, Fax: ++41 1 6331101, e-mail: nocera@qeod. ethz . ch 
ISPRS Commission VI, Working Group 3 
KEY WORDS: Spatial analysis, Non - linear regression, Variance analysis, Geomarketing. 
ABSTRACT 
In this paper, it is presented the way used to examine data coming from a GIS which has been built, in order to be an 
useful instrument to people who work in economical and social fields. 
The monitored territory is the city of Reggio Calabria. Firstly the acquisition of data has been made: digitizing of the 
map, collecting data from many different archives, surveying, etc. Then the GIS has been built up, finally some 
examples of data analysis have been presented. 
Starting from the implemented GIS, it’s possible to obtain data or attributes, corresponding to some, previous defined, 
entities. A statistical analysis of those data has been done, in order to study the relations among them and to allow for 
doing some models, able to represent the real situation and to show the real trend. 
1. THE PRINCIPAL STEPS TO BUILD UP 
THE STATISTICAL MODEL 
The implemented GIS allows to know better the territory, 
giving geographic information, integrated and merged, 
with many other types of information (statistical, detected 
and results from market studies). 
In the present case the most important types of data are: 
• data which describe the physical aspect of the 
territory; 
• data which describe social and economical aspects. 
All the collected data, the GIS technology and the 
statistical analysis allow for creating a model which 
represents the real situation and shows the real trend. 
The statistical analysis have, at its heart, a model which 
attempts to describe the structures or relationships, in 
some objects or phenomena on which measurements (the 
data) are taken. 
The process of developing a statistical model varies 
depending on whether a classical hypothesis - driven 
approach (confirmatory data analysis) or a more modem, 
data - driven approach (exploratory data analysis) is 
followed. 
In many data analysis projects, both approaches are 
frequently used. In classical regression analysis, the 
residuals are usually examined, by using of exploratory 
data analytic methods, for verifying whether underlying 
assumptions of the model hold. The goal of either 
approach is a model which imitates, as closely as 
possible, in as simple a way as possible, the properties of 
the objects or phenomena being modelled. Creating a 
model usually involves the following steps: 
1. Determine the variables to observe (preliminary data 
modeling). In a study involving a classical modeling 
approach, these variables correspond to the 
hypothesis being tested. For data - driven modeling, 
these variables are the link to the phenomena being 
modeled. 
2. Collect and record the data observations (data 
acquisition). 
3. Study graphics and summaries of the collected data 
to discover and remove mistakes and to reveal low 
dimensional relationships between variables 
(qualitative robustness).