DATASTANDARDIZATION

and

CLASSIFICATION

Cartographic Design for GIS (Geog. 340)Prof. Hugh HowardAmerican River College

STANDARDIZATION

STANDARDIZATION• Normalization• Transformation of raw data values to

different, more meaningful values– To map densities instead of “raw” values

– To map proportions between variables

– To map other relationships between variables

– To map statistical summaries

MAPPING DENSITY• How much of a particular thing exists

within a given area• Larger enumeration units often have

"more" of a particular thing– Mapping density is not necessary if all

you want to do is show where “more” is– Accounting for the varying sizes of

enumeration units can be more revealing

MAPPING DENSITY

Population/Area

“persons per square mile”

MAPPING DENSITY

Bushels/Area

“bushels per acre”

MAPPING PROPORTIONS• Proportions represent the relationship

of a part to a whole• Several ways to express proportions

– Quotient: 0.0-1.0 – Percentage: 0-100%– Rate: 7 per 1,000

MAPPING PROPORTIONS

Persons 60 and Over/Total Persons*100

“percentage of seniors”

Persons 60 and Over

MAPPING PROPORTIONS

Non Grads/Total Population*100

“percentage of non grads”

MAPPING RELATIONSHIPS• It is often revealing to show how two

variables are related (in a manner that is not strictly proportional)

• Several ways to express relationships– Quotient: 0.0-infinity – Percentage: 0-infinity%– Rate: 1,500 per 100

MAPPING RELATIONSHIPS

Females/Males

“ratio of females to males”

MAPPING RELATIONSHIPS• It is often revealing to show how two

variables are related (in a manner that is not strictly proportional)

• Several ways to express relationships– Quotient: 0.0-infinity – Percentage: 0-infinity%– Rate: 1,500 per 100

MAPPING RELATIONSHIPS

Acres of Cropland/Population

“acres per 1,000 people”

MAPPING STAT. SUMMARIES• Enumeration units can be represented

according to calculated statistics– Median– Mean (average)– Standard Deviation, etc.

MAPPING STAT. SUMMARIES

Animation showing raw and standardized values

(slow version)

Animation showing raw and standardized values

(fast version)

STANDARDIZATION• Transformation of raw data values to

different, more meaningful values– Densities, Proportions, Relationships,

and Statistical Summaries

• In conjunction with data classification, normalization allows us to craft our message…

DATACLASSIFICATION

DATA CLASSIFICATION• The act of organizing attribute values

into categories, or groups• Can be qualitative or quantitative, and

based on any of the four measurement scales– Nominal– Ordinal – Interval– Ratio

0 - 500

501 - 1 ,000

1 ,001 - 1,500

RATI O(Popu lat i on )

2 .4 - 4 .7

4.8 - 6.3

6.4 - 8 .6

I NTE RVAL(Qu al i t y of L i fe)

Poor

Fai r

Good

ORD I NAL(Vi si b i l i t y)

Com m er ci al

Residen t i al

In du st r i al

NOM I NAL(Z on i n g)

DATA CLASSIFICATION

DATA CLASSIFICATION• One of the most interesting aspects of

thematic mapping– One set of attribute values can yield

many different maps, depending on the classification scheme

– The scheme you choose can strongly influence how your map is perceived

DATA CLASSIFICATION

DATA CLASSIFICATION• Animation showing population using

equal interval, quantile, and natural breaks classification methods

DATA CLASSIFICATION

There is no “best” method

Certain methods are not well suited to particular situations

DATA CLASSIFICATION• How many classes should you use?

– Anywhere from 3 to 7 – 5 is probably optimal– An odd # has a “middle” class

Difficult to differentiate large numbers of tints

DATA CLASSIFICATION• Animation showing agricultural sales

using 2, 4, and 6 classes

DATA CLASSIFICATION

DATA CLASSIFICATION• Equal Interval

– Each class occupies an equal interval along the number line, or histogram

TOWN POPULATION

No gaps between classes

DATA CLASSIFICATION• Advantages of Equal Interval

– Can be easy to understand and interpret– Good for attributes that are normally

represented using uniform classes: elevation, precipitation, temperature

0 – 2021 – 4041 – 6061 – 8081 – 100

DATA CLASSIFICATION• Disadvantage of Equal Interval

*

DATA CLASSIFICATION• *Considers distribution of data along a

number line (poor)– Doesn't work well with skewed

distributions (can result in empty classes)

DATA CLASSIFICATION• Quantile

– Each class contains the same (or similar) number of attribute values

4 classes: quartiles5 classes: quintiles6 classes: sextilesTOWN

Gaps between classes

POPULATION

DATA CLASSIFICATION• Advantage of Quantile

– Ensures that a choropleth map will have the same number of darkest polygons as lightest, etc.

≈13 Counties per Class

67 Counties5 Classes

DATA CLASSIFICATION• Disadvantage of Quantile

*

DATA CLASSIFICATION• *Considers distribution of data along a

number line (poor)– Doesn’t work well with skewed

distributions (one or two classes can occupy the majority of the range)

DATA CLASSIFICATION• Natural Breaks

– Each class contains clusters of attribute values, and “natural” breaks between

More subjectiveTOWN

Gaps between classes

POPULATION

DATA CLASSIFICATION• Advantage of Natural Breaks

*

DATA CLASSIFICATION• *Considers distribution of data along a

number line (very good)– Considers how the data are distributed

along the number line; each classification is “custom tailored”

– Works well with skewed data distributions

DATA CLASSIFICATION• Disadvantages of Natural Breaks

– Subjective, and results will differ– More difficult to compare with other maps– One or two classes can end up occupying

the majority of the data's range

DATA CLASSIFICATION• Classification for map comparison

– Use the same method for all maps (if possible)

– Equal interval with identical break values often works best (shown here)

– Quantile can also work well– By definition, natural breaks will result in

different classifications on different maps, making comparison difficult

DATASTANDARDIZATION

and

CLASSIFICATION

Cartographic Design for GIS (Geog. 340)Prof. Hugh HowardAmerican River College