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Space-Time The ESRI Time Project – Comments by Steve Kopp Time series and ArcGIS: What can I use now? –Tracking Analyst –Plotting graphs of Attribute Series.

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Presentation on theme: "Space-Time The ESRI Time Project – Comments by Steve Kopp Time series and ArcGIS: What can I use now? –Tracking Analyst –Plotting graphs of Attribute Series."— Presentation transcript:

1 Space-Time The ESRI Time Project – Comments by Steve Kopp Time series and ArcGIS: What can I use now? –Tracking Analyst –Plotting graphs of Attribute Series using CRWR TS Plotter A true Temporal GIS: What does ArcGIS need? –Hydrologic Flux calculations: Florida Example –A new file type?: NetCDF

2 Tracking Analyst Simple Events –1 feature class that describes What, When, Where Complex Event –1 feature class and 1 table that describe What, When, Where Arc Hydro

3 Simple Event IDTimeGeometryValue 1T1X1,Y10.1 2T2X2,Y20.3 1T3X3,Y30.7 2T4X4,Y40.4 3T5X5,Y50.5 2T6X6,Y60.2 4T7X7,Y70.1 1T8X8,Y80.8 1T9X9,Y90.3 Unique Identifier for objects being tracked through time Time of observation (in order)Geometry of observation Observation

4 Complex Event (stationary version) IDGeometry 1X1,Y1 2X2,Y2 3X3,Y3 4X4,Y4 IDTimeValue 1T10.1 2T20.3 1T30.7 2T40.4 3T50.5 2T60.2 4T70.1 1T80.8 1T90.3 The object maintains its geometry (i.e. it is stationary) Cases 1, 2, 3, 4, 5

5 Complex Event (dynamic version) IDGage Number IDGeometryTimeValue 1X1,Y1T10.1 2X2,Y2T20.3 1X3,Y3T30.7 2X4,Y4T40.4 3X5,Y5T50.5 2X6,Y6T60.2 4X7,Y7T70.1 1X8,Y8T80.8 1X9,Y9T90.3 The object’s geometry can vary with time (i.e. it is dynamic) Cases 6 and 7

6 Fecal Coliform in Galveston Bay, Texas

7 Tracking Analyst Demo Show the Galveston Bay Monitoring Point feature class and Time Series Table Show the temporal layer Show the tracking analyst time “Playback Manager” Animate bacteria concentrations

8 Time SeriesFeature Series Raster SeriesAttribute Series Time Variable Time and Space in GIS x y Value t1t1 t2t2 t3t3 Time t1t1 t2t2 t3t3 t3t3 t2t2 t1t1

9 Time Series and Temporal Geoprocessing Time SeriesFeature Series Raster SeriesAttribute Series Time Variable x y Value t1t1 t2t2 t3t3 Time t1t1 t2t2 t3t3 ArcGIS Temporal Geoprocessing t3t3 t2t2 t1t1 DHI Time Series Manager Adobe picture

10 TSDateTime FeatureID TSType TSValue Arc Hydro Attribute Series TSType Table Feature Class (point, line, area)

11 Arc Hydro Attribute Series Feature Class (HydroID) Attribute Series Table (FeatureID)

12 Map time series e.g. Nexrad Collections of values recorded at various locations and times e.g. water quality samples This is current Arc Hydro time series structure Type TSType UnitsRegular…. 1 Attribute Series FeatureID Time Value * Type Attribute Series Typing

13 Plotting Attribute Series One feature with a time-dependent Attribute –Observed or Modeled Complications –Regular or Irregular in time –Many Types (rainfall, streamflow, dissolved oxygen, etc.) –Instantaneous, cumulative, averaged, min, max, etc. –Different units (cfs, m 3 /d, gpd, etc.) Plot the data in ArcMap

14 TS Plotter Demo Show TSType Table Plot time series for a few MonitoringPoint features Summarize data into yearly averages Export data and chart to Excel Show exported data and chart in Excel

15 South Florida Water Management Project Prototype Area Prototype region includes 24 water management basins, More than 70 water control structures managed by the South Florida Water Management District (SFWMD) Includes natural and managed waterways Lake Okeechobee Lake Istokpoga Lake Kissimmee

16 Questions that SFWMD wants Answered –How much water is there? –Where is the water in the District? –How much water will enter the canal system? –How can water be routed from one basin to another?

