Space-Time

1
Space-Time

• Arc Hydro time series structure
• Tracking Analyst
• A true Temporal GIS What does ArcGIS need?
• Time series, attribute series, raster series,
  feature series
• Space-time grids NetCDF

2

• In 1905, Albert Einstein published his famous
  Special Theory of Relativity and overthrew
  commonsense assumptions about space and time.

http//archive.ncsa.uiuc.edu/Cyberia/NumRel/NumRel
Home.html
3
(No Transcript)
4
Additional reading
5
Space-Time

• Arc Hydro time series structure
• Tracking Analyst
• A true Temporal GIS What does ArcGIS need?
• Time series, attribute series, raster series,
  feature series
• Space-time grids NetCDF

6
Space-Time Cube
TSDateTime
Time
TSValue
Data Value
FeatureID
Space
TSTypeID
Variable
7
Time Series Data
8
Time Series of a Particular Type
9
A time series for a particular feature
10
A particular time series for a particular feature
11
All values for a particular time
12
MonitoringPointHasTimeSeries Relationship
13
TSTypeHasTimeSeries
14
Arc Hydro TSType Table
Type Of Time Series Info
Regular or Irregular
Units of measure
Time interval
Recorded or Generated
Type Index
Variable Name

• Arc Hydro has 6 Time Series DataTypes
• Instantaneous
• Cumulative
• Incremental
• Average
• Maximum
• Minimum

15
Time Series Types
Incremental
Instantaneous
Average
Cumulative
Minimum
Maximum
16
Space-Time

• Arc Hydro time series structure
• Tracking Analyst
• A true Temporal GIS What does ArcGIS need?
• Time series, attribute series, raster series,
  feature series
• Space-time grids NetCDF

17
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

18
Simple Event
ID Time Geometry Value
1 T1 X1,Y1 0.1
2 T2 X2,Y2 0.3
1 T3 X3,Y3 0.7
2 T4 X4,Y4 0.4
3 T5 X5,Y5 0.5
2 T6 X6,Y6 0.2
4 T7 X7,Y7 0.1
1 T8 X8,Y8 0.8
1 T9 X9,Y9 0.3
Unique Identifier for objects being tracked
through time
Observation
Time of observation (in order)
Geometry of observation
19
Complex Event (stationary version)
ID Time Value
1 T1 0.1
2 T2 0.3
1 T3 0.7
2 T4 0.4
3 T5 0.5
2 T6 0.2
4 T7 0.1
1 T8 0.8
1 T9 0.3
ID Geometry
1 X1,Y1
2 X2,Y2
3 X3,Y3
4 X4,Y4
Cases 1, 2, 3, 4, 5
The object maintains its geometry (i.e. it is
stationary)
20
Complex Event (dynamic version)
ID Geometry Time Value
1 X1,Y1 T1 0.1
2 X2,Y2 T2 0.3
1 X3,Y3 T3 0.7
2 X4,Y4 T4 0.4
3 X5,Y5 T5 0.5
2 X6,Y6 T6 0.2
4 X7,Y7 T7 0.1
1 X8,Y8 T8 0.8
1 X9,Y9 T9 0.3
ID Gage Number
1 1001
2 1002
3 1003
4 1004
Cases 6 and 7
The objects geometry can vary with time (i.e. it
is dynamic)
21
Tracking Analyst Display
22
Feature Class and Time Series Table
23
Temporal Layer
Shape from feature class is joined to time series
value from Time Series table
24
Space-Time

• Arc Hydro time series structure
• Tracking Analyst
• A true Temporal GIS What does ArcGIS need?
• Time series, attribute series, raster series,
  feature series
• Space-time grids NetCDF

25
Time and Space in GIS
Time Series
Feature Series
t3
t2
Value
t1
Time
Raster Series
Attribute Series
Value
t3
t2
t1
t1
t2
t3
y
x
26
Time Series and Temporal Geoprocessing
DHI Time Series Manager
Time Series
Feature Series
t3
t2
Value
t1
Time
Raster Series
Attribute Series
Value
t3
t2
t1
y
x
ArcGIS Temporal Geoprocessing
Adobe picture
27
South Florida Water Management Project

