1. All lecture material by Austin Troy (c) 2003 except where noted Lecture 2: Introduction to GIS Part 1. Understanding Spatial Data Structures Part 2. Legend editing, choropleth mapping and layouts

2. All lecture material by Austin Troy (c) 2003 except where noted Introduction to GIS Part 1. Understanding Spatial Data Structures

3. All lecture material by Austin Troy (c) 2003 except where noted Perception, Semantics, and Space How do we deal with representing semantic constructions of spatial objects, like “mountain,” “river,” “street,” “city,” How about representing more conceptual semantic constructions like “temperature,” “migration pattern,” “traditional homeland,” “habitat,” “geographic range,” etc? Answer: we have various data models which use different abstractions of reality Introduction to GIS

4. All lecture material by Austin Troy (c) 2003 except where noted Entities and Fields There are two general approaches for representing things in space: –Entities/ Objects: precise location and dimensions and discrete boundaries (remember, points are abstractions). –Fields, or phenomena: a Cartesian coordinate system where values vary continuously and smoothly; these values exist everywhere but change over space Introduction to GIS

5. All lecture material by Austin Troy (c) 2003 except where noted Entities and Boundaries There are two general types of boundaries, bona fide and fiat (D. Mark, B. Smith, A. Varzi) Pure bona fide boundaries represent real discontinuities in the world, like roads, faults, coastlines, power lines, rivers, islands, etc. Pure Fiat boundaries are a human cognitive or legal construction, based on a categorization, such as administrative unit, nation state, hemisphere Some have elements of both, like soil type areas Introduction to GIS

6. All lecture material by Austin Troy (c) 2003 except where noted Two major data models Entity approach roughly corresponds with the vector model Field approach roughly corresponds with raster model Any geographic phenomenon can be represented with both, but one approach is usually better for a particular circumstance Introduction to GIS

7. All lecture material by Austin Troy (c) 2003 except where noted Raster Spatial features modeled with grids, or pixels Cartesian grid whose cell size is constant Grids identified by row and column number Grid cells are usually square in shape Area of each cell defines the resolution Raster files store only one attribute, in the form of a “z” value, or grid code. Consider the contrary…. Introduction to GIS

8. All lecture material by Austin Troy (c) 2003 except where noted Vector layers either represent: –Points (no dimensions) –Lines, or “arcs” (1 dimension) or –Areas, or “polygons” (2 or 3 dimensions) Points are used to define lines and lines are used to scribe polygons Each point line or polygon is a “feature,” with its own record and its own attributes Introduction to GIS Vector

9. All lecture material by Austin Troy (c) 2003 except where noted Raster and Vector representations of the same terrain Introduction to GIS Raster: great for surfacesVector: limited with surfaces

10. All lecture material by Austin Troy (c) 2003 except where noted Introduction to GIS Raster and Vector representations of the same land use

11. All lecture material by Austin Troy (c) 2003 except where noted Introduction to GIS Raster and Vector representations of the same land use: closer in

12. All lecture material by Austin Troy (c) 2003 except where noted Vector vs. Raster: bounding Introduction to GIS Raster: bad with boundingVector: boundary precision

13. All lecture material by Austin Troy (c) 2003 except where noted Introduction to GIS Vector vs. Raster: Sample points Cancer rates across space

14. All lecture material by Austin Troy (c) 2003 except where noted In Arc View and Arc GIS, we can covert vector layers to grids, based on an attribute, or grids to vector layers The disadvantage of vector to raster is that boundaries can be imprecise because of cell shape Each time you convert, you introduce more error too Moving between vector and raster Introduction to GIS

15. All lecture material by Austin Troy (c) 2003 except where noted WHEN TO USE RASTER OR VECTOR??? Introduction to GIS

16. All lecture material by Austin Troy (c) 2003 except where noted where boundaries are not precise that occur everywhere within a frame and can be expressed as continuous numeric values where change is gradual across space where the attribute of a cell is a function of the attributes of surrounding cells Raster data analysis is better for representing phenomena: Introduction to GIS

17. All lecture material by Austin Troy (c) 2003 except where noted Simple file structure Simple overlay operations Small, uniform unit of analysis Raster technical advantages : Introduction to GIS Raster technical disadvantages : Big file size, especially for fine-grained data Difficult and error-prone reprojections Square pixels are unrealistic

18. All lecture material by Austin Troy (c) 2003 except where noted Vector analysis is better : Where there are definable regions Where the relative position of objects is important Where precise boundary definition is needed Where multiple attributes are being analyzed for a given spatial object For modeling of routes and networks For modeling regions where multiple overlapping attributes are involved EG: units with man-made boundaries (cities, zip codes, blocks), roads, rivers Introduction to GIS

