Temporal GIS for Meteorological Applications

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. Time-stamped values Time-stamped records Salary Department Name [11,44] Toys [11,60] John [11, 49] 15K Price From To Stock [45, 60] Shoes [50, 54] 20K [55, 60] 25K IBM 16 10-7-91 10:07am 10-15-91 4:35pm [0, 20] Hardware [0,20] U [41,51] 10-15-91 4:35pm 10-30-91 4:57pm [0, 20] 20K IBM 19 Tom [41, 51] Clothing [41, 51] 30K 10-30-91 4:57pm 11-2-91 12:53pm IBM 16 [0,44] U [50, Now] [0,44] U [50, 11-2-91 12:53pm 11-5-91 2:02pm [0,44] U [50, Now] 25 IBM Mary Now] 25K Credit Snodgrass and Ahn (1985): Gadia and Yeung (1988): Adding Time in RDBM Time-stamped tables 1993 Avg. Income County Population 1994 20,000 Nixon 17,000 Income Avg. County . Population 19,800 Nixon 20,000 1995 32,000 Cleveland 35,000 County Population Avg. Income Nixon 20,900 21,000 Cleveland 35,000 32,000 Oklahoma 86,000 28,000 Gadia and Vaishnav (1985):

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. http://www.rrcc-online.com/~gey235/bpop.html

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

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

10. Time Semantics Space 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.

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” Peuquet and Duan (1995) Event-based SpatioTemporal Data Model (ESTDM) Changes from ti-1 to ti

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

21. UNDERSTANDING SEE SEE what CONCEPTUALIZE THINK why what how REPRESENTATION why CONCEPTUALIZE how ANALYSIS SEE UNDERSTANDING UNDERSTANDING Temporal GIS: “reversed engineering” 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 trigger process event drive state measured by spatiotemporal data

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

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

37. Levels of Organization Extratropical Cycle Supercell Squall-lines Tornado Hail

38. Data, States, Processes, and Events

39. Objects formed through spatial aggregation

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

41. Levels of Observations

42. Objects formed by temporal aggregation

43. Zones Zone 0 mm/hr threshold 2 mm/hr threshold 4 mm/hr threshold

44. Sequences Sequence

45. Process Process

46. Event Event

47. Time Series of Gridded Snapshots Time Series of Gridded Snapshots Time Series of Gridded Snapshots Data Structures objects fields

48. A Case Study Collaborator: Dr. John McIntosh

49. 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

50. Storm paths and velocity