Temporal GIS for Meteorological Applications

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Temporal GIS for Meteorological Applications

• Visualization, Representation, Analysis,
  Visualization, and Understanding

May Yuan Department of Geography College of
Atmospheric and Geographic Sciences The
University of Oklahoma
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Outline

• A brief history of time in GIS
• Temporal GIS for Meteorological Applications a
  new approach visualization gt representation gt
  analysis gt visualization gt understanding
• A case study

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A brief history of time in GIS
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GIS development no room for time

• Mapping tradition
• Static views of the world
• Space-centered
• Using location to get information

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Adding Time in RDBM
Time-stamped tables
1993
Avg. Income
County
Population
1994
Nixon
17,000
20,000
Avg.
.
County
Income
Population
Nixon
20,000
19,800
1995
Cleveland
35,000
32,000
County
Population
Avg. Income
Nixon
20,900
21,000
Cleveland
35,000
32,000
Oklahoma
86,000
28,000
Gadia and Vaishnav (1985)
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Adding Time in GIS

• Snapshot Time-stamping layers
• Space-time composite Time-stamping spatial
  objects (records)
• Spatiotemporal object Time-stamping attributes

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Snapshots

• Temporal time sets

Metro Denver Temporal GIS Project by Temporal
GIS, Inc. http//www.rrcc-online.com/gey235/bpop.
html
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Space-Time Composite

• Spatial change over time
• History at location
• Cadastral mapping

Langran and Chrisman (1988)
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Spatiotempoal Object Model

• Spatial objects with beginning time and ending
  time

Worboys (1992)
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Change at Location

• History at a location
• Nothing moves

Geographic semantics something (concrete or
abstract) meaningful in geographic worlds,
including objects, fields, ideas, authority, etc.
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Commercial TGIS

• 4Datalink (2002)
• STEMgis (2003?)
• TerraSeer (2004)

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4Datalink (2002)

• Time Travel Through Data
• Spatiotemporal objects with initial time (ti) and
  finishing time (tf)
• AM/FM applications

No considerations on changes in geometry or
attributes.
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STEMgis (2003)

• Time-stamp spatial objects
• Hierarchical database

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TerraSeer (2004)

• Object chains
• Public health and surveillance

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Current Temporal GIS Technology

• Mostly point data
• Change-based information
• Uniform change
• Do not consider
• Change with spatial variation
• Split
• Merge
• Development (temporal lineage)

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TGIS based on event and change
Peuquet and Duan (1995)
Event-based SpatioTemporal Data Model (ESTDM)
Changes from ti-1 to ti
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Temporal GIS for Meteorological Applications
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New temporal GIS approach

• Shift our emphasis
• From storage how data are ingested
• Observation based
• Organize data accordingly how data were collected
  by sensors or observers
• To analysis what we want to get from the data
• Process based
• Organize data according to how data were resulted
  from geographic processes

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Lets start with a scenario
May 3, 1999 Oklahoma City Tornado outbreaks
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Visualization

• An entry point for
• Investigation
• Exploitation
• Hypothesis generation
• Understanding
• A means to
• Communicate results
• Discern correlation and relationship
• Reveal patterns and dynamics

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Temporal GIS reversed engineering
TEMPORAL GIS
HUMAN
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Digital Precipitation Arrays
See ST Data gt Think Geog. Processes
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What do we have?

• Observations from sensor networks satellites,
  radars, or ground-based stations at discrete
  points in time.
• Model simulation data
• Raster or point-based data
• Point-based data may be transformed to raster
  data through spatial interpolation.

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What do we want?

• Information beyond pixels and points
• How does something vary in space?
• How does something change over time?
• How does something progress in space?
• How does something develop over time?
• How often do similar things occur in space and
  time?
• Want to know about something

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What is something?
Event, Process and State
trigger
process
event
drive
state
measured by
spatiotemporal data
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Events and Processes

• An event introduces additional energy or mass
  into a system
• Triggers processes to adjust the system

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State

• Fields
• Objects
• Fields of objects
• Objects of fields

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Temporal GIS for understanding and discovery

• Representation
• identify constructs for geographic processes
• organize ST data based on geographic processes
  that generate the data
• Analysis
• elicit process signatures and their implications
• diagnose how a geographic process evolves
• examine how a geographic process relates to its
  environment
• categorize and relate processes in space and time
• Visualize

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Issues

• Scale
• Granularity
• Uncertainty

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Considerations

• Integration of fields and objects
• Hierarchies of events, processes, and states

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Koestler (1967) holons

• Duality of a holon
• Self-assertive tendency preserve and assert its
  individuality as a quasi autonomous whole
• Integrative tendency function as an integrated
  part of an existing or evolving larger whole.
• Field of objects rainfield of storms
• Object of fields storm of rainfields

objects
fields
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Weinberg (1975) General Systems Theory

• Small-number simple systems
• Individuals behaviors
• Mathematical
• Large-number simple systems
• Collective behaviors
• Statistics
• Middle-number complex systems
• Too large for math
• Too small for stats
• Both individually and collectively

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Hierarchy Theory Is For

• Middle-number complex systems in which elements
  are
• Few enough to be self-assertive and noticeably
  unique in their behavior.
• Too numerous to be modeled one at a time with
  any economy and understanding.
• A hierarchy is necessary to understand
  middle-number complex systems (Simon 1962).

