…<click here>
to review the Report
Writing Tips
Part 2, Email Dialog 2010 covering
discussions for weeks 6 through 10
<click
here> for Part 1, Email Dialog 2010 covering discussions for
weeks 1 through 5
___________________________
3/11/10
Jeremy—good
to hear from you …responses embedded below.
Joe
Dr. Berry--Quick question regarding Exercise 9
question 6. Regards, Jeremy
Discuss any relationships you
detect among the three maps. (Aside: don’t do the Composite analysis to
determine how separable the clusters are).
The cluster algorithm compares data values
between maps at the same location and depending on the number of cluster
categories specified, calculates data distances and places data locations into
the aforementioned number of categories. …you might mention something about the “iterative
nature” of the algorithm and that it attempts to minimize the
intra-cluster (within each cluster) data distances and maximize the
inter-cluster (among the different clusters) data distances.
In the simple 2 category example the categories
specify locations of similar and dissimilar data values. As can be seen
these areas are approximately evenly distributed over the map with similar
regions (green) of approximately the same size as dissimilar locations (red).
Is
something like the above what is required with additional mention of 3 and 4
clusters or should more mention of ISODATA classification be added? Is this what the question is asking?
___________________________
3/10/10
Elizabeth—sorry for the delayed response …meetings. meetings,
meetings. Responses embedded below. Joe
Hello Joe-- A couple questions for you on exercise 9.
Thanks! Elizabeth
1. For question 2, the coincidence summary map ends up
with discrete data in classes 1-9. I haven't figured out yet how to know
which intersections are producing which resulting numbers, since it is not the
2-digit code we have seen before.
The classes represent a binary progression (1, 2, 4 for 1997
yield classes and 8, 16, 32 for 1998 yield classes). The addition of a
binary progression of numbers results in a unique sum. For example, the
only way you can get a sum of 9 is by the coincidence of class 1 in 1997 and
class 1 in 1998.
2. Also, for curiosity's sake, why are the
values for the 1998 yield classes map 8, 16, and 32? And the highest
value for the 1997 yield class map a 4 instead of a 3? Does the pattern
give an advantage in the results?
…yep—the values form a binary progression. I purposely set
the numbers up as a binary progression so some of you would take the subtle
hint (extension of the question) to use “Compute plus” as an alternate way to
form the coincidence map. Or others might renumber to 1, 2, 3 for both
maps, then “Compute times 10 plus” to form the coincidence map.
3. I ran a cross-tab query to get the
cell counts since I hadn't yet figured the relation of 1-9 to categories of
intersections, described above. In the results, shown on the table
below, the total cell count did not matching up between the two layers and it
seems it should. So, I went back and looked at cell counts for each of
the two input map layers, and found the strangeness of the attached screen
shot---the 1997_Yield_Classes map shows blue cells on it for the higher than 1
standard deviation, but when looking at the it says the count is 0. Why
would that be? If the count for that value is truly 0, then there should
be no blue specks on the map.
4. I'm not sure if my discrepancy in the count of the cells matching up in the table. I'm also attaching the results of the cross-tab query so you can see if I interpreted it correctly. It does seem like there should be 3289 cells (both input maps had that value).
For those who followed the “Intersect completely” approach the
resulting values are automatically assigned a sequential number to all value
pairs between the two reclassified yield maps—1,8= 1 (low, low); 1,16= 2; 1,32= 3;
2,8 = 4; 2,16 = 5; 2,32= 6; 4,8= 7; 4,16= 8;
4,32= 9 (high, high).
This is the coincidence table statistics I got—
___________________________
3/9/10
Hi Dr. Berry-- I have a question
about Exercise 9. Why in Question #7 after we use the Scan function, does
it say to make 34 user defined ranges starting from 0? This would means
that a value of 35 is not labeled in this map.
Why is that?
I also am having a hard time with
question 8 and 9 because I have not had a statistics course yet, so I am having
a hard time with residuals, R-squared, and regression. Would you mind
taking a look at my answers and seeing if I am on the right track? Also
is there some time Thursday that we could met to go over this. Thank you, Luke
Luke—you are right, my
mistake. The “Number of ranges” needs to be 35 …coupled
with a lot of patient number entry to get from 0-1 (light grey) to 34-35
(green) with a yellow color inflection point at 16-17 (see Shading Manager below).
As to your concern about
regression, residuals and r-squared in predictive spatial statistics, the
following links…
-
http://www.innovativegis.com/basis/MapAnalysis/Topic10/Topic10.htm#Map_correlation,
topics “Use Scatterplots to Understand Map
Correlation” and “Can Predictable Maps
Work for You?”
-
Slides #34
through #39 that we didn’t get to during last week’s class (Wk9_lec.ppt)
and
-
Online regression
short primer at http://www.epa.gov/bioindicators/statprimer/regression.html
…might help in your
understanding of predictive spatial statistics using regression, residuals and
r-squared. Please review these materials before we get together on
Thursday. Joe
___________________________
3/7/10
Professor Berry-- I am currently
working on question #5 of this week’s lab. My question regards the calculation of
similarity values. I feel that I mostly understand the procedure, but I am not
sure about the actual calculation.