17 DBHydro TimeSeries Achieve of Water Related Time Series Data currently used by SFWMD Example of Flow Data: Daily Average Flow [cfs] at Structure S65 (spillway) Unique 5-digit alphanumeric code called DBKEY Date/TimeValue Spatial Information About point of measurement DBHydro can be accessed at:

18 Coupling Table: Linking Control Volume to Features Q in Q out Q rain Q evap Water Balance performed over a Control Volume (i.e.: Basin) Coupling Table links the Control Volume (basin) to all features that transfer water into and out of the Control Volume Horizontal (structures) Vertical (rainfall, ETp) S65BC Basin

19 Water Balancing in ArcHydro Q S65A +Q RAIN - Q ETp –Q S65C = Storage

20 Coupling Table Design ObjectID HydroID of Control Volume HydroID of Inflow or Outflow Feature that contains Time Series Information Direction of Flow 1 = IN, 2 = OUT If Inflow/Outflow is a flux, include an area over which the flux acts

21 Demo of Flow and Flux Calculations using TSViewer Links Control Volume Feature with Inflow and Outflows

22 Multidimensional Data Representation for the Geosciences Ocean Science Earth Science Atmospheric Science Hydrology

23 Weather and Hydrology Weather Information –Continuous in space and time –Combines data and simulation models –Delivered in real time Hydrologic Information –Static spatial info, time series at points –Data and models are not connected –Mostly historical data Challenges for Hydrologic Information Systems How to better connect space and time? How to connect space, time and models? How to connect weather and hydrology?

24 TSDateTime FeatureID TSType TSValue Arc Hydro Attribute Series TSType Table Feature Class (point, line, area)

25 Time Space (x,y,z) Variables Value NetCDF Data Model (developed at Unidata for distributing weather data) Attributes Dimensions and Coordinates NetCDF describes a collection of variables stored in a dimension space that may represent coordinate points in the (x,y,z,t) dimensions

26 NetCDF File for Weather Model Output of Relative Humidity (Rh) dimensions: lat = 5, long = 10, time = unlimited; variables: lat:units= “degrees_north”; long:units= “degrees_east”; time:units= “hours since ”; data: lat = 20, 30, 40, 50, 60; long = -160, -140, -118, -96, -84, -52, -45, -35, -25, -15; time = 12; rh =.5,.2,.4,.2,.3,.2,.4,.5,.6,.7,.1,.3,.1,.1,.1.,.1,.5,.7,.8,.8,.1,.2,.2,.2,.2,.5,.7,.8,.9,.9,.1,.2,.3,.3,.3,.3,.7,.8,.9,.9.0,.1,.2,.4,.4,.4,.4,.7,.8,.9; rh (time, lat, lon);

27 Relative Humidity Points

28 Interpolate to Raster GeoTiff format, cell size = 0.5º

29 Zoom in to the United States

30 Average Rh in each State Determined using Spatial Analyst function Zonal Statistics with Rh as underlying raster and States as zones

31 Integrated Data Viewer (Developed by Unidata) Data Probe Vertical Profile Time/Height display Vertical cross-section Plan view Isosurface Note: IDV = Integrated Data Viewer

32 RUC20 – Output Samples Precipitable water in the atmosphere Cross-section of relative humidity Images created from Unidata’s Integrated Data Viewer (IDV) Wind vectors and wind speed (shading)

33 IDV Demo For RUC20 predicted temperature (4D dataset) show: Plan view changes over time Cross-section changes over time Vertical Profile changes over time Data Probe changes over time


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