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Collecting & Using

Geospatial Data in the Field

Digital Government Collaboration

ISU UCSB v NSF v Census BLS USDA USGS

www.statlab.iastate.edu/dg

Geospatial info used throughout

data collection process

Locate

Site

Planning &

Assignments

Collect &

Check Data









Check &

Analyze Process Data

Data

2

NRI Sample Design

 Stratified two-stage probability sample

of land in US and territories

– 300,000 area segments (PSUs ~ ¼ section)

– 800,000 points selected within PSUs

– Repeated measurements on sample units

over time









3

Data Collection via Photo-

interpretation & Field Observations









a 386

1 3 b 330

2 9







6

a 330

b 600

a 330

c 386

7

a 378

b 600

d 412

a 391a



5



a 362

4





8 4

a 330

b 600

PSU Measurements

 Areas of polygons

with specific land

uses

– Built-up areas

– Farmsteads

– Water

– Federal land



 Lengths of linear

features

– Streams



5

Point Measurements

 Land cover / use

classification



 Agricultural &

conservation practices



 Soil and erosion

factors



 Ecological

classifications



6

Updating Digital NRI Locations



• History

Sample segment and

point locations

transferred from

maps and photographs

to 7.5’ quadrangle for

digitizing

• Current goal

Update locations with

accurate & precise

measurements 7

Johnson Co Pilot



 Historical maps &

data for target

location

 Current digital

coordinate

 GPS receiver (PPS)









8

Integrate Handheld Data Collection

Instrument & GPS Receiver









9

Integrated User Interface



 Receiver set-up, active

link

 User’s position &

digital coordinate

 Navigation aids

– Turning direction

– Current position relative

to target

– Current path

 Accuracy information

– Satellite configuration

 Capture button

– Location metadata 10

Satellite

Configuration









11

Location

Metadata









12

Research Team

 Computer-assisted survey data collection

Sarah Nusser, Iowa State U

 Infrastructures for adaptive systems

Les Miller, ISU

 Digital geographic information resources

Mike Goodchild, U Calif, Santa Barbara

 Emerging technologies for geospatial data

Keith Clarke, UCSB





13

Research Vision



 To formulate, develop & test an extensible

framework that

– supports flexible & effective use of

distributed & heterogeneous geospatial

information resources

– applies to a broad range of field

environments

– takes full advantage of emerging information

technologies

14

Framework Model

Field User









Infrastructure







Repositories Specially Ad Hoc Information

Prepared for Data Resources

Collection Campaign (via Internet)



15

Research Areas



 Mediating infrastructures

 Field tools (e.g., conflation, sampling)

 Wearable technologies

 Statistical survey applications









16

Today’s Architecture





Data

Collection

Environment

Data Samples

Collection

Reposi- Attributes

tories







17

External MEDIATION

Reposi- Tiger

tories Dissect

DOQQs query

Maps

DRGs

Data

Search for

Resources Collection

options

Environment

Prioritize

Data Samples results

Conflation

Collection Sampling



Reposi- Attributes Extract

Photos views /

tories metadata

Geospatial

Process,

transform

data for user

18

Iowa Map

Data Server

Mediator wrapper DRGs

Ortho

- Spawns agents

Photos

to get product

NRI Data

User wrapper Repository

- Sends info PSUs

request to

mediator Photos

- Adds field

computing info Compute Server NRI Data

- Spawns agents to Local

get data Cache



- Manipulates, integrates data

19

- Creates view or data (product)

Field Conflation



 Merge & compare geographic

information from various sources

while in the field

– Very limited hardware & software

environments

– Resolve registration problems

– Merge any combination of raster and

vector data

20

22

23

24

Towards a general approach to

conflation

 Between all data types (raster and vector)

 Dealing with all types of disagreement

(positional, semantic)

 Running in very small field devices

 Account for different objectives when

combining

– Average of multiple good images

– Pick the best as a reference image

– Remove differences via smoothing

25

Emerging Field Technologies



 Beyond personal computing

– Personal computer fully engages or occupies user

– Desktops, laptops, most handhelds

 Research & explore alternative technologies

for mobile field data collection

– Propotype development

– Interface research







26

Wearable computing



 Wearable computers have the potential to

experience the life of the user in a first-

person sense

 The computer is working even when the user

is not giving explicit commands (e.g. health

monitors, communication systems)

 The user is doing something

besides interacting with

the computer



27

Context awareness



 Context (non-explicit user

input) is gathered through “sensing”

the person's environment, state, task

 Use context (e.g., where the person is,

what they are doing) to create systems that

proactively support, anticipate, and

facilitate the person's task

 Key spatial context is from GPS



28

User Interface for Augmented

Vision





Augmented: see-through

map plus locator









Viewed reality





29

View Options









30

Version 0 of the UCSB self-

contained wearable computer



 Consisting of:

 CharmIT™ Developer's Kit with a PC/104 platform

 GPS module

 head-mounted, see-through visual display

 Twiddler2 as text input device

 Fanny-pack or vest

 Version 2 will be web-enabled



31

CharmIT™ Developer's Kit

 CharmIT™ is built on the

PC/104 specification, which has

been an industry standard for

embedded computing for

nearly ten years

 hundreds of companies

manufacture a wide variety of

PC/104 hardware

 majority of components are

low power and ruggedized

 CharmIT™ Developer's Kit is

lower cost (approximately

$2000), low power

(approximately 7 watts with

Jumptec 266) and offers

enough computing power for

most everyday wearable tasks

32

Typical CharmIT™ kit configuration:



 Customizable, lightweight

aluminum case

 Jumptec Pentium 166 or 266

Mhz core board - includes on-

board 10/100 ethernet

 USB and SVGA

 PCMCIA board with two slots or

SoundBlaster-compatible sound

card

 Power conversion/distribution

board

 Two Sony NP-F960 batteries

(approx 5.5 hours runtime

each)

 All necessary cables and

connectors

33

Head-mounted displays









Integrated Eyeglassisplays ($5000)



ClipOn Display ($2500)

-evaluation kit comes with a belt-worn, VGA interface

box connected to the display by a 4’ cable

Display format: 640x480, 24-Bit color, 60 Hz refresh rate

Field of View: Approximately 16 degrees horizontal



34

Text input



 The Twiddler2 chorded

keyboard is designed for one-

handed input with an array of

12 finger keys and six thumb

keys.



 Frequent users can enter text

at close to two-hand touch-

typing speeds.









35

Wearable computer vests









36

Future applications…

Eyetracker is essentially an eye driven cursor or "visual

mouse" that positions the cursor in the direction that the eye

is looking. The eye's ability to rapidly shift gaze in a

changing environment makes it an ideal input device for

computer and video applications.







Nogatech Video Capture Products

USB Digital Video Camera CaptureVision™

Imagine real-time full motion video capture and display

plus high resolution still frame capture on a notebook…

Geospatial Information as

Reference

 Settings

– Locating sample outlets & households

– Locating unmarked field points

 Questions

– Strategies for using map & GPS

info in planning, navigation, locating

– Role & effectiveness of alternative

geospatial info resources

– Appropriate materials, formats

– Impact of screen size, interface mode

38

NRI Testbed



 9 sq mi area in

north Ames

 Transitioning from

riparian &

agricultural land to

urban development

 3 faux PSUs





39

iPaq w/ wireless connection









40

41

Collecting & Using

Geospatial Data in the Field

Digital Government Collaboration

ISU UCSB v NSF v Census BLS USDA USGS

www.statlab.iastate.edu/dg



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