In the question, we are using
values of P and K content in the soil at a certain grid cell location as
comparison values. When the final Similarity Value is calculated, does the
algorithm assign an equal weight to a the similarity between P values
(comparison and location values) and K values? In other words, is the final
Similarity Value calculated based on 50% of the K similarity and 50% of the P
similarity? For example if a value is 100% similar concerning K and 0% similar
concerning P, will the final Similarity Value be 50% or not?
Also, how is the cutoff for a
value to have 0% similarity determined; I would have thought that it would have
been if there was absolutely NO P or K in the soil, but the table in Question 5
suggests otherwise. Any help would be
great as I work on the response. Curtis
Curtis—it
sounds like you are comfortable with the concept of “data distance” the
multi-dimensional distance between two data patterns in numeric space. If not, checkout …http://www.innovativegis.com/basis/MapAnalysis/Topic16/Topic16.htm
… where Topic “Geographic Software Removes Guesswork from Map Similarity” describes the
similarity index calculation.
In
short, the procedure calculates the data distance between the data patterns
at every map location in a project area.
The maximum data distance becomes the reference for 0 (percent
similar) indicating the least similar location.
The calculation for all other locations is (1 – mapValue /
(maxDdistance) * 100 to express as a percent similar. In the case of the location with the maximum
data distance, the calculation becomes (1 – maxDdistance / (maxDdistance) * 100
= 0%. For the comparison location
itself, the calculation becomes (1 – 0 / (maxDdistance) * 100 = 100%
identifying a data pattern exactly the same as the comparison point. All of the other data distances are similarly
processed to yield a similarity scale from 0 (least similar) to 100 (most
similar). Joe
___________________________
3/7/10
Hey Joe-- quick question on question 1 in Exercise
9. Since the range of P is from 0-102
and the range of K is from 88-310, using the same legend does not seem
appropriate. When you say, the same legend do you mean the same
number of ranges and the same color pattern, and not necessarily the same map
value breaks within the ranges? Thanks,
Jason
Jason— good
question. By now in the course it should
be readily apparent that the default displays are not “best”. The question then is to determine the best
surface map display strategy for the two maps viewed side-by-side.
The map
surfaces characterize two different nutrient concentrations for a given field
…sort of apples and oranges (related but different mapped phenomena). This perspective would suggest that they
should be displayed with a similar interval step but both colored ramped to
indicate 0 though their different maximum concentrations (Option 1 below).
But the two
maps are viewed as crop nutrients and have the same base unit (ppM) but just
differ in their ranges …sort of orange and tangerine (same mapped phenomena of
nutrient concentration). This
perspective would suggest that they should be displayed with the same interval
step and color ramp indicating 0 though the maximum concentration (Option 2
below).
Your must
decide on the “best” display strategy for both maps (or you can introduce another
display strategy …maybe utilizing 3-D?) and explain your reasoning of your
choice in terms of the nature of the data and cartographic sensitivities. The good part is that there isn’t a
right/wrong to the question …just degrees of better reasoning. Joe
Option 1 – both are in steps of 20 ppm and color
ramped from 0 (green) to their individual Maximum value (red) with a yellow
inflection at mid-range.
Option 2 – both are in steps of 20 ppm and color
ramped from 0 (green) to the highest Maximum value (red) with a yellow
inflection at mid-range.
___________________________
3/6/10
Professor Berry-- In question #3
of this week's lab, the last part asks us to “Briefly describe the statistical
summary in the Percent_Difference Map’s Shading Manager. Be sure to investigate the Statistics and
Histogram tabs.”
Because there are some values
that are very extreme positive percent change values, the histogram shows that
the majority of values are near 0% plus or minus change. Is it possible to
change the display of the histogram so that I can explain it better? Thank you, Curtis
Curtis—you
have hit on the “nugget” of question …the default display (shading Manager
Summary) is not useful because of the extreme outliners. The Statistics
tab shows—
You can
get a better Histogram display by selecting MapSetà New Graphà Histogram and choosing the Percent_Difference map…
A quick
and dirty way to get rid of the outliers would be to enter “RENUMBER
Percent_Difference ASSIGNING 140 TO 140 THRU 5000 ASSIGNING -50 TO -100 THRU -50 FOR Percent_Difference_noOutliers”…
Since
this is “Map Analysis” one can’t just “throw away” outliers (assign the values
to Null or No Data), as that would leave holes in the map display, so the
generally accepted method is to “cap” the upper and lower tails …-50 and
150% in this case. Since N is so big,
“capping” instead of “disregarding” outlier data is thought to be OK. Joe
___________________________
3/4/10
Luke--
responses embedded below. Joe
Hi Dr. Berry-- I have three
questions about GIS modeling:
1) I guess I am a geek at heart. I have a modeling question that I wanted to
see how to solve using MapCalc. How
would one make a model that looks at which way is faster driving to Durango, CO
from Du or driving from DU to the airport then waiting on line and waiting
though security and then finally flying to Durango, CO? One would also have to calculate driving from
Durango airport to the end location because offend the airport itself is not
the end location. How would one make a
model using MapCalc to look at which way is faster? Also how could you make the model so that
there was not only one start location?
For instance what if instead of starting at DU one started in Conifer
how would that effect the model. What
commands would be needed to make this model in MapCalc? I hope this makes sense.
MapCalc’s
Spread operation (Costdistance in
ESRI) is the key. You would need to 1)
convert a vector road map to grid (PolyGrid in ESRI); 3) use Renumber
(Reclassify in ESRI) to assign friction values (typical time to cross a grid
cell of each road type) to the different road types; 3) create a “starter” map
for DIA; Spread starter thru Friction for DIA travel-time surface that contains
the estimated driving time to the airport.
