introduction: statistics in geography module 03. reading read chapters 1 & 2read chapters 1...
TRANSCRIPT
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Introduction:Introduction:Statistics in GeographyStatistics in Geography
module 03module 03
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ReadingReading
• read Chapters 1 & 2read Chapters 1 & 2
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Introduction:Introduction:Statistics in GeographyStatistics in Geography
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Collection of Primary DataCollection of Primary Data
• samplingsampling– observationobservation
– fieldfield
– questionnairequestionnaire• face-to-faceface-to-face
• mailmail
• telephonetelephone
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Collection of Primary DataCollection of Primary Data
• nature of problemnature of problem
• wordingwording
• sequencesequence
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Other ConsiderationsOther Considerations
• explicitly spatialexplicitly spatial– retail market arearetail market area
– ethnic clusteringethnic clustering
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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
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VariablesVariables
• discretediscrete
• continuouscontinuous
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VariablesVariables
• quantitativequantitative
• qualitativequalitative
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Levels of MeasurementLevels of Measurement
• nominalnominal
• ordinalordinal
• intervalinterval
• ratioratio
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Measurement ConceptsMeasurement Concepts
• precisionprecision– level of exactnesslevel of exactness
– instrumentationinstrumentation
– spurious precisionspurious precision
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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)
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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
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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)
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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
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Basic Classification MethodsBasic Classification Methods
• equal intervals based on rangeequal intervals based on range
range = largest value – smallest valuerange = largest value – smallest value
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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
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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
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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 ...
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Basic Classification MethodsBasic Classification Methods
• quantile breaksquantile breaks– equal number of intervals per equal number of intervals per
categorycategory
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ExampleExample
• income groupingsincome groupings
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Basic Classification MethodsBasic Classification Methods
• natural breaksnatural breaks
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ExampleExample
• influence of extreme valuesinfluence of extreme values
––––> > Frank StronachFrank Stronach
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ExampleExample
• who is Frank Stronach?who is Frank Stronach?–retired (?) manufacturer of retired (?) manufacturer of
automobile partsautomobile parts
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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
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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?
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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)
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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
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Home WorkHome Work
• read section on graphic read section on graphic proceduresprocedures– pp.31-33pp.31-33