FNRM 3131 Introduction to GIS in Natural Resource Management

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1 FNRM 3131 Introduction to GIS in Natural Resource Management Instructors: Andrew C Jenks 215 Green Hall Paul V Bolstad 301H Green Hall pbolstad@umnedu

2 Course Topics Motivation: why GIS? Data Models Map Projections and Coordinate Systems Data entry - digitzing Data entry - GPS Data entry - digital data Image data Tables Basic Spatial Analysis Raster Analyses Spatial Models and Modeling Interpolation Data Quality The Future

3 What is a GIS? Geographic Information System - collection, organization, maintenance, analysis, and output of spatiallyreferenced information Two important components - What, and Where

4 We usually understand GIS to be computerfacilitated system GIS is NOT only software/hardware Also includes: Trained personnel Supporting Institution Protocols for use

5

6 Motivation

7 Why GIS?

8 Why GIS?

9 Pavement quality inventory and monitoring

10

11

12 Why GIS? Sustaining Lobster Fisheries in Penobscot Bay Why is it important? Fisheries stocks dwindling, lobster an economic mainstay with sustainable harvests

13 Lobster habitat (green) in outer Penobscot Bay

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15 From Observations and Process Knowledge to Prediction and Action stituteorg/penbayc oll_understandinglif ephp

16 Courtesy Virginia Institute of Marine Science Dynamic Spatial Models

17 GIS Software Tools GIS started at universities as research tools Harvard, Yale, Minnesota, Clark University GIS software have evolved to robust (sort of) tools capable of a wide variety of tasks ESRI (ArcGIS) Quantum GIS (QGIS) Microimages Autocad MapInfo ERDAS Idrisi Manifold GRASS Intergraph

18 Science and specific tools often lacking Caveat Tools for data management Tools for generic analysis

19 What is in it for me? Help us understand your goals and expectations Please answer the questions on the Moodle site: Direct link is: Questionnaire Or type/paste it in:

20 Steps for Successful GIS Analysis (Goals defined, methods exist) Choose best data model Define bounds, geographic region of interest Identify existing spatial data Determine coordinate system for analyses Develop digital database Document database (origin, quality) Perform analyses Report results Update Database

21 Famous Environmental Thinkers/Writers Aldo Leopold Sand County Almanac (1949) Rachel Carson Silent Spring (1962)

22 Famous Environmental Thinkers/Writers Aldo Leopold Sand County Almanac (1949) Rachel Carson Silent Spring (1962) Ian McHarg Design With Nature (1969)

23 Ian McHarg Design With Nature (1969)

24 Some terms Ian McHarg created: Environmental impact assessment New community development Coastal zone management Brownfields restoration River corridor planning Sustainability & Regenerative design

25 wwwlarchumdedu/classes/ /Planned_Communities_11_3 _08pdf

26 wwwlarchumdedu/classes/ /Planned_Communities_11_3 _08pdf

27

28 GIS Fundamentals

29 Real World phenomena that exist Data Model Data Structure 12, 47 58, 36 89, 72 computer representation An abstraction, relevant phenomena and properties

30 Data Model Define spatial objects in a database, And represent relationships among objects (connection, adjacency, proximity, influence)

31 REPRESENTATION AND DATA STRUCTURES We approximate entities with objects Entity This approximation is biased Terms Entities - "things" in the real world we represent (Rivers, buildings, soil types, wetlands) Object Objects - our representation in a data model

32 REPRESENTATION AND DATA STRUCTURES Coordinates and Attributes

33 Many objects for a theme, with properties recorded for each object

34 REPRESENTATION AND DATA STRUCTURES Most common data models define thematic layers Typically, layers, one layer for each distinct view of a theme

35 Cartesian Coordinates

36

37

38 Degree Units

39 Sexagesimal (base sixty)

40 Sexagesimal (base sixty) Originated with ancient Sumerians and Babylonians (2000 BC) 60 has 12 factors ( 1, 2, 3, 4, 5, 6, 10, 12, 15, 30, 60) Allows many simple fractions also worked well with cuneiform script

41 We Can Convert!

42 Common Data Models Vector Raster Y Points Points ( x,y ) Line Area Line Area X

43 Vectors Define Discrete Features

44 Points Lines Areas

45 Three Types of Vector Features Points Organization Point ID X Y etc Lines Polygons A B 1 C Line Begin End ID Point Point A 6 9 B 9 1 C etc Polygon ID Lines A 11, 12, 52, 53, 54 B 52, 53, 9, 41, 22, 13

46 REPRESENTATION AND DATA STRUCTURES We can get multiple objects from a single entity, eg, lake may be a municipal water source recreation area flood control sink wildlife habitat We typically store different object types (even from the same entity) in different layers

