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AGILE Conference 2012, Avignon (France). Describing change in the real world: from observations to events. Gilberto Camara Karine Reis Ferreira Antonio Miguel Monteiro INPE – National Institute for Space Research. Useful References.

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describing change in the real world from observations to events

AGILE Conference 2012, Avignon (France)

Describing change in the real world: from observations to events

Gilberto Camara

Karine Reis Ferreira

Antonio Miguel Monteiro

INPE – National Institute for Space Research

useful references
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.
welcome to the age of data intensive giscience
Welcome to the Age of Data-intensive GIScience!

Capabilities

Vantage Points

L1/HEO/GEO TDRSS & Commercial

Satellites

Far-Space

Permanent

LEO/MEO Commercial Satellites and Manned Spacecraft

Near-Space

Aircraft/Balloon Event Tracking and Campaigns

Airborne

Deployable

Terrestrial

User

Community

Forecasts & Predictions

slide4
Data-intensive GIS = principles and applications of geoinformatics for handling very large data sets
challenges for data intensive giscience
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?

slide6
Data-intensive GIS is not “more maps”

Spatio-temporal data that captures change

We need new theories and methods

objects and events
Objects and events

The coast of Japan is an object

The 2011 Tohoku tsunami was an event

processes and events
Processes and events

Flying is a process - Virgin flight VX 112 (LAX-IAD) on 26 Apr 2012 is an event

aral sea an object disaster an event
Aral Sea (an object) – disaster (an event)

When did the Aral Sea shrank to 10% of its original size?

slide11

objectsexist, eventsoccur

Mount Etna is an object

Etna’s 2002 eruption was an event

a view on processes and events
A view on processes and events

(Worboys & Galton)

Space

Time

Count

Mass

football or game?

water or lake?

a pragmatic view on objects and events
A pragmatic view on objects and events

Space

Time

Observable

Abstract

football or game?

water or lake?

data types for moving objects guting
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

data types for moving objects guting1
Data types for moving objects (Guting)

flight (id: string, from: string, to: string, route: mpoint)

weather (id: string, kind: string, area: mregion)

detecting flood gauges in netherlands
Detecting flood (gauges in Netherlands)

Source: LlavesandRenschler, AGILE 2012

event processing architecture
Event processing architecture

Source: ENVISION project (http://www.envision-project.eu/)

slide19

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

slide22

Observationsallowustosenseexternal reality

Anobservationis a measureof a value in a location in spaceand a position in time

building blocks basic types
Building blocks: Basic Types

type BASE = {Int, Real, String, Boolean}

operations: // lots of them…

building blocks geometry ogc
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

building blocks time iso 19108
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

slide26

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

why do we need interpolators
Why do we need interpolators?

Howlong do youtakefrom Frankfurt toBeaune?

why do we need interpolators1
Why do we need interpolators?

We cannot sample every location at every moment – we need to estimate in space-time

slide30

Sensors: water monitoring

  • Brazilian Cerrado
  • Wells observation
    • 50 points
    • 50 semimonthly time series
    • (11 Oct 2003 – 06 March2007)

Rodrigo Manzione, Gilberto Câmara, Martin Knotters

slide31

Estimates of water table depth for an area in Brazilian Cerrado

JUNE

JULY

MAY

AUGUST

SEPTEMBER

Manzione, Câmara, Knotters

three types of interpolators
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

slide33

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

time series
Time Series

Continuous variation of a property value over time

(water table depth sensors)

time series1
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

moving objects
Moving objects

MOVING OBJECTS

Objects whose position and extent change continuously

slide37

Moving objects

individual entity that varies its location (and its extent) over time

slide38

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

slide39

Moving Object data type

distance: MovingObject x MovingObject → TimeSeries

distance (mo1, mo2) {

ObsSet oset

for t = mo1.begin(); t <= 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

}

slide41

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.
slide42

source: USGS

type Coverage [T: TIME, G1: GEOM, G2: GEOM, B: BASE] uses ST

operations:

new: {Obs [T, G1, B}

x IntInSpaceTime[T, G1, B] x G2 → Coverage

value:Coverage x G1 x T → B

slide43

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

}

slide45

From a coverage to a time series

timeSeries: Coverage x S → TimeSeries

timeSeries (c1, loc)

ObsSet oset

for t = c1.begin(); t <= 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

}

slide46

When did the large flood occur in Angra? When precipitation was > 10mm/hour for 5 hours

Coverage set (hourly precipitation grid)

Event (precipitation > 10 mm/hour for 5 hrs)

slide47

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.

slide48

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

slide49

Eventcomposition

Forest loss > 20%

Exploração intensiva

time

Event 1

Perda >50% do dossel

Loss > 50%

Event 2

Perda >90% do dossel

Loss > 90%

Event 3

Corte raso

Clearcut

Event 4

Floresta

Floresta

slide51

When did the large flood occur in Angra?

Coverage prec = getData (weather forecast)

flood = new Event()

from t0 = prec.begin(); t0 <= prec.end(); t.next()

if getRain (prec, t0, t0 + 24) > 100

strong = new Event (prec, t0, t0 + 24)

flood.compose (strong)

slide52

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

}

slide53

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 <= aralSea.end(); t.next()

if getWaterArea (aralSea,t) < 0.1* areaOrig

disaster = new Event (aralSea, t, t.aralSea.end())

break

return disaster

}

terralib spatio temporal database as a basis for innovation
TerraLib: spatio-temporal database as a basis for innovation

Modelling (TerraME)

Visualization (TerraView)

Spatio-temporal

Database (TerraLib)

Data Mining(GeoDMA)

Statistics (aRT)