Announcement Lab 1 is on the class website. Due: 9/21/17 Also due that day is your project proposal. Lecture 4B

Lab Deliverables Report Sheet (in data folder) with the answers typed under the question. Map layout on a “clean” page. In general, layouts should include: An appropriate title. Legend North arrow Your name Date Lecture 4B

Lecture 4B

ArcInfo Coverages Lecture 4B

A coverage is a geo-relational data model that stores vector data—it contains both the spatial (location) and attribute (descriptive) data for geographic features. Coverages use a set of feature classes to represent geographic features. Each feature class stores a set of points, lines (arcs), polygons, or annotation (text). Coverages can have topology, which determines the relationships between features. http://desktop.arcgis.com/en/arcmap/10.3/manage-data/coverages/what-is-a-coverage.htm Lecture 4B

More than one feature class is often required to define the features in a coverage. For example, line and polygon feature classes both exist in a coverage representing polygon features. Polygon features also have label points, which appear as a separate feature class. Every coverage has a feature class containing tic points, which represent known, real-world coordinates. http://desktop.arcgis.com/en/arcmap/10.3/manage-data/coverages/what-is-a-coverage.htm Lecture 4B

http://desktop. arcgis. com/en/arcmap/10 http://desktop.arcgis.com/en/arcmap/10.3/manage-data/coverages/what-is-a-coverage.htm Lecture 4B

Ch. 4 Problems 4.1 Which is the larger scale map: a) 1:5000 or b) 1:15,000? 4.3 Describe three different types of generalization. 4.4 Categorize the generalizations in the following image as: fused, simplified, displaced, omitted or exaggerated. Lecture 4B

fused, simplified, displaced, omitted or exaggerated Lecture 4B

4.9 Complete the following table: Ground Distance Map Distance Map Scale 13,280 ft 6.4 in 1:24,900 126.4 km 25.28 cm 1:56110 123.6 mi 22.8 in 40.7 m cm 1:502.5 km 4.62 in 1:249,685 Lecture 4B

4.12 Identify the digitizing error in the figure below. Lecture 4B

Maps & Data Entry Chapter 4 – pp 170-End Lecture 4B

When do we need a coordinate transformation? Digitizing from legacy maps Digitizing from suitable aerial photos Lecture 4B

Coordinate Transformation Also called registration, as it registers the map layers to a map coordinate system. Requires a set of control points. Must be as accurate as the desired outcome. Evenly distributed over the area. Sufficient number. The number of control points depends on the mathematical form of the transformation. Additional control points usually improves the overall accuracy, Lecture 4B

Developing the Transformation Equations Coordinates in the source system Coordinates in the target system Estimate equations that allow us to calculate the target coordinates given any set of source coordinates Lecture 4B

Similarity Transformation The method allows rotation of the rectangle and preserves its shape but not size. Lecture 4B

Projective Transformation The method allows angular and length distortion, thus allowing the rectangle to be transformed into an irregular quadrilateral. Lecture 4B

Affine Transformation The method allows angular distortion but preserves the parallelism of lines. While preserving line parallelism, the affine transformation allows rotation, translation, skew, and differential scaling on the rectangular object. Lecture 4B

Affine Transformation Target coordinates a first-order linear function of source coordinates Known as an Affine Transformation: Easting = Te + a1x + a2y Northing = Tn + b1x + b2y There are 6 parameters to be estimated T shifts origin bi & ai changes scale & rotation Lecture 4B

Example Road intersection: Real world coordinates: E=500,000 & N=4,800,000 Digitized coordinates: x=125 & y= 100 There are six parameters to be estimated, so we need a minimum of six equations. Each control point provides two equations. Need a minimum of 3 control points Lecture 4B

Lecture 4B

The Best Fit We start with as many control points as possible. After the first iteration, check the residuals, and remove the control points with the largest error. Repeat as necessary to reduce the RMSE to <5. Lecture 4B

Root Mean Square Error When the general formula is derived and applied to the control point, a measure of the error—the residual error—is returned. The error is the difference between where the “from” point ended up as opposed to the actual location that was specified—the “to” point position. The total error is computed by taking the root mean square (RMS) sum of all the residuals to compute the RMS error. Lecture 4B

Root Mean Square Error (RMSE) Lecture 4B

Root Mean Square Error (RMSE) Although the RMS error is a good assessment of the transformation's accuracy, don't confuse a low RMS error with an accurate registration. For example, the transformation may still contain significant errors due to a poorly entered control point, or errors in the image. The more control points of equal quality used, the more accurately the polynomial can convert the input data to output coordinates. Lecture 4B

Higher order polynomial transformation Named for the largest exponent in the transformation equation. ArcMap supports first to third order transformation. Command line supports first to twelfth order transformation. Use the lowest order transformation that provides acceptable results Lecture 4B

Lecture 4B

Higher Order Polynomials and RMSE Second order, RMSE = 3.82 First order (affine), RMSE = 7.2 Lecture 4B

Higher Order Polynomials and RMSE First order (affine), RMSE = 7.2 Third order, RMSE = 0.00033 Selecting the best transformation is a subjective process, guided by multiple goals. Lecture 4B

Raster Geometry and Resampling Data must often be resampled when converting between coordinate systems or changing the cell size of a raster data set. Common methods: Nearest neighbor Bilinear interpolation Cubic convolution Lecture 4B

Nearest Neighbor Nearest neighbor assignment does not change any of the values of cells from the input layer; for this reason it is often used to resample categorical or integer data (for example, land use, soil, or forest type), or radiometric values, such as those from remotely sensed images. Lecture 4B

