Thematic map - population change in the U.S.

https://www.e-education.psu.edu/natureofgeoinfo/book/export/html/1553 

 

The theme depicted in this thematic map of the U.S. is population change across time.  In this case, the change is shown in percentages by county, over a 10 year span (1990-2000).  This thematic map can more specifically be referred to as a classed choropleth map – the shadings represent different ranges of percentage of change.  Thematic maps like these are very useful for examining a phenomenon and its occurrence across space.  We are able to see that during this time period, the population increased the most drastically in the West, particularly in Arizona and Washington State, as well as in Florida. 

Mental map - Boston

http://ocw.mit.edu/ans7870/MAS/MAS.965/f04/assignments/tripti/Assignment10/index.htm

 

This is quite obviously a mental map for several reasons.  First of all, it is hand-drawn.  Secondly, it has no fixed scale – buildings and streets are color-coded, but we are not sure of the distances and proportions.  This mental map of a part of Boston serves a certain purpose:  not necessarily an accurate depiction of the whole picture, but rather a workable picture of the necessary landmarks in order for a viewer to navigate him- or herself, as determined by the person who drew the map.  Mental maps, though subjective, can be very helpful in day-to-day activities, as well as in determining what things characterize a neighborhood, or stand out in memory.

Star plot - MER ID sample designs

http://start1.jpl.nasa.gov/caseStudies/autoTool.cfm

 

In this star plot, four different design samples (MER IDD) are compared and sorted by seven metrics variables (accuracy, collision, trajectory completion, time, mass, actuator saturation, and link deflection).  According to the description underneath, in this star plot, the center represents the most desirable result. Each design represented on the plot has a different color; in red, they also added a handcrafted MER ID design, for comparison and validation purposes.  It is clear that each design has its setbacks and its advantages, so the way to determine the best design is to decide which metrics are the most important, and then to see which design fulfills these metrics the best.  In this case, the website description says design samples 3 and 4 fulfill the important metrics the best.

Correlation matrix - Cancer types

http://www.livestockgenomics.csiro.au/Genome_to_Phenome/  - click on Figure 8

 

This correlation matrix is examining possible correlations between types of cancers.  In a typical correlation matrix, there is a diagonal line of correlation=1 (in this case red, meaning that brain cancer correlates with brain cancer, etc.)  What we are looking for are other red boxes underneath and above the diagonal line.  It is interesting and perplexing to see that “Brain_Normal” is seemlingly correlated with “Brain_Cancer”, “Breast_Cancer” and “Colon_Cancer”.  However, since this diagonal correlation line is beneath the standard red correlation line, it means these variables are negatively correlated – so, if you have a “normal” brain, you are likely not to have brain cancer, breast cancer, or colon cancer.  Again, it is important to note that this matrix depicts correlation, not necessarily causation.

Similarity matrix - behavioral malware clusters

http://cgi.mtc.sri.com/Cluster-Lab/

 

In this similarity matrix, we can see a diagonal line of red boxes, which decrease in size from left to right.  These red boxes represent a strong similarity (close to identical).  The diagonal line of red boxes is surrounded by mostly blue boxes – this color indicates small similarity.  These similarity matrices can be used for various purposes; this one is being used to determine malware behavioral patterns, and hopefully to predict future patterns by their similarities.

Stem-and-leaf plot - height in elderly women

http://what-when-how.com/statistics/skewness-to-systematic-review-statistics/ 

 

This stem-and-leaf plot shows us that of the 351 elderly women in the sample, most of them are either 158 or 159 or 163 inches tall.  At first, I had trouble understanding the system, but once I did, it was very easy to interpret the results:  the “stem” portion on the left stands for the first 2 digits of the height, while the “leaves” represent the heights of each woman in the sample.  If you turn this stem-and-leaf plot around to be vertical instead of horizontal, you get a histogram.

Box plots - foreign country visits

http://cnx.org/content/m46174/latest/?collection=col11521/latest

 

Box plots are helpful for depicting groups of numerical data through their quartiles.  This box plot compares three countries – Germany, America and China.  Specifically, the data shows results of a survey of 20 year-olds in each country when asked how many foreign countries they had visited in their lifetime.  We can see that 20 year-old Germans have visited the most foreign countries (between 4 and 8 countries), with American 20 year olds behind (between 0 and 5 countries) and Chinese 20 year olds without any data (which I assume means 0 countries). 

