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
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/NumRelHome.html
Additional reading
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
Space-Time Cube
Time
TSDateTime
Data Value TSValue
FeatureID
Space
Variable
TSTypeID
Time Series Data
Time Series of a Particular Type
A time series for a particular feature
A particular time series for a
particular feature
All values for a particular time
MonitoringPointHasTimeSeries Relationship
TSTypeHasTimeSeries
Arc Hydro TSType Table
Type
Index
Variable
Name
Units
of
measure
Regular Time
or
interval
Irregular
Arc Hydro has 6 Time Series DataTypes
1.
2.
3.
4.
5.
6.
Instantaneous
Cumulative
Incremental
Average
Maximum
Minimum
Type Recorded
Of
or
Time Generated
Series
Info
Time Series Types
Instantaneous
Cumulative
Incremental
Maximum
Average
Minimum
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
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
Simple Event
ID
Unique Identifier
for objects being
tracked through
time
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
Time of observation (in order)
Geometry of observation
Observation
Complex Event (stationary version)
ID
Geometry
ID
Time
Value
1
X1,Y1
1
T1
0.1
2
X2,Y2
2
T2
0.3
3
X3,Y3
1
T3
0.7
4
X4,Y4
2
T4
0.4
3
T5
0.5
2
T6
0.2
4
T7
0.1
1
T8
0.8
1
T9
0.3
Cases 1, 2, 3, 4, 5
The object maintains its geometry (i.e. it is stationary)
Complex Event (dynamic version)
ID
Gage Number
ID
1
1001
1
X1,Y1
T1
0.1
2
1002
2
X2,Y2
T2
0.3
3
1003
1
X3,Y3
T3
0.7
4
1004
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
Cases 6 and 7
Geometry Time Value
The object’s geometry can vary with time (i.e. it is dynamic)
Tracking Analyst Display
Feature Class and Time Series
Table
Temporal Layer
Shape from feature class is joined to time series value from Time
Series table
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
Time and Space in GIS
Feature Series
Value
Variable
Time Series
t1
t2
t3
Time
Time
Attribute Series
Raster Series
Value
t1
t2
t3
t3
t2
t1
y
x
Time Series and Temporal Geoprocessing
DHI Time Series Manager
Feature Series
Value
Variable
Time Series
t1
t2
t3
Time
Time
Attribute Series
Raster Series
Value
t1
t2
t3
Adobe picture
t3
t2
t1
y
ArcGIS Temporal Geoprocessing
x
South Florida Water Management Project
•Prototype region
includes 24 water
management basins,
Prototype Area
•More than 70 water
control structures
managed by the South
Florida Water
Management District
(SFWMD)
Lake
Kissimmee
Lake
Istokpoga
Lake
Okeechobee
•Includes natural and
managed waterways
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/org/ema/dbhydro/index.html
Arc Hydro Attribute Series
TSDateTime
TSValue
Feature Class
(point, line, area)
FeatureID
TSType
TSType Table
Attribute Series Typing
TSType
Type Units Regular …. 1
• 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
Attribute Series
* FeatureID Type Time Value
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
Fecal Coliform in Galveston Bay
(Irregularly measured data, 1995-2001)
Coliform Units per 100 ml
Tracking Analyst Demo
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
Nexrad data as Attribute Series
Attribute
series
Display as a temporal
layer in Tracking Analyst
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, ….)
Compile Gage Time Series into an
Attribute Series table
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
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
Time sequence of hydraulic
head maps
z
t3
t2
t1
Hydraulic head, h
x
y
Attribute Series to Raster Series
Inundation
d
L h
Depth of inundation = d
IF (h - L) > 0 then
d=h–L
IF (h – L) < 0 then
d=0
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
Ponded Water Depth
Kissimmee River
June 1, 2003
Show Generate Rasters Model
Hydroperiod Tool TimeSeries Framework
Feature Series
Variable
Time Series
Time
1
3
Attribute Series
Raster Series
2
t
4
y
x
Depth Classification
Depth
5
4
3
Class
11
9-10
7-8
5-6
2
3-4
1
0
1-2
0
-1
Value_ From_ To_
-1
-100 -0.0001
0
0
0
1 0.0001
0.5
2
0.5
1
3
1
1.5
4
1.5
2
5
2
2.5
6
2.5
3
7
3
3.5
8
3.5
4
9
4
4.5
10
4.5
5
11
5
100
Feature Series of Ponded Depth
Show Classify Depths Model
Attribute Series for Habitat Zones
Show Zonal Stats Model
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
Multidimensional Data
Representation for the Geosciences
Atmospheric Science
Hydrology
Ocean
Science
Earth Science
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?
Arc Hydro Attribute Series
TSDateTime
TSValue
Feature Class
(point, line, area)
FeatureID
TSType
TSType Table
NetCDF Data Model
(developed at Unidata for distributing weather data)
Time
Dimensions and
Coordinates
Value
Space (x,y,z)
Variables
NetCDF describes a collection
of variables stored in a dimension space
that may represent coordinate points in
the (x,y,z,t) dimensions
Attributes
NetCDF File for Weather Model
Output of Relative Humidity (Rh)
dimensions:
lat = 5, long = 10, time = unlimited;
variables:
rh (time, lat, lon);
lat:units
long:units
time:units
= “degrees_north”;
= “degrees_east”;
= “hours since 1996-1-1”;
data:
lat
long
time
rh
= 20, 30, 40, 50, 60;
= -160, -140, -118, -96, -84, -52, -45, -35, -25, -15;
= 12;
= .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;
Relative Humidity Points
Interpolate to Raster
GeoTiff format, cell size = 0.5º
Zoom in to the United States
Average Rh in each State
Determined using Spatial Analyst function Zonal Statistics with
Rh as underlying raster and States as zones
Integrated Data Viewer
(Developed by Unidata)
•
•
•
•
•
•
Data Probe
Vertical Profile
Time/Height display
Vertical cross-section
Plan view
Isosurface
Note: IDV = Integrated Data Viewer
RUC20 – Output Samples
Precipitable water in the atmosphere
Cross-section of relative humidity
Wind vectors and wind speed (shading)
Images created from Unidata’s
Integrated Data Viewer (IDV)