Repeat for the Durango airport.
Identify a Begin and an End location (any road location) on the
travel-time surfaces estimates the time to and from the airport, then add a
time estimate for parking, shuttle time, check-in, security, train, gate wait,
flight time and any other times you can think of after leaving the car.
The
accumulated surface contains the estimated travel-time from any location in
your departing (e.g., DU to DIA) and destination (e.g., Durango home)
locations. You calculate this surface
“once and use many” as different departure and destination addresses are
entered (geo-coding to convert street address to Lat/Lon coordinates in
ESRI). The information provided in the
grid approach is radically different from vector’s Network Analysis. It has the major advantages of 1) generating
a continuous solution from a location to everywhere instead of a point-to-point
solution that has to be calculated for every departing and destination address,
2) can include off-road travel, and 3) less demanding of input data (no
requirement for defining “turntable” specifications for every
intersection). On the other hand,
vector-based Network Analysis 1) responds to the very real one-way streets,
left turn delays, etc. and 2) provides navigation information (take this
street, then turn left, etc.).
The
bottom line is that vector’s Network Analysis is best for point-to-point navigation and raster’s
Accumulation Surface Analysis is better for large data sets, such as modeling LA basin retail store
travel-time for with thousands and thousands of customers and their
purchases. Remember “raster is faster
but vector is corrector.”
2) In the reading it states that typically in
kriging the values are brought down below IDW. What is the math behind
this? I know the kriging uses the
statistics of surrounding values, but how does that pull down the values? I also have read Introduction to GIS by Chang
and I am still unclear about why this is.
Do you have a math table to explain the numbers better?
Krig’s
approach can generate interpolated estimates that are outside the range (min to
max) of original sample data set. This
usually happens in 1) the “extrapolation” for areas outside the extent of the
data where it is carrying the localized trend of the data, or 2) in areas where
data is rapidly changing (e.g., a peak to a valley back to a peak Krig will
over-shoot the valley). I do not have a
data table that explains the effect as that requires “human calculation” of a
lot of ugly equations. However, the
effect the effect can be seen in slide #28, week 8 PowerPoint where the minimum
value is -1 (impossible for the data).
3) For question four in this week’s lab can we
combine a post map with the IDW and kriging map? I think in order to do this one need a file
called sample3.dat which is not in the file list. Does this mean that we cannot do this? Visually it would have made the maps easier
to look at because one would know the values of the valleys and peaks.
Someone
in your group must have found the Sample3.dat file or you couldn’t have created
the surfaces in the exercise. In part 2,
“…then
select the SAMPLE3.dat file in
the \Samples folder.”
Thank you, Luke
___________________________
3/3/10
Joe-- I was trying to figure out what the cell size is
in Surfer. I was asking help and read a bit about cell properties, but I
still haven't stumbled across the cell size. Could you point me in the
right direction? Thanks! Elizabeth
Elizabeth—
you have hit on a really knurly question.
“Cell size” is determined by “Grid Line Geometry” during gridding—
Grid
line geometry defines the grid limits and grid density. Grid limits are the
minimum and maximum X and Y coordinates for the grid. Grid density is usually defined by the number
of columns and rows in the grid. The # of Lines in the X Direction is the number of grid
columns, and the # of Lines in
the Y Direction is the number
of grid rows. By defining the grid
limits and the number of rows and columns, the Spacing values are automatically determined as the distance in
data units between adjacent rows and adjacent columns.
From the
Grid Line Geometry fields in the Grid dialog box for the Sample3 data set…
…it appears
that the XY coordinates likely extend from 0 to 50 in both directions (just no
samples at the borders of the project area).
But the process uses the min/max in the sample data set to set the cell
size, so the values are slightly different in the two planer directions. This might work for simply making a good plot
of the data but hardly sufficient for consistent grid-based “analysis frame”
which needs to be the same distance in both the X and the Y directions for the
analytical function to work …a basic assumption of grid-based map analysis.
The bottom
line is that the default Grid Line Geometry does not generate true
raster/grid data. To be true grid
data the analysis frame should be configured as 100 columns by 100 rows with a
cell size (spacing) at 0.5 in both the X and the Y directions. If my assumptions are correct, the user needs
to “force” the X/Y min and max to 0 and 50, respectively, and set the spacing
to 0.5 to form a true analysis frame that can be used in grid-based map
analysis. As for the X,Y units (Z for
that matter), I don’t know …I haven’t found where that metadata is found in
Surfer.
Joe
___________________________
2/28/10
Exercise 8, Question 4. What is the maximum,
minimum and average difference between the two interpolated surfaces? Be sure your answer discusses the
interpretation of the “sign” and “magnitude” of the differences.
I am having trouble answering
this question because I do not understand what the units are on the Z-Values.
The class discussion lead me to believe that it was not possible to have such a
large difference (Almost 2000) between the two models. Could you please clarify
how to interpret the values in this table?