Histogram - British Columbia and age-disease correlation

http://www.bccancer.bc.ca/nr/bcca/asp/PrintPreview.asp?GUIDS={00AE441B-4AEB-4AF5-9B64-E76ECE24484C}

 

A histogram displays the distribution of measured data on a graph through the use of vertical bars.  This particular histogram examines data from patients in British Columbia diagnosed with either benign disease or malignant tumors between 1987 and 1996.  The variable on the x-axis is age at diagnosis, while the variable on the y-axis is the number of cases for the corresponding age.  The histogram shows us that the most cases occurred in patients between the ages of 65 and 70.  We can also see that the number of cases increases steadily from the age of 15 to 70, after which the number decreases steadily until it reaches a low at age 90.  However, one must also consider that there probably were not as many 90-year old patients as there were younger patients.

Parallel coordinate graph - 6 variable example

http://www.evl.uic.edu/aej/424/kyoung/Training-parallelcoordinate.html

 

Although this parallel coordinate graph may seem confusing at first, it is helpful when you are looking for similarities and differences in profiles of datasets while taking several variables into consideration.  According to the author of the website, the subset of the data in the lowest decile of the variable LLTI is shown in black while the remainder of the dataset is in gray.  This helps the eye to catch similarities and trends – both a general trend and outliers become clear.  In addition, low values are seen under the variables of “Density” and “Unemp”.

Triangular plot - Kauai tsunami

http://www.pmel.noaa.gov/maillists/tmap/ferret_users/fu_2007/msg00384.html

 

A triangular, or ternary, plot depicts the ratios of three different variables in an equilateral triangle.  This type of plot is helpful for systems that are composed of three different elements.  The triangular plot of Kauai at hand uses three different tsunami wave trains with the goal of modeling the linear combination of these trains that would cause the greatest wave at the impact site.  The various colors, from purple to red, represent the size of the resulting wave from these tsunami wave trains. 

Windrose - Maine

http://www.maine.gov/dep/air/meteorology/Windrosehome.html 

Besides having a colorful, peacock-like appearance, windroses are helpful to meteorologists because they depict wind direction frequency in a given location in a visually interesting and effective way.  According to this Maine website, data is presented for 36 directions, in order to reduce any bias from using a particular windrose software.  In addition, this windrose represents five years of data, which helps in determining trends.  We can see that most of the winds in this area in Maine came from the northwest – the winds from this direction were also some of the strongest winds (black).  The least of the winds in this area come from the southeast.  It is important to consider that windroses such as these are site-specific and really depend on the topography (mountains versus valleys). 

Climograph - Atlanta, Georgia

http://drought.unl.edu/archive/climographs/AtlantaMetric.htm

 

A climograph shows the precipitation and temperature in a given place over the course of one year (or an average of several years).  Usually precipitation is displayed by bars (and in mm), and temperatures are displayed by a line connecting dots of average monthly temperatures (in degrees C).  This climograph shows us the average precipitation and temperature in Atlanta, Georgia as measured between 1971 and 2000.  There is a clear peak in temperature in July, but precipitation levels fluctuate a bit.  There are several high points in precipitation, the peak being in April, with other significant highs in January, February, and August.

Population profile - Galashiels, Scotland

http://galashielshistory.com/page3.html

 

This population profile has a typical pyramid shape.  This particular profile is comparing the population of Galashiels, Scotland, in 1881 (dark blue) to the population 30 years earlier in 1851 (light blue).  We can see that overall the population increased, keeping its pyramid shape for the most part (typical for growing populations), with an especially large growth in the age range of 20-24.  From today’s perspective, it is alarming to see the birth rate equal the death rate of young people as it is here, but it is not so surprising when one considers that this data is from the 19^th century!

