KGA172 Space, Place and Nature

1. KGA172 Space, Place and Nature Accuracy in Mapping Dr Christopher Watson

3. Part 1 Looking forward, looking back

4. Revising Lecture 1.2 Focus on terms: work alone, with others in class or with your workshop coordinator to ensure you understand and can exemplify the following terms as they apply to spatial science: spatial location attributes relationships proximity visibility topology scale datum GPS GIS cadastre Auguste Rodin, A man thinking

5. Learning Objectives Module 1 Lecture 3 KGA172 Know and be able to (a) employ basic geographical terminology and concepts, (b) find, evaluate, analyse and reference appropriate literature, (c) contribute to debates about development and sustainability Comprehend and be able to explain spatial patterns, generate basic maps, field sketches and graphs, and communicate in written and graphical forms Apply key academic skills and (a) engage in critical thinking, discussion and listening, and in self-reflection and reflection upon the viewpoints of others and (b) research, plan and conduct fieldwork to collect data Analyse and interpret basic spatial, numerical and qualitative information Synthesize and integrate knowledge of social and Earth systems be able to describe and demonstrate that you understand • accuracy and precision • factors that limit spatial data accuracy • conventions used to define accuracy • different spatial data sources

6. Textbook Reading Bergman and Renwick (2008) Chapter 1 Critical reading What is the author’s purpose? What key questions or problems does the author raise? What information, data and evidence does the author present? What key concepts does the author use to organize this information, this evidence? What key conclusions is the author coming to? Are those conclusions justified? What are the author’s primary assumptions? What viewpoints is the author writing from? What are the implications of the author’s reasoning? [from Foundation for Critical Thinking] A man in a library

7. Part 2 Why bother with accuracy?

8. Accuracy: Measure of how close an observation is to the ‘true’ value. Precision: Measure of how repeatable one’s observations are.

9. A Sea Level Example… X (Comparison Point) What would we see if we compared satellite altimeter sea level against GPS buoy sea level…

10. A Sea Level Example… Buoy sea level (truth) – Altimeter sea level = the data below Altimeter sea level is rather precise, but not overly accurate (eeekk – hence the requirement for calibration)

11. Part 3 Data quality

12. Data Quality I How sound are your data? • Scale • Ratio of distance on a map to the equivalent distance on Earth's surface. • Primarily an output issue; at what scale do you wish to display your data? • Precision or Resolution • Exactness of the measurement or description. • Determined by input; can output at lower (but not higher) resolution. • Accuracy • Degree of correspondence between data and the real world. • Fundamentally controlled by quality of input.

13. Data Quality II • Lineage • Original sourcesof the data and the processing steps it has undergone. • Currency • Degree to which data represents the world at the present moment in time. • Documentation or Metadata • Data about data: recording all of the above. • Standards • Common or “agreed-to” ways of doing things. • Data built to standards is more valuable since it is more easily shareable.

14. Surveying and Spatial Science… • Nearly all aspects of industry, science and society are finding an increasing need for high quality information in order to make reliable decisions. • Much of that information has a spatial component, a location on Earth, and involves an integrated approach to the science and technologies of measurement, mapping, analysis and visualisation of data. • Translated: Huge demand for people with “spatial skills” to work in very diverse roles. What do you notice on this screen image from Google Street View?

15. Part 4 Scale, resolution and accuracy

16. Scale I • Reduce real things and places to a more convenient scale of a drawing, for example, model car/plane, diagram, map. • Scale: Ratio of image on map to real world; that is, relates 1 unit on map to similar unit on ground. • Different map scales: - written scale - representative fraction - graphic scale

17. Scale II • Advantage of graphic scale: If map enlarged or reduced, scale also changes accordingly. • Scales are small, medium, and large depending on the ratio – note these are subjective but no less valuable terms. • Rule of thumb: • large scale: 1:25,000 or larger [fine scale] • medium scale: 1:500,000 to 1:25,000 • small scale: 1:500,000 or smaller [coarse scale] • Large scale map shows a small area with a large amount of detail: smalldenominator  large representative fraction.

18. Scale III • Scale can never be constant everywhere on a map because of map projection. • Problem is worst for small scale maps and certain projections (e.g. Mercator). • Can be true from a single point to everywhere. • Can be true along a line, or a set of lines. • On large scale maps, adjustments often made to achieve ‘close to true’ scale everywhere (e.g. UTM system).

19. Scale, Resolution and Accuracy I • On paper maps, scale is hard to change, thus it generally determines resolutionand accuracy– and consistent decisions are made for these. • A GIS is scale independentsince output can be produced at any scale, irrespective of the characteristics of input data – at least in theory. 1:100,000 1:25,000

20. Scale, Resolution and Accuracy II • In practice, an implicit range of scales or maximum scale for anticipated output should be chosen and used to determine: • what features to show • e.g. manholes only on large scale maps • how features will be represented • e.g. manhole a polygon at 1:50; cities a point at 1:1,000,000 • appropriate levels for accuracy and precision • larger scale generally requires greater resolution • larger scale necessitates a higher level of accuracy

21. Scale, Resolution and Accuracy III • GIS also helps with the generalisation problem implicit in paper maps. • A road drawn with 0.5 mm wide line (the smallest for decent visibility): • at 1:25,000 implies the road is 12.5m wide • at 1:250,000 implies the road is 125m wide • At least in a GIS you can store the true road width, but be careful with plots and map output!

