Midterm

MidtermMultiple choice on scantron/bring #2 pencilMajor concepts moreso than detailsReviewing LECTURES is key PPT files

background & extra in Chapters 1, 3-4, 9, 20 in Longley et al.

Will not include Web Sites of the Week (WSWs)Labs

Learning Assessment/Practice Questions on class web site

GIS Data Capture:Getting the Map into the Computer

Chapter 9, Longley et al.

Overview

IntroductionPrimary data captureSecondary data captureData transferCapturing attribute dataManaging a data capture projectError and accuracy

Data Collection

Can be most expensive GIS activityMany diverse sourcesTwo broad types of collection

Data capture (direct collection)Data transfer

Two broad capture methodsPrimary (direct measurement)Secondary (indirect derivation)

Data Collection Techniques Field/Raster Object/

VectorPrimary Digital remote

sensing imagesGPS measurements including VGI

Digital aerial photographs

Survey measurements

Secondary Scanned maps Topographic surveys

DEMs from maps

Toponymy data sets from atlases

Stages in Data Collection Projects

Planning

Preparation

Collection / TransferEditing / Improvement

Evaluation

Primary Data Capture

Capture specifically for GIS useRaster – remote sensing

e.g., SPOT and IKONOS satellites and aerial photography, echosounding at seaPassive and active sensors

Resolution is key considerationSpatialSpectral, AcousticTemporal

Vector Primary Data Capture

SurveyingLocations of objects determines by angle and distance measurements from known locationsUses expensive field equipment and crewsMost accurate method for large scale, small areas

GPSCollection of satellites used to fix actual locations on Earth’s surfaceDifferential GPS used to improve accuracy

Total Station

GPS “Handhelds”geographic coordinates text

photos

audiovideo

Bluetooth, WiFi

cell towers+/- 500 mGoogle db of

tower locations

Graphic courtesy of Wired, Feb. 2009

Wi-Fi+/- 30 mSkyhook

servers and dbGPS+/- 10 m

iPhone uses reference network

“Power to the People:”VGI & PPGIS“Volunteered Geographic Information”

Wikimapia.orgOpenstreetmap.orgAka “crowdsourcing”

“Public Participation GIS”GEO 599, Fall 2007Papers still online at dusk.geo.orst.edu/virtual/

Example:

A Boon for International Development Agencies

Robert Soden, www.developmentseed.org

Kinshasa, Democratic Republic of Congo

International Development, Humanitarian Relief

Robert Soden, www.developmentseed.org

Mogadishu, Somalia

Haiti Disaster, MapAction.org

UCLA Center for Embedded Networked Sensing, http://peir.cens.ucla.edu

“Citizen Sensors”

Societal Issues(privacy, surveillance, ethics)

e.g., Google StreetView

Google Maps Mania Blog

Early and late May 2008

More surveillance(electronic, video, biological,

chemical)integrated into national system

From Chris Peterson, Foresight Institute

As presented at OSCON 2008, Portland

Graphic: Gina MillerFrom Chris Peterson, Foresight InstituteAs presented at OSCON 2008, Portland

Sewer monitoring has begun

“The test doesn’t screen people directly but instead seeks out evidence of illicit drug abuse in drug residues and metabolites excreted in urine and flushed toward municipal sewage treatment plants.”

From Chris Peterson, Foresight InstituteAs presented at OSCON 2008, Portland

Secondary Geographic Data Capture

Data collected for other purposes, then converted for use in GISRaster conversion

Scanning of maps, aerial photographs, documents, etc.Important scanning parameters are spatial and spectral (bit depth) resolution

Scanner

Vector Secondary Data Capture

Collection of vector objects from maps, photographs, plans, etc.Photogrammetry – the science and technology of making measurements from photographs, etc.Digitizing

Manual (table) Heads-up and vectorization

Digitizer

GEOCODING

spatial information ---> digital formcapturing the map (digitizing, scanning)sometimes also capturing the attributes“mapematical” calculation, e.g.,

address matchingWSW

The Role of ErrorMap and attribute data errors are the data producer's responsibility,

GIS user must understand error.Accuracy and precision of map and attribute data in a GIS affect all other operations, especially when maps are compared across scales.

Accuracycloseness to TRUE values

results, computations, or estimates

compromise on “infinite complexity”

generalization of the real worlddifficult to identify a TRUE value

e.g., accuracy of a contourDoes not exist in real worldCompare to other sources

Accuracy (cont.)accuracy of the database = accuracy of the products computed from databasee.g., accuracy of a slope, aspect, or watershed computed from a DEM

Positional Accuracytypical UTM coordinate pair might be:Easting 579124.349 mNorthing 5194732.247 mIf the database was digitized from a 1:24,000 map sheet, the last four digits in each coordinate (units, tenths, hundredths, thousandths) would be questionable

Map scale Ground distance corresponding to 0.5 mm map distance

1:1250 62.5 cm

1:2500 1.25 m

1:5000 2.5 m

1:10,000 5 m

1:24,000 12 m

1:50,000 25 m

1:100,000 50 m

1:250,000 125 m

1:1,000,000 500 m

1:10,000,000 5 km

A useful rule of thumb is that positions measured from maps are accurate to about 0.5 mm on the map. Multiplying this by the scale of

the map gives the corresponding distance on the ground.

Positional Accuracy

Testing Positional AccuracyUse an independent source of higher accuracy:

find a larger scale map (cartographically speaking)

use GPS

Use internal evidence:digitized polygons that are unclosed, lines that overshoot or undershoot nodes, etc. are indications of inaccuracysizes of gaps, overshoots, etc. may be a measure of positional accuracy

not the same as accuracy!repeatability vs. “truth”not closeness of results, but number of decimal places or significant digits in a measurement A GIS works at high precision, usually much higher than the accuracy of the data themselves

Precision

Accuracy vs. PrecisionAccuracy vs. Precision

High AccuracyLow Precision

Low AccuracyHigh Precision

Many darts in reproduceable clusters, but not in the bullseye.

Darts are near the bullseye (the "true value"), but there aren't very many clusters of them (not reproduceable).

Accuracy vs. PrecisionAccuracy vs. Precision

High AccuracyLow Precision

Low AccuracyHigh Precision

Many darts in reproduceable clusters, but not in the bullseye.

Darts are near the bullseye (the "true value"), but there aren't very many clusters of them (not reproduceable).

Components of Data Quality

positional accuracyattribute accuracylogical consistencycompletenesslineage