19. All lecture material by Austin Troy (c) 2003 except where noted Smaller file size (in general) More graphically interpretable Allows for topology (see further on) Vector technical advantages : Introduction to GIS Vector technical disadvantages : Complicated file structure Minimum mapping units are inconsistent between overlapping layers

20. All lecture material by Austin Troy (c) 2003 except where noted Specific Vector Usages All legal and administrative boundaries (zip codes, states, property lines, land ownership) Building footprints and 3-D models Roads Bedrock geology Pipelines, power lines, sewer lines Flight paths and transportation routes Coastlines Introduction to GIS

21. All lecture material by Austin Troy (c) 2003 except where noted Specific Raster Usages Terrain modeling where micro-locational variability is present and matters Groundwater modeling, where surface flow outside of channels is important Representation of slope and aspect Representations of distance and proximity to features Spatial representation of probabilities (logit) Modeling phenomena in nature with continuous spatial variability and numeric attributes, like soil moisture, depth to bedrock, percent canopy cover, vegetative greenness index, species richness index Introduction to GIS

22. All lecture material by Austin Troy (c) 2003 except where noted In many cases, though, the choice between raster and vector may not be so clear. Often it depends on the application The following are some examples where you could go either way: Tossups Introduction to GIS

23. All lecture material by Austin Troy (c) 2003 except where noted Vector-based models used for terrain, including contours and TIN –Problem: creates distinct terrain entities that distort reality: terraces and triangular facets Raster based grids are more commonly used –They are optimal for showing spatial micro- variation in elevation although still have the problem of being like miniature “steps” –Lattices deal with this through interpolation Terrain Introduction to GIS

24. All lecture material by Austin Troy (c) 2003 except where noted Soil Soil type: Vector –Soil types are meant to represent discrete and homogeneous areas and are qualitative. There is no “slight gradation” between soil types like with pH Soil pH: raster –pH is numeric, not categorical, and that number may vary slightly within a single soil type polygon –If pH were turned into categories, like High, Medium and Low, vector might be better Introduction to GIS

25. All lecture material by Austin Troy (c) 2003 except where noted Weather Weather station data: Vector, coded with points Average precipitation surface: Raster interpolation of points Average precipitation contours: vector lines Both are interpolations, but one may be more accurate in a given situation Downside of contours: terrace effect, fewer intervals, more categorical Introduction to GIS

26. All lecture material by Austin Troy (c) 2003 except where noted Rivers Most people think of a river as a discretely bounded entity, hence vector What about where the river size fluctuates seasonally, e.g. desert rivers? Or where the location of the river bed changes slowly and gradually over the years Or where the river becomes delta, and the distinction between “river” and “swamp” becomes fuzzy? Or where the river has a certain probability of flowing or being dry at any given location and time Introduction to GIS

27. All lecture material by Austin Troy (c) 2003 except where noted Depends on the type of analysis being done With vector can do network modeling of stream and river system, but only in the arcs –Vector stream model can take advantage of topologically enabled analysis tools With raster, can do surface flow modeling –More realistic, because when it rains water flows everywhere, not just in channels, shows accumulation –Think of every piece of land as mini stream channel Rivers Introduction to GIS

28. All lecture material by Austin Troy (c) 2003 except where noted No official administrative boundary for this Where does one metro area begin and another end? Look at the New York New Jersey area. For a precise bounding, say for administrative purposes, use vector Can also include “fuzzy boundaries” To represent a gradual change from one urban area to another, use raster Metropolitan Areas Introduction to GIS

29. All lecture material by Austin Troy (c) 2003 except where noted Vector works well for modeling vegetation stand type where categories are broad, e.g. mixed conifer, deciduous hardwood Raster works better where there is micro-locational heterogeneity in species distribution Raster also works better for representing ecotones, or edges between two stands The more specific and variable the classification, the more likely the raster approach will be needed Vegetation Mapping Introduction to GIS

30. All lecture material by Austin Troy (c) 2003 except where noted Introduction to GIS Part 2. Legend editing, choropleth mapping, and layouts

31. All lecture material by Austin Troy (c) 2003 except where noted Visual Analysis The most intuitive form of vector analysis is visual analysis, where we code features with colors or symbols to deliver information Frequently, we code features by an attribute value and let the color or symbol express the attribute value Understanding legend editing and map classification is critical to making maps that effectively deliver information Introduction to GIS

32. All lecture material by Austin Troy (c) 2003 except where noted Mapping of Attribute Data In GIS, each feature can have a number of attributes attached to it (e.g. land parcel>> property ID, assessed value, square footage) We can map out these attribute values by their corresponding geography Two basic approaches for classifying the data: 1.Quantities approach 2.Category approach Introduction to GIS

