1. Geographic Information Systems SGO 1910, SGO 4030 October 10, 2006

2. Updates Midterm quiz 1 (will be returned next week) Midterm quiz 1 (will be returned next week) ArcGIS CDs – Geodata will give us more, and I hope to pick them up before next week. ArcGIS CDs – Geodata will give us more, and I hope to pick them up before next week. GIS Day – November 15 GIS Day – November 15 Oslo Project: Will be described next week – please start thinking about groups (of 3). Oslo Project: Will be described next week – please start thinking about groups (of 3).

3. Masters students: start thinking about papers as an alternative to the Oslo Project (both are options) Masters students: start thinking about papers as an alternative to the Oslo Project (both are options) Read Steinberg and Steinberg Read Steinberg and Steinberg

5. Where are we? What is GIS? What is GIS? Geographic representations: discrete objects and fields Geographic representations: discrete objects and fields Digital representations: vector and raster models Digital representations: vector and raster models Geographic data: scale, spatial autocorrelation, sampling, interpolation Geographic data: scale, spatial autocorrelation, sampling, interpolation Georeferencing systems Georeferencing systems Map projections Map projections

6. Where are we going? One more theoretical issue: uncertainty One more theoretical issue: uncertainty Then onto more practical issues: GIS data collection (including GPS) GIS data collection (including GPS) GIS databases GIS databases Cartography and map production Cartography and map production Geographical analysis Geographical analysis GIS and society GIS and society

7. Uncertainty

8. Uncertainty arises from the way that we conceive of the world (geographic representation), how we measure and represent it (digital model), and how we analyze the representations. Uncertainty arises from the way that we conceive of the world (geographic representation), how we measure and represent it (digital model), and how we analyze the representations. All of these add up – uncertainty increases from the start of the project to the end. All of these add up – uncertainty increases from the start of the project to the end.

10. Terms to consider Error (differences between observers or between measuring instruments) Error (differences between observers or between measuring instruments) Accuracy (difference between reality and our representation of reality) Accuracy (difference between reality and our representation of reality) Precision (the repeatability of measurements) Precision (the repeatability of measurements) Quality (attribute accuracy, positional accuracy, logical consistency, completelness, and lineage) Quality (attribute accuracy, positional accuracy, logical consistency, completelness, and lineage)

11. Filter 1: Conception Spatial uncertainty Spatial uncertainty Do Natural geographic units exist? Do Natural geographic units exist? Scales for bivariate/multivariate analyses? Scales for bivariate/multivariate analyses? Discrete objects more reliant on “natural units” Discrete objects more reliant on “natural units” Vagueness (in boundaries, membership) Vagueness (in boundaries, membership) Statistical, cartographic, cognitive Statistical, cartographic, cognitive Ambiguity Ambiguity Different labels by different national or cultural groups, language (GIS is not value-neutral!!) Different labels by different national or cultural groups, language (GIS is not value-neutral!!)

12. Indicators Direct – a clear correspondence with mapped phenomenon Direct – a clear correspondence with mapped phenomenon Indirect (or proxy) – best available measure Indirect (or proxy) – best available measure Selection of indicators is subjective Selection of indicators is subjective Differences in definitions are a major impediment to integration of geographic data over wide areas Differences in definitions are a major impediment to integration of geographic data over wide areas

13. Direct indicator for joblessness: Number of unemployed (measured as those collecting unemployment benefits) Direct indicator for joblessness: Number of unemployed (measured as those collecting unemployment benefits) Indirect indicators of joblessness: crime rates, visits to soup kitchens Indirect indicators of joblessness: crime rates, visits to soup kitchens

14. Fuzzy Approaches to Uncertainty In fuzzy set theory, it is possible to have partial membership in a set In fuzzy set theory, it is possible to have partial membership in a set membership can vary, e.g. from 0 to 1 membership can vary, e.g. from 0 to 1 this adds a third option to classification: yes, no, and maybe this adds a third option to classification: yes, no, and maybe Fuzzy approaches have been applied to the mapping of soils, vegetation cover, land use, and vulnerability Fuzzy approaches have been applied to the mapping of soils, vegetation cover, land use, and vulnerability

