Introduction:Introduction:Statistics in GeographyStatistics in Geography

module 03module 03

ReadingReading

• read Chapters 1 & 2read Chapters 1 & 2

Introduction:Introduction:Statistics in GeographyStatistics in Geography

Collection of Primary DataCollection of Primary Data

• samplingsampling– observationobservation

– fieldfield

– questionnairequestionnaire• face-to-faceface-to-face

• mailmail

• telephonetelephone

Collection of Primary DataCollection of Primary Data

• nature of problemnature of problem

• wordingwording

• sequencesequence

Other ConsiderationsOther Considerations

• explicitly spatialexplicitly spatial– retail market arearetail market area

– ethnic clusteringethnic clustering

Other ConsiderationsOther Considerations

• implicitly spatialimplicitly spatial– observations (places) but locations observations (places) but locations

not part of analysisnot part of analysis

– associationassociation

• ecological fallacyecological fallacy

VariablesVariables

• discretediscrete

• continuouscontinuous

VariablesVariables

• quantitativequantitative

• qualitativequalitative

Levels of MeasurementLevels of Measurement

• nominalnominal

• ordinalordinal

• intervalinterval

• ratioratio

Measurement ConceptsMeasurement Concepts

• precisionprecision– level of exactnesslevel of exactness

– instrumentationinstrumentation

– spurious precisionspurious precision

Measurement ConceptsMeasurement Concepts

• accuracyaccuracy– systemwide biassystemwide bias

– can be precise without being can be precise without being accurateaccurate• US Census example (S. Bronx) US Census example (S. Bronx)

Measurement ConceptsMeasurement Concepts

• validityvalidity– nature, meaning, definition of a nature, meaning, definition of a

concept or variableconcept or variable

– resort to operational definitionsresort to operational definitions• housing affordabilityhousing affordability

Measurement ConceptsMeasurement Concepts

• reliabilityreliability– consistency, stabilityconsistency, stability– timetime– comparison of data from different collection comparison of data from different collection

sourcessources• CMAs (Census Metropolitan Areas) & CMAs (Census Metropolitan Areas) &

SMSAs (Standard Metropolitan SMSAs (Standard Metropolitan Statistical Areas)Statistical Areas)

Basic Classification MethodsBasic Classification Methods

• organize, simplify, generalizeorganize, simplify, generalize– degree of similaritydegree of similarity

• subdivisionsubdivision

• agglomeration based on defined agglomeration based on defined criteriacriteria

Basic Classification MethodsBasic Classification Methods

• equal intervals based on rangeequal intervals based on range

range = largest value – smallest valuerange = largest value – smallest value

ExampleExample

• range of incomes in a group such range of incomes in a group such as this room as this room (GEOG 2420 F 2001)(GEOG 2420 F 2001)

• $ 6,500 to $ 47,800$ 6,500 to $ 47,800

• range = $ 41,300range = $ 41,300

• 4 equal intervals $ 10,3254 equal intervals $ 10,325

ExampleExample

• classesclasses$ 6,500 - $ 16,825$ 6,500 - $ 16,825

$ 16,826 - $ 27,150$ 16,826 - $ 27,150

$ 27,151 - $ 37,475$ 27,151 - $ 37,475

$ 37,476 - $ 47,800$ 37,476 - $ 47,800

Basic Classification MethodsBasic Classification Methods

• equal intervals not based on equal intervals not based on rangerange– 100-0 percent 100-0 percent ––>––> 10 class 10 class

intervalsintervals

– each 10 percenteach 10 percent

– name each class A+, A, B, C ...name each class A+, A, B, C ...

Basic Classification MethodsBasic Classification Methods

• quantile breaksquantile breaks– equal number of intervals per equal number of intervals per

categorycategory

ExampleExample

• income groupingsincome groupings

Basic Classification MethodsBasic Classification Methods

• natural breaksnatural breaks

ExampleExample

• influence of extreme valuesinfluence of extreme values

––––> > Frank StronachFrank Stronach

ExampleExample

• who is Frank Stronach?who is Frank Stronach?–retired (?) manufacturer of retired (?) manufacturer of

automobile partsautomobile parts

Example Example 33

• assumptionsassumptions– neighbourhood of 4,000 peopleneighbourhood of 4,000 people

– average income of earners $65,000average income of earners $65,000

––––>> neighbourhood income $260,000,000 neighbourhood income $260,000,000

–Frank Stronach’s income in 2002Frank Stronach’s income in 2002

––––>> $54,100,000 $54,100,000

ExampleExample

• resultresult– average income jumps from average income jumps from

$65,000$65,000 to to $78,505$78,505

• which which average incomeaverage income figure is figure is most representative?most representative?

Figure 2.2 Figure 2.2 (p.26)(p.26)

• same data set but different same data set but different categoriescategories• look at resulting maps look at resulting maps (pp.28-30)(pp.28-30)

InterpretationInterpretation

• hypothesis can be supported or hypothesis can be supported or rejectedrejected– dependency on how you classify dependency on how you classify

your datayour data

– direct impact on your personal direct impact on your personal reputationreputation

Home WorkHome Work

• read section on graphic read section on graphic proceduresprocedures– pp.31-33pp.31-33