Space-Time

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

http://cs.alfred.edu/~boersma/images/ein2.jpg

Additional reading

Space-Time





Space-Time Cube

TSDateTime

TSTypeID

TSValue

FeatureID

Time

Space

Variable

Data Value

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

TypeIndex

VariableName

TypeOf

TimeSeries

Info

Regular or

Irregular

Unitsof

measure

Timeinterval

Recordedor

Generated

Arc Hydro has 6 Time Series DataTypes1. Instantaneous2. Cumulative3. Incremental4. Average5. Maximum6. Minimum

Instantaneous

Cumulative

AverageIncremental

Maximum Minimum

Time Series Types

Space-Time





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 EventID 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 throughtime

Time of observation (in order) Geometry of observation

Observation

Complex Event (stationary version)

ID Geometry

1 X1,Y1

2 X2,Y2

3 X3,Y3

4 X4,Y4

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

The object maintains its geometry (i.e. it is stationary)

Cases 1, 2, 3, 4, 5

Complex Event (dynamic version)

ID Gage Number

1 1001

2 1002

3 1003

4 1004

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

The object’s geometry can vary with time (i.e. it is dynamic)

Cases 6 and 7

Tracking Analyst Display

Feature Class and Time Series Table

Temporal Layer

Shape from feature class is joined to time series value from TimeSeries table

Space-Time





Time Series Feature Series

Raster SeriesAttribute Series

Time

Variable

Time and Space in GIS

xy

Value

t1

t2

t3

Val

ue

Time

t1

t2

t3

t3

t2

t1

Time Series and Temporal Geoprocessing



Time

Variable

xy

Value

t1

t2

t3

Val

ue

Time

t1

t2

t3

ArcGIS Temporal Geoprocessing

t3

t2

t1

DHI Time Series Manager

Adobe picture

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 waterwaysLake

Okeechobee

Lake Istokpoga

Lake Kissimmee

DBHydro TimeSeriesAchieve 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/Time Value

Spatial Information About point of measurement

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

http://www.sfwmd.gov/org/ema/dbhydro/index.html

http://www.sfwmd.gov/org/ema/dbhydro/index.html

TSDateTime

FeatureID

TSType

TSValue

Arc Hydro Attribute Series

TSType Table

Feature Class(point, line, area)

• 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

Units Regular …. 1Attribute Series

FeatureID Time Value* Type

Attribute Series Typing

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

Attributeseries

Display as a temporallayer 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

Hydraulic head is the water surface elevation in a standpipeanywhere in a water system, measured in feet above mean sealevel

h

Land surface

Mean sea level(datum)

Map of hydraulic head

X

Y

Z

Hydraulic head, h

xy

h(x, y)

A map of hydraulic head specifies the continuous spatialdistribution of hydraulic head at an instant of time

Time sequence of hydraulic head maps

x

y

z

Hydraulic head, h

t1

t2

t3

Attribute Series to Raster Series

Inundation

hL

d

Depth of inundation = d IF (h - L) > 0 then d = h – LIF (h – L) < 0 thend = 0

Inundation Time Series

t

h(x,y,t)LT(x,y)

Time

d(x,y,t)

d(x,y,t) = h(x,y,t) – LT(x,y)

DEMO: DHI Time Series

Ponded Water DepthKissimmee River

June 1, 2003

Show Generate Rasters Model



Time

Variable

y

t

x

1

2

3

4

Hydroperiod Tool TimeSeries Framework

Depth Classification

Value_ From_ To_-1 -100 -0.00010 0 01 0.0001 0.52 0.5 13 1 1.54 1.5 25 2 2.56 2.5 37 3 3.58 3.5 49 4 4.5

10 4.5 511 5 100

0

5

4

3

2

1

Depth Class

11

9-10

7-8

5-6

3-4

1-20-1

Feature Series of Ponded Depth

Show Classify Depths Model

Attribute Series for Habitat Zones

Show Zonal Stats Model

Space-Time





Multidimensional Data Representation for the Geosciences

Ocean Science

Earth Science

Atmospheric Science

Hydrology

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?

TSDateTime

FeatureID

TSType

TSValue

Arc Hydro Attribute Series

TSType Table

Feature Class(point, line, area)

Time

Space (x,y,z)

Variables

Value

NetCDF Data Model (developed at Unidata for distributing weather data)

Attributes

Dimensions andCoordinates

NetCDF describes a collection of variables stored in a dimension space that may represent coordinate points in the (x,y,z,t) dimensions

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 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);

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 SamplesPrecipitable water in the atmosphere

Cross-section of relative humidity

Images created from Unidata’s Integrated Data Viewer (IDV)

Wind vectors and wind speed (shading)

Space-Time

Documents

Transcript of Space-Time