Describing change in the real world: from observations to events

1
Describing change in the real world from
observations to events
AGILE Conference 2012, Avignon (France)

• Gilberto Camara
• Karine Reis Ferreira
• Antonio Miguel Monteiro
• INPE National Institute for Space Research

2
Useful References

• AU Frank, One step up the abstraction ladder
  combining algebras from functional pieces to a
  whole, COSIT 1999
• RH Guting et al., A foundation for representing
  and querying moving objects, ACM Transactions on
  Database Systems, 2000
• M Worboys, Event-oriented approaches to
  geographic phenomena, IJGIS, 2003
• A Galton R Mizoguchi, The Water Falls but the
  Waterfall does not Fall New Perspectives on
  Objects, Processes and Events, Applied Ontology,
  2009.
• W Kuhn, A Functional Ontology of Observation and
  Measurement, GeoS 2009.

3
Welcome to the Age of Data-intensive GIScience!
Vantage Points
Capabilities
L1/HEO/GEO TDRSS Commercial Satellites
Far-Space
Permanent
LEO/MEO Commercial Satellites and Manned
Spacecraft
Near-Space
Airborne
Aircraft/Balloon Event Tracking and Campaigns
Deployable
Terrestrial
User Community
Forecasts Predictions
4

• Data-intensive GIS principles and applications
  of geoinformatics for handling very large data
  sets

5
Challenges for data-intensive GIScience
Which data is out there? How to organize big
spatial data? How to get the data I need?
How to model big data? How to access and use big
data?
6

• Data-intensive GIS is not more maps
• Spatio-temporal data that captures change
• We need new theories and methods

7
(No Transcript)
8
Objects and events
The coast of Japan is an object The 2011 Tohoku
tsunami was an event
9
Processes and events
Flying is a process - Virgin flight VX 112
(LAX-IAD) on 26 Apr 2012 is an event
10
Aral Sea (an object) disaster (an event)
When did the Aral Sea shrank to 10 of its
original size?
11
objects exist, events occur
Mount Etna is an object Etnas 2002 eruption was
an event
12
A view on processes and events
(Worboys Galton)
Space
Time
Count
Objects Events
Matter Processes
Mass
football or game?
water or lake?
13
A pragmatic view on objects and events
Space
Time
Observable
Objects Events
Matter Processes
Abstract
football or game?
water or lake?
14
Object (GPS buoy) event (tsunami)
15
Data types for moving objects (Guting)
 mpoint instant ? point  mregion instant ?
region
Frank, Kuhn, Guting algebras are better than
1st order logic for modelling geo-things
16
Data types for moving objects (Guting)
flight (id string, from string, to string,
route mpoint) weather (id string, kind
string, area mregion)
17
Detecting flood (gauges in Netherlands)
Source Llaves and Renschler, AGILE 2012
18
Event processing architecture
Source ENVISION project (http//www.envision-proj
ect.eu/)
19
source USGS
Events are categories (Frank, Galton) identity
id a a composition ?a, ?b, ?c, c a.b
associativity a (b c) (a b) c
20

• How can we design an algebra for spatiotemporal
  data that represents change?