33. All lecture material by Austin Troy (c) 2003 except where noted Mapping of Attribute Data Quantity approach: applies to numeric attributes that are ordinal (have order to them); this means one values is greater than or less than another; good for continuous data. Category approach: applies to categorical data, where the categories can have, but don’t need to have, order. If they do have order, the category approach ignore that order The same layer can have some quantitative and some categorical attributes Introduction to GIS

34. All lecture material by Austin Troy (c) 2003 except where noted Mapping of Attribute Data Category approach, example: vegetation type Introduction to GIS

35. All lecture material by Austin Troy (c) 2003 except where noted Mapping of Attribute Data Quantity approach, example: population Introduction to GIS

36. All lecture material by Austin Troy (c) 2003 except where noted Mapping Categories This is the simplest type of mapping: we are simply assigning a different color or symbol to each feature with a given category value Examples: vegetation types, land use, soil types, geology types, forest types, party voting maps, land management agency, recategorizations of numeric data (“bad, good, best” or “low, medium, high’). Can you think of any others? Introduction to GIS

37. All lecture material by Austin Troy (c) 2003 except where noted Mapping Categories To map categories in ArcGIS, we simply double click on the layer in the TOC and, in “layer properties,” click on the “symbology” tab Generally,we will choose “Categories>> Unique values” Introduction to GIS The we choose our values field that contains the attribute and then click the “Add all values” button

38. All lecture material by Austin Troy (c) 2003 except where noted Mapping Categories The symbology in the last slide gives us conservation lands, categorized by type of ownership Introduction to GIS

39. All lecture material by Austin Troy (c) 2003 except where noted Mapping Categories Often categories must be aggregated and redefined: this land use map had over 110 categories that were condensed to 12 Introduction to GIS

40. All lecture material by Austin Troy (c) 2003 except where noted Mapping Categories Do do this, we must group the “group values” function in the symbology properties window Introduction to GIS We can then give that grouping a label In this case 1262, 1263, 1264, 1265, etc. refers to different subcategories of commercial land use This classification is saved when I save my ArcMap Document

41. All lecture material by Austin Troy (c) 2003 except where noted Quantity Mapping This is more complex, because there are so many ways to map out quantities Mapping options depends on the feature type: For points, lines and polygons, we can darken or lighten the color to express magnitude: this is called graduated color, or color ramping For lines and points we can increase symbol size to express greater magnitude: this is called graduated symbol; we can do this because points and lines have fewer than 2 dimensions Introduction to GIS

42. All lecture material by Austin Troy (c) 2003 except where noted Choropleth Mapping a thematic mapping technique that displays a quantitative attribute using ordinal classes applied as uniform symbolism over a whole areal feature. Sometimes extended to include any thematic map based on symbolism applied to areal objects. -Nick Chrisman A map that shows numerical data (but not simply "counts") for a group of regions by (i) classifying the data into classes and (ii) shading each class on the map. -Keith Clarke Introduction to GIS

43. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color In Arc GIS layer properties>>symbology, we choose Quantities>>graduated color We then choose a value to represent In this case we choose median house value It automatically chooses five classes for the data Introduction to GIS

44. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color The resulting map shows high housing value areas with dark colors and low with light Introduction to GIS

45. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color In that case we used 5 classes. Changing the number of classes changes the information delivered; more classes: more info, but harder to see differences Introduction to GIS 3 classes for median value

46. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color In that case we used 5 classes. Changing the number of classes changes the information delivered; more classes: more info, but harder to see differences Introduction to GIS 15 classes for median value

47. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color The Classification Method also affects how the mapped attributes will look. Arc GIS normally defaults to the Jenks, or natural breaks, method Introduction to GIS These are the breaks it makes, based on the distribution of the data largesmall

48. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color Now, here’s an equal interval approach. Notice how all the breaks are evenly spaced. With a fairly normal distribution of data, this is usually OK Introduction to GIS

49. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color Here’s what the same distribution looks like with only 5 equal intervals. Introduction to GIS

50. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color However, when the distribution is skewed, or there are significant outliers, then equal interval is problematic because most intervals have no data in them. Here’s an example, with number of vacant houses per tract—most have near none, but a very few have a lot Introduction to GIS

51. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color This map of vacant properties tells us almost nothing, because almost all the records fall into the first class Introduction to GIS

52. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color Notice how with natural breaks there are now more classes on the left side, where most of the data are Introduction to GIS

53. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color Introduction to GIS This map, made with Natural Breaks, is more intelligible

54. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color There is a similar approach to Natural Breaks called Quantile. This method sets class boundaries so each class has equal numbers of observations in it Introduction to GIS

55. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color This essentially sets the class boundaries so as to maximize the perceived variation in the map, as we see here Natural Breaks is similar, but does not necessarily result in an equal number of data points in each class; rather it uses Jenks' Goodness of Variance Fit (GVF) statistic Introduction to GIS

56. All lecture material by Austin Troy (c) 2003 except where noted Graduated Color Graduated color can also be applied to points. Here are houses display by sales price Introduction to GIS Natural breaks Equal interval

57. All lecture material by Austin Troy (c) 2003 except where noted Graduated Symbol Since points and lines are not dimensionally realistic, the symbols representing them can also be graduated. Here the size of the dot represents the house price Introduction to GIS

58. All lecture material by Austin Troy (c) 2003 except where noted Graduated Symbol The same thing can also be done with lines—for instance, the width of a line feature showing rivers can be made to represent the flow of that river segment. For many line features, like streets, ArcGIS comes preloaded with symbol palettes that recognize the attribute codes and put the appropriate symbol Introduction to GIS

59. All lecture material by Austin Troy (c) 2003 except where noted Symbol Styles We can also choose to “match to symbols in a palette” and then apply the “transportation.style” palette to the CFCC, or road category, attribute in our roads layer Introduction to GIS Results in this map Must click here to match Choose your style palette here

60. All lecture material by Austin Troy (c) 2003 except where noted Symbol Styles One could also manually create symbol styles for each street type. Clicking on each symbol in either the TOC or properties windows brings up a manual symbol selector. You can assign a separate one to each category. Introduction to GIS Includes many more classes of symbols that are industry standar

61. All lecture material by Austin Troy (c) 2003 except where noted Symbol Styles There are also a huge variety of industry-specific point symbols that can be either assigned through matching symbols to a predefined style or manually assigning those symbols Introduction to GIS

62. All lecture material by Austin Troy (c) 2003 except where noted Charts displayed geographically Attributes for point, line or polygon features can also be displayed as charts on the map Introduction to GIS

63. All lecture material by Austin Troy (c) 2003 except where noted Normalization With graduated color or symbol, we can also show an attribute normalized by another attribute or expressed as a percentage of total. Here we have number of vacancies per tract as a percentage of total households. Otherwise we’re only tracking total number. Introduction to GIS numerator denominator

64. All lecture material by Austin Troy (c) 2003 except where noted Layouts You can very simply create a map for layout in Arc GIS by simply clicking View>>Layout view. Layouts are designed to cartographically acceptable, which means they must have the key elements of a printed map, such as scale bars, north arrows, legends and titles. These can be added from the Insert menu Introduction to GIS

65. All lecture material by Austin Troy (c) 2003 except where noted Layouts Example layout (from lab 6) Introduction to GIS legend North arrow Scale bar title

66. All lecture material by Austin Troy (c) 2003 except where noted Layouts Legends are edited in the Legends property window, which can be accessed by double clicking the legends. Best way to learn about it is try it out Introduction to GIS Legends can show layer name as well as intervals for quantitative data and category names for categorical data

67. All lecture material by Austin Troy (c) 2003 except where noted Layouts You can change names of the layers for the sake of your layout legend (since most layers have pretty unintuitive names) in the layer properties window Introduction to GIS

68. All lecture material by Austin Troy (c) 2003 except where noted Layouts In layouts you can have detailed and highly formatted labeling and annotation. You can use an attribute field to label; this is specified in layer properties Introduction to GIS

69. All lecture material by Austin Troy (c) 2003 except where noted MXD Files You can save your layout, along with all other preferences and settings by saving an Arc Map Document (MXD) file. However, this is not saving your data, only the settings, including the layout. If you move the MXD, you must move the layers with it. This is one reason why a geodatabase is easier than multiple shapefiles To save, just go to File>>save as Introduction to GIS

70. All lecture material by Austin Troy (c) 2003 except where noted Layer Files Layer (.lyr) files save all your settings and preferences for one single file. It is primarily for saving legend settings. So, for instance, if I a layer with 300 land use categories, and I create a legend classification that regroups them into 30 categories, each with a special color or hatching, I can save that as a layer file. Once created, opening a layer file will open the data layer with all the preferences saved. You can move the data around without moving the layer file as long as both are on the same system. Introduction to GIS

71. All lecture material by Austin Troy (c) 2003 except where noted Layer Files This is done in Arc Catalog, by right clicking and clicking “create layer.” Then I can create the legend preferences in Arc Catalog Introduction to GIS Then, double clicking in Arc Catalog will give me the layer properties, which can be changed