Thanks, Curtis
Curtis—actually
I don’t know the nature of the data either …knowing Surfer it is likely
environmental data like parts per million of lead in the soil. However, numbers are numbers and the computer
never knows the “real-world” legacy of the numbers it analyzes (seems fair that
we should know either). The descriptive
statistics for the “Sample3, Z1 column” data that you interpolated using IDW
and Krig DEFAULT SETTINGS
are…
…indicating
that the range of the sample data is Min= 9.3 and Max= 5277.7. And from the difference surface statistics
for IDW – Krig you had attached to your email…
Hopefully
these ramblings stir some thinking.
Class discussion suggested that usually aren’t large differences (which
the preponderance of tan tones suggests) but in this case that “blue plateau”
seems to suggest differently. Your
charge is to suggest some possible reasons based on the sample data pattern and
differences in the spatial interpolation approaches of IDW and Krig.
Joe
___________________________
2/26/10
Dr Berry-- A couple questions regarding Question 2 and
5. Regards, Jeremy.
What is the
visual effect of perceived precision/accuracy by decreasing the contour
interval from 5 to 10?
Question 2. Decreasing the contour interval from 5
to 10 feet produces contours with greater distance between them. The
contour lines describe elevation points. Any point on the line given an
elevation value of that line. A decrease in contour intervals
decreases the precision and accuracy of the contour map. A point’s elevation
not on a contour line will be estimated from the nearest contour line.
The greater the contour interval the greater the distance between contour
lines. The greater the contour interval the lower the accuracy and
precision of the estimation of the elevation point on the map surface. Are both accuracy and precision
lowered or does there need to be a distinction between the two?
Keep in mind that “Precision” refers to the correct positioning of
spatial information (contour bands, in this case) and “Accuracy” refers to the
correct classification of spatial information (contour interval ranges). In “noodle-ing” this question, also keep in
mind that a contour map is a discrete map representation of a continuous
surface and not a traditional polygon map of property ownership or cover
type parcels.
Is the actual
“level of detail” of the elevation data in the display increased in a contour
map with more intervals?
The actual detail of the elevation data is
unchanged. The estimation of points of elevation on the surface map is
increased with increased contour internals. Elevation locations are more
accurately estimated with increased contour intervals. True?
I believe you are
on the right tract …the elevation data is unchanged, just the visual rendering
of the continuous surface as discrete map features (contour bands) is
changed. Merge this thought with your
“noodle-ing” above and I think it might help you formulate your responses to
both questions.
What defines
the actual “spatial resolution” contained in any map surface (data)?
The actual spatial resolution is determined by the
density of the spatial distribution of the elevation data points. The greater the density of the
elevation points over the map surface the higher the spatial resolution of the
map surface. Accurate?
However, the elevation data is a grid-based representation (DEM) of
a continuous surface, not a set of points.
So what characteristic determines the spatial resolution of grid
data? If you want to extend the
discussion a bit you might see if you can figure out the actual resolution for
this data set—requires some snooping about with Surfer to locate the metric.
Question
5. I wish to
create a difference map of Average minus IDW or Kriging interpolation.
How do you create an average map? I don't see this option
in the Grid Methods provided.
Not an “Average map” …just identify the “maximum, minimum and average difference statistics (three scalar numbers) between the two interpolated surfaces you just created. These are “descriptive statistics”
summarizing the map values comprising the Difference Surface; not a new map
layer.
Also how do you add a legend to the difference map?
___________________________
2/24/10
Dr. Berry-- It seems as though you have two versions
of Surfer out there. Surfer 8 and 9. Surfer 9 demo version from the website
does not have the full functionality that 8 has. Could you confirm this before
I waste another evening trying to create an Overlay Map. Regards, Jeremy.
Jeremy—I didn’t know they had released a version 9 …must be
their new upgrade. I recommend working with version 8 as
that is what the exercise write-up anticipates. Version 8 is on the Map
Analysis book CD, in the class folder in the GIS lab, and available for
download from my website—
s8demo.exe
—
click on this link and select Open. Follow the onscreen installation
instructions. It is recommended that you accept the default
specifications as the exercise write-ups assume this installation location.
Joe
___________________________
2/22/10
Dr. Berry-- is there an exercise 7? Regards, Jeremy
Jeremy—no Exercise 7 due this Thursday.
In the original materials I referred to Exercise #7, as following exercise #6 (mini-project). Since there isn’t an exercise for week 7, the new naming scheme referees to the last two exercises as Exercise #8 (week 8) and Exercise #9 (week 9). The exercise number now refers to the week the material is introduced …not the sequence order; I apologize for the confusion.
The next Exercise #8 will cover the material presented in this week’s class (week 8) on Spatial Interpolation considerations and is due the following class. You will form your own 1-3 member teams.
Or you can do a short paper (4-8 pages) on a GIS Modeling related topic of your choosing in place of Exercise 8 or/and Exercise 9. For an even more extended experience, you could design you own mini-project. For example, you might be interested in developing a GIS Modeling exercise for Junior High or High School students. In this case, the week 8 “special project” (Part 1) would be to develop the plan and sketch-out what needs to be done; week 9 “special project” (Part 2) would be to complete the exercises(s) and create a self-contained CD with all the materials needed for teachers. Make me an “offer I can’t refuse” about what you want to do.
However, just for fun (we’re still having fun, right?) Exercise 8 for this week uses Surfer software to investigate concepts and practice in surface modeling (spatial interpolation)—what could possibly be more fun. Surfer is installed in the GIS lab, or you can download and install the Surfer software on your own computer from the book CD, from class website, from the class materials in the GIS Modeling course folder or by downloading directly from Golden Software’s site …http://www.goldensoftware.com/demo.shtml.