• 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

Prototype Area
Lake Kissimmee
Lake Istokpoga
Lake Okeechobee
28
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)

Spatial Information About point of measurement
Unique 5-digit alphanumeric code called DBKEY
Date/Time
Value

• DBHydro can be accessed at http//www.sfwmd.gov/o
  rg/ema/dbhydro/index.html

29
Arc Hydro Attribute Series
TSDateTime
Feature Class (point, line, area)
TSValue
FeatureID
TSType
TSType Table
30
Attribute Series Typing
TSType
Attribute Series
1

Type
Units
Regular
.
Time
Type
FeatureID
Value

• 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

31
Irregularly recorded water quality data form an
Attribute Series

• A point feature class defines the spatial
  framework
• Many variables defined at each point
• Time of measurement is irregular
• May be derived from a Laboratory Information
  Management System

Field samples
Laboratory
Database
32
Fecal Coliform in Galveston Bay(Irregularly
measured data, 1995-2001)
Coliform Units per 100 ml
Tracking Analyst Demo
33
Nexrad over South Florida

• Real-time radar rainfall data calibrated to
  raingages
• Received each 15 minutes
• 2 km grid
• Stored by SFWMD in Arc Hydro time series format

34
Nexrad data as Attribute Series
Attribute series
Display as a temporal layer in Tracking Analyst
35
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, .)

36
Compile Gage Time Series into an Attribute Series
table
37
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
38
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
39
Time sequence of hydraulic head maps
z
t3
t2
t1
Hydraulic head, h
x
y
40
Attribute Series to Raster Series
41
Inundation
d
h
L
Depth of inundation d IF (h - L) gt 0 then d
h L IF (h L) lt 0 then d 0
42
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
DEMO DHI Time Series
43
Ponded Water Depth Kissimmee River June 1, 2003
44
Show Generate Rasters Model
45
Hydroperiod Tool TimeSeries Framework
Time Series
Feature Series
Raster Series
Attribute Series
46
Depth Classification
Depth
Class
11
5
9-10
4
7-8
3
5-6
2
3-4
1
1-2
0
0
-1
47
Feature Series of Ponded Depth
48
Show Classify Depths Model
49
Attribute Series for Habitat Zones
50
Show Zonal Stats Model
51
Space-Time

• Arc Hydro time series structure
• Tracking Analyst
• A true Temporal GIS What does ArcGIS need?
• Time series, attribute series, raster series,
  feature series
• Space-time grids NetCDF

52
Multidimensional Data Representation for the
Geosciences
Atmospheric Science
Hydrology
Ocean Science
Earth Science
53
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?

54
Arc Hydro Attribute Series
TSDateTime
Feature Class (point, line, area)
TSValue
FeatureID
TSType
TSType Table
55
NetCDF Data Model (developed at Unidata for
distributing weather data)
Time
Dimensions and Coordinates
Value
Space (x,y,z)
NetCDF describes a collection of variables
stored in a dimension space that may represent
coordinate points in the (x,y,z,t) dimensions
Variables
Attributes
56
NetCDF File for Weather Model Output of Relative
Humidity (Rh)
dimensions lat 5, long 10, time
unlimited variables latunits
degrees_north longunits
degrees_east timeunits hours since
1996-1-1 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)
57
Relative Humidity Points
58
Interpolate to Raster
GeoTiff format, cell size 0.5º
59
Zoom in to the United States
60
Average Rh in each State
Determined using Spatial Analyst function Zonal
Statistics with Rh as underlying raster and
States as zones
61
Integrated Data Viewer(Developed by Unidata)

• Data Probe
• Vertical Profile
• Time/Height display
• Vertical cross-section
• Plan view
• Isosurface

Note IDV Integrated Data Viewer
62
RUC20 Output Samples
Precipitable water in the atmosphere
Cross-section of relative humidity
Wind vectors and wind speed (shading)
Images created from Unidatas Integrated Data
Viewer (IDV)