17. Scale & Geographic Individuals Regions Regions Uniformity (internal homogeneity) Uniformity (internal homogeneity) Functional zones (boundaries as breakpoints) Functional zones (boundaries as breakpoints) Relationships typically grow stronger when based on larger geographic units Relationships typically grow stronger when based on larger geographic units

18. Scale and Spatial Autocorrelation No. of geographicCorrelation areas 48.2189 24.2963 12.5757 6.7649 3.9902

20. Filter 2: Measurement/representation Representational models filter reality differently Representational models filter reality differently Vector (requires a priori conceptualization of geographic features as discrete objects) Vector (requires a priori conceptualization of geographic features as discrete objects) Raster (boundaries seldom resemble natural features, but convenient and efficient…) Raster (boundaries seldom resemble natural features, but convenient and efficient…)

21. 0.9 – 1.0 0.5 – 0.9 0.1 – 0.5 0.0 – 0.1

22. Other issues Measurements only accurate to a limited extent Measurements only accurate to a limited extent ‘Continuous’ scales are in practice discrete ‘Continuous’ scales are in practice discrete Discrete isopleth/choropleth map display Discrete isopleth/choropleth map display Choropleth mapping in multivariate cases Choropleth mapping in multivariate cases Box 4.3 explains the difference! Box 4.3 explains the difference!

23. Spatially Intensive versus Extensive Variables Choropleth maps use values describing properties of non-overlapping areas (municipalities, states, countries) Choropleth maps use values describing properties of non-overlapping areas (municipalities, states, countries) Extensive variables: values true for the entire area are the same color: E.g. Total population Extensive variables: values true for the entire area are the same color: E.g. Total population Intensive variables: values could potentially be true for every part of the area (but actuall represent an average). E.g. Population density. Intensive variables: values could potentially be true for every part of the area (but actuall represent an average). E.g. Population density.

24. Measurement Error Digitizing errors Digitizing errors Automated solutions Automated solutions Conflation of adjacent map sheets Conflation of adjacent map sheets

27. Data Integration and Lineage Concatenation Concatenation E.g. polygon overlay E.g. polygon overlay Conflation Conflation E.g. rubber sheeting E.g. rubber sheeting Persistent error indicates shared lineage Persistent error indicates shared lineage Errors tend to exhibit strong positive spatial autocorrelation Errors tend to exhibit strong positive spatial autocorrelation

31. Filter 3: Analysis Can good spatial analysis develop on uncertain foundations? Can good spatial analysis develop on uncertain foundations? Can rarely correct source Can rarely correct source More usually tackle operation (internal validation) More usually tackle operation (internal validation) Conflation/concatenation allows external validation of zonal averaging effects Conflation/concatenation allows external validation of zonal averaging effects Error propagation measures impacts of uncertainty in data on the results Error propagation measures impacts of uncertainty in data on the results

32. Ecological Fallacy Inappropriate inference from aggregate data about the characteristics of individuals Inappropriate inference from aggregate data about the characteristics of individuals Fundamental difference between geography and other scientific disciplines is that definitions of objects of study is almost always ambiguous. Fundamental difference between geography and other scientific disciplines is that definitions of objects of study is almost always ambiguous.

33. Modifiable Areal Unit Problem (MAUP) Scale + aggregation = MAUP Scale + aggregation = MAUP can be investigated through simulation of large numbers of alternative zoning schemes can be investigated through simulation of large numbers of alternative zoning schemes Apparent spatial distributions which are unrepresentative of the scale and configuration of real-world geographic phenomena (example: urban density) Apparent spatial distributions which are unrepresentative of the scale and configuration of real-world geographic phenomena (example: urban density)

34. Summary Uncertainty is more than error Uncertainty is more than error Richer representations can create uncertainty! Richer representations can create uncertainty! Need for a priori understanding of data and sensitivity analysis Need for a priori understanding of data and sensitivity analysis Spatial analysis is often context-sensitive (you need to know your data and place!) Spatial analysis is often context-sensitive (you need to know your data and place!)

35. Living with Uncertainty Acknowledge that uncertainty is inevitable Acknowledge that uncertainty is inevitable Data should never be taken as truth (assess whether it is suitable) Data should never be taken as truth (assess whether it is suitable) Uncertainties in outputs may exceed uncertainties in inputs because many GIS processes are highly non- linear Uncertainties in outputs may exceed uncertainties in inputs because many GIS processes are highly non- linear Rely on multiple sources of data Rely on multiple sources of data Be honest and informative in reporting the results of GIS analysis. Be honest and informative in reporting the results of GIS analysis.

