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Temporal GIS for Meteorological Applications Visualization, Representation, Analysis, Visualization, and Understanding May Yuan Department of Geography.

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Presentation on theme: "Temporal GIS for Meteorological Applications Visualization, Representation, Analysis, Visualization, and Understanding May Yuan Department of Geography."— Presentation transcript:

1 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

2 Outline A brief history of time in GIS Temporal GIS for Meteorological Applications: a new approach: visualization > representation > analysis > visualization > understanding A case study

3 A brief history of time in GIS

4 GIS development: no room for time Mapping tradition Static views of the world Space-centered Using location to get information

5 Adding Time in RDBM Time-stamped tables Gadia and Vaishnav (1985): County Nixon Population 17,000 Avg. Income 20, Avg..County Nixon Cleveland Population 20,000 35,000 Income 19,800 32,000 County Nixon Cleveland Oklahoma Population 20,900 35,000 86,000 Avg. Income 21,000 32,000 28,000 Time-stamped records Snodgrass and Ahn (1985): FromToPrice IBM :57pm :07am :35pm :53pm Stock :35pm :57pm :53pm :02pm Time-stamped values Department [11,60] John [0,20] U [41,51] Tom Salary [11, 49] 15K [50, 54] 20K [55, 60] 25K [0, 20] 20K [41, 51] 30K [0, 20] Hardware [41, 51] Clothing [11,44] Toys [45, 60] Shoes Name [0,44] U [50, Now] Mary [0,44] U [50, Now] 25K [0,44] U [50, Now] Credit Gadia and Yeung (1988):

6 Adding Time in GIS Snapshot: Time-stamping layers Space-time composite: Time-stamping spatial objects (records) Spatiotemporal object: Time-stamping attributes

7 Snapshots Temporal time sets Metro Denver Temporal GIS Project by Temporal GIS, Inc.

8 Space-Time Composite Spatial change over time –History at location –Cadastral mapping Langran and Chrisman (1988)

9 Spatiotempoal Object Model Worboys (1992) Spatial objects with beginning time and ending time

10 Change at Location History at a location Nothing moves Space Semantics Time Geographic semantics = something (concrete or abstract) meaningful in geographic worlds, including objects, fields, ideas, authority, etc.

11 Commercial TGIS 4Datalink (2002) STEMgis (2003?) TerraSeer (2004)

12 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.

13 STEMgis (2003) Time-stamp spatial objects Hierarchical database

14 TerraSeer (2004) Object chains Public health and surveillance

15 Current Temporal GIS Technology Mostly point data Change-based information Uniform change Do not consider: –Change with spatial variation –Split –Merge –Development (temporal lineage)

16 TGIS based on “event” and “change” Changes from t i-1 to t i Peuquet and Duan (1995) Event-based SpatioTemporal Data Model (ESTDM)

17 Temporal GIS for Meteorological Applications

18 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

19 Let’s start with a scenario May 3, 1999 Oklahoma City Tornado outbreaks

20 Visualization An entry point for –Investigation –Exploitation –Hypothesis generation –Understanding A means to –Communicate results –Discern correlation and relationship –Reveal patterns and dynamics SEE THINK what why how UNDERSTANDING CONCEPTUALIZE SEE

21 Temporal GIS: “reversed engineering” ANALYSIS UNDERSTANDING CONCEPTUALIZE SEE REPRESENTATION what why how UNDERSTANDING SEE THINK what why how UNDERSTANDING CONCEPTUALIZE SEE TEMPORAL GIS HUMAN

22 Digital Precipitation Arrays See ST Data > Think Geog. Processes

23 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.

24 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”

25 What is “something”? Event, Process and State state process spatiotemporal data event measured by trigger drive

26 Events and Processes An event introduces additional energy or mass into a system Triggers processes to adjust the system Time Energy or mass

27 State Fields Objects Fields of objects Objects of fields

28 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

29 Issues Scale Granularity Uncertainty

30 Considerations Integration of fields and objects Hierarchies of events, processes, and states

31 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

32 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

33 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).

34 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.

35 Key HT Elements Grain (resolution) Scale (extent) Identification of entities Hierarchy of levels Dynamics across levels Incorporation of disturbances

36 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.

37 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.

38 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.

39 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.

40 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.

41 Levels of fields and objects Spatial scale and granularity Temporal scale and granularity

42 Levels of Organization Extratropical Cycle Squall-lines Supercell Hail Tornado

43 Data, States, Processes, and Events

44 Objects formed through spatial aggregation

45 A process is formed… State 1State 2 State 3 Extent 1Extent 2 Extent 3 Process 1 Spatial aggregation of observatory data Temporal aggregation of state sequences

46 Levels of Observations

47 Objects formed by temporal aggregation

48 Zone 0 mm/hr threshold2 mm/hr threshold4 mm/hr threshold Zones

49 Sequence Sequences

50 Process

51 Event

52 Data Structures Time Series of Gridded Snapshots fields objects

53 A Case Study Collaborator: Dr. John McIntosh

54 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

55 Storm paths and velocity

56 4/15/98/04 0 m 3 4/15/98/00 116,670 m 3 4/15/98/01 2,193,379 m 3 4/15/98/02 697,902 m 3 4/15/98/03 491,908 m 3 Duration: 4 hours Cumulative volume: 3,499,857 m 3 How long did a storm last and how much rainfall was received in this watershed? Interactions with a geographic feature

57 Find storms occurring at certain time and duration A query builder dialog to support queries based on the modeled relationships and object attribute values Colorado Oklahoma Kansas Colorado Oklahoma Kansas

58 Characterization indices

59 Cross Correlation Matrices Little shared information among the indices for both process and event objects. process event

60 Find storms with rotations

61 Similar change from T1 to T2 Cases from a cluster determined by the six indices

62 Similar change from T1 to T2 a. c. b. d. Cases from a cluster determined by the six indices

63 Compare two processes Dynamic time warping: the sequences are stretched so that Imperfectly aligned common features align Time Value Time Value

64 Return: storm systems with similar behaviors Find matching storms … Query

65 Categorize processes

66 Hierarchical Clustering

67 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 interestFeatures Spatial bounds Temporal bounds Radar data Satellite data In-situ observations specify access

68 Events and Processes to Identify Correlates Spatiotemporal relationships among features (e.g. NDVI, ENSO, and LULC) Spatial lags Temporal lags

69 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

70 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

71 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

72 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

73 Questions, Comments?


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