Project 3 - Report2: Route Optimization

Report

Project 4 - Managing Sales Territories

REPORT

Project 4 - Prepare: Sales Patterns and Outlets

Project 3 - Report

Click here for Report Presentation.

Project 3 Analyze

Project 3 Prepare: GIS / Economic Development

Project 2 - Report

Project 2 - Analyze Trees/ Carbon

Project 2 - Prepare Landcover Classification

It took quite a few tries but I was finally able to create a signature file with 50 classes for a mosaic of all clipped photos in the project. The maximum likelihood tool worked well on the mosaic signature but the slice tool did not do a very good job of reclassifying the image. I manually reclassified with reclassify tool by pixel value and used the results in this weeks submitted map.

Project 1 - Report

Project 1 - Base Map

Hospitals?

Special Topics - Project 1 Metadata

Links to new table metadata files for project 1:
Asthma
Census 2000
Ozone
Particulate 2.5

Not being from sea level I feel disadvantaged doing this analysis. The light blue areas to the north seem like they would be water. At .35 m. elevation would this be concidered wet or water? Is ocean elevation 0.0 m. elevation? Is a mile high elevation in the mountains?

Mod 4 - Supervised Classification and Recode

Germantown Land Use Map
The purpose of this map is to view and assess the city of Germantown, Maryland's land use pattern and growth trends in order to locate and build a new town center with respect to Maryland's Smart, Green and Growing Initiative. Satellite imagery has been classified and coded based on areas ground truthed in each category. Classification signatures were evaluated with histogram values and mean plot charts. Spectrally confused classes were edited until the values were accebtable then similar classes were grouped and recoded as reflected in the legend.

Evaluating and revising the signatures waas the hard part of this challenge for me. I wasn't sure if my criteria for evaluating the histograms and mean plots was valid.

Week 3 _ Orthorectification

Mod 3 Challenge Link

A simple undo button on the ERDAS GUI would make this program so much more user friendly. Oh and don't forget to save often and wear your helmet. This was otherwise a fun exercise, I like learning new software.

Module 2 Challenge - Spectral Bands

Links to this weeks challenge:
EMAGINE Map M2v1
EMAGINE Map M2v2
EMAGINE Map M2v3

Module 1 Challenge

The following link will take you to this weeks challenging challenge. ERDAS Imagine was a bit twitchy at times but I managed to complete this simple map with a few do-overs.

JEL_Mod1Challenge

Final_Cartographic Skills

It was a tough choice between the SAT or ACT. I took the SAT in High School. This time I chose The ACT. Very interesting, the trend in preference for one over the other by location but the test scores showed no particular correlation to the percent of students participating.

Google Earth

JEL World Wind Eco-Farm
Site Selection
Michigan’s coastal Wind Resources and Transmission lines are rated good/ excellent by the U.S. Department of Energy, National Renewable Energy Laboratory (2004). Michigan has potential capacity of 16,560 mega watts (13th in US) and relatively low existing capacity at 449 mw. Originally the site was located in Lake Huron before learning that most new wind power development in the U.S. has been onshore, due to the higher costs and risks of offshore wind power, along with delays due to opposition to projects such as Cape Wind (Wikipedia) off Cape Cod, Massachusetts. As In the Bowling Green case, the World Wind Eco-Farm, if embraced by the local community can become a green attraction and pilot project for the area.

Wind speed; 6.5-7 m/s predicted mean annual wind speeds at 80-m height (U.S. Dept. of Energy).

Ornithology: This site has very few flyways, corridors, or narrow routes but several converging migratory routes as does much of the Great lakes area per the Northern Prairie Wildlife Research Center (USGS)

Ecology: Although a coastal area, this site is inland and agricultural in character minimizing the ecological impacts.

Noise: Vicinity consists of agricultural and limited residential areas within a one mile radius.

Shadow Flicker: Very few residents in shadow zones especially in the east/ west impact zone.

Electro-Magnetic interference: Negligible

Access: 11 miles to major shipping at Port Huron and 2.5 miles to State Highway 25.

Aeronautical and Military: No airports or military installations in vicinity.

