Lab 7: Mapping Census 2000 with ArcGIS

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The first map shows counties in the contiguous United States color-coded by the number of people living in them in year 2000.  The darkest counties have the most people, while the lightest counties have the least.  These values are calculated by counting how many people live in each county; then the counties are sorted into ranges of numbers of people to be represented by different shades of color.  The gradient color ramp, from dark purple to light blue, is great for this kind of data because it easily shows at first glance where the counties with the most people are compares to the ones with the least.

The second map (top right) shows counties in the contiguous United States color-coded by the difference from 1990 to 2000 in the number of people per county.  The difference in number of people per county is calculated by taking the number of people in each county in year 2000 and subtracting the number of people in that same county ten years ago in 1990.  If the number is positive, it means there was a net growth in that county's population; conversely, if it is negative it means there was a net loss.  The color ramp, from bright pink to dark green, is good for this kind of data because it shows the contrast very well between counties with the biggest gains or losses, which we are more interested than counties that did not change very much.  The colors help to show a clear migration away from the middle of the country towards the coasts and big cities.

The third map (bottom left) shows counties in the contiguous United States color-coded by the percent change from 1990 to 2000 in the total population of each county.  The percent change in the total population of each county is calculated by subtracting the total population in 1990 from the total population in 2000 in each county, then dividing that sum by the total population in 2000 in each county, and finally multiplying the resulting number by 100 to get your percent change.  The selected color ramp, from orange to purple but with most of the ranges a shade of purple, is great for this kind of data because the states with a negative percent change really stick out and there are far fewer counties that experienced a loss than a gain in population, so the gains are all in shades of purple while the negative percentages are orange.

The fourth and last map shows counties in the contiguous United States color-coded by their population densities in year 2000.  The population density is calculated by dividing the total population in each county in year 2000 by the total area of each county (in square miles).  This calculation gives you the population density in people per square mile for each county.  The selected color ramp from almost white to green to dark navy blue is ideal for this data because the lighter colors naturally indicate space and openness whereas the dark colors are more condensed and represent the counties with the highest population densities.

Lab 6: DEMs in ArcGIS

Shaded Relief Model

 Slope Map

 Aspect Map

3D Image (Angle 1)

 

 3D Image (Angle 2)

 

The area I selected contains the mountains that make up Killington Ski Resort in Killington, Vermont and some of the surrounding terrain.  Growing up in the Boston area, I spent many weekends of my youth skiing at Killington, so the area was particularly interesting for me to look at.  The extent information (in decimal degrees) is:

Top: 43.9711111109

Left: -73.0333333299

Right: -72.481944441

Bottom: 43.5247222219

The geographic coordinate system used was "GCS North American 1983," and the area I selected was located in UTM Zone 18N from the 1983 North American Datum.

Lab 5: Projections in ArcGIS

GCS & Mercator Map Projection

The first depiction of the world shown is drawn from the geographic coordinate system.  GCS is different from a projected map because GCS is a coordinate system based on a three-dimensional model of the Earth not a projection system, so when GCS is put directly on a two-dimensional map distortion occurs.  The second map is a Mercator map projection, which is a cylindrical map projection and is conformal meaning that all angles are preserved but distances and areas are distorted.  In the Mercator map, the world looks like a vertical rectangle with land to the far south and north (e.g. Greenland, Antarctica) especially stretched out vertically.  The measured distance from Washington D.C. to Kabul is significantly longer on the Mercator map projection than on the GCS map, about 10,100mi compared to 7,000mi.

Cylindrical & Bonne Equal Area Map Projections

The cylindrical equal area map projection is a cylindrical map projection that makes the world look like a rectangle on a two-dimensional map.  Everything appears to be stretched out from east to west, and the measured distance from Washington D.C. to Kabul is longer than on the Bonne map projection below, about 10,100mi compared to 6,700mi.  The Bonne equal area map projection is a pseudoconical map projection that makes the world look heart-like in shape.  In both map projections, area is preserved but angles and distances are distorted.

Conic & Cylindrical Equidistant Map Projections

The equidistant conic map projection is a conic map projection in which distances along the meridians are preserved, but angles and areas are distorted.  The equidistant conic map projection makes the world look like a circle with a wedge removed (or a pizza with a slice removed) on a two-dimensional map.  All continents beside Antarctica look small and somewhat compressed, but Antarctica stretches all the way around the map projection in a strip.  The equidistant cylindrical map projection is a cylindrical map projection in which distances along the meridians are also preserved but angles and areas are distorted.  This map projection, however, makes the world look square-like in shape, reminiscent of the Mercator map projection in appearance.  The measured distance from Washington D.C. to Kabul is shorter on the equidistant cylindrical map projection than on the equidistant conic map projection, about 5,100mi compared to 7,000mi.

Lab 4: Getting started with ArcGIS

Tutorial Exercise 1:
In Exercise 1, the first step we did after learning how to start ArcMap and open an existing map document was to change the display symbol for "schools" from a dot to the more appropriate School 1 symbol.  This makes it easier for the experienced viewer to recognize right away where the schools are located on the map without having to check the legend.  Looking at the noise contour and the locations of schools close to the airport shows us which if any schools are at risk of experiencing too much noise from the airport.  We then added text to identify the one school within the noise contour as Northwestern Prep.  Finally, we added a title, legend, North arrow, and scale bar to further help with understanding and interpreting the map.

Tutorial Exercise 2:
In Exercise 2, we first created a new data frame with the parcels data.  We then ensured that both data frames were the same size before copying the layers for the noise contour and airport area from the Schools data frame to the parcels data frame.  Next we adjusted the symbols and color scheme of the parcels so that instead of all having the same symbols they had a different color for each land-use type.  We then made a table to show the total area of each land-use type and the number of parcels of each type within the noise contour.  We used this table to make a bar graph displaying the number of parcels of each land-use type.

Tutorial Exercise 3:
In Exercise 3, we created another new data frame to display the population density data for the county, which shows where people are located in the area.  To do this we first opened ArcCatalog and copied the layers for arterials, tracts, and airport_area to the new data frame.  Then we added the population data by adding the table that contained the population data to the data frame.  We joined the table with the population data to the census tract data table so that each tract would have its own population data.  Finally in order to map population density we created a new field in the tracts table with a calculation that measured population density by dividing the population of the tract by the area of the tract converted into square miles.  We then changed the colors of the tracts to reflect their population densities, the darkest having the most dense populations.  This map allows you to easily see where the most people are concentrated in the county.

 

Tutorial Exercise 4: 

 In Exercise 4, we edited the data in the first map of Schools and Noise Contour to elongate and curve the airport road to make a loop that connects back to an arterial road at another point.  First we started editing using the Editor Toolbar and set snapping so that any new arterial lines we drew would "snap" to the lines that were already on the map.  We then started drawing the new road by clicking with the sketch tool and inputting various command such as making a segment parallel to an existing line or making a ninety-degree turn to the right.  Finally we finished the loop by connecting the new road segment back to an existing arterial and naming it "Airport Dr". 

Tutorial Exercise 5:
In Exercise 5, we tweaked the appearance of all the existing data frames to make them easier to understand.  We added backgrounds, titles, and simplified legends to the data frames that did not have them.  We relocated the north arrow from the Schools data frame to the bottom right corner of the layout because the same north arrow applies to all of the maps.  We gave the entire layout the title of Proposed Airport Expansion to show at first glance what the data is about.  Finally we added an extent rectangle to show where the proposed airport expansion area is on the map of the entire county.  To beautify the map we added a neatline and some drop shadows to the data frames.

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