47 Representations and Data Structures We typically have a one-to-one correspondence between entities (the real world thing) and objects (the computer representation) in a layer but not always

48 One-to-One

49

50 A Many to One Correspondence Two lakes, one polygon

51 We typically represent entities as one of four types of objects points lines areas 3-D objects

52 Representation Enforced Uniformity

53 Vector Assumptions Dimensionality 0, 1 st, 2 nd points have no dimension, lines have no width

54 Enforced Uniformity

55 Assignment Ambiguity

56 Vector Topology Topology geometric properties that to not change with shape: Adjacency, connectivity, containment

57 Why Topology Matters We frequently deform our data (map projections) We can better achieve accuracy in analysis Some calculations are faster

58 Planar Topology no overlaps

59

60

61

62

63

64 Can you locate Iran? How about Afghanistan?

65

66 Rasters Fixed Cell Size, Grid Orientation

67 Rasters Discrete or Continuous Features discrete continuous

68

69

70

71 29 36? Rasters as Sampling Points continuous variables

72 Raster The Mixed Pixel Problem Landcover map Two classes, land or water Cell A is straightforward What category to assign For B, C, or D?

73 Raster The Storage Space/Resolution Tradeoff Decreasing the Cell Size by one-half causes a Four-fold increase in the storage space required

74

75 Resampling

76 What if the cells aren t well behaved

77 Orientation and/or Cell Size May Differ

78 Resampling - Distance-weighted averaging bilinear interpolation

79

80 Comparisons, raster vs vector Characteristics Positional Precision Can be Precise Vector Raster Defined by cell size Attribute Precision Analytical Capabilities Data Structures Poor for continuous data Good for spatial query, adjacency, area, shape analyses Poor for continuous data Most analyses limited to intersections Slower overlays Often complex Good for continuous data Spatial query more difficult, good for local neighborhoods, continuous variable modeling Rapid overlays Often quite simple Storage Requirements Coordinate conversion Network Analyses Relatively small Usually well-supported Easily handled Often quite large Often difficult, slow Often difficult Output Quality Very good, map like Fair to poor - aliasing

81 No Decision is Final We Can Convert

82 Triangular Irregular Network (TIN) Typically used to represent terrain or other spotsampled continuous variables Connect sample points in a network of triangles Why? to preserve sample accuracy, save space samples connected network - TIN

83 TIN Parts Points sample locations Edges connecting lines Facets triangles, faces

84 TIN Triangle Formation TIN triangles defined such that Three points on a circle Circles are empty they don t contain another point These are convergent circles

85 Multiple Representations

86 Data and File Structures Real World Data Model Data Structure 12, 47 58, 36 89, 72

87 Data and File Srtuctures Data are stored as binary numbers Bits are 0 or 1 Bytes are 8 bits Data (eg, raster cells) are often references as 1 byte, two byte, etc

88

89

90 Cells Have a Type, Size Type eg, Real, unsigned integer, signed integer, text Size 8 bit, 32 bit, 64 bit, long, short, character width These control the size of the datasets, and type of data that may be stored Mixing types, sizes, often requires some care

91 Raster data set properties We can store a number up 32 To 2 or 4,293,967,296 in a cell

92 Characters, assign a number to each character value, eg, ASCII (American Standard Coding for Information Interchange) 65 = A, 66 = B, 67 = C 97 = a, 98 = b, 99 = c But 50 = 2 51 = 3

93 Positive Integers are easy Positive Integers as is, so interpret all 8 bits for the number eg, 8 bit unsigned integer is the number 13, as in our previous example Negative Integers interpret 1 st bit as sign bit, eg, 0 is positive, 1 is negative number, and interpret next bits as number 8 bit signed integer is - 13

94 Floating point numbers (real numbers) are a bit more complicated Generally a sign, exponent, and a fraction part eg, a 4-byte floating point number has the first bit as a sign, the next 8 as the exponent, and the next 23 as the fractional part, S EEEEEEEE FFFFFFFFFFFFFFFFFFFFFFF WHY SHOULD YOU CARE?

95 When you want this You get this:

96 Data and File Structures Data often have specific organization to reduce size speed access ease updates

97 Data and File Structures Compression Reducing size eg, raster run-length coding

98 GIS Hardware Typical of most computer systems, with a little more storage, a few added tools for input/output Coordinate input devices are the primary specialized hardware Digitizing Tablets GPS receivers

99 Summary GIS are systems for the creation, maintenance, analysis, and conveyance of spatial data We represent abstractions of our world into spatial and attribute components using data models and data structures Two major data models raster and vector

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