36. The more scientific knowledge we gain, the more uncertain we are likely to be: “Richness of representation and computational power only make us more aware of the range and variety of established uncertainties, and challenge us to integrate new ones” (Longley et al. 2005, p. 152).

37. Data Acquisition: Getting the Map into the Computer

38. Data transfer Input of data from other systems (via Internet, CD ROMs, tapes, etc.) Input of data from other systems (via Internet, CD ROMs, tapes, etc.)

39. Data capture Primary (direct measurement, e.g. remote sensing and surveying) Primary (direct measurement, e.g. remote sensing and surveying) Secondary (derivation from other sources; digitizing, scanning, etc.) Secondary (derivation from other sources; digitizing, scanning, etc.)

40. GIS maps are digital Real maps: traditional paper maps that can be touched Real maps: traditional paper maps that can be touched Virtual maps: an arrangement of information inside the computer; the GIS can be used to generate the map however and whenever necessary. Virtual maps: an arrangement of information inside the computer; the GIS can be used to generate the map however and whenever necessary.

41. GIS Data Conversion Traditionally the most time-consuming and expensive part of a GIS project Traditionally the most time-consuming and expensive part of a GIS project Involves a one-time cost Involves a one-time cost Digital maps can be reused and shared. Digital maps can be reused and shared. Requires maintenance (eg. updating) Requires maintenance (eg. updating)

42. GIS data can be Purchased. Purchased. Found from existing sources in digital form. Found from existing sources in digital form. Captured from analog maps by GEOCODING. Captured from analog maps by GEOCODING.

43. Finding Existing Map Data Map libraries Map libraries Reference books Reference books State and local agencies State and local agencies Federal agencies Federal agencies Commercial data suppliers Commercial data suppliers

44. Existing Map Data Existing map data can be found through a map library, via network searches, or on media such as CD-ROM and disk. Existing map data can be found through a map library, via network searches, or on media such as CD-ROM and disk. Many major data providers make their data available via the Internet. Many major data providers make their data available via the Internet.

45. Statenskartverk http://ngis.statkart.no/katalog/java/katalog.asp Rasterdata Rasterdata Temakart Temakart Vektordata Vektordata Primærdata Primærdata Prosjekter Prosjekter

46. 1. Accessing GIS Data Example: Costa Rica Example: Costa Rica

53. Data Collection One of most expensive GIS activities One of most expensive GIS activities Many diverse sources Many diverse sources Two broad types of collection Two broad types of collection Data capture (direct collection) Data capture (direct collection) Data transfer (exchange) Data transfer (exchange) Two broad capture methods Two broad capture methods Primary (direct measurement) Primary (direct measurement) Secondary (indirect derivation) Secondary (indirect derivation)

54. Data Collection Techniques RasterVector Primary Digital remote sensing images GPS measurements Digital aerial photographs Survey measurements Secondary Scanned maps Topographic surveys DEMs from maps Toponymy data sets from atlases

55. Toponymy? Toponymy is the scientific study of toponyms (place-names), their origins, meanings, use and typology. The word is derived from the Greek τόπος topos, place, and oνομα ōnoma, name. It is itself a branch of onomastics, the study of names of all kinds. Toponymy is the scientific study of toponyms (place-names), their origins, meanings, use and typology. The word is derived from the Greek τόπος topos, place, and oνομα ōnoma, name. It is itself a branch of onomastics, the study of names of all kinds.GreekonomasticsGreekonomastics A toponym is a name of a locality, region, or some other part of Earth's surface or an artificial feature. In some cultures, most or all such place names have a definite meaning in the language; this is not the case, generally, for place names in the English language. A toponym is a name of a locality, region, or some other part of Earth's surface or an artificial feature. In some cultures, most or all such place names have a definite meaning in the language; this is not the case, generally, for place names in the English language.nameEnglish languagenameEnglish language

56. GEOCODING Geocoding is the conversion of spatial information into digital form. Geocoding is the conversion of spatial information into digital form. Geocoding involves capturing the map, and sometimes also capturing the attributes. Geocoding involves capturing the map, and sometimes also capturing the attributes.