Scatterplot - egg consumptioni and colon cancer mortality in women

http://perfecthealthdiet.com/category/food/eggs/

A scatterplot displays data in form of dots on a graph.  Oftentimes these dots are scattered rather than lined up, so you draw what is called a trend line in order to see the general direction of the data.  This scatterplot shows the association between egg consumption and colon cancer mortality in women.  The trend line indicates that with increased egg consumption, colon cancer mortality in women increases.  It is important to note that this trend line in the scatterplot graph shows a correlation, but not necessarily a causation.  There may be other factors involved in higher colon cancer mortality besides increased egg consumption.

Lorenz curve - income inequality in the U.S.

http://iopscience.iop.org/1367-2630/12/7/075032/fulltext/

 

The Lorenz curve, when used in economics, graphically represents the cumulative distribution function of the empirical probability distribution of wealth.  The data in this Lorenz curve is taken from IRS data in the years of 1996 and 2007.  The distance between the diagonal line and the curve represents income inequality.  We can see that the Lorenz curve of 1996 is closer to the diagonal line than that one of 2007, meaning that the income inequality worsened from 1996 to 2007 from 18.5% to 21.5%.

Bilateral graph - Trade deficits with China

http://www.theatlantic.com/international/archive/2010/10/china-and-international-rules-self-interested-or-malign/64747/

 

This graph displays the U.S. trade relationship with China from 2006 to 2010, specifically focusing on trade deficits in clean energy products.  The graph is considered bilateral because it displays two (or more – in this case, three) sets of data.  The blue bar represents exports, the green bar represents imports and the red bar represents trade balance.  It is easy to see that trade between the U.S. and China has become more and more unbalanced during this time frame because the red bar has grown in the negative direction while the green bar (imports) has grown in the positive direction.  This means that the U.S. has imported much more from China than China has from the U.S. in clean energy products. 

Range-graded proportional circle map - cartography courses in the U.S.

http://go.owu.edu/~jbkrygie/krygier_html/geog_353/geog_353_lo/geog_353_lo11.html

 

A range-graded proportional circle map is one of two types of proportional circle maps.  The other type uses continuously variable symbols.  This type, as opposed to using a limited amount of symbols, is graded and each symbol represents a certain range.  The latter type is used in this map:  We can see course offerings in cartography, sorted by state (in the U.S.), using six different sizes of circles for the number of courses offered and different levels of shading for relative importance of these courses.  From the map, it is evident that there are more course offerings with a higher level of relative importance in the Northeast than in the rest of the country, although there is a rather high amount of courses with high relative importance in Kansas.  Although there is no date on this map, I would venture to guess that it is a little dated – I wonder if the course offerings have changed much since this map was developed.

DOQQ - West Virginia

http://www.geomart.com/products/geodata/doqq/index.htm

 

What distinguishes a DOQQ, or Digital orthquarter quads, image from a normal aerial photo is that it is a georectified raster image, meaning it digitally removes camera tilt and image displacement caused by terrain relief.  Thus, a DOQ can combine the characteristics of a photo with the geometric qualities of a map.  This DOQQ map gives an aerial, normal-color, view of one of West Virgina’s state forests, Cooper’s Rock State Forest, as well as Mont Chateau State Park.  The contour lines show equal lines of height while the color shadings are realistic depictions of reality, since they are taken from aerial photos.

DEM map - Sacramento Valley

http://academic.emporia.edu/aberjame/student/alvarez1/551proj.html 

 

A DEM, or Digital Elevation Model, is just that – a digital model – created from the terrain elevation data in the chosen area.  This map uses a DEM to show a close-up of the features in the “project area” of Sacramento Valley, California.  The colors make the differences in elevation very clear – orange and red (Coast Ranges and the Sierra Nevada) mean higher elevation, while blue (Sacramento Valley) indicate lower elevation.  This kind of map is especially useful to developers, because it allows them to see the elevation differences in a given area at a glance, which (in California’s case) is important for development because of such natural hazards, such as flooding, landslides and wildfires.

DLG map - Illinois and Missouri

http://www.mapcruzin.com/free-geodata-shapefiles/usgs-geospatial-data-geodata-geographic-download.htm

 

Although a DLG, Digital Line Graph, uses USGS data to create maps, it does not rely on previously constructed maps, like the DRG (which scans USGS topographic maps).  Instead, a DLG uses digital vector form to represent data on a map.  This DLG map uses the SDTS (Spatial Data Transfer Standard), which makes it easier for spatial data to be transferred between different computer systems.  This DLG map shows Illinois and Missouri on a 1:24,000 scale.

