temporal gis for agent-based modeling of complex spatial...
TRANSCRIPT
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Temporal GIS for Agent-Based Modeling of Complex Spatial SystemsMay Yuan ([email protected])University of Oklahoma
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The great divide and the opportunity for bridging
Now
Data and Cases Simulation and Prediction
Temporal GIS Agent-Based Modeling
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All about representation
• ABM: – How to represent agent, landscape, and their interactions– discretization– Disaggregation and aggregation– Mathematical functions, probability, and decision models
• Temporal GIS: – How to represent facts and knowledge– Philosophical– Cognitive– Ontological– Data modeling, logic, and calculus
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ABM Representation Complex Spatial Systems• Agents
– Simple behavior rules– Autonomous and adaptive nature– Individual decisions– Randomness
• Landscape– Simple cellular structures– Variations, changing, and modifiable nature– Multiple themes
• Emergence– Meaningful orderly patterns from the non-linear interactions among
autonomous disaggregated parts (agents) and the landscape
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TGIS Representation of Complex Spatial Systems• What, When, How• Snapshots• Spatiotemportal object model• Spatiotemporal event-based model• Process model• Event model• Lifelines• Helixes
Complexity?
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Time in DBMS and GIS
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From Space-Time to Geographic Dynamics• Space-time is the reference frame• Geographic dynamics
– Drivers: activities, events, and processes– Observables: change and movement
• Forms and patterns in space and time• Works of activities, events, and processes• Observable through means of change and movement
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Geographic dynamics and GIS data
Object/State in (S,T)
Activity, event, process
spatiotemporal data
Transform
GIS approaches:GIS approaches:• ontology and conceptualizationontology and conceptualization• representation and data modelingrepresentation and data modeling• algorithm development for query, analysis and modelingalgorithm development for query, analysis and modeling
captured by
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Goodchild, Yuan, and Cova (2007): all possible changes
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Kwan (2004): Activities
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Stefanidis, Agouris, Partsinevelos (2003)
SpatioTemporal Helixes for Event Modeling
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Pequet and Duan (1995) SpatioTemporal Event-based Data Model
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A severe weather case
• A severe weather event: 1999 May 3 OKC tornado outbreaks
• Severe weather processes: development of convective storms, supercell thunderstorms, tornadoes
• Severe weather states: spatial distributions of storms, supercells, and tornadoes at a given time
• Spatiotemporal data: radar data, GEOS data, sounding data, photos, etc.
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Complexity and Hierarchy
• Weinberg (1975) General Systems Theory– Small-number simple systems: individuals’
behaviors– Large-number simple systems: averages– Middle-number complex systems
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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.• Koestler (1967) 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.
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Examples of Middle-number Complex Systems
• Ecological systems (Allen and Ahl, 1982).• Geographic systems.
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Hierarchy Theory
• Reality may or may not be hierarchical.• Hierarchical structures facilitate observations and
understanding.• Processes operate at different levels of scale• Higher levels constrain 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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Sequence BSequence A
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Sequence BSequence A
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0 mm/hr threshold2 mm/hr threshold4 mm/hr threshold
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T 23T 22
Sequence BSequence A
Sequence CT1
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T 2 T3 T4
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T5T 6 T 8T 7
Event B
T 23T 22
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T 2 T3 T4
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Sequence BSequence A
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T1 T 2 T 3T 4
T4T5
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T 2 T3 T4
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T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
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T 2 T3 T4
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T 23T 22
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T 23T 22
Sequence BSequence A
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T 23T 22
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Sequence CT1
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T1 T 2T 3T 4
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T1 T 2T 3T 4
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T1 T 2T 3T 4
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T5 T 6 T8T7
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T 2 T3 T4
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T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
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T1 T 2T 3T 4
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T1 T 2T 3T 4
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Event AT1
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Event B
T 23T 22
Event AT1
T 2 T3 T4
T 4T5
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Event B
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Sequence BSequence A
Sequence CT1
T2 T 3
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Sequence BSequence A
Sequence CT1
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T1 T 2T 3T 4
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Event AT1
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Event B
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Sequence BSequence A
Sequence CT1
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T1 T 2T 3T 4
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T1 T 2T 3T 4
T4T5
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Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Event AT1
T 2 T3 T4
T 4T5
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Event B
T 23T 22
Sequence BSequence A
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T1 T 2T 3T 4
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T 2 T3 T4
T 4T5
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Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
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T1 T 2T 3T 4
T4T5
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Process B
Process A
T1 T 2T 3T 4
T4T5
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Event AT1
T 2 T3 T4
T 4T5
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T5T 6 T 8T 7
Event B
T 23T 22
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Zone A
Zone BZone C
T 1 T2T3
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Process
Process
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Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Leve
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Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Event
Process
Sequence
Zone
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Zone A
Zone BZone C
T 1 T2T3
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Zone A
Zone BZone C
T 1 T2T3
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Zone A
Zone BZone C
T 1 T2T3
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Process B
Process A
T1 T 2T 3T 4
T4T5
T 5
T5 T 6 T8T7
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Event AT1
T 2 T3 T4
T 4T5
T 5
T5T 6 T 8T 7
Event B
T 23T 22
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Sequence BSequence A
Sequence CT1
T2 T 3
T4
T5
T5
Zone A
Zone BZone C
T 1 T2T3
Zone A
Zone BZone C
T 1 T2T3
Leve
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Event
Event
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Data and Information
• Information– Event: an incidence of rain – Process: the development of rain (e.g.
