77
Nevertheless, some systems of this type do exist. For
instance, the Digital Environmental Library of the
University of Berkeley (http://elib.cs.berkeley.edu/) allows
the retrieval of digital aerial photographs, by pointing their
position on a map (see fig. 1) and is developing into a
multimedia document catalogue.
The TIGER system of the US Bureau of Census
(http://tiger.census.gov/), besides allowing to access a
detailed map of any location in the US (see in fig. 2 a
portion of New York City), makes the map searchable for
some geographic entity (such as zip code, county name,
and so on). A user can also modify a Tiger map to display
or not specific information layers.
We Options Navhjale Annotate Help \
Discuaiöü URL: jMap, /y fri gzr
i TKi ER Map Service
\ The toi lowirç map is produrr ort-ttie- fly 'from ¿sp«c i«i binary vweton of
: T'-h- *1 ii it fn*
{."oó- mom 1
•• in I
>Yi; ;
Y>
A .A
LEGEND
; Lake/Porul/Ocean Connector
; Stream Highway
National Park Expressway
; Other Park
: Mi Iitary Area
City
'IF I- -■ " < ‘ ' - Cj t " ill “ ' I ’ - i
Fig.2 - A map supplied by the TIGER system
In both applications, the data supplied are homogeneous
and very basic so that they may be of use for different
studies.
GRASSLinks (http://regis.berkeley.edu/grasslinks; Huse,
1995) and Opgis (http://www.ogis.lst.se/htlmfrm.html) are
similar systems, where the data associated to each
location are richer, but still very structured.
On the other hand, systems like SINTESI
(http://cidoc.iuav.unive.it/sintesi/) and ISTAT
(http://www.istat.it) are developed specifically to draw a
thematic map as a result of a query on a standard
database.
When the size of data, and consequently their
heterogeneity, grows it becomes impossible to fit them
into a pre-defined data structure. This is why, projects
such as the Master Environmental Library (MEL) of the
Defense Modeling and Simulation Office - US Department
of Defense (http://mel.dmso.mil) or Tioga (Stonebraker et
al., 1993) are more similar to data catalogues than to
databases. Indeed, they basically store metadata
information (within which, the geographical location) and
allow a search on these values to direct the user to the
actual source of information (see fig. 3).
Display Map Display Icons GeoBrowr.e Help
Enter place name:
Mil
: Cancel Display
Fig. 3 - Tioga geographical browser
3. DATA STRUCTURES
All the above implementations share the characteristic of
being created and maintained by a central institution
which has designed the system to fit its own specific
needs and purposes. In a complex reality as the Italian
one, where the responsibility over a certain territory
belong to a number of, often conflicting, organizations,
such an approach is practically unfeasible. Furthermore,
there is an enormous amount of environmental data (for
instance, flows on some rivers have been measured daily
for more than 150 years) that have been collected in
different ways and must be preserved because it
constitutes an important legacy on past environmental
conditions.
One feasible alternative is thus to build a "light" storage
and retrieval system that can accommodate the
contributions of different parties, providing the minimum of
structure to allow for an efficient retrieval. The system we
have developed is a data repository, similar in some
respect to MEL, but the metadata structure is highly
simplified.
MEL utilizes an implementation of the metadata standard
structure proposed by the US Federal Geographic Data
Committee (FGDC, 1994) that implies the use of 219 data
description fields. These have been proved to be
redundant for most applications (Foresman et al., 1996).
On the contrary, the structure used in our system is closer
to the few essential features that studies like Miller and
Bullock (1994) have shown to be common to all
applications.
The metadata structure describing a certain data set is
simply constituted by:
Entity name
Entity type
Geographical position
Data category
Provider of the data
Frequency of measurements
Units