Joe
___________________________
2/17/10
Folks—some of are
“over-driving” the purpose of a Prototype Model—to demonstrate a viable
approach and stimulate discussion. It is important to “keep it
simple stupid (KISS) to insure clients focus on model approach and logic”
during the early discussion phase of a project.
Anticipated refinements are
reserved for the “Further Considerations” section ...discussion only of additional
enhancements at this stage, not implementation.
If model refinement
simultaneously accompanies prototype development, there isn’t a need for a
prototype. But that is the bane of a “waterfall approach” to modeling
…you can easily drown by jumping off edge at the onset; whereas calmly walking
into the pool with your client (baby steps) engages and involves them, as well
as presenting a manageable first cut of the approach and logic for
discussion.
This type of thinking is the foundation of the “Agile” project management approach that is sweeping the software development and business worlds (http://en.wikipedia.org/wiki/Agile_software_development) …baby steps with a client, not top-down GIS’er solutions out-of-the-box. --Joe
___________________________
2/16/10
Jeremy—I am not sure what might cause the problem …maybe there
is some preference setting that goes for the auto-resize. I mostly use
Office 2007 on both XP and Vista machines and don’t have the problem. You
might try “forcing” fixed dimensions for the table and cells—
What is the optimal size of the MapCalc window to
convey the map images appropriately? I don’t think there is
an “optimal” size window BUT BE SURE it is consistent when
grabbing a series of screen shots. MapCalc will resize to the window, so
if it is not consistent the aspect ratios of the maps won’t be consistent. I suppose the best thought is to always
maximize the MapCalc window to insure consistency (and highest resolution
captures). It is best to not use the “Restore down” window and “dink”
with its sizing (might be hard to do if you are constantly moving to different
computers). MapCalc keeps the display of
the grid cells “square” but attempts to maximize the screen “real estate”
hence you see different display relationships among the map, title and legend—
a screen display, not a stable paper map.
I personally think the one of the right looks more
appropriate. I disagree.
The Tutor25.rgs analysis frame is 25 columns x 25 rows so it is a square, not a
vertically elongated rectangle. You must be used to topographic sheets
that have elongated borders, but the UTM and Lat/Lon coordinates do their best
to be square. So in grid terms, each cell is a “square” and the grid
lines have to be displayed that way (except for 3D perspective of
course). --Joe
Dr. Berry-- I have a question regarding copying images from
MapCalc into tables.
With the aspect ratio turned on they look
different, and they are not 2.5x2.5. With the aspect ratio turned off they are
2.5x2.5 but are "distorted". I personally think the one of the right
looks more appropriate.
Regards. Jeremy
___________________________
2/15/10
Folks—I
have received some emails about solutions to the “word problems” in Question 5
of Exercise #4. As mentioned in class, I purposely ordered the questions
from easiest to progressively harder. However, some of you correctly
noted that the “hardest part” was interpreting what a question
was asking.
First,
as a general rule in solving word problems you need to state any
“assumptions” you make in its solution. With MapCalc and Surfer it is
“fair” to assume that the question intends to use system defaults, unless there
is a specific note in the question to use a different option. Don’t
over-reach a question by cluttering it with a lot of “yes, but I could…”
variations—like Luke Skywalker, go with your force, but state any assumptions.
A
particularly useful tool that most you probably learned in grade school is to
use a “parsing” outline. This means to break the question into a set of
specific tasks required, and then order the tasks into a logical sequence of
steps leading to the solution.
Generally
speaking, most GIS modeling problems are a good fit for an ordered “Parsing
Outline” as the solutions are usually linear with few logical jumps (rarely are
there “Do While” loops or “If <this>, Then <that>” jumps in
modeling logic) ...the models are more like a recipe for Banana Bread—do this,
then this, then this and bake until brown. (Aside: don’t
confuse this statement with programmer’s coding of algorithms for the basic
map analysis tools …lots and lots of loops, tests and stacks).
Below
are the solutions for all five word problems with discussion that ought
to help in understanding the spatial reasoning behind their solutions. (Aside: hence the
Optional Question 4-1 about completing the other two is off the table, but send
me an email and I will substitute a couple of new ones …I have a million of
them).
Joe
________________________________________
Exercise #4 – Question 5
·
Q5-1) Using
the Tutor25.rgs database, determine the average visual exposure
(Vexposure map from question #4) for each of the administrative districts
(Districts map). Screen grab the important map(s) and briefly describe
your solution as a narrative flowchart.
Parsing the question into
processing considerations:
1) Visual
exposure— derived in Question 4 by
ANALYZE Ve_roads_sliced with Ve_housing_sliced Mean for Vexposure
…involved a process sequence
that used visual exposure to Roads and weighted visual exposure to Houses to
create an overall visual exposure index to human activity with a potential
range of 1 (low) to 5 (high). The question did not ask you to re-compute
the average VE index, just use the existing map.
2) Districts—a given
base map with eight contiguous regions defining the Districts in the project
area.
3) Average VE per
District—use region-wide overlay to calculate the average VE index (data
map) within each of the Districts (template map) by
COMPOSITE Districts with Vexpose average for AvgVE_Districts
The hardest part was recalling that COMPOSITE is the region-wide
operator in MapCalc (ZONALmean in ESRI Grid/Spatial Analyst).