22. Scale, Resolution and Accuracy IV Red: roads from 1:5,000 mapping Green: roads from 1:25,000 mapping

23. Precision or Resolution • Not the same as scale or accuracy! • Precision: Exactness of measurement or description • Resolution: “Size” of “smallest” feature which can be displayed, recognised, or described • Can apply to space, time (e.g. daily vs. annual), or attribute • For GIS raster data, it is pixel size (resolution)

24. Resolution Example Where are we?

25. Resolution Example How about now?

26. Resolution Example What are we looking at?

27. Precision or Resolution • Resolution and Scale: • Generally, increasing to larger scale allows features to be observed better and requires higher resolution • Resolution and Positional Accuracy: • You can see a feature (resolution), but it may not be in the right place (accuracy) • Higher accuracy generally costs much more to obtain than higher resolution • Accuracy cannot be greater (but may be much less) than resolution (e.g. if pixel size is one metre, then best accuracy possible is one metre)

28. Accuracy • Positional Accuracy (Quantitative Accuracy): • Spatial: • Horizontal accuracy (distance from true location) • Vertical accuracy (difference from true height) • Temporal: • Difference from actual time and/or date • Attribute Accuracy or Consistency: • Validity concept in experimental design and statistical information • A feature iswhat GIS/map declares it to be • A railroad is a railroad, and not a road • A soil sample agrees with the type mapped

29. Accuracy • Completeness: • Reliabilityconcept in experimental design and statistical information • Are all instances of a feature the GIS/map claims to include, in fact, there? • Partially a function of the criteria for including features: When does a road become a track? • Simply put, how much data is missing?

30. Accuracy – Logical Consistency • Presence of contradictory relationships in database • Non-Spatial: • Data for one country are for 2000, for another it is 2001 • Some crimes recorded at place of occurrence, others at place where report was taken • Annual data series not taken on same day/month (sometimes called lineage error) • Data use different source or estimation technique for different years (again, lineage) • Spatial: • Overshoots and gaps in road networks or parcel polygons

31. Part 5 Other matters

32. Sources of Error • Inherent instability of the phenomenon itself • e.g. random variation of most phenomena (e.g. leaf size) • Measurement • e.g. surveyor or instrument error • Model used to represent data • e.g. choice of ellipsoid, or classification systems • Data encoding and entry • e.g. keying or digitising errors • Data processing • e.g. algorithms used • Propagation from one dataset to another, • e.g. using inaccurate layer as source for another layer

33. Sources of Error Issues that relate to positional accuracy • Choice of ellipsoid and datum • Choice of map projection and its parameters • Accuracy of measured locations (surveying) of features on Earth • Media stability (stretching, folding, wrinkling of maps, photos) • Human drafting, digitising or interpreting error • Resolution and/or accuracy of drafting/digitising equipment • Thinnest visible line 0.1-0.2mm  2-4m at 1:20,000 (20,000  0.2 = 4,000mm = 4m) • Machine precision: coordinate rounding error in storage and manipulation

34. Lineage • Identifies the original sources from which the data were derived • Details processing steps through which data have gone to reach their current form • Bothhave impact on accuracy • Both should be in the metadata, and are usually required by standards • Metadata: Data that describe a dataset to allow others to find and evaluate it.  i.e. data about data

35. Examples of Metadata • Identification: Title? Area Covered? Themes? Currency? Restrictions? • Data Quality: Accuracy? Completeness? Logical Consistency? • Spatial Data Organisation: Indirect? Vector? Raster? Type of Elements? Number? • Spatial Reference: Projection? Grid System? Datum? Coordinates? • Entity and Attribute Information: Features? Attributes? Attribute Values? • Distribution: Distributor? Formats? Media? Online? Price? • Metadata Reference: Metadata currency? Responsible party?

36. Currency: Are your data up-to-date? • Data always relative to specific point in time, which must be documented. • Important applications for historical data (e.g. analysing trends), so do not readily trash old data. • Data require specific plan for maintenance. • May be continuous, or at defined points in time. • Otherwise, data become outdated very quickly. • Currency not really independent quality dimension; it is simply a factor contributing to lack of accuracy: • Consistency: Some features do not match those in the real world today • Completeness: Some real world features are missing from the GIS database or map

37. Standards ANZLIC: http://www.anzlic.org.au/infrastructure_standards.html GA: http://www.ga.gov.au/standards/standards.jsp US: http://www.fgdc.gov/standards/ World: http://ncl.sbs.ohio-state.edu/ica/3_spatial.html • Common or “agreed-to” ways of doing things. • May exist for: • Data • Data accuracy • Documentation about the data (metadata) • Transfer of data and its documentation • Symbology and presentation • May address: • Content (what is recorded) • Format (how it is recorded, e.g. file format) • May be product of: • Organisation’s internal actions (private standards) • External government body (public standards) • Market-place forces leading to one dominant approach (industry standards)

38. Spatial Data Sources • Data source influences accuracy... How sound are the data? • Google Earth • Topographic Map http://earth.google.com/

39. Spatial Data Sources Remote sensing Aerial photographs

40. Spatial Data Sources Terrestrial or Airborne Laser Scanning

41. Spatial Data Sources Handheld GPS

42. Spatial Data Sources LISTmap and SurCoM SurCoM (Survey Control Marks): http://surcom.dpiw.tas.gov.au/surcom/jsp/index.jsp LISTmap (Land Information System Tasmania): http://www.thelist.tas.gov.au/

43. Spatial Data Sources Census Data Dwellings being purchased as a percentage of all occupied private dwellings (from 2001 Hobart Social Atlas) Australian Bureau of Statistics: http://www.abs.gov.au/

44. Summary • How sound are my data? The answer influences interpretation! • Accuracy and precision • Factors limiting spatial data accuracy: scale, precision / resolution, accuracy, lineage, currency, metadata, standards • Accuracy: positional accuracy, attribute accuracy, completeness, logical consistency • Sources of error important to consider • Different spatial data sources • Next lecture: Spatial Technologies