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Space and Time

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Diagram. Time series from gages in Kissimmee Flood Plain ... Data telemetered to central site using SCADA system ... humidity, incident solar radiation, vapor ... – PowerPoint PPT presentation

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Title: Space and Time


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
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
9
ODM and HIS in an Observatory Settinge.g.
http//www.bearriverinfo.org
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 from sensors (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
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
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
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
TSDateTime
Time
TSValue
Data Value
FeatureID
Space
TSTypeID
Variable
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
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

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) gt 0 then d
h L IF (h L) lt 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
51
Attribute Series for Habitat Zones
52
Space and Time
  • Introductory concepts
  • Discrete space-time model Arc Hydro
  • Temporal Geoprocessing
  • Continuous space-time model netCDF
  • Tracking Analyst

53
Multidimensional Data
  • Data cube (3D) or hypercube (4D,5D)
  • Temperature varying with time
  • Temperature varying with time and altitude

T
Y
X
54
Multidimensional Data
Time 3
Time 2
Time 1
55
Multidimensional Data
Time 3
Time 2
Time 1
56
Multidimensional Data
Time 1
Time 2
Data Cube
Time 3
Time Slices
57
Multidimensional Data
Includes variation in (x,y,z,t)
58
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

59
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 ...

60
Storing Data in a netCDF File
61
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/

62
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.

63
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.

64
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

65
NetCDF in ArcGIS
  • NetCDF data is accessed as
  • Raster
  • Feature
  • Table
  • Direct read (no scratch file)
  • Exports GIS data to netCDF

66
Gridded Data
Raster
Point Features
67
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

68
Space and Time
  • Introductory concepts
  • Discrete space-time model Arc Hydro
  • Temporal Geoprocessing
  • Continuous space-time model netCDF
  • Tracking Analyst

69
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

70
Simple Event
Unique Identifier for objects being tracked
through time
Observation
Time of observation (in order)
Geometry of observation
71
Complex Event (stationary version)
Cases 1, 2, 3, 4, 5
The object maintains its geometry (i.e. it is
stationary)
72
Complex Event (dynamic version)
Cases 6 and 7
The objects geometry can vary with time (i.e. it
is dynamic)
73
Tracking Analyst Display
74
Feature Class and Time Series Table
75
Temporal Layer
Shape from feature class is joined to time series
value from Time Series table
76
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

77
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)

78
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
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