Transcript Interoperating with GIS and Statistical

Interoperating with GIS and
Statistical Environment
for an Interactive
Spatial Data Mining
Didier Josselin, THEMA, UPRESA 6049 du CNRS, Besançon, GDR CASSINI
[email protected] http://thema.univ-fcomte.fr/didier.htm
Xlisp-Stat programming :
@ D. Betz, L. Tierney, C. Brunsdon, D. Josselin, L. Guerre, B. Dancuo
French Research Group about GIS
(1990-2000 : GDR CASSINI, 2000...?)
The spatial data mining quest
Finding significant relations
between geographical objects
in order to cluster them
Examples of geographical purpose
Sub-objectives at
geographical entity scale
 1st
door : the statistical dependency
some entities have common characteristics...
 2nd
door : the spatial relation
some entities are contiguous, closed from each others…
 3rd
door : the combination of spatial and statistical relation
some entities are similar and closed...
Sub-objectives at territory and
geographical space scale

1st door : the spatial cutting out and data aggregation : a succession
of deriving ...
Analysing spatial repartition,
Identifiing gradients,
Detecting discontinuities...

2nd door : the spatial auto-correlation measure
Global and local

3rd door : the identification of geographical composite
(heterogeneous) entities
Geographical agricultural
flows analysis
Agricultural flows between French communes
Commune A
Commune B
Various flows status
LES FLUX SORTANTS EN FRANCHE-COMTÉ
Outgoing
flowsENin1988
Franche-Comté
Source : RGA 1988
What are we looking for ?
Commune aggregate with its key and boundary
Commune described by an attribute
Commune couple flow
Which softwares may be
available and convenient ?
Geographical Information
Systems
+

Various structured query languages
 Existing
tools to build clean structured databases

Graphical and mapping functionalities

generally open to other softwares
-

Poor in statistical functions

Rarely integrate Exploratory Data Analysis

Need to write queries rather execute them in a graphic
way
ESDA Environment
+

Numerous statistical functions

Numerous graphic representations

Ease to select objects on screen
 Dynamic

link between objects
generally open to development by programming
-

Poor in geographical and semiologic functionalities

Does not integrate structured databases functions

Does not include geometrical or topological models
Any solutions ?
Modifying existing softwares
First methodological choice
Adding to a statistical
environment some mapping and
relational functionalities
ARPEGE’ : a tool to Analyse
Robustly in Practice and
Explore Geographical
Environment (XlispStat)
The « visioner » in ARPEGE’
Using ARPEGE’ to analyse flows
+

Dynamic link between multiple objects

Relative fastness to support expert decision making

Facilities to implement relations and triggers between
objects

Possibility to focus on many crossed selections

Difficult to manage with multiscaling
 Users
may miss some synthetic statistical indicators
or automatic methods

Application must be quite simple (RAM limitations)

Combinatory explosion risk !
Coupling two complementary
softwares
Second methodological choice
Interoperating with a GIS and a
statistical environment software
LAVSTAT : a dynamic Link
between ArcView and XlispSTAT
Interaction
LAVSTAT principles
Services, DDE
Server
XlispStat
ArcView
+

Dynamic link between GIS and Statistical software
 The

whole functionalities access to both systems
Increases the ways to investigate spatial data

A screen is not enough to explore data

A few time loss to make interoperating the two
softwares

Not already stable (memory conflicts)
CONCLUSION
A few advices for spatial
analysis to take reliant
decisions in order
to shape the future ...
If you have some objectives to
reach with data to explore...
Choose the appropriate
methods ...
0
Keep a critical look on tools
and methods ...
1
Choose most robusts methods
to analyse your data ...
2
Check hypothesis without too
tight assumptions
3
Try to dominate time during
anaysis and to be inside
learning process ...
4
Keep in touch with all
individual data
5
Bring to light all aspects of
your problem by multiple
representations
6
Use dynamic links and
interactivity
7
Study the fringe as the trend...
8
...and model deviation,
residuals ...
7
9
… and relations between
geographical objects through
different scales...
10
… which may be well defined
(semantic,topology, structural,
functional ...)
11
Validate your results by maths
and
expertise
12
f ( x)   x.dx
And consider the
“measurement density” is not
constant
13