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Hierarchy Theory (HT)

• Reality may or may not be hierarchical.
• Hierarchy structures facilitate observations and
  understanding.
• Processes at higher levels constrain processes at
  lower levels.
• Fine details are related to large outcomes across
  levels.
• Scale is the function that relates holons and
  behavior interconnections across levels.

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Key HT Elements

• Grain (resolution)
• Scale (extent)
• Identification of entities
• Hierarchy of levels
• Dynamics across levels
• Incorporation of disturbances

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Grain and Scale

• Related to observations and measurements.
• The observed remains the same.
• Grain and scale determine what and how much of
  the observed that the observer is able to obtain
  for examination.

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Identification of Entities

• Definitional entities
• Observer- generated to outline what is expected
  to examine.
• Fixed the level of observation at the outset.
• Empirical entities
• Observed and measured in the field.

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Hierarchy of Levels

• Levels of organization.
• For definitional entities.
• Theoretical structures how things are organized.
• Predictive models.
• Levels of observations.
• For empirical entities.
• Derived from empirical studies.
• Provide suggestions to fine tune the levels of
  organization.

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Dynamics Across Levels

• Hierarchical levels are dynamically and
  functionally related.
• Higher-level entities in a non-nested hierarchy
• Behave at a lower frequency.
• Provide a context and set environmental
  constraints to the lower-level entities.
• In a nested hierarchy
• The behavior of higher-level entities is
  determinable from knowledge of its component
  levels.

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Incorporation of Disturbances

• The evolvement of a hierarchy system to handle
  disturbances
• Collapse to a diffuse, low level of organization
  Or
• Move to a higher level of organization via a new
  set of upper-level constraints.

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Levels of fields and objects
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Levels of Organization
Extratropical Cycle
Supercell
Squall-lines
Tornado
Hail
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Data, States, Processes, and Events
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Objects formed through spatial aggregation
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A process is formed
Temporal aggregation of state sequences
Spatial aggregation of observatory data
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Levels of Observations
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Objects formed by temporal aggregation
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Zones

Zone
0 mm/hr threshold
2 mm/hr threshold
4 mm/hr threshold
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Sequences

Sequence
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Process

Process
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Event

Event
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Data Structures
objects
fields
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A Case Study

• Collaborator Dr. John McIntosh

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Data for Our Case Study

• The Arkansas Red River Basin Forecast Center
  generates hourly radar derived digital
  precipitation arrays
• 8760 raster layers per year
• Organized as temporal snapshots and available
  online

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Storm paths and velocity
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How long did a storm last and how much rainfall
was received in this watershed?
Interactions with a geographic feature
4/15/98/03 491,908 m3
4/15/98/00 116,670 m3
4/15/98/01 2,193,379 m3
4/15/98/02 697,902 m3
Duration 4 hours Cumulative volume 3,499,857 m3
4/15/98/04 0 m3
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Find storms occurring at certain time and duration
A query builder dialog to support queries based
on the modeled relationships and object attribute
values
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Characterization indices
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Cross Correlation Matrices
process
event

• Little shared information among the indices for
  both process and event objects.

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Find storms with rotations
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Similar change from T1 to T2
Cases from a cluster determined by the six indices
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Similar change from T1 to T2
Cases from a cluster determined by the six indices
b.
a.
c.
d.
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Compare two processes

• Dynamic time warping the sequences are stretched
  so that Imperfectly aligned common features align

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Find matching storms
Return storm systems with similar behaviors
Query
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Categorize processes
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Hierarchical Clustering
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Events and Processes (Features) for Data Retrieval

• As a catalog to identify what is of interest
• As a filter to specify time and area of interest

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Events and Processes to Identify Correlates

• Spatiotemporal relationships among features (e.g.
  NDVI, ENSO, and LULC)
• Spatial lags
• Temporal lags

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Features for impact analysis

• Use features to retrieve environmental and
  socio-economic data
• Case based evaluation
• Case comparison
• Impacts along the evolution of a process

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Temporal GIS for Meteorology

• Ingest meteorological data
• Analyze patterns and behaviors
• Identify anomalies
• Find spatiotemporal relationships among weather
  events (e.g. teleconnections)
• Incorporate model output and observations with
  environmental data Model validation
• Elicit environmental correlates
• Evaluate environmental consequences
• Assess socio-economic impacts
• Facilitate emergency planning, rescue, and
  decision making

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Concluding Remarks

• A new representation consists of hierarchy of
  events, processes, sequences, and states
• Fields of objects and objects of fields
• Built upon Hierarchy Theory
• Extend GIS queries to geographic dynamics about
  events and processes
• New GIS support for geospatial data mining and
  knowledge discovery
• For observations
• For extracted features

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What next?

• Ontology of meteorological events and processes
• Categorization and environments
• Data scaling
• Volume NEXRAD Level II data
• Sources remotely sensed, in-situ, and report
• Space and time domain climate change
• A theory of geographic representation and analysis

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Questions, Comments?
THANK YOU