Lecture Material
UP507
Winter, 2003

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Instructor will keep you posted on City Planning Commission meetings.

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Week 1:

• Syllabus--go over webpage
• Latitude and Longitude
• chalkboard explanation
• link to brief explanatory material
• Dot density maps
• One page biosketch
• Academic background
• General and particular interests
• Software use background
• Do you already have a webpage and if so, what software do you like to use.
• GIS experience
• Anything else you would like to share
• Discussion; project ideas
• Your own project
• City of Ann Arbor
• Planning Department
• Creating a more interactive CIP presence on our webpage, including having maps & projects interrelated.
• Help Planning create a more integrated approach to putting development petition info on our webpage (creating links, adding staff reports, maybe creating a map to show where active petition-related property is located, etc.).
• Conduct a land use & zoning analysis in the Montgomery Wellfield area to supplement other work underway.
• Helping catalog data in the GIS and indicate directions for use.
• Building Department--Historic Preservation--continue with an inventory already begun.
• Environmental Coordinator--continue with work already begun on phosphorus.
• Other possibilities
• I-maps improve communication giving residents direct access to municipal information
• Cell towers--location map (use GPS)--look at study from Simon Fraser--locational criteria for new towers/antennae and colocational issues.
• City of Detroit--contacts through TCAUP
• Other municipal contacts--on your own.
• Projects from the past--some links given on home page.  An archive that will hold all is under construction.
• Websites--set one up.

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Week 2:

• Media Union field trip:  starts at 5:45p.m. from our classroom.  See the various facilities available to you.  Later we will see the CAVE.  Today we see what all is available to use.
• Thiessen polygons
• Link to article about them:  these polygons serve as a limiting position for constructing nested circular buffers.
• Demonstration of ArcView to calculate these polygons on an arbitrary distribution of dots:  Spatial Analyst extension must be loaded.
• Create a new layer
• Edit the table and add a field with some numerical information
• Use Analysis|Assign Proximity to create Thiessen polygons--convert layers to shape files.
• Modify them by using a rectangle
• Modify using convex hull of distribution--draw it if Animal Movement extension/Home Range is not loaded.  Clip, if desired.
• Set distance--use the polygons to estimate maximum buffer radius for the distribution.
• Maps brought in on CD:  some in response to student request--Ann Arbor, Japan and Korea.  Others, as well.
• More suggestions from Planning Department (AA)
• Map impact of Chapter 63 changes in each watershed--that is, how much previously undetained impervious has been corrected
• Map the location of ash trees
• Map the footing drain disconnect program
• Site plan project interactive map
• Transportation plan project map--detours, progress, etc.
• Transit boardings and adjacent land use
• Comparison between zoning and existing land use to find areas where rezoning is warranted.

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Week 3:
Martin Luther King Day:  no formal class.  I will be in the classroom during class/lab time if you wish to work on your project; there will be no lecture.  Also, I will be in my office earlier in the day for scheduled office hours.

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Week 4:

• Map projection:  the goal is to send each point on the globe-sphere to a unique point in the plane:  a one-to-one transformation.
• Stereographic projection--trapped in Euclidean geometry.
• The One-point Compactification Theorem (blackboard demonstration): shows that the skin of a spherical globe cannot be perfectly flattened into the plane; it fails to do so by at least one point. Thus, there can be no perfect map in the plane:  the stated goal cannot be attained.
• Open questions:  geographic maps in the non-Euclidean world.
• Map projection as a transformation:  Thompson's fish--classification schemes are not unique, either.  There is an infinite number of them (Tobler, Map Transformations of Geographic Space).
• Links to various projections.
• Link to review sheet on scale.
• Geosystems Handouts on Map Projections
• Alternative geometric classification schemes for maps:
• move the center of projection; alter the plane of capture.
• Developable surfaces:   roll a plane rectangle into a cylinder, Möbius strip, Klein bottle.  The cone.
• Infinity of others.
• Cylindrical and conical projections--choose a projection suited to need.

