Geog 7 Lab 7

The first map in the top left corner shows the change in population throughout the country from 1990 to 2000, the years when the census was taken.  The values were calculated by taking count within the census, and applying the data within the tables.  The map's color ramp is appropriate for this kind of data because the areas of greatest change are denoted by dark green, denoting that this area is prospering.  Like crops, the greener it is, the better.  The pink colors seem appropriate because they are very light and show that population is lightening up in these areas.  In fact, it is decreasing in these areas.

The second map in the top right corner shows the number of people in different areas around the country in the year 2000.  The values in this map, much like the values in the other map, were likely calculated by the counts taken within the census in the year 2000.  The map shows similar statistics to the first map.  The middle of the country, encompassing the Great Plains and much of states such as Idaho, Wyoming and Montana, has the least population, with much of these towns averaging less than 10,000 people.  The color ramp, like the other map, seems appropriate, because the greatest populations are the darkest, while the lightest populations are in a light blue color.  The colors clearly transition nicely from the middle of the country outwards, as the greatest populations do seem to be near the coasts, such as in California, New York and Florida, and cities like Chicago which is near Lake Michigan, another large water source.

The third map in the lower left corner shows the percentage change in population between 1990 and 2000.  This is likely the most accurate depiction of the population change in different areas, as it is based on percentages based on the areas' original numbers, not just a display of actual numbers as in the first map.  In that map, it is obvious that the big cities will have the greatest amount of people, but that does not necessarily mean that the percentage change is the greatest.  These values were calculated by taking the rough count of the change in population and likely dividing this by the midpoint between the population of 1990 and 2000.  This color ramp is very good for this type of map because it clearly displays the areas which have a greatly increased percentage in population, and also a very obvious color for areas which have decreased percentage in population greatly.  Everything else in between is rather nonchalant, which shows just basic percentage changes in population either way

The fourth map in the lower right corner shows the population density calculated in the year 2000.  These values were calculated by dividing the populations of different towns by the entire national population.  Based on these numbers, the map can be created to show which areas have the greatest populations.  And as this graph shows, the greatest population density, not surprisingly, is centered around the major cities throughout the United States.  What is also apparent from this map is that, besides the major populations centers, there seems to be much greatest population density overall in the eastern half of the United States versus the western half.  The color ramp seems appropriate because it clearly displays the areas of greatest population density, yet at the same time shows clearly which areas have the least population.  The most important part though is that one can also distinctly see which areas have medium population density, as these closer are distinct from the areas of highest population density as well as the areas of lowest population.

Geography 7 Lab 6

For this lab, I chose to take a digital elevation model of the San Fernando Valley, where I am from.  It was an easy choice since I am from there and live there when I'm not at UCLA, so I know the area best.  The San Fernando Valley is exactly as it name implies, surrounded by mountains with a wide valley below.  The surrounding mountains reach a peak of about 1,700 feet, and the lowest point in the valley drops down to 300 feet above sea level.  As is clearly displayed in the 3-D model in the bottom right corner, the highest mountains are in the northern portion of the valley, leading into Santa Clarita and Valencia and Newhall, among others.  The mountains leading into West L.A. on the south of the valley are not as high comparatively.  The extent of this digital elevation model 34.358 degrees at the top, 34.099 degrees at the bottom, -118.686 degrees on the left and -118.281 degrees on the right.  The UTM Zone for this area is Zone 11.

Geog 7 Lab 5 (Week 6)

These maps, the GCS and the Mercator projections, are different in a few different ways.  For one, the distances between Washington D.C. and Kabul on each map are significantly different.  It is 7,000 miles approximately between the two cities on the GCS projection, and about 10,000 miles between them on the Mercator projection.  The Mercator projection distorts distances far more, as the GCS projection is far closer to the actual distance.  The GCS projection also has equal area squares on its map, while the same is not necessarily true for Mercator.  The GCS projection preserves direction, distance, shape and area, which makes it a fairly accurate projection.