·
Q5-2) Using
the Tutor25.rgs database, identify the visual exposure to roads (Vexpose
map) within a 300m simple buffer (3 cells) around roads (Roads
map). Screen grab the important map(s) and briefly describe your solution
as a narrative flowchart.
Parsing the question into
processing considerations:
1) Visual exposure to
roads— use the “completely” option to count the number of viewer cells
connected to each map location by
RADIATE Roads to 100 Completely For Vexpose
2) 300m proximity buffer
around roads— must calculate a simple proximity map that extends to 4 (or
more 100 meter cell lengths) away from roads so you can renumber to a 300 meter
(3 cell lengths) binary buffer
SPREAD Roads to 4 For Road_prox4
RENUMBER Road_prox4 Assign 0 to 0 thru 4 Assign 1 to 0 thru 3 For Road_buffer3
3) Identify Vexpose
values within Road_buffer3— use grid math to multiply the binary buffer by
the visual exposure values
COMPUTE Road_buffer3 Times Vexpose for VE_ Road3 (could
use CALCULATE)
The hardest part of this question was interpreting the question.
One interpretation was to just determine the visual exposure within just a 300
meter reach of a road by RADIATE Roads to 3 Completely For Vexpose3. The problem with this solution is that there might be
locations that are within the 300 meter buffer that are seen from road locations
that are farther away than 300 meters. Another interpretation just
calculated the viewshed (binary map of just seen or not seen) not visual
exposure.
·
Q5-3) Using
the Tutor25.rgs database, create a map (not just a display) that
shows the locations that have the highest (top 10%) visual exposure to
houses (Housing map). Screen grab the important map(s) and briefly
describe your solution as a narrative flowchart.
Parsing the question into
processing considerations:
1) Visual exposure to
Houses— some confusion in interpretation as whether to use “completely”
the counts the number of Housing locations visually connected, or to use “weighted”
that sums the Housing values for the total number of Houses
RADIATE Housing to 100 Weighted For Vexpose_houses
2) Determine top 10%—
need to use the Shading Manager to set 10 ranges that will identify the
top 10% (1/10). Confusion can arise as whether the question is asking to
use “equal count” that identifies intervals of 10% area, or to use “equal
ranges” that identifies intervals 10% values. Since area wasn’t
specified, the map value for the lower bounds of the tenth interval is best to
use.
3) Create a map—
reclassify the VE map to a binary map that identifies the top 10% by
RENUMBER Vexpose_houses Assigning 0 to 0 thru <maxValue> Assign 1 to
<lower 10% cutoff> thru <maxValue> For Vexpose_houses_top10pct
To just use the Shading Manager to set the color for the first 9
intervals to light grey and the 10th interval to red creates the
same visual display, BUT it is not a binary map that the computer can use in
further processing, such as determining the average elevation for the most
visually exposed areas (COMPOSITE Vexpose_houses_top10pct with Elevation
Average).
·
Q5-4) Using
the Island.rgs database, create a map that identifies locations that are
fairly steep (15% or more on the Slope map) and are westerly oriented
(SW, W and NW on the Aspect map) and are within 1500 feet inland from
the ocean (hint: the analysis frame cell size is a “property” of any map
display). Screen grab the important map(s) and briefly describe your
solution as a narrative flowchart.
Parsing the question into
processing considerations:
1) Fairly steep— some
confusion could arise about what slope algorithm to use. Best to use the
default “fitted” (and state in your assumption) unless otherwise directed to
use another method (maximum, minimum, average).
SLOPE Elevation Fitted For Slopemap
RENUMBER Slopemap Assign 0 to 0 thru <maxValue> Assign 1 to 15 thru
<maxValue> For Fairly_steep
2) Westerly oriented—
easiest to use “octants” as the question implies octants, but you could use
“Precisely” and then determine the azimuth cutoffs for the bearings (1=N, 2=NE, 3=E, 4=SE, 5=S, 6=SW, 7=W, 8=NW, 9=Flat). Since the question didn’t specify
what to do for flat areas without a dominant terrain orientation, it seems safe
to assume (and state your assumption) that such areas are not of interest.
ORIENT
Elevation Octants For Aspectmap
RENUMBER Aspectmap Assign 0 to 0 thru 9 Assign 1 to 6 thru 8 For
Westerly_oriented
3) Ocean— the tricky
part here was determining which map to use in the calculation …Land_mask.
Mask_all has “coast” as a feature but that would mean establishing proximity
from the coast cells (one cell inland) not the ocean cells.
RENUMBER Land_mask Assigning 1 to -1 For Ocean
4)
Within 1500 feet inland— checking the Propertiesà Source tab as suggested identifies the
cell size of the Island.rgs database as 82.5 feet. Thus the “reach”
inland is 1500/82.5 = 18.18, rounded to 18 cell lengths (plus one for “or more”
that needs to be eliminated when renumbering for the binary mask)
SPREAD
Ocean to 19 For Inland_1500plus
RENUMBER Inland_1500plus Assign 1 to 0 thru 18 Assign 0 to 18 thru 19 For
Inland_1500 (could use 18.0001 thru 19 if concerned about
an exact “tie”)
3)
Locations meeting all three criteria—
COMPUTE
Fairly_steep Times Westerly_oriented Times Inland_1500 For
All_three (could use CALCULATE)
Displaying the result on a 3D Grid or Lattice plot of the
Elevation surface is best as it lets the viewer “see” the terrain
relationships.
