Midterm

Multiple choice on scantron/bring #2 pencil Major concepts moreso than details Reviewing 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 Introduction Primary data capture Secondary data capture Data transfer Capturing attribute data Managing a data capture project Error and accuracy

Data Collection Can be most expensive GIS activity Many diverse sources Two broad types of collection Data capture (direct collection) Data transfer Two broad capture methods Primary (direct measurement) Secondary (indirect derivation)

Data Collection Techniques Field/RasterObject/Vector Primary Digital remote sensing images GPS measurements including VGI Digital aerial photographs Survey measurements Secondary Scanned mapsTopographic surveys DEMs from mapsToponymy data sets from atlases

Stages in Data Collection Projects PlanningPreparation Collection / Transfer Editing / Improvement Evaluation

Primary Data Capture Capture specifically for GIS use Raster – remote sensing e.g., SPOT and IKONOS satellites and aerial photography, echosounding at sea Passive and active sensors Resolution is key consideration Spatial Spectral, Acoustic Temporal

Vector Primary Data Capture Surveying Locations of objects determines by angle and distance measurements from known locations Uses expensive field equipment and crews Most accurate method for large scale, small areas GPS Collection of satellites used to fix actual locations on Earth’s surface Differential GPS used to improve accuracy

Total Station

GPS “Handhelds” geographic coordinates text photos audio video Bluetooth, WiFi

cell towers +/- 500 m Google db of tower locations Graphic courtesy of Wired, Feb Wi-Fi +/- 30 m Skyhook servers and db GPS +/- 10 m iPhone uses reference network

“Power to the People:”VGI & PPGIS “Volunteered Geographic Information” Wikimapia.org Openstreetmap.org Aka “crowdsourcing” “Public Participation GIS” GEO 599, Fall 2007 Papers still online at dusk.geo.orst.edu/virtual/

Example: A Boon for International Development Agencies Robert Soden, Kinshasa, Democratic Republic of Congo

International Development, Humanitarian Relief Robert Soden, Mogadishu, Somalia

Haiti Disaster, MapAction.org

UCLA Center for Embedded Networked Sensing, “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 Miller From Chris Peterson, Foresight Institute As 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 Institute As presented at OSCON 2008, Portland

Secondary Geographic Data Capture Data collected for other purposes, then converted for use in GIS Raster 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 form capturing the map (digitizing, scanning) sometimes also capturing the attributes “mapematical” calculation, e.g., address matching WSW

The Role of Error Map 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.

Accuracy closeness to TRUE values results, computations, or estimates compromise on “infinite complexity” generalization of the real world difficult to identify a TRUE value e.g., accuracy of a contour Does not exist in real world Compare to other sources

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

Positional Accuracy typical UTM coordinate pair might be: Easting m Northing m If 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 scaleGround distance corresponding to 0.5 mm map distance 1: cm 1: m 1: m 1:10,0005 m 1:24,00012 m 1:50,00025 m 1:100,00050 m 1:250, m 1:1,000, m 1:10,000,0005 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 Accuracy Use 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 inaccuracy sizes 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. Precision

Components of Data Quality positional accuracy attribute accuracy logical consistency completeness lineage