A mercator projection is a cylindrical projection.

 The two equal area maps shown are the Sinusoidal projection and the Mollweide Projection.  Sinusoidal projections show size, or area, relatively accurately, but distort shape and direction.  The Mollweide projection preserves area as well, but distorts shape.  The sinusoidal projection looks more horizontally stretched, thereby distorting direction between the land masses.  The Mollweide on the other hand does not seem to distort direction because the relative locations of the continents are kept in check, as it appears to just be like an oval on its side.  Though each 30-30 latitude-longitude square is equal area on each map, they still look different comparatively, which comes naturally with the different manners in which the maps are projected.  Both maps are made using a pseudocylindrical projection.

The two equidistant map projections shown are the Plate Carree projection and the Equidistant Cylindrical projection.  The Plate Carree projection preserves distance and direction, but distorts area, and is also not conformal.  The Equidistant Cylindrical also preserves distance and direction, but also distorts area, and is not conformal.  The features on the maps look different in the sense that equidistant cylindrical map seems to distort area even more so than the plate carree map.  It appears to be more scrunched up, which therefore accounts for its very short estimated distance between Washington, D.C. and Kabul.  The Plate Carree map still distorts area, but seems to make land masses larger, as the estimated distance between Washington, D.C. and Kabul on this map is 10,000 miles.  The Equidistant Cylindrical projection is made using a cylindrical projection.  The Plate Carree map is also made using a cylindrical projection.

Geog 7 Lab 4

This is the result for exercise 1.  The blue part of the map shows the area of the airport, and the lines within it show the runway.  The shape surrounding it represents the raise that the noise from the airport can travel, and the map also includes surrounding roads.  In this first exercise, it is important to determine which schools, represented by the little dots throughout the map, are within the airport's noise contour.  This helps determine where to avoid building schools in the future.  From the map, it appears that only a couple of schools are within the noise contour, including Northwestern Prep, which was required to be labeled for this particular exercise.  ArcGIS technology allows us to know where to build schools, as well as providing information for other practical uses.

This map in the second exercise shows the original map from exercise 1, in addition to a bar graph and a map of the distribution of land use within the noise contour.  Based on these images, it appears that there is lots of agricultural and residential development within this noise contour.  This implies that there are lots of families that could potentially be bothered by this noise contour extending out from the airport.  Surprisingly this exercise does not show a huge amount of commercial development around the airport area.  With the air traffic noise in that area, it is possible that homes are cheaper within the surrounding area.  With cheaper homes comes more buyers, which leads to more residential development.

 This map in exercise 3, specifically the map added at the bottom, displays the population density in this particular city presented in this tutorial.  Based on the map, it appears that the population is by far the densest in the center of the city, where the major roads intersect.  This major population center is about 15-20 miles from the airport.  This is typical of many major cities, since airport areas generally are not the spots of major populations.  The dark green parts of the map probably represents the downtown of this particular city.  Outside of this huge population concentration, the population is relatively light throughout the rest of the city, where not many major roads intersect as they do in the downtown area.

This map in exercise 4 is not much different from the previous exercise.  In particular, road names are added to the original map at the top.  In addition, this exercise showed how to add a new feature to a data range, which in this case meant creating a new street, called Airport Road.  The ability of developers to easily create new features on an ArcGIS map makes it possible to design and plan city centers much easier than ever before.  Developers can decide how to use the land and where to develop based on the technology available her in ArcGIS.

This final exercise, exercise 5, sums up the entire airport expansion plan into one complete layout view.  First off, exercise 5 introduced a new map, a very general map showing in the red box in the top left corner the airport expansion area.  This map does not need any features, but rather is very direct and shows clearly the general area where the planned airport expansion is set to occur.  This type of layout view with multiple maps is an exceptional tool at the disposal of developers everywhere because it simultaneously shows roads, schools, land use charts, land use graphs, population density and a general view of the proposed airport expansion all in one simple layout view.