21
Observations allow us to sense external reality
22
Observations allow us to sense external reality
An observation is a measure of a value in a
location in space and a position in time
23
Building blocks Basic Types
type BASE Int, Real, String,
Boolean operations // lots of them
24
Building blocks Geometry (OGC)
type GEOM Point, LineString, Polygon,
MultiPoint, MultiLineString, MultiPolygon
operations equals, touches, disjoint,
crosses, within, overlaps, contains, intersects
GEOM x GEOM ? Bool
25
Building blocks Time (ISO 19108)
type TIME Instant, Period operations
equals, before, after, begins, ends, during,
contains, overlaps, meets, overlappedBy, metBy,
begunBy, endedBy TIME x TIME ? Boolean
26
Observation data type
type Obs T TIME, G GEOMETRY, B BASE
operations new T x G x B ? Obs value
Obs ? B geom Obs ? G time Obs ? T
27
From observations to events
28
Why do we need interpolators?
How long do you take from Frankfurt to Beaune?
29
Why do we need interpolators?
We cannot sample every location at every moment
we need to estimate in space-time
30
Sensors water monitoring
Brazilian Cerrado Wells observation 50 points
50 semimonthly time series (11 Oct 2003 06
March2007)
Rodrigo Manzione, Gilberto Câmara, Martin Knotters
31
Estimates of water table depth for an area in
Brazilian Cerrado
JUNE
JULY
MAY
AUGUST
SEPTEMBER
Manzione, Câmara, Knotters
32
Three types of interpolators
IntValueInTime T TIME, B BASIC estimate
Obs x T ? B
IntSpaceInTime T TIME, G GEOM estimate Obs
x T ? G
IntInSpaceTime T TIME, G GEOM, B
BASIC estimate Obs x (T,G) ? B
33
What do ST types have in common?
type STgen T TIME, G GEOM, B BASE
operations getObs ST ?
Obs begins, ends ST ? T boundary
ST ? G after, before ST x T ?
ST during ST x Period ? ST
34
Time Series
Continuous variation of a property value over
time (water table depth sensors)
35
Time Series
Type TimeSeries T TIME, B BASE uses
ST operations new Obs T,S,B x
IntValueInTime T,B ? TimeSeries value
TimeSeries x T ? B
36
Moving objects
MOVING OBJECTS Objects whose position and extent
change continuously
37
Moving objects
individual entity that varies its location (and
its extent) over time
38
Moving Object data type
type MovingObject T TIME, G GEOM uses
ST operations new Obs T,G,B x
IntSpaceInTime T,G ? MovingObject value
MovingObject x T ? G
39
Moving Object data type
distance MovingObject x MovingObject ?
TimeSeries distance (mo1, mo2) ObsSet
oset for t mo1.begin() t lt mo1.end()
t.next() Point p1 mo1.value (t)
Point p2 mo2.value (t) o1 new
Obs (t, dist (p1, p2)) oset.add (o1)
ts new TimeSeries (oset) return ts
40
How many walruses reached Baffin island?
41
source USGS
Coverage T ? G ? B Multi-temporal collection of
values in space. Two-dimensional grids whose
values change Samples from fixed or moving
geosensors.
42
source USGS
type Coverage T TIME, G1 GEOM, G2 GEOM, B
BASE uses ST operations new Obs T, G1,
B x IntInSpaceTimeT, G1, B x G2 ?
Coverage value Coverage x G1 x T ? B
43
Functions on coverages
getWaterArea (Coverage cov, Time t) area
0 forall g inside cov.boundary() if
cov.value (g,t) "water area
area g return area
44
From a coverage to a time series
45
From a coverage to a time series
timeSeries Coverage x S ? TimeSeries timeSeries
(c1, loc) ObsSet oset for t
c1.begin() t lt c1.end() t.next()
Real v c1.value (loc, t) o1 new Obs
(t, loc, val) oset.add ( o1 ) ts new
TimeSeries ( oset ) return ts
46
When did the large flood occur in Angra? When
precipitation was gt 10mm/hour for 5 hours
Coverage set (hourly precipitation grid) ? Event
(precipitation gt 10 mm/hour for 5 hrs)
47
The event data type
An event is an individual episode with a
beginning and end, which define its character as
a whole. An event does not exist by itself.
Its occurrence is defined as a particular
condition of one spatiotemporal type.
48
The event data type
Type Event T1 TIME, T2 TIME uses
ST operations new ST x (T1, T2) ?
Event compose Event x Event ?
Event intersect Event x Event ? Event
49
Exploração intensiva
Event composition
Forest loss gt 20
time
Event 1
Perda gt50 do dossel
Loss gt 50
Event 2
Perda gt90 do dossel
Loss gt 90
Event 3
Corte raso
Clear cut
Event 4
Floresta
Floresta
50
When did the large flood occur in Angra?
51
When did the large flood occur in Angra?
Coverage prec getData (weather forecast) flood
new Event() from t0 prec.begin() t0 lt
prec.end() t.next() if getRain (prec, t0,
t0 24) gt 100 strong new Event
(prec, t0, t0 24) flood.compose
(strong)
52
When did the Aral Sea shrank to 10 of its
original size?
getWaterArea (Coverage cov, Time t) area
0 forall g inside cov.boundary() if
cov.value (g,t) "water area
area g return area
53
When did the Aral Sea shrank to 10 of its
original size?
aralSea new Coverage (images) findDisaster
(aralSea) t0 aralSea.begin() areaOrig
getWaterArea (aralSea,t0) for t
aralSea.begin() t lt aralSea.end() t.next()
if getWaterArea (aralSea,t) lt 0.1 areaOrig
disaster new Event (aralSea, t,
t.aralSea.end()) break return disaster

54
From observations to events
55
TerraLib spatio-temporal database as a basis for
innovation
Modelling (TerraME)
Visualization (TerraView)
Spatio-temporal Database (TerraLib)
Data Mining(GeoDMA)
Statistics (aRT)
56
GIS technology for big data
57

• Algebras for spatio-temporal data are a powerful
  way of representing change