Instructor will keep you posted on City Planning Commission meetings.
Syllabus--go over webpage
Latitude and Longitude
to brief explanatory material
One page biosketch
General and particular interests
Software use background
Do you already have a webpage and if so, what software do you like to
Anything else you would like to share
Discussion; project ideas
Your own project
City of Ann Arbor
Creating a more interactive CIP presence on our webpage, including
maps & projects interrelated.
Help Planning create a more integrated approach to putting development
petition info on our webpage (creating links, adding staff reports,
creating a map to show where active petition-related property is
Conduct a land use & zoning analysis in the Montgomery Wellfield
to supplement other work underway.
Helping catalog data in the GIS and indicate directions for use.
Building Department--Historic Preservation--continue with an inventory
Environmental Coordinator--continue with work
already begun on phosphorus.
I-maps improve communication giving residents direct access to
Cell towers--location map (use GPS)--look at study
from Simon Fraser--locational criteria for new towers/antennae and
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.
Media Union field trip: starts at 5:45p.m. from our
See the various facilities available to you. Later we will see
CAVE. Today we see what all is available to use.
to article about them: these polygons serve as a limiting
for constructing nested circular buffers.
Demonstration of ArcView to calculate these polygons on an arbitrary
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
to shape files.
Modify them by using a rectangle
Modify using convex hull of distribution--draw it if Animal Movement
Range is not loaded. Clip, if desired.
Set distance--use the polygons to estimate maximum buffer radius for
Maps brought in on CD: some in response to student request--Ann
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
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
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;
will be no lecture. Also, I will be in my office earlier in the
for scheduled office hours.
Map projection: the goal is to
each point on the globe-sphere to a unique point in the plane: a
Stereographic projection--trapped in
The One-point Compactification Theorem
demonstration): shows that the skin of a spherical globe cannot be
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
Open questions: geographic maps in
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
Four Colors are sufficient for any map,
adjacent regions have a line segment in common. Open
corresponding material involving other adjacency assumptions.
in the plane
on a sphere, as well (link 04).
An application of the one-point compactification theorem.
on developable surfaces that result may not
case (links 05,
Given that no more than four colors are ever needed to color a map in
plane, subject to the initial adjacency assumption, why should a GIS
so many choices for color?
Four colors are sufficient to color any map in the plane; one never
more than four. Thus, when choosing coloring schemes, bear this
in mind and have a rationale for color selection based on the
known theorem about coloring.
Thematic maps (aka, ranged fill or choropleth maps) with many ranges
a variety of colors
Ranges need to make sense so that changes in data intensity are
in changes in color intensity
- 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
hypothetical height values in the underlying database (map file sent to
you to put into your ifs space).
color the parcels as "unique value" using "height" as the variable
check to see that both the Spatial Analyst and 3D Analyst extensions
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
vertical component--or click on "vertical exaggeration" in the
Use the various buttons to move the Scene around--rotate it, zoom in
out: left-click and drag--rotate; right-click and drag--zoom in
out; both-click and drag--pan.
Export to vrml and put on a website: load Netscape plug-in to
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
sky raised from very low to low and direction of sun changed from
to southwest (north is at the top as it comes up). Look in the
and also look at a website
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
Ann Arbor regarding maximum height in the downtown.
Classification and Adjacency in thematic maps: rationale for
different schemes for classification (after ArcView documentation).
Natural Breaks (Jenk's Optimization, statistical formula that minimizes
variation within each created class): identifies breakpoints by
for "natural" groupings and patterns in the data under
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
the suitability for choosing this method of data partition as the
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
to extreme values
Quantile--each class is assigned the same number of features (insofar
Well-suited to data that are linearly distributed--data that does not
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
that a commercial establishment is in the top quarter of sales of all
in the region.
Distinctions among intermediate values, grouped in natural breaks, may
be easier in quantile.
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
that act then adds clutter to the map
Clear replication of results is easier than with natural breaks but
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
is similar. This approach is similar in nature to the quantile
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
distinctions among them may be difficult to make
Equal interval--partitions the range of attribute values into equal
Familiarity: a natural legend in terms of ease in reading (at
when the nature of the entire range of possibilities is clear, as in
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.
Hides variation between features with fairly similar values
When the data range does not already make natural sense, a different
scheme may be better.