DRG map - Washington, D.C.

http://egsc.usgs.gov/isb//pubs/factsheets/fs08801.html

 

This DRG is interesting because it looks a bit old-fashioned.  In fact, DRGs or Digital Raster Graphics, are scanned images of U.S. Geological Survey topographic maps.  Once these maps are scanned, the map neatline is georeferenced to the surface of the Earth.  On this DRG map, we can see part of Washington D.C., West (probably from the 1990s) including the Potomac River and some contour lines, which is typical for a topographic map.  Since this is an urban area, parts of town are also labeled, as are some green spaces and a bridge.

Isopleth map - Spain

http://geographyfieldwork.com/DataPresentationMappingTechniques.htm

 

Isopleth is a more general term referring to lines of equal data types (like temperature or height).  Isopleth maps are generally more suited to displaying continuous data, rather than “patchy” distributions of data (compared to choropleth maps).  The isopleth used in this particular map is precipitation (in northeastern Spain on June 10, 2000), measured in mm (although the legend on the right says m – that is incorrect).  We can see that the most precipitation is just north of Montserrat (dark blue).  The tight isopleths (or isohyets, to be exact) indicate that the amount of precipitation increases more around this area than in other areas where the isohyets are further apart.

Isopach map - Weir-Pittsburgh Coal

http://www.kgs.ku.edu/PRS/Coal/weir.html

 

Isopachs in a map represent lines of equal thickness of rock.  This map of the Weir-Pittsburgh Coal uses isopachs to show the thickness of the coal in this area.  It seems to be that the thickest coal lies between Wilson and Montgomery (red) and the least thick coal lies west of Wilson and Montgomery, in Crawford, and in the center of the map (purple and blue).

Isohyets - Hong Kong in June 2012

http://www.hko.gov.hk/wxinfo/pastwx/mws201206.htm

 

Isohyets represent lines of equal rainfall or precipitation.  This rainfall map not only features isohyets in Hong Kong during June of 2012, but the colors between the isohyets also represent differing amounts of rainfall in millimeters.  The worst of the rainfall seems to have been in the North (red) and the least of the rainfall was in the Southeast (grayish blue) during this month.

Isotachs - Current Winds in the U.S.

http://www.e-missions.net/wvstorm/?cat=2&sid=1&pid=73&page=Weather%20Satellites%20and%20Maps 

 

This map features isotachs – lines of equal wind speed.  The isotachs in this particular map also have small arrows, indicating the wind direction.  It looks like the Southwest and the Southeast (particularly Alabama and Mississippi) are experiencing faster winds than the rest of the country on this day (December 2, 2008).

Isobar map - Northern Hemisphere

http://www.srh.noaa.gov/ffc/?n=mapslast - then click on "Northern Hemisphere"

Isobars are lines of equal pressure.  This map features the Northern Hemisphere and its pressure systems with isobars at a glance.  The isobars are in orange, the High Pressure areas are in blue and the Low Pressure areas are in red.  On this particular day, July 22, 2014, Central America seemed to have a concentrated area of high pressure, while low pressure dominated a bit more in Eastern Asia.  It also looks like there is a potential low pressure system developing in the Atlantic – you can see this by the circular isobars.  If the pressure continues to drop, this may form a tropical depression, but that is difficult to predict from this map.

LIDAR map - San Francisco

http://architecture2030.org/slr/san_francisco_ca

 

LIDAR maps are some of the most futuristic-looking of all maps, using remote sensing technology and reflected light (from a target illuminated by a laser) to measure distances.  LIDAR maps are often used to show projections of an area in three dimensions.  In this LIDAR map of San Francisco, we can see the flooding that is projected for the city in 2030 due to sea level rise.  The projection is distinguished from the actual photo of San Francisco today by a green-blue color.  Although the projection may or may not be 100% accurate, the LIDAR technology is undoubtedly fascinating and effective.

