The above map is a cartogram which was orginally created with ScapeToad and later exported and modified within ArcGIS. The spatial distortion aspect of the cartogram shows the amount of CO2 emissions produced by country. Note that China and the United States are the two greatest emitters. The choropleth aspect of the map shows the GDP of each country (in billions USD): the darker the green, the higher the GDP in each country. The color ramp used for this map was chosen through colorbrewer. See the graphic on the right.
Q1: Which column is being mapped (give the column name)? Which column corresponds to a country's gross domestic product (GDP)?
The column being mapped is TOTCO2_200. These data are represented within the countries shape layer with a quantitative classification method. GDP_USD is the column which corresponds to each country's annual GDP.
Q2: Which type of color ramp should we use here (diverging, sequential, etc.) and why?
A sequential color scheme suits these data best. The single hue with a varying saturation based on GDP value effectively illustrates ranking. 5 classes are chosen to show some variation among countries, but not so much as to confuse the viewer with similar looking values.
The above maps are a proportional symbol map and dot density map, which show global internet use and global telephone subscriptions, respectively. Both maps were created within ArcGis Desktop. Two data frames were visulaized on one layout view to create the effect of "stacked" maps. Symbols were not changed drastically from the those suggested by instructions apart from the color. The background grid was also removed.
Q3: Is it possible to make a map which shows the internet access data as a dot-density and the phone subscriptions as a proportional symbols? If so, explain why it would or would not be a good idea.
It is possible to represent the data in this way, however it may not be ideal because Internet use is measured from 0 to 100, whereas the telephone subscription is based on an absolute number. Dot density is better when working with such a large range of data values.
The above map created using Qgis, shows certain infrastrucutre elements of Lima, Peru including majors roadways, and electrical towers. The base map was generated using Open StreetMap.