Standard deviation--shows extent to which attribute values differ from
Merits--Easy to visually assess which regions have values above or
the mean for all data.
Drawbacks--The data may skew class count and position in relation to
mean: many high values may cause low values to be grouped in a
class below the mean and produce multiple classes above the mean, so
the mean class does not, itself, occupy a visual central position on
Normalizing data--divides each of the attribute values by some
Divide by the sum total of the attribute's values, so that the
ratios represent percentages of the total. Enables comparison
one region to the next, using percentages of the total (region 1
50% of the sales while region 2 contains a mere 17% of the sales)
than absolute totals.
Divide by values in another attribute: may take into account
variation influencing the original attribute. Population density,
dividing total population per unit by area of the unit is a common
If total count is important, then normalization of data is not
For example it may be more important to know how many members of a
group are present in a particular region, to trigger some funding
that it is to know what the density of population is within that
group. In a group of 100, 35 members of a minority group may
fairly "dense"; however, if 50 members are required as a floor for
programs to be realized, then the density is irrelevant.
Do not normalize data that has already been normalized: rates,
per unit area, and so forth.
Think about what you want, though, when using census tracts which are
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
some of the on-board population that comes with ArcView. Open up
the Michigan Block Group shape file. Zoom in on southeastern
A more detailed look at the clustering process might consider
out those block groups in which black exceeds white and has a
block group in which white exceeds black--denote this situation as
There are then four logical alternatives: BW, WB, BB, and
the first letter represents which variable dominates; the second letter
indicates which variable dominates in neighboring blocks.. To
out the appropriate block groups:
make the B layer active.
In ArcView 3.2: Go to Theme|Select by Theme. In the top
in the next pull down, select the W layer. Choose "new
When the selected polygons come up, convert them to a shape file and
it a deeper green. This process will pick out the edge of the B
that is adjacent to the W layer.
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
When the selected polygons come up, convert them to a shape file and
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
area--similar groups are clustered. In Washtenaw county some
groups are clustered, some are not. Clustering of either similar
or dissimilar groups is highest in Detroit--this would fit with field
(often referred to as "spatial autocorrelation). This process can
be iterated indefinitely (limited by the size of the base file) and
a sort of "contouring".
In terms of simple conditions, each of the four conditions, BB,
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
colors, it is easy to look at a map
of the whole state.
Policy makers and municipal authorities may find maps such as these
Academic research may modify how to interpret what the adjacencies mean
and what sort of quantitative significance to assign to them; it may
consider definitional matters, such as how polygons are adjacent--at
only, at edges only, or at corners and edges; or, it might also
how such variables might be made more meaningful through
Chess analogies are often-used jargon to describe relative adjacency
The subject of "spatial statistics" delves more deeply into different
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
offer an opportunity to find critical points
CAVE demo...starts promptly at 5:30--you will
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
The Jordan Curve Theorem:
permits correct assignment of addresses
side of streets--suppose that the path is composed of two squares
at a point. When the path is separated into two squares, a
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
the software if geocoding is to work on matched addresses.
permits visually appropriate coloring of
illustrates the need to split complex
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
Defining regions in the absence of regional information: making
from nothing? (All three cases below use ESRI software,
with Spatial Analyst Extension, 3D Analyst Extension; also Animal
extension from the Alaska Biological Center, and Xtools from Oregon
one way of defining regions in the absence of regional information.
polygons: another way of defining regions in the absence of
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
spatial elements together. Use of Adobe ImageReady.
Work on projects.
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
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
both layers show.
Use of DreamWeaver (by MacroMedia) on web pages
Use of extensions (if loaded in the load set). Projector!; Animal
Movement; XTools; EdTools; or others.
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
has 20 minutes in which to present material to the class.
Week 8: Midterm Presentations--Party afterwards at
Hyoung Bae Park
Week 9: MOVING THINGS AROUND...
Diffusion of an innovation: one approach to moving things around
at the theoretical level. Numbers can create spatial
Consider the following locational
model of Hagerstrand. In an urban planning context, these
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
files around in the plane.
Register.avx permits image registration to shape files.
Movement extension offers a variety of tools for tracking movement
in the plane.
Week 10: Spatial Hierarchy and Fractal
Review of where we have been.
Week 14: Final Presentations
Hyoung Bae Park
Week 15: Final project websites due.
copyright, S. Arlinghaus.