Nearest Neighbor Lecture 4B http://support.esri.com/other-resources/gis-dictionary/term/nearest%20neighbor%20resampling Lecture 4B

Bilinear Interpolation Distance weighted averaging Lecture 4B

Cubic Convolution Similar to bilinear interpolation, but it uses the 16 nearest cells in the calculation. http://support.esri.com/other-resources/gis-dictionary/term/cubic%20convolution Lecture 4B

Types of GIS Output Maps: Everyone recognizes this most common output from a GIS. Cartograms: These special maps that distort geographic features based on their output values rather than their size. Charts: GIS can produce pie charts, histograms (bar charts), line charts, and even pictures in addition to maps. Lecture 4B

Types of GIS Output Directions: Another common output, directions show you how to get from one place to another. Customer lists: Business GIS applications often produce customer lists, sometimes with printed mailing labels. 3D diagrams and movies: These forms of GIS output help you see the results of your work realistically and dramatically. Lecture 4B

Maps as Output The map is still the most common form of output. Map design elements to be considered: Frame of reference –title, graticule, inset map Projection Features to be mapped Level of generalization Annotations/Labels Symbolism Maps should show only as much detail as necessary to get the point across. Lecture 4B 37

Cartographic Design Most design choices are compromises Design is a process Stage 1 – type of map, data to be represented, size, shape, basic layout. Data type is the most important factor in determining map type and symbols Stage 2 – kinds of symbolism, number of classes, class limits, color, line weights. Stage 3 – define all symbols, typography (font, size, positions etc.) Lecture 4B

Legends Qualitative Data Make symbols as intuitive as possible Use professional standards whenever possible Lecture 4B 39

Contrast – bad example Lecture 4B

Contrast – good example Lecture 4B

Typography & Lettering Use concise formulated captions Avoid using more than four fonts Establish a typographic hierarchy Develop legibility Black lettering on yellow ---- most legible Red lettering on green ---- least legible Lecture 4B

Example: hello world hello world Hard to read better Lecture 4B

Text Placement Placing text on a map is one of the most time consuming tasks, as much as 50% of the final map production time. Poor placement of text affects the readability of the map, this is especially true in regions where map symbols are densely clustered. Situations often arise in which text must overwrite other symbols with which it has no logical association. Lecture 4B

Automated Name Placement Components of name placement systems: Specification of map features and text characteristics. Generation of trial name positions. Selection of optimal labels. Scale at which labels will be displayed Font type, color and size must also be specified. It is desirable to name as many features as possible, while recognizing that some features will remain unlabeled. Named features should be ranked in some way to resolve conflicts. Lecture 4B

Non-Traditional Maps Cartogram Multimedia output Hybrid – Map overlaying an image 3D Virtual GIS Lecture 4B

Cartogram Shows different rail lines – the circles represent stops, the larger circles are stops where you can change lines. This type of map is referred to as a cartogram Lecture 4B 47

Results on A Population Cartogram 2008 Election Results by State http://www-personal.umich.edu/~mejn/election/2008/ Lecture 4B

http://amphibiaweb.org/amphibian/cartograms/ Lecture 4B

Lecture 4B Figure 8.9 Example of multimedia content in GIS displays 50 Source: Screenshot shows ESRI Graphical User Interface (GUI). ArcMap, ArcView and ArcInfo Graphical User Interfaces are the intellectual property of ESRI and is used herein with permission. Copyright © 2005 ESRI all rights reserved Lecture 4B 50

Making Great Maps Lecture 4B

THE OUTPUT FROM GIS ANALYSIS TABLES AND CHARTS Used with maps or alone to improve understanding of cartographic results Whenever map output is less immediately understood by audience Tables and charts are generally more understood by general public than maps are Map is not appropriate for the output Show summaries of attribute data and relationships among them Explicitly spatial Implicitly spatial Presents complex tabular information effectively. Provides an immediate impact and takes less effort to understand. Complements map information. Shows the same information in a different way, or provides additional information about map features. Lecture 4B

TABLE AND CHART DESIGN Should be readily understood with minimal explanations Appropriate titles for each table and chart presented Label axes on all graphs Provide legends wherever appropriate Use fonts types and sizes that are easily read – Arial Chose colors wisely as not to mislead audience Avoid plotting more than 3 distinct attributes per plot Lecture 4B

Good for comparing values and showing trends Bar Chart Good for comparing values and showing trends Lecture 4B 54

Column Chart Good for comparing values and showing trends Lecture 4B 55

Area Chart Good for showing the relative value for each category as well as the total. Lecture 4B 56

Cumulative Bar Chart Combines features of both the bar and area charts Lecture 4B 57

Pie Charts Shows relationships between the parts and the whole, particularly useful for showing proportions and ratios. Lecture 4B 58

Line Charts Emphasizes rate of change. Particularly good for representing trends over a period of time. Lecture 4B 59

Scatter Charts Reveals trends or patterns in the data. Can help reveal associations, sometimes cause-and-effect relationships. Lecture 4B 60

What is Metadata? Data about data Any information that makes data useful for another user Background information that describes content, quality, condition, availability, use conditions, and distribution methods for data. Structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource. Lecture 4B

Types of Metadata? Descriptive metadata – describes a resource for discovery and information. Structural metadata – indicates how compound objects are put together . Administrative metadata – provides information to help manage a resource. Lecture 4B

Why is it Important? Supports data sharing Helps users to find data Helps users to judge quality/utility of the data Extends useful life-span of data Saves time and money Lecture 4B

Roles of Metadata Supports access (search, browse, and retrieval) Supports transfer Supports evaluation of fitness for use Supports use Lecture 4B

Assignment Finish reading chapter 4. Problems: 20, 21, 28. Lecture 4B