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Midterm. 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

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Midterm


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    Presentation Transcript
    1. Midterm

    2. 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

    3. GIS Data Capture:Getting the Map into the ComputerChapter 9, Longley et al.

    4. Overview • Introduction • Primary data capture • Secondary data capture • Data transfer • Capturing attribute data • Managing a data capture project • Error and accuracy

    5. 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)

    6. Data Collection Techniques

    7. Preparation Evaluation Planning Collection / Transfer Editing / Improvement Stages in Data Collection Projects

    8. 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

    9. 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

    10. Total Station

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

    12. cell towers +/- 500 m Google db of tower locations Wi-Fi +/- 30 m Skyhook servers and db GPS +/- 10 m iPhone uses reference network Graphic courtesy of Wired, Feb. 2009

    13. “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/

    14. Example: A Boon for International Development Agencies Kinshasa, Democratic Republic of Congo Robert Soden, www.developmentseed.org

    15. International Development, Humanitarian Relief Mogadishu, Somalia Robert Soden, www.developmentseed.org

    16. Haiti Disaster, MapAction.org

    17. “Citizen Sensors” UCLA Center for Embedded Networked Sensing, http://peir.cens.ucla.edu

    18. Google Maps Mania Blog Societal Issues(privacy, surveillance, ethics)e.g., Google StreetView Early and late May 2008

    19. More surveillance (electronic, video, biological, chemical) integrated into national system From Chris Peterson, Foresight Institute As presented at OSCON 2008, Portland

    20. From Chris Peterson, Foresight Institute As presented at OSCON 2008, Portland Graphic: Gina Miller

    21. 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

    22. 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

    23. Scanner

    24. 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

    25. Digitizer

    26. GEOCODING • spatial information ---> digital form • capturing the map (digitizing, scanning) • sometimes also capturing the attributes • “mapematical” calculation, e.g., • address matching WSW

    27. 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.

    28. 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

    29. 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

    30. Positional Accuracy • typical UTM coordinate pair might be: • Easting 579124.349 m • Northing 5194732.247 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

    31. 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 Positional Accuracy 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.

    32. 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

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

    34. Accuracy vs. Precision

    35. Accuracy vs. Precision

    36. Components of Data Quality • positional accuracy • attribute accuracy • logical consistency • completeness • lineage