Space and Time

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Space and Time

• By David R. Maidment
• with contributions from Steve Kopp, Steve Grise,
  and Tim Whiteaker

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Space and Time

• Introductory concepts
• Discrete space-time model Arc Hydro
• Temporal Geoprocessing
• Continuous space-time model netCDF
• Tracking Analyst

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Space and Time

• Introductory concepts
• Discrete space-time model Arc Hydro
• Temporal Geoprocessing
• Continuous space-time model netCDF
• Tracking Analyst

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Linking GIS and Water Resources
Water Resources
GIS
Water Conditions (Flow, head, concentration)
Water Environment (Watersheds, gages, streams)
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Data Cube
A simple data model
Time, T
When
D
Where
Space, L
Variables, V
What
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Discrete Space-Time Data ModelArcHydro
Time, TSDateTime
TSValue
Space, FeatureID
Variables, TSTypeID
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Continuous Space-Time Model NetCDF (Unidata)
Time, T
Coordinate dimensions X
D
Space, L
Variable dimensions Y
Variables, V
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CUAHSI Observations Data Model

• 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

Streamflow
Groundwater levels
Precipitation Climate
Soil moisture data
Flux tower data
Water Quality
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ODM and HIS in an Observatory Settinge.g.
http//www.bearriverinfo.org
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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 from sensors (regular time series)
• Data from field sampling (irregular time points)

Variables (VariableID)
Space (HydroID)
Time
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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
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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
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Space, Time, Variables and Geoprocessing
Geoprocessing is 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
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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
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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
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Space-Time Datasets
CUAHSI Observations Data Model
Sensor and laboratory databases
From Robert Vertessy, CSIRO, Australia
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Space and Time

• Introductory concepts
• Discrete space-time model Arc Hydro
• Temporal Geoprocessing
• Continuous space-time model netCDF
• Tracking Analyst

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Space-Time Cube
TSDateTime
Time
TSValue
Data Value
FeatureID
Space
TSTypeID
Variable
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Time Series Data
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Time Series of a Particular Type
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A time series for a particular feature
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A particular time series for a particular feature
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All values for a particular time
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MonitoringPointHasTimeSeries Relationship
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TSTypeHasTimeSeries
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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

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Time Series Types
Incremental
Instantaneous
Average
Cumulative
Minimum
Maximum
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A Theme Layer
Synthesis over all data sources of observations
of a particular variable e.g. Salinity
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Texas Salinity Theme
7900 series 347,000 data
7900 series TPWD 3400 TCEQ 3350 TWDB 150
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Copano and Aransas Bay Salinity
Number of Data 0 50 50 150 150 400 400
1000 1000 3000
Copano Bay
Aransas Bay
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Texas Daily Streamflow Theme
USGS Data 1138 sites (400 active)
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Austin Travis Lakes Streamflow
Years of Data 0 10 10 20 20 40 40 60 60
110
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Texas Water Temperature Theme
22,700 series 966,000 data
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Austin Travis Lakes Water Temperature
Number of Data 0 50 50 150 150 400 400
1000 1000 5000
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http//data.crwr.utexas.edu
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Data from Individual Sites
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HydroPortal to access Themes
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Space and Time

• Introductory concepts
• Discrete space-time model Arc Hydro
• Temporal Geoprocessing
• Continuous space-time model netCDF
• Tracking Analyst

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Time Series value, time
Feature Series shape,value, time
Four Panel Diagram
Raster Series raster, time
Attribute Series featureID, value, time
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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, .)

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Compile Gage Time Series into an Attribute Series
table
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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
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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
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Time sequence of hydraulic head maps
z
t3
t2
t1
Hydraulic head, h
x
y
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Attribute Series to Raster Series
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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
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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
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Ponded Water Depth Kissimmee River June 1, 2003
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Depth Classification
Depth
Class
11
5
9-10
4
7-8
3
5-6
2
3-4
1
1-2
0
0
-1
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Feature Series of Ponded Depth
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Attribute Series for Habitat Zones
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Space and Time

• Introductory concepts
• Discrete space-time model Arc Hydro
• Temporal Geoprocessing
• Continuous space-time model netCDF
• Tracking Analyst

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Multidimensional Data

• Data cube (3D) or hypercube (4D,5D)
• Temperature varying with time
• Temperature varying with time and altitude

T
Y
X
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Multidimensional Data
Time 3
Time 2
Time 1
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Multidimensional Data
Time 3
Time 2
Time 1
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Multidimensional Data
Time 1
Time 2
Data Cube
Time 3
Time Slices
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Multidimensional Data
Includes variation in (x,y,z,t)
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What is NetCDF?

• NetCDF (network Common Data Form)
• A platform independent format for representing
  multi-dimensional array-orientated scientific
  data.
• Self Describing - a netCDF file includes
  information about the data it contains.
• Direct Access - a small subset of a large dataset
  may be accessed efficiently, without first
  reading through all the preceding data.
• Sharable - one writer and multiple readers may
  simultaneously access the same netCDF file.
• NetCDF is new to the GIS community but widely
  used by scientific communities for around many
  years

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What is a NetCDF file?