rain storms, squall lines, orographic effects, etc.)
– State: the distribution of rain at a given time
• Data– Images– In-situ observations
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Key Elements in Hierarchy Theory
• Grain (resolution)• Scale (extent)• Identification of entities• Hierarchy of levels• Dynamics across levels• Incorporation of disturbances
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Two kinds of entities in HT
• 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.
<< Agents
<< Data objects
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Identification of Entities
• Definitional entities vs. Empirical Entities• Agents vs. data objects• Agents can represent any kinds of autonomous
entities (including drivers of geographic dynamics)• Data objects, same, but in databases
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Bridge Agents and Data Objects
• Data objects >> empirical objects >> inform definitional objects >> agents
• Agents >> definitional objects >>inform observations >> data objects
• Inform agents: refine behavior rules, interaction and relationship
• Inform observations: where to look, what patterns to look, and when patterns may emerge or converge
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• ABM: control factors at local, regional, and global scales
• ABM: Local optimal vs. global optimal solutions
Bridge Landscape and Data Objects
From Strout and Li Intro Agent Analyst
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Hierarchy of Geographic Dynamics• Levels of agents:
– Fine grain agents: drivers– Coarse grain agents: conveys
• Levels of landscape situations– Fine grain: traffic lights, distance between vehicles– Coarse grain: highway types, traffic flows
• Agent-based modeling needs to incorporate the hierarchical nature of geographic dynamics
• Temporal GIS needs to support the necessary data in forms that enable the simulation of agent actions within and across levels of geographic dynamics.
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Similarity
• Pattern transitions from ABM• Pattern recorded in TGIS• Similarity• Comparison and calibration
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Measures of Similarity
Index NameIndex Type
elongation, orientationObject
static distribution, arrangementObject-object
flux (inflow and outflow)Object-environment
relative movementObject-object
growthObject
Dynamic
edge condition, situationObject-environment
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Comparison
• Dynamic time warping: the simulated ABM pattern transitions and observed patterns transitions are stretched so that Imperfectly aligned common features align
Time
Value
Time
Value
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Return: storm systems with
similar behaviors
Find matching storms …
Query
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Organize data based on processes and similarity
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
1 2
3
4
56
7
8
9
10
11
12
13
1415
16 1718
19
20
12
34
Figure 10. Dendogram of test set clusters.
1 2
3
4
56
7
8
9
10
11
12
13
1415
16 1718
19
20
12
34
Figure 10. Dendogram of test set clusters.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
h o u r 1 h o u r 2 h o u r 3 h o u r 4 h o u r 5
Figure 8. Test set of processes with durations between 3 and 5 hours.
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Summative Remarks
• Integrate ABM and TGIS• Hierarchy Theory serves the conceptual framework• Levels of aggregations > Levels of geographic dynamics• Geographic dynamics
– Drivers: activities, events, and processes– Observables: change and movement– Possible changes: attributes, geometry, internal structure, movement
• ABM and TGIS for all ranges of geographic dynamics– Definitional entities vs empirical entities– Levels of dynamics and complexity
• TGIS supports ABM: behavior rules, landscape situations and comparison
• ABM benefits TGIS: what, when, and where to look or take observations• Cross-fertilization
– Comparison and calibration– Patterns, behavior, landscape effects, and outcome – The past is the key to the future– The future promotes the rethinking and a deeper understanding of the past