Cartographic animation - Tsunami

 http://www.globalsecurity.org/eye/andaman-ani2.htm

 

This cartographic animation of a tsunami caused by the Sumatra Earthquake in 2004 is interesting to watch, but it is not really helpful in the moment of the earthquake and tsunami.  However, we can see the pattern of wave distribution over time, without looking at several maps at once.  We can see that it started on a small strip of islands, from which waves spread out both to the east and to the west (the two colors perhaps representing intensity) and eventually crashing onto the shores after about 100 minutes (or more for the coast to the west of the earthquake).

Black and white aerial photo - New District of Doha

http://catnaps.org/islamic/planning.html

 

This black and white aerial photo documents the creation of the New District of Doha in Quatar.  One can see how the bay was dredged and the new land is beginning to be created on the sea for new development.  The aerial perspective makes it easy to compare large areas and developments over time.  The black and white choice for this aerial photo is helpful in distinguishing between land and water (color might be too confusing).

Infrared aerial map - Property in South Carolina

http://sclandauction.com/t66_73.htm

 

This map is an infrared aerial map of a property in South Carolina.  The tracts that make up portions of the map are for the potential buyer – the website explains where each tract is and what the borders are.  The colors on this infrared map allow the viewer to assess what type of land is in this area – the red indicates vegetation (from the looks of it, the vegetation on the west side is a bit healthier compared to that on the east side – the red shade is darker on the west side than on the east side).  The moisture content in the soil towards the center and east side of the map (blueish-gray color) is lower than in the rest of the area.  These sorts of maps are very helpful for someone who wants a quick overview of the characteristics of the area.

Doppler Radar - Hurricane tracking

http://www.noaanews.noaa.gov/stories/s1192.htm 

A Doppler radar, such as this one on the NOAA website, is helpful for tracking weather events, such as hurricanes.  The velocity data gathered by the radar using the Doppler effect is displayed on this map, forming the well-known circular shape of a hurricane with its eye in the middle (low velocity).  The different colors represent different precipitation intensities.  We can see on this map that for the most part, the hurricane is pretty middle to high intensity (mostly yellow and green) and that the eye is currently on the shore.

Statistical map - oil consumption around the world

http://www.theguardian.com/news/datablog/2010/jun/09/bp-energy-statistics-consumption-reserves-energy

 

This statistical map uses oil consumption as the property that represents distances and sizes of countries.  We can see that the U.S. and China are leading countries in oil consumption, with Japan, India, Brazil and Russia not too far behind.  It is interesting to see the percentage of change that took place between 2008 and 2009, as represented by the small numbers in each country’s circle.  Overall, it seems there was a change of -2%, which means that there was a bit less overall oil consumption in 2009 than in 2008.

Unstandardized choropleth map - race in the U.S.

http://www.censusscope.org/us/map_common_race.html

 

This map is an unstandardized choropleth map because although it sorts the U.S. counties by the variable of race and uses colors to distinguish the data, it does not do so in one standardized, areally-averaged way.  According to the legend, counties are shown “according to which races have a population greater than the national average”, but this does not help the viewer know exactly how big said population is or what other populations are represented in that county (again, the legend says that if more than one race “has a population greater than the average” they are shown as “multiethnic”).

Standardized choropleth map - Gini index in U.S. counties

http://www.incontext.indiana.edu/2014/mar-apr/article1.asp

 

This map is standardized or areally averaged.  It uses the Gini index, a measure of statistical dispersion, to sort counties in the U.S. by income.  From this map, you can see that over this five-year estimate, the South and coastal areas in the U.S. seem to have the highest Gini indices, meaning that there is high equality in income in these areas.

Univariate choropleth map - Percentage of people in poverty in the U.S.

http://www.citylab.com/work/2011/12/americas-new-poverty-belt/677/ 

A Univariate choropleth map means that only one type of data is being examined and displayed on the map.  This map, like many choropleth maps, uses varying values (or lightness/darkness of shading) of the hue (in this case blue) to differentiate between the different interval steps.  We can see from this map that from 2006 to 2010, the states with the largest percentage of people living in poverty areas are in the southern/central part of the U.S. (from New Mexico to Alabama and up through Kentucky).