·
Q5-5) Using
the Agdata.rgs database, create a map that locates areas that have unusually
high phosphorous levels (one standard deviation above the mean on the
1997_Fall_P map) and are within 300 feet of a “high yield pocket” (top
10% on the 1997_Yield_Volume map). Screen grab the important map(s) and
briefly describe your solution as a narrative flowchart.
Parsing the question into
processing considerations:
1) Unusually high
Phosphorous levels— need to use the Shading Manager’s “Statistics
Tab” to identify the Mean and StDev of the 1997_Fall_P mapped
data. The lower cutoff for “unusually high levels” is Mean + 1 StDev.
RENUMBER 1997_Fall_P map Assign 0 to 0 thru <maxValue> Assign 1 to
<Mean + 1StDev> thru <maxValue> For High_P
2) High yield pocket—
need to display the 1997_Yield_Volume and use the Shading Manager
to set 10 ranges that will identify the top 10% (1/10). Confusion can
arise as whether the question is asking to use “equal count” that
identifies intervals of 10% area, or to use “equal ranges” that
identifies intervals 10% values. Since area wasn’t specified, the map
value for the lower bounds of the tenth interval is best to use.
RENUMBER 1997_Yield_Volume Assigning 0 to 0
thru <maxValue> Assign 1 to <lower 10% cutoff> thru <maxValue>
For High_yield
3)
Within 300 feet of a high yield pocket— checking the Propertiesà Source tab as suggested identifies the
cell size of the AgData.rgs database as 50.0 feet. Thus the “reach” away
from high yield pockets is 300/50.0 = 6 cell lengths (plus one for “or more”
that needs to be eliminated when renumbering for the binary mask)
SPREAD
High_yield to 7 For Pockets_300plus
RENUMBER Pockets_300plus Assign 1 to 0 thru 6 Assign 0 to 6 thru 7 For
Pockets_300 (could use 6.0001 thru 7 if concerned about
an exact “tie”)
4)
Locations meeting both criteria—
COMPUTE
High_P Times Pockets_300 For HighP_HighYield (could use CALCULATE)
Displaying the result on a 3D Grid or Lattice plot of the
1997_Fall_P surface is best as it lets the viewer “see” the relationship with P
levels.
___________________________
2/13/10
Katie, Jason and Luke— great scoping
session yesterday!!! I think our clients
are comfortable with the proof-of-concept prototype that emerged from the
meeting. Below is a somewhat more
refined version of the draft flowchart for the Basic and Extended Forest Access
models you sent (aside for all GIS
Modeling students: this is a good flowchart for “Techy-guy”— note how the boxes
align and match the levels of abstraction from Descriptive mapped data
to Prescriptive map solution).
Hopefully you captured the “Future Considerations” thoughts and
discussion as well—cover type, soils, effective proximity buffer, etc.
Because this is a “mini-Project” I have
decided not to require you
to implement and report the additional models for identifying the Landings and characterizing their Timbersheds as we outlined on the
whiteboard …these are fairly complex solutions and outside the workload bounds
of a mini-Project. All you need to do is
implement the Basic and Extended models flowcharted above …and generate
a comparison between the output from Basic model alone
and the Extended model.
Your comparison should include a map showing the difference and a table
summarizing the difference (easier than you think …hint: Shading Manager table).
The roughest conceptual part is in
understanding the extended “adjustments” and how they affect the Basic Access
Model. The idea is that the Basic model
considers direct factors determining absolute and relative barriers to
harvesting— landscape features (Ownership, Water and Sensitive Areas) and
machinery operating conditions (Slope).
The Extended Model considers indirect human-related factors that
suggests it more attractive to harvest in certain circumstances—areas of low
visual exposure and low housing density (out of sight and away) have the effect
of lowering the direct factor “impedance” by as much as 50% (.5). The effect is to favor harvesting in areas
with less people impact.
Another conceptual hurdle is the final step
that summarizes the total forest accessible area within each of the watersheds. The “Renumber” provides for infusing end user
expertise and judgment. If the
renumbering for the binary map is to one unit below the maximum “To
<value>” it will identify all of the accessible forested areas as the
“Composite” will count the total number of accessible cells. However, if the user wants just the “most”
accessible forested areas (i.e., low hanging fruit) they would renumber to a
smaller relative access value—less of a costly reach into the woods. The procedure is like saying “we only want to
harvest within a quarter mile of a road,” except the harvesting reach from
roads is in realistic terms that considers the availability and
accessibility dictated by the intervening terrain.
It looks like all of the maps are in the Bighorn.rgs database except “Ownership”
and “Watershed” maps that I will create and send to you as a text file for
importing …by tomorrow 5:00 pm (earlier if all goes well).
I will complete the processing for the MBA
Team client and forward to them with your report a week from Sunday. I’ll share the Landings/Timber-sheds Addendum
with you and the rest of the class ...and who knows how many others as I plan
to use the model in my Beyond Mapping column for GeoWorld as a three-part
series to illustrate the anatomy of a GIS access model.
Have a great weekend, Joe
___________________________
1/18/10
Folks—in
grading your Exercise #1 reports I have made numerous “red-line comments”
embedded in your responses. When you get the reports back, PLEASE take
note.
