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Space and Time

Space and Time. By David R. Maidment with contributions from Steve Kopp, Steve Grise, and Tim Whiteaker. Space and Time. Introductory concepts Discrete space-time model – Arc Hydro Temporal Geoprocessing Continuous space-time model – netCDF Tracking Analyst. Space and Time.

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Space and Time

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  1. Space and Time By David R. Maidment with contributions from Steve Kopp, Steve Grise, and Tim Whiteaker

  2. Space and Time • Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst

  3. Space and Time • Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst

  4. Linking GIS and Water Resources Water Resources GIS Water Conditions (Flow, head, concentration) Water Environment (Watersheds, gages, streams)

  5. Data Cube A simple data model Time, T “When” D “Where” Space, L Variables, V “What”

  6. Discrete Space-Time Data ModelArcHydro Time, TSDateTime TSValue Space, FeatureID Variables, TSTypeID

  7. Continuous Space-Time Model – NetCDF (Unidata) Time, T Coordinate dimensions {X} D Space, L Variable dimensions {Y} Variables, V

  8. A relational database at the single observation level (atomic model) Stores observation data made at points Metadata for unambiguous interpretation Traceable heritage from raw measurements to usable information CUAHSI Observations Data Model Streamflow Groundwater levels Precipitation & Climate Soil moisture data Water Quality Flux tower data

  9. ODM and HIS in an Observatory Settinge.g. http://www.bearriverinfo.org Pre Conference Seminar

  10. Space, Time, Variables and Observations An observations data model archives values of variables at particular spatial locations and points in time • Observations Data Model • Data fromsensors (regular time series) • Data from field sampling (irregular time points) Variables (VariableID) Space (HydroID) Time

  11. Space, Time, Variables and Visualization A visualization is a set of maps, graphs and animations that display the variation of a phenomenon in space and time • Vizualization • Map – Spatial distribution for a time point or interval • Graph – Temporal distribution for a space point or region • Animation – Time-sequenced maps Variables (VariableID) Space (HydroID) Time

  12. Space, Time, Variables and Simulation A process simulaton model computes values of sets of variables at particular spatial locations at regular intervals of time • Process Simulation Model • A space-time point is unique • At each point there is a set of variables Variables (VariableID) Space (HydroID) Time

  13. Space, Time, Variables and Geoprocessing Geoprocessingis the application of GIS tools to transform spatial data and create new data products • Geoprocessing • Interpolation – Create a surface from point values • Overlay – Values of a surface laid over discrete features • Temporal – Geoprocessing with time steps Variables (VariableID) Space (HydroID) Time

  14. Space, Time, Variables and Statistics A statistical distribution is defined for a particular variable defined over a particular space and time domain • Statistical distribution • Represented as {probability, value} • Summarized by statistics(mean, variance, standard deviation) Variables (VariableID) Space (HydroID) Time

  15. Space, Time, Variables and Statistical Analysis A statistical analysis summarizes the variation of a set of variables over a particular domain of space and time • Statistical analysis • Multivariate analysis – correlation of a set of variables • Geostatistics– correlation space • Time Series Analysis – correlation in time Variables (VariableID) Space (HydroID) Time

  16. Space-Time Datasets CUAHSI Observations Data Model Sensor and laboratory databases Pre Conference Seminar From Robert Vertessy, CSIRO, Australia

  17. Space and Time • Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst

  18. Space-Time Cube Time TSDateTime Data Value TSValue FeatureID Space Variable TSTypeID

  19. Time Series Data

  20. Time Series of a Particular Type

  21. A time series for a particular feature

  22. A particular time series for a particular feature

  23. All values for a particular time

  24. MonitoringPointHasTimeSeries Relationship

  25. TSTypeHasTimeSeries

  26. Arc Hydro TSType Table Units of measure Regular or Irregular Time interval Type Of Time Series Info Recorded or Generated Type Index Variable Name • Arc Hydro has 6 Time Series DataTypes • Instantaneous • Cumulative • Incremental • Average • Maximum • Minimum

  27. Time Series Types Incremental Instantaneous Average Cumulative Minimum Maximum

  28. A Theme Layer Synthesis over all data sources of observations of a particular variable e.g. Salinity

  29. Texas Salinity Theme 7900 series 347,000 data 7900 series TPWD 3400 TCEQ 3350 TWDB 150

  30. Copano and Aransas Bay Salinity Number of Data 0 – 50 50 – 150 150 – 400 400 – 1000 1000 – 3000 Copano Bay Aransas Bay

  31. Texas Daily Streamflow Theme USGS Data 1138 sites (400 active)

  32. Austin – Travis Lakes Streamflow Years of Data 0 – 10 10 – 20 20 – 40 40 – 60 60 – 110

  33. Texas Water Temperature Theme 22,700 series 966,000 data

  34. Austin – Travis Lakes Water Temperature Number of Data 0 – 50 50 – 150 150 – 400 400 – 1000 1000 – 5000

  35. http://data.crwr.utexas.edu

  36. Data from Individual Sites

  37. HydroPortal to access Themes

  38. Space and Time • Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst

  39. Time Series {value, time} Feature Series {shape,value, time} Four Panel Diagram Raster Series {raster, time} Attribute Series {featureID, value, time}

  40. Time series from gages in Kissimmee Flood Plain • 21 gages measuring water surface elevation • Data telemetered to central site using SCADA system • Edited and compiled daily stage data stored in corporate time series database called dbHydro • Each time series for each gage in dbHydro has a unique dbkey (e.g. ahrty, tyghj, ecdfw, ….)

  41. Compile Gage Time Series into an Attribute Series table

  42. Hydraulic head Land surface h Mean sea level (datum) Hydraulic head is the water surface elevation in a standpipe anywhere in a water system, measured in feet above mean sea level

  43. Map of hydraulic head Z Hydraulic head, h h(x, y) x y X Y A map of hydraulic head specifies the continuous spatial distribution of hydraulic head at an instant of time

  44. Time sequence of hydraulic head maps z t3 t2 t1 Hydraulic head, h x y

  45. Attribute Series to Raster Series

  46. Inundation d h L Depth of inundation = d IF (h - L) > 0 then d = h – L IF (h – L) < 0 then d = 0

  47. Inundation Time Series d(x,y,t) = h(x,y,t) – LT(x,y) h (x,y,t) LT(x,y) d(x,y,t) t Time

  48. Ponded Water Depth Kissimmee River June 1, 2003

  49. Depth Classification Depth Class 11 5 9-10 4 7-8 3 5-6 2 3-4 1 1-2 0 0 -1

  50. Feature Series of Ponded Depth

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