Landscape and Visual: Scenic Highway area through farmland and proximity to coastline may be an issue of opposition. Immediate vicinity has no major resort areas or parks and limited costal homes.

Week8_Isarithmic Mapping

Here is my Isarithmic map for this weeks deliverable. Manually interpolating the observation data was straight forward. I had to learn some new tricks to complete the map with shading but overall a good learning experience.

Week9_Flow Map

Week 9’s Flow map was a challenging exercise for illustrating the movement of people to the U.S. This is a distributive flow map utilizing conceptual point locations. I chose a Robinson compromise world projection to make the map layout more interesting. Using the square root of the data values to calculate my line widths made the range more compact and worked well graphically. There was plenty of room to add annotation to each flow line making a legend redundant and unnecessary. The empty space at the lower corners was perfect place for a legend if it were needed. I decided to add the Homeland Security logo and image for visual balance. Illustrator is starting to be more useful with each map completed and I hope to get more proficient and quicker as the weeks go by.

Week8_Dot Maps

While placing dots for this housing density map I considered the provided ancillary data (lakes, rivers and wetlands) as limiting attributes. I also added cities, towns and urban areas as related ancillary data to further influence the dot placement. Add the random/ human factor and the results are this weeks Areal Frequency Map of Florida’s housing units. The biggest challenge was avoiding geometric pattern, false clusters and voids as suggested in the module reading. I chose not to label the counties because my intent was to portray the distribution pattern for the entire state. County lines and lakes are included for reference.

Week7_Proportional Symbols

Another interesting week with Illustrator vs ArcGIS. Used both programs extensively for this map. I Learned a lot about the capabilities of each as well as some essential formulas in excel for scaling symbols correctly. I used the world map shape file from week 2 to fill in the rest of the land mass and projected from space for the key map.

Week6_Choropleth

Both maps were easy enough to start. The devil was in the details.
I used ColorBrewer values for both maps and they look ok on my screen but I have not printed to critique. Aggregating the census data was interesting but tedious. I realized how much it can effect the look of a map and possibly the interpretation. I am starting to find and use more of the tools in Illustrator. Layers and groups are proving to be most useful.

Week5_MapComposition

This weeks lab was more time consuming than I had planned. Learning new software always does that to me, like some kind of time warp. I concentrated on the use of color and composition to tie the map elements together. Labeling each county would have added to the usefulness of this map but I used my time to learn more about the layer and group features in Illustrator.

Week4_Typography

I learned lots of new tricks with Illustrator through trial and error^3. Annotation will never seem the same old boring task. The basics were easy enough but I got caught up in small refinements. I am going to spend some more time with this program because now I am hooked.

Data Classification Deliverable 2

Natural Breaks classification was my method of choice. Although the Quantile method had its strong points I liked the natural breaks map for its grouping of like values in the data. By considering the distribution I think the results are better suited to show the pattern in the data. This may not have been the case if the data were not expressed as a percentage.

Data Classification Deliverable 1

This exercise to display and compare four methods of classifying the data was very interesting. The standard deviation method was the most challenging. I used a gray scale color ramp to compare the four maps without the distraction of color, and the winner is....

For my “Good Map” I have a map from SERTIS-CNES-INTERNATIONAL CHARTER to illustrate the clear communication of data in a humanitarian crisis. The response maps were compiled within hours of the Haiti earthquake on January 12th, 2010. I can imagine the value of these maps in the first days after the quake as invaluable in assessing and communicating the damage and need in a time sensitive situation. This and many other thematic maps of the earthquake zone were produced quickly by SERTIS-CNES without sacrificing sound cartographic design. The Tufteian principles and John Krygier’s commandments are represented well in this concise map. Although there are almost no labels, the explanatory text, legend, and key map make for easy interpretation.

Bad Map

Here is a map that is hard to look at. Although one can glean the intended information with a bit of study and squinting, the message is lost in the details. Background and the major theme, rivers are so close in color as to be almost indistinguishable. The choice of blue, green and yellow stippling for the entire state tells us nothing of Idaho and its fascinating terrain that supports the rivers. The text is dominant when it should be secondary and redundant with overprint leaders were it should be the primary focus. The rivers are blue I will give you that.