AJ Lashway Week 2

Chapter 1

Notes:

Map projection will be dependent on the scale of data, level of precision required, and where the data is located.

Definitions:

• Discrete data– points or lines in space where a given feature is either there, or isn’t; there are ‘gaps’ in the map. Typically uses a vector model.

• ex; streams, parcels of land, businesses

• Continuous data– data covers the entire map, and you can determine the value for any given point. These are typically numeric values in raster, but can also be mapped using vector.
  • ex; temperature/heat maps, precipitation, soil type
• Summarized data–  a given value applies to an entire area, not a specific location. Typically uses a vector model.
  • ex; number of businesses in a zip code, total length of streams in a watershed.
• Vector model– features are shapes defined by “x, y” locations in space.
  • Can be discrete locations, events, lines, or areas.
  • Uses geographic coordinates (x, y).
  • Lines are a series of coordinate pairs.
  • Areas are closed polygons.
• Raster model– features are a matrix of cells in continuous space.
  • Consists of multiple layers (typically), with each layer representing one attribute.
  • Can use varying cell size (examples on page 11).
    • Small cell sizes result in a more defined map, but requires more storage space. Large cell sizes will show patterns, but they lose the level of detail achieved with smaller sizes.
• Attribute values– identify what the feature is, describe it, or represent some magnitude associated with the feature.
  • Types: categories, ranks, counts, amounts, ratios
• Categories– groups of similar things
  • ex; roads: freeways, highways, local roads
  • ex; crimes: burglaries, thefts, assaults
• Ranks– put features in order from high to low. Most often used when direct measurements are difficult, or if the quantity represents a combination of features.
  • ex; “scenic value” of rivers; area in mountain gorge ranks higher than area near a dairy farm
  • You can rank based on different attribute values
    • ex; soils of a certain type ranked the same in relation to suitability for growing a particular crop.
• Counts & Amounts– shows you total numbers. Count is the actual number of features. Amount can be any quantity associated with the feature.
  • ex; amount: number of employees at a given business
  • They let you see the actual value of each feature as well as its magnitude compared with other features.
• Ratios– shows the relationship between 2 quantities, created by dividing 1 quantity by another for each feature. They more accurately show the distribution of features.
  • ex; dividing (# of people in each tract)/(# of households)=(average # of people/household)
  • Proportions– show what part of a total each value is.
    • ex; number of 18-30 year olds/total population
    • They are often shown as percentages
  • Densities– show the distribution of features or values per unit area
    • ex; population of county/land area in miles squared= people/square mile
• Selecting– used to specify features to work with, or to assign new attribute values to specific features.
  • select ATTRIBUTE = VALUE
  • Can also use (>), (<), and unequal (<>)
• Calculating– used to assign NEW values to features in the data table.
  • select FIELD = VALUE → calculate ATTRIBUTE = VALUE
• Summarizing– [summarize] the values for specific attributes to get statistics.
  • ex; create a new table → list a value for each type → add count of features

Questions:

Would the census population data from GEOG 112 be considered summarized data?

Why would you use a rank based on an attribute rather than just using the secondary attribute?

Chapter 2

Notes:

The amount of information shown on a particular map depends on what the map will be used for. You need to know the intended audience for the map and its purpose before starting, and plan accordingly.

The category values discussed in the previous chapter may have subtypes that add varying levels of detail. The same base map can then be expanded upon, depending on its purpose and the intended audience at the moment.

Even if you’re intending on focusing on a certain set of data, having surrounded data can help to contextualize the information and resulting patterns. If the data is discrete, showing these data sets on separate maps may make information more digestible. If the data is continuous, displaying all or a couple of categories on the same map is favorable in many cases. When it comes to categories and how many should be displayed, 7 is a good rule of thumb for a maximum. However, the distribution of features and scale of the map can affect this. You can display more features if they’re scattered than if they’re clustered together.

So, it’s good to experiment with how many categories are being displayed. Getting another set of eyes that aren’t familiar with the data set is probably crucial to ensure the map is understandable. This is also a good way of figuring out how the data is being perceived by the reader. Depending on how categories are grouped, that perception can change dramatically.

Definitions: 

• Single type– when the same symbol is used for all features.
• Reference features– landmarks/locations that can be used to ground a map in a certain area, and convey more meaning to the reader.
  • ex; major roads, locations of cities/towns, stores
  • They should be mapped in light colors or greys to avoid dominating the map.

Chapter 3

Notes:

Mapping based on quantities can give additional context that can give a better picture of what’s being represented. Again, knowing the purpose of the map being created will tell you how to make it and whether quantities will be beneficial.

Discrete data uses graduated symbols or shaded areas, while continuous data uses graduated colors, contours, or 3D perspective views.

When mapping based on quantities, you will want to start off with the basic data set and figure out what patterns are present. Then, make a map that helps highlight these patterns. Each feature included in the data set should only be incorporated in a way that best represents the data.

Definitions:

• Quantities– a data set/set of points that have variation amongst the features.
  • These can be counts or amounts, ratios, or ranks
• Class– a grouping of a range of similar data, typically used when features all (or mostly) have different values, and the data range is large. Classes will make it easier to identify patterns.
  • Natural Breaks– natural groupings of data values present in the individual sets.
  • Quantile– each class contains an equal number of features.
  • Equal Interval– the difference between the high and low values of each class is the same.
  • Standard Deviation– features are broken into classes based on how much their values vary from the mean.

Chapter 4

Notes:

Density mapping is helpful in cases where there are many features. It will be easier to read in some cases than individual points representing each feature. You’ll have to decide two major things: 1) whether to shade defined areas, or create a continuous density surface and 2) decide if you’re focusing on features themselves or on values associated with features.

In general, summarizing data with map density can make patterns more general, but easier to look at and identify specific numbers for overall areas. Map density should be used for already summarized data with defined borders. Density surfaces provide the most detail, but require the most effort by far to put together. These are best for concentrated data.

The level of specificity in a data set/range of area can greatly affect what the resulting map looks like. In density surface mapping, areas between features are estimated through interpolation. Interpolation can cause extreme highs and lows to vanish. So, while patterns are easier to see, there should be another map that shows locations of features to provide context.

Definitions:

• Density– used to show where the highest concentration of features is.