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

58. Secondary Geographic Data Capture Data collected for other purposes can be converted for use in GIS Data collected for other purposes can be converted for use in GIS Raster conversion Raster conversion Scanning of maps, aerial photographs, documents, etc Scanning of maps, aerial photographs, documents, etc Important scanning parameters are spatial and spectral (bit depth) resolution Important scanning parameters are spatial and spectral (bit depth) resolution

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

60. Vector Secondary Data Capture Collection of vector objects from maps, photographs, plans, etc. Collection of vector objects from maps, photographs, plans, etc. Digitizing Digitizing Manual (table) Manual (table) Heads-up and vectorization Heads-up and vectorization Photogrammetry – the science and technology of making measurements from photographs, etc. Photogrammetry – the science and technology of making measurements from photographs, etc. COGO – Coordinate Geometry COGO – Coordinate Geometry

61. Managing Data Capture Projects Key principles Key principles Clear plan, adequate resources, appropriate funding, and sufficient time Clear plan, adequate resources, appropriate funding, and sufficient time Fundamental tradeoff between Fundamental tradeoff between Quality, speed and price Quality, speed and price Two strategies Two strategies Incremental Incremental ‘Blitzkrieg’ (all at once) ‘Blitzkrieg’ (all at once) Alternative resource options Alternative resource options In house In house Specialist external agency Specialist external agency

62. Summary Data collection is very expensive, time- consuming, tedious and error prone Data collection is very expensive, time- consuming, tedious and error prone Good procedures required for large scale collection projects Good procedures required for large scale collection projects Main techniques Main techniques Primary Primary Raster – e.g. remote sensing Raster – e.g. remote sensing Vector – e.g. field survey Vector – e.g. field survey Secondary Secondary Raster – e.g. scanning Raster – e.g. scanning Vector – e.g. table digitizing Vector – e.g. table digitizing

63. Digitizing Captures map data by tracing lines from a map by hand Captures map data by tracing lines from a map by hand Uses a cursor and an electronically-sensitive tablet Uses a cursor and an electronically-sensitive tablet Result is a string of points with (x, y) values Result is a string of points with (x, y) values

64. Digitizer

65. The Digitizing Tablet

66. Digitizing Stable base map Stable base map Fix to tablet Fix to tablet Digitize control Digitize control Determine coordinate transformation Determine coordinate transformation Trace features Trace features Proof plot Proof plot Edit Edit Clean and build Clean and build

67. Selecting points to digitize

68. Scanner

69. Scanning Places a map on a glass plate, and passes a light beam over it Places a map on a glass plate, and passes a light beam over it Measures the reflected light intensity Measures the reflected light intensity Result is a grid of pixels Result is a grid of pixels Image size and resolution are important Image size and resolution are important Features can “drop out” Features can “drop out”

70. Scanning example This section of map was scanned, resulting in a file in TIF format that was bytes in size. This was a file of color intensities between 0 and 255 for red, green, and blue in each of three layers spaced on a grid 0.25 millimeter apart. How much data would be necessary to capture the features on your map as vectors? Would it be more or less than the grid (raster) file?

71. Field data collection

72. Pen Portable PC and GPS

73. Data Transfer Buy vs build is an important question Buy vs build is an important question Many widely distributed sources of GI Many widely distributed sources of GI Key catalogs include Key catalogs include US NSDI Clearinghouse network US NSDI Clearinghouse network Geography Network Geography Network Access technologies Access technologies Translation Translation Direct read Direct read

74. Attribute data Logically can be thought of as in a flat file Logically can be thought of as in a flat file Table with rows and columns Table with rows and columns Attributes by records Attributes by records Entries called values. Entries called values.

75. Database Management Systems Data definition module sets constraints on the attribute values Data definition module sets constraints on the attribute values Data entry module to enter and correct values Data entry module to enter and correct values Data management system for storage and retrieval Data management system for storage and retrieval Data definitions can be listed as a data dictionary Data definitions can be listed as a data dictionary Database manager checks values with this dictionary, enforcing data validation. Database manager checks values with this dictionary, enforcing data validation.

76. The Role of Error Map and attribute data errors are the data producer's responsibility, but the GIS user must understand error. Map and attribute data errors are the data producer's responsibility, but the 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 and precision of map and attribute data in a GIS affect all other operations, especially when maps are compared across scales.