• NetCDF is a binary file
• A NetCDF file consists of
• Global Attributes Describe the contents of
  the file
• Dimensions Define the structure of the data
• (e.g Time, Depth, Latitude, Longitude)
• Variables Holds the data in arrays shaped
  by Dimensions
• Variable Attributes Describes the contents of
  each variable
• CDL (network Common Data form Language)
  description takes the following form
• netCDF name
• dimensions ...
• variables ...
• data ...

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Storing Data in a netCDF File
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NetCDF Data Sources

• Community Climate Systems Model (CCSM) 
  http//www.ccsm.ucar.edu, https//www.earthsystemg
  rid.org/
• The CCSM is fully-coupled, global climate model
  that provides state-of-the-art computer
  simulations of the Earth's past, present, and
  future climate states.
• 100 yrs of climate change forecast data
  (2000-2099)
• Control runs (1870-1999) and scenario runs
• Temperature, precipitation flux, surface snow
  thickness, snowfall flux, cloud water content,
  etc.
• Program for Climate Model Diagnosis and
  Intercomparison (PCMDI) http//www-pcmdi.llnl.gov/

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NetCDF Data Sources

• Vegetation and Ecosystem Modeling and Analysis
  Project (VEMAP) http//dataportal.ucar.edu/vemap/m
  ain.html
• VEMAP was a large, collaborative, multi-agency
  program to simulate and understand ecosystem
  dynamics for the continental United States.
• The VEMAP Data Portal is a central collection of
  files maintained and serviced by the NCAR Data
  Group
• Climate data interval Annual, monthly, and
  daily.
• Data type Historical and model results
• Data Temperature, irradiance, precipitation,
  humidity, incident solar radiation, vapor
  pressure, elevation, land area, vegetation, water
  holding capacity of soil, etc.

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NetCDF Data Sources

• British Atmospheric Data Center (BADC)
  http//badc.nerc.ac.uk/data/
• The role of the BADC is to assist UK atmospheric
  researchers to locate, access and interpret
  atmospheric data.
• Many datasets are publicly available but datasets
  marked with key symbol have restricted access.
• Datasets are organized by projects or
  organizations.
• Climatology Interdisciplinary Data Collection
  (CIDC) has monthly means of over 70 Climate
  Parameters.
• Met Office - Historical Central England
  Temperature Data has the monthly series, which
  begins in 1659, is the longest available
  instrumental record of temperature in the world.
  The daily series begins in 1772.

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NetCDF Data Sources

• National Oceanic Atmospheric Administration
  (NOAA)
• National Digital Forecast Database (NDFD)
  http//www.nws.noaa.gov/ndfd/
• Radar Integrated Display with Geospatial Element
  (RIDGE) http//www.srh.weather.gov/ridge/
• Precipitation Analysis http//www.srh.noaa.gov/rfc
  share/precip_download.php
• Climate Diagnostics Center http//www.cdc.noaa.gov
  /
• NCDC THREDDS Catalog http//www.ncdc.noaa.gov/thre
  dds/catalog.html
• NCDC NCEP Stage IV Radar Rainfall
  http//www.ncdc.noaa.gov/thredds/catalog/radar/StI
  V/catalog.html

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NetCDF in ArcGIS

• NetCDF data is accessed as
• Raster
• Feature
• Table
• Direct read (no scratch file)
• Exports GIS data to netCDF

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Gridded Data
Raster
Point Features
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NetCDF Tools

• Toolbox Multidimension Tools
• Make NetCDF Raster Layer
• Make NetCDF Feature Layer
• Make NetCDF Table View
• Raster to NetCDF
• Feature to NetCDF
• Table to NetCDF
• Select by Dimension

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Space and Time

• Introductory concepts
• Discrete space-time model Arc Hydro
• Temporal Geoprocessing
• Continuous space-time model netCDF
• Tracking Analyst

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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

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Simple Event
Unique Identifier for objects being tracked
through time
Observation
Time of observation (in order)
Geometry of observation
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Complex Event (stationary version)
Cases 1, 2, 3, 4, 5
The object maintains its geometry (i.e. it is
stationary)
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Complex Event (dynamic version)
Cases 6 and 7
The objects geometry can vary with time (i.e. it
is dynamic)
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Tracking Analyst Display
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Feature Class and Time Series Table
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Temporal Layer
Shape from feature class is joined to time series
value from Time Series table
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Summary Concepts

• Hydrologic variables are defined as a function of
  space and time
• Although space and time seem alike as independent
  dimensions they are not
• Space can be discrete or continuous and is
  multidimensional
• Time is one-dimensional
• This leads to idea of spatially-referenced time
  series of data

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Summary Concepts (II)

• In Arc Hydro, discrete spatial features are
  associated with time series values through a
  HydroID-FeatureID relationship
• Time series associated with individual features
  become Attribute Series associated with a Feature
  class
• Attribute series can be transformed to Raster
  Series and Feature Series by temporal
  geoprocessing (Four panel diagram)

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Summary Concepts (III)

• ArcGIS explicitly supports time representations
  through
• By allowing operations on netCDF files for
  spatially continuous fields
• By allowing visualization of moving features
  using Tracking Analyst