The most flagrant mistake was failing to link
the model logic to the commands and results by thoroughly considering the
map values For example,
“…looking at the
interpretation results shows that the Gentle Slope criterion is the least
selective—just about everywhere is rated as “pretty good.” However,
if the model is moved to another project area that is predominantly
east-facing, the aspect consideration might be the most restrictive. You
missed a chance to comment on which criteria map layer was the most restrictive
and which was least restrictive.” …you
need to “fully digest and think
about” the map values,
as well as simply “completing” the exercise.
Just to reinforce the “deadline
policy” for your reports—
…the “deadline etiquette” spiel in class is part
of the sidebar education opportunities that comes with the course …more bang
for your tuition buck. In the “real-world” there are often a lot of folks
counting on a sequence of progressive steps—if one is missed the procession can
get off kilter. Outside factors are part of reality but a “heads-up” of likely
missing a deadline lessens the impact, as others appreciate the courtesy and,
provided you announce a new expected deadline, they can adjust their schedules
as needed …softens the blow by recognizing others and demonstrates you
are a team player. The opposite reaction occurs if the deadline is
disregarded …hardens the blow by ignoring others and suggests that you
are a soloist.
Also, below is a list of general “report writing
tips” that might be useful in future exercises. Hopefully these tips
will help the “final” polishing of your Exercise #2 reports (and
beyond!!!) --Joe
Underlying Principle: Report writing is all about helping
the “hurried” reader 1) see the organization of you thinking, as well as
2) clearly identify the major points in your discussion.
…Report Writing Tip #1: enumeration is useful in report writing as the
reader usually is in a hurry and wants to “see” points in a list.
…Report Writing Tip #2: when expanding on an enumerated
list you might consider underlining the points to help the hurried
reader “see” your organization of the extended discussion/description.
…Report Writing Tip #3: avoid long paragraphs with several
major points—break large, complex paragraphs into a set smaller ones
with each smaller paragraph containing a single idea with descriptive
sentences all relating to the one thought. Don’t be “afraid” to have a
paragraph with just one sentence.
…Report Writing Tip #4: it is a good idea to use two
spaces in separating sentences as it makes paragraphs less dense …makes it
easier to “see” breaks in your thoughts—goes with the “tip” to break-up long
paragraphs as both are distracting/intimidating to a hurried reader as they
make your writing seem overly complex and difficult to decipher. Most professional reports do not indent
paragraphs—appears more “essay-like” than report-like. A report is not a literary essay.
…Report Writing Tip #5: avoid using personal pronouns (I, we, me, etc.) in a
professional report. A report is not a letter (or a text message).
…Report Writing Tip #6: “In order to…” is a redundant
phase and should be reduced to simply “To…” For example, “In order to
empirically evaluate the results …” is more efficiently/effectively written as
“To empirically evaluate the results…” This and two other points of
grammar are often used to “differentiate” the Ivy scholar from the inferior
educated masses. The other two are 1) the split infinitive ( e.g.,
This thing also is going to be big, not “…is also going to be…”; don’t
stick adjectives or adverbs in the middle of a compound verb) and extraneous
hyperbole (e.g., “That’s a really good map for…” versus “That’s a good map
for…”; avoid using “really”).
…Report Writing Tip #7: need to ALWAYS include a caption
with any embedded graphic or table. Also, it is a general rule is that if
a figure is not discussed in the text it is not needed—therefore, ALWAYS
direct the reader’s attention to the graphic or table with a statement of
its significance to the discussion point(s) you are making.
…Report Writing Tip #8: ALWAYS have Word’s Spelling
and Grammar checkers turned on. When reviewing a document, right click on Red (spelling error) and
Green (grammar error)
underlined text and then correct.
…Report Writing Tip #9: it is easiest/best
to construct (and review) a report in “Web Layout” as page breaks do not
affect the placement of figures (no gaps or “widows”). Once the report is in final form and ready
for printing, you can switch to “Print Layout” and cut/paste figures and
captions as needed.
…Report Writing Tip #10: be sure to use a
consistent font and pitch size throughout the report. Change font only to highlight a special point
you are making or if you insert text from another source (include the copied
section in quotes).
…Report
Writing Tip #11: don’t use “justify” text alignment as it
can cause spacing problems when a window is resized in “Web Layout” view; the
document will not be printed ...it’s the “paperless society,” right? Also, be consistent with line spacing …usually single space
(or 1.5 space) is best …avoid double spacing as it takes up too much
“screen real estate” went viewing a report.
…Report
Writing Tip #12: it is easier (and more professional) to use a table for
the multiple screen gabs and figure #/title/caption as everything is
“relatively anchored” within the table and pieces won’t fly around when
resizing the viewing window—
|
CoverType
map |
CLUMP
dialog box |
CLUMPED
CoverType map |
|
Figure 2-1. Script construction and map output for the CLUMP
operation. The left inset shows the
CLUMP operation settings. The
CoverClumps output map on the right identifying unique map values for each
“contiguous Covertype grouping” is displayed in discrete 2D grid format with
layer mesh turned on. |
||
…the easiest (and
best) way to center items in the table is to click on each item and choose
“Center” from the Paragraph tools; to create upper and lower spacing Select the
entire table and the Table Propertiesà Cell tabà Cell Optionsà uncheck Cell Margins
boxà specify .08 as both top and bottom margins.
___________________________