Lab 6 - Post-Pre layer map

Lab 6 - Cut/Fill Map with Volume Change

Lab 6 - Pre-Dem with Volume

Supervised Classification

I have to start off by saying I rather perform an unsupervised classification rather than a supervised classification. Although the supervised classification is more accurate. It is a very time consuming process, but it is also so easily to make mistakes. Once I viewed by end result I noticed that my residential area was much larger than my forest areas. I believe that I had to add more AOI for my forest areas to make sure that I had all shades of green. I also compared my histograms from the unsupervised and supervised classification to notice my error. The legend on the right is for the supervised classification. To get the image to look more accurate I would have to go back and select more AOI for the forest and high density urban areas.

Lab5 Unsupervised Classification

Not being familiar with the area I used the satellite images from www.maps.google.com to be able to assign the land types.

Rectification

The usefulness of rectification as well as the pitfalls of image rectification.

The usefulness of rectification is that you can take an image and align it with a map to get actual x and y coordinates from land.

The pitfalls of image rectification is that it's a time consuming process.

Residential Area Fort Worth, TX - Thermal Imagery

Brief description of why the following features appear as they do in this thermal infrared image:

Roads- bright (hot) because of the heat that has been absorb from the sun.

Natural and man-made vegetation-some areas are bright (hot) because of the soil being dried and some areas are dark (cool) because the soil have moisture.

Sidewalks and patios-bright (hot) because the sidewalks and patios are concrete, which absorb the heat from the sun.

Storage sheds in back yards-dark (cool) because of the material on the roof.

Automobiles-some are dark (cool) because they have been parked and unused and some are bright (hot) because they are releasing heat from the engine from being on or recently being used.
Bright spots on many of the roof tops-bright spots (hot) because the vents or chimneys are releasing heat from the houses.

Lab 2-Question 7

Panchromatic view of Marco Island. This image shows the water as light gray, the islands in black, land is shown in light gray with some white areas, and sand as bright white along the coast. This image compared to the multispectral has a better resolution and shows the features better.

Multispectral RBG 2, 3, 4 view of Marco Island. This image shows all the features differently from the above image, except for the sand areas, which is represented similar to the above image. Bright areas along the coast. The water is shown as brown/green, the islands are in a dark blue and land is shown in a lighter shade blue with some with areas.

Exercise II

What problems might you infer or identify in using this type of photograph?

The problem that I might have using this type of photograph is the ability to distinguish features on the image properly. This photograph has various shades of red, which is ideal for distinguishing vegetation, but will make it difficult to point out the other features. Although the shades of red point out vegetation I can’t properly select some areas because the shades of red are blending in with some features on the photograph. Also, the photograph being at a small scale, which shows a large area makes it difficult to identify the feautres correctly.

Lab11 Google Earth Wind Power

I selected a location near Goodhart, MI as being the best location for a wind farm in the Great Lakes. I used the Wind Power America website to select my location. After looking over the data for IL, WI, OH, IN and MI, I decided that Michigan will be the best. Near Lake Michigan the areas were rated as being outstanding, excellent and good with a wind power class from 6 to 4. The location that I selected has a wind class of between 4 and 5 being closer to a 5. The wind power density at 50m W/m2 is between 500-600. I also checked the population for concerns on health, noise, and visual. The population for Goodhart, MI is 49,737. The nearest establishments that I found, such as, hotels or church are at least 3 miles.

It is said that electricity with as little as 5 miles per hour wind speeds can be generated. Today's (5/2/09) wind is at 9 mi/hr and it's a regular weather day. I also made sure that there was a major road for acess to the wind farm, which is N Lake Shore Dr.

This will be a good thing for the community, which most of the time there is little to any complaints.

Lab 10 Isohyet Map

Lab 9 Flow Map

Mercator Projection was used to create this map. My max line width is .25 obtained from using the Width of line = (max line width) x (value/max value). My smallest width of line is .0009.

Non-Continuous Cartogram

The projection for this map is GCS WGS 1984. This projection was used so that the cartogram tool worked. I found this map being more difficult to create. In order for me to show the outlines of the world I had to add a second data frame and placing it on top to be able to see through the cartogram. But I'm still not satisfied with my map. Not sure if it was created correctly.

Continuous Cartogram

The projection for this map is Robinson.

Dot Distribution Mapping

I took the total from Housing Density divided by 1850 dots to get my dot value of 4.

Proportional Symbol Mapping

Projection used for this map is Lambert Azimuthal Equal Area since it can handle large areas and I'm displaying an entire continent.

Lab 5B Choropleth Map

I created my 5 equal interval classes using the equal interval method, which is Range/# of classes = high-low/# of classes. I used the Color Brewer website to get by RGB for my grey tones. Once I created the map I ran it through Vischeck to see if the shades will look right for the color vision-impared.

Lab 5A- Choropleth Map

The projection that I used for this map was Lambert Azimuthal Equal Area. This projection preserves the area of the polygons and one of it's general use is for thematic maps. The data progresses from low to high, which is unipolar so I used the sequential scheme color. I used the Color Brewer website to get my RGB for my map. Once I created the map I ran it through the Vischeck website to make sure that color vision-impared can see the difference in shades.

Lab 4B Map Composition

Lab 4A Typography

Data Classification

Four Data Classification's

Data Classification - Natural Break

Map Critique Lab

Good Map
I consider this map to be a good one. I was looking for population totals of the Chicago area. I found this map that shows me population by community areas. The communities are outlined in black and numbered. I can reference the numbers to the legend where it tells me the community name. From viewing this map I can see that majority of my community has a population of 5,001 to 10,000.
Bad Map
I consider this map to be a bad map. I was searching for a map of the United States that will show me the states labeled. When I selected the map by the title, which is United States of America, I noticed that not one state was labeled. The map doesn’t contain a legend telling me what the labels on the map are. It seems that the map has major cities labeled, but not for all states.

Mental Mapping Exercise

My one and only top choice to work at will be Chicago. I was born and raised in the city of Chicago. I still live in the city and love it. Yes, our winters are cold and our summers are hot and humid, but having all of my family within a 5 mile radius from me keeps me here.

I have traveled and seen many states, so my second choice will be California for its beautiful weather and Florida because it’s near the Caribbean. If I had to pick a third choice it will be all other states.