Mercator--conformal--well-suited as a navigation chart.  Equal area projection better-suited to showing map of the world's landmasses--when areal comparisons matter.
• More compromise projections
• Four Colors are sufficient for any map, assuming adjacent regions have a line segment in common.  Open questions:   corresponding material involving other adjacency assumptions.
• in the plane (links 01, 02, 03)
• on a sphere, as well (link 04).  An application of the one-point compactification theorem.
• on developable surfaces that result may not be the case (links 05, 06, 07, 08).
• Given that no more than four colors are ever needed to color a map in the plane, subject to the initial adjacency assumption, why should a GIS have so many choices for color?
• Thematic maps (aka, ranged fill or choropleth maps) with many ranges need a variety of colors
• Ranges need to make sense so that changes in data intensity are reflected in changes in color intensity
Four colors are sufficient to color any map in the plane; one never needs more than four.  Thus, when choosing coloring schemes, bear this fact in mind and have a rationale for color selection based on the underlying known theorem about coloring.
• Color, extrusion, and use of GIS to move from 2D to 3D.
• Load up the map sample of the downtown Ann Arbor parcel map, loaded with hypothetical height values in the underlying database (map file sent to you to put into your ifs space).
• In ArcView,
• color the parcels as "unique value" using "height" as the variable
• check to see that both the Spatial Analyst and 3D Analyst extensions are enabled
• Go to View|3DScene|Themes
• Go to Theme|3DProperties
• In the extrude section, hit the calculator button and choose "height"
• Go back to the extrude section and multiply "height"*5, to exaggerate the vertical component--or click on "vertical exaggeration" in the "properties" menu.
• Use the various buttons to move the Scene around--rotate it, zoom in and out:  left-click and drag--rotate; right-click and drag--zoom in and out;  both-click and drag--pan.
• Export to vrml and put on a website:  load Netscape plug-in to view--free from, http://www.cai.com/cosmo/html/win95nt.htm
• Here is Sandy's first attempt at this (link)--you try it with the same files--do better!!  Second attempt:  background changed to light green; position of sun in sky raised from very low to low and direction of sun changed from northeast to southwest (north is at the top as it comes up).  Look in the "properties" menu.
• and also look at a website from UM College of Engineering Virtual Reality Lab to see other projects and student projects from Eng 477 (Prof. Peter Beier).
• Real-world application--project making alternative scenarios for City of Ann Arbor regarding maximum height in the downtown.

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Week 5:

• Classification and Adjacency in thematic maps:  rationale for choosing different schemes for classification (after ArcView documentation).
• Natural Breaks (Jenk's Optimization, statistical formula that minimizes variation within each created class):  identifies breakpoints by looking for "natural" groupings and patterns in the data under consideration--ArcView Default
• Merits
• Big jumps in the data appear at class boundaries.
• Extreme values are visually obvious.
• The two merits taken together may produce a "realistic" view of the data--hence, the suitability for choosing this method of data partition as the default.
• Drawbacks
• Class intervals difficult to read
• Clear replication of results may be difficult
• Merging or mosaicking maps will produce different classifications
• Coloring may need to be adjusted so as not to give undue visual importance to extreme values
• Quantile--each class is assigned the same number of features (insofar as divisibility permits).
• Merits
• Well-suited to data that are linearly distributed--data that does not have disproportionate numbers of features with similar values ("clusters").
• Easy to explain to others how it works.
• Useful for making comparisons in relation to the partition:  to show that a commercial establishment is in the top quarter of sales of all stores in the region.
• Distinctions among intermediate values, grouped in natural breaks, may be easier in quantile.
• Drawbacks
• Features close in value to each other may lie in different classes
• Features ranging widely in value may be included within the same class
• Increasing the number of classes may help to overcome these drawbacks but that act then adds clutter to the map
• Clear replication of results is easier than with natural breaks but still problematic when merging files.
• Equal area (polygons only)--classifies polygons by finding breakpoints in the attribute values so that the total area the polygons in each class is similar.  This approach is similar in nature to the quantile approach except here each feature is given a weight in the classification equal to its area (instead of 1).
• Merits--Polygons that are largest in area are in classes by themselves
• Drawbacks--Polygons that are smallest in area are grouped in classes and distinctions among them may be difficult to make
• Equal interval--partitions the range of attribute values into equal subintervalues.
• Merits
• Familiarity:  a natural legend in terms of ease in reading (at least when the nature of the entire range of possibilities is clear, as in percentages, temperature, and so forth).
• Emphasis on ranking in relation to the partition:  to show that a store is part of a group of stores in the top quarter in sales.
• Drawbacks
• Hides variation between features with fairly similar values
• When the data range does not already make natural sense, a different classification scheme may be better.
• Standard deviation--shows extent to which attribute values differ from the mean.
• Merits--Easy to visually assess which regions have values above or below the mean for all data.
• Drawbacks--The data may skew class count and position in relation to the mean:  many high values may cause low values to be grouped in a single class below the mean and produce multiple classes above the mean, so that the mean class does not, itself, occupy a visual central position on the map.
• Normalizing data--divides each of  the attribute values by some other value.
• Merits
• Divide by the sum total of the attribute's values, so that the resulting ratios represent percentages of the total.  Enables comparison from one region to the next, using percentages of the total (region 1 contains 50% of the sales while region 2 contains a mere 17% of the sales) rather than absolute totals.
• Divide by values in another attribute:  may take into account spatial variation influencing the original attribute.  Population density, dividing total population per unit by area of the unit is a common example.
• Drawbacks
• If total count is important, then normalization of data is not appropriate.  For example it may be more important to know how many members of a minority group are present in a particular region, to trigger some funding mandate, that it is to know what the density of population is within that particular group.  In a group of 100, 35 members of a minority group may appear fairly "dense"; however, if 50 members are required as a floor for certain programs to be realized, then the density is irrelevant.
• Do not normalize data that has already been normalized:  rates, attribute per unit area, and so forth.
• Think about what you want, though, when using census tracts which are already scaled in area to include roughly equivalent numbers of population.
• Adjacency patterns:  points and regions.   The clustering of regions and regional definition.
• How are regions clustered in space?  Are similar ones next to each other or are dissimilar ones next to each other.  Consider for example some of the on-board population that comes with ArcView.  Open up the Michigan Block Group shape file.  Zoom in on southeastern Michigan.
• Go to Theme|Query;  click on the little "update values" box.  Then, double click on "black" then click on > then double click on "white". Choose "New Set".  Once the selected set comes up, go to to Theme|Convert to Shapefile.  Then, color the new shapefile with a green interior to produce this map of block groups in which quantity of black exceeds quantity of white.
• Go to Theme|Query;  click on the little "update values" box.  Then, double click on "white" then click on > then double click on "black".  Choose "New Set".  Once the selected set comes up, go to to Theme|Convert to Shapefile.  Then, color the new shapefile with a purple interior to produce this map of block groups in which quantity of white exceeds quantity of black.
• A more detailed look at the clustering process might consider separating out those block groups in which black exceeds white and has a neighboring block group in which white exceeds black--denote this situation as BW.  There are then four logical alternatives:  BW, WB, BB, and WW:  the first letter represents which variable dominates; the second letter indicates which variable dominates in neighboring blocks..  To pick out the appropriate block groups:
• BW:  make the B layer active.
In ArcView 3.2: Go to Theme|Select by Theme.  In the top pull-down, select "intersect"; in the next pull down, select the W layer.  Choose "new set".  When the selected polygons come up, convert them to a shape file and color it a deeper green.  This process will pick out the edge of the B layer that is adjacent to the W layer.
• WB:  make the W layer active.
Go to Theme|Select by Theme.  In the top pull-down, select "intersect"; in the next pull down, select the B layer.  Choose "new set".  When the selected polygons come up, convert them to a shape file and color it a deeper purple.  This process will pick out the edge of the W layer that is adjacent to the B layer.
• Look at the whole map.  Clustering of like groups is evident in the Detroit metro area--similar groups are clustered.  In Washtenaw county some dissimilar groups are clustered, some are not.  Clustering of either similar or dissimilar groups is highest in Detroit--this would fit with field evidence (often referred to as "spatial autocorrelation).  This process can be iterated indefinitely (limited by the size of the base file) and creates a sort of "contouring".
• In terms of simple conditions, each of the four conditions,  BB, BW, WB, WW would be expected, with no constraints, to occur 25% of the time.
• When all the block group outline boundaries are removed from the different colors, it is easy to look at a map of the whole state.
• Policy makers and municipal authorities may find maps such as these useful.
• Academic research may modify how to interpret what the adjacencies mean and what sort of quantitative significance to assign to them; it may also consider definitional matters, such as how polygons are adjacent--at corners only, at edges only, or at corners and edges; or, it might also consider how such variables might be made more meaningful through normalization.  Chess analogies are often-used jargon to describe relative adjacency pattern.  The subject of "spatial statistics" delves more deeply into different measures for clustering and for levels of significance once clustering is found (a different course from this one).
• Animated maps based on GIS maps showing clustering of regions:  Link 1; Link 2
• "Mapplets" offer an opportunity to find critical points

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Week 6:

• CAVE demo...starts promptly at 5:30--you will need to remove shoes.  Visit to University of Michigan virtual reality immersion CAVE.  The files are similar to the ones you created in 3D Analyst in ArcView (using the Ann Arbor parcel map of downtown).
• How can you tell the inside from the outside
• Jordan Curve Theorem; implications for mapping.
• Page 1.
• Page 2.(keep a fixed direction)
• Page 3.
• The Jordan Curve Theorem:
• permits correct assignment of addresses on either side of streets--suppose that the path is composed of two squares touching at a point.  When the path is separated into two squares, a consistent assignment procedure for addressing may be given.  If the squares were not split apart, then the set of addresses would be misallocated, at least in part.  One must have the Jordan Curve Theorem built into the software if geocoding is to work on matched addresses.
• permits visually appropriate coloring of polygons
• illustrates the need to split complex curves apart at nodes where the curve crosses itself in order to ensure that the two properties above will hold on maps.  This fact is important in digitizing (and elsewhere).
• Defining regions in the absence of regional information:  making something from nothing?  (All three cases below use ESRI software, ArcView3.2 with Spatial Analyst Extension, 3D Analyst Extension; also Animal Movement extension from the Alaska Biological Center, and Xtools from Oregon Forestry).
• Spider diagrams:  one way of defining regions in the absence of regional information.
• Thiessen polygons:  another way of defining regions in the absence of regional information
• Contours:  Partitioning the plane in various ways (level curves of a surface--but hard to find--requires knowing an equation for the volume).
• Other ways to make maps come alive:  animation--brings temporal and spatial elements together.  Use of Adobe ImageReady.
• Work on projects.

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Week 7:

• Developing world application
• Use of transparency in making multiple layers in maps show on the web
• Create a map with two layers.  In the top layer, make a pattern and a transparent background.
• Put the map in Layout--adjust the units first so the scale is correct
• To get rid of the grid points:
• In the view window, change the background color to white
• In the layout window, hide the remaining outer grid points.
• Save it in .jpeg format:  File|Export|jpeg
• Then, load the .jpeg onto a web page; notice that only one layer shows.
• Instead, use the alt+prntscreen approach and save the map in PhotoShop--then both layers show.
• Use of DreamWeaver (by MacroMedia) on web pages
• clickable maps
• mouse-over text
• Use of extensions (if loaded in the load set).  Projector!; Animal Movement; XTools; EdTools; or others.

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Spring Break--SA will be available by e-mail throughout much of the break.  Presentations will be the Monday after spring break.  There are 12 students, and four hours of class time.  Thus, each student has 20 minutes in which to present material to the class.

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Week 8: Midterm Presentations--Party afterwards at Sandy's home.

• Dan Shoup
• Howard Tsai
• Angkana Chairatananon
• Luci Kim
• Hyoung Bae Park
• Chris Eckman
• Katie Kozarek
• Wayne Buente
• Zeb Acuff
• Pat Sloan
• Simon VanLeeuwen
• Chuck Hahn

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Week 9:  MOVING THINGS AROUND...

• Diffusion of an innovation:  one approach to moving things around at the theoretical level.  Numbers can create spatial pattern.  Consider the following locational model of Hagerstrand.  In an urban planning context, these ideas might parallel
• spatial infill and extent of urbanization (limit to sprawl).
• possibility of introducing new ordinance material--leapfrogging effect (reference to creeksheds material).
• Extensions to ArcView:  several approaches to moving things around at the practical level.
• Projector!  reprojects maps (not datums)
• EdTools permits translation and rotation of shape files:  slide shape files around in the plane.
• Register.avx permits image registration to shape files.
• Animal Movement extension offers a variety of tools for tracking movement in the plane.
• E-mailed information:
• i-maps
• search engine

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Week 10:  Spatial Hierarchy and Fractal Transformations.

• Fractals and Space Filling--Fractals and the Periphery:  an Application
• Fractals and Centrality:  an Application, Part I.

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Week 11:

• Fractals and Centrality:  an Application, Part II.
• Looking inside a single point, representing an urban area, can be as complex as looking at a broad system!

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Week 12:
Continuation...

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Week 13:
Finish up...
Review of where we have been.

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Week 14: Final Presentations Dan Shoup
Howard Tsai
Angkana Chairatananon
Luci Kim
Hyoung Bae Park
Chris Eckman
Katie Kozarek
Wayne Buente
Zeb Acuff
Pat Sloan
Simon VanLeeuwen
Chuck Hahn

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Week 15:  Final project websites due.

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copyright, S. Arlinghaus.