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Geographical referencing and the modifiable areal unit problem

Geographical referencing and the modifiable areal unit problem. Geographical referencing and the MAUP. Lecture overview: Objectives of lecture Introductory questions Geographical referencing of social data Modifiable areal unit problem Lecture summary. Objectives.

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Geographical referencing and the modifiable areal unit problem

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  1. Geographical referencing and the modifiable areal unit problem

  2. Geographical referencing and the MAUP • Lecture overview: Objectives of lecture Introductory questions Geographical referencing of social data Modifiable areal unit problem Lecture summary

  3. Objectives • To understand that there are multiple options for the geographical referencing of social data • To be familiar with the principal datasets and approaches • To understand the representational difficulties associated with areal aggregation

  4. Introductory questions… How do I find out which census area this postcode falls into? What on earth is the modifiable areal unit problem??

  5. Geographical referencing • Most commonly points and areas • Generally indirect • Census zones • Service delivery districts • Postal geography • Regular grid (esp. from RS modelling) • Aggregation from individuals • Arbitrary boundary location cf. phenomena of interest (‘imposed’ vs ‘natural’ areal units)

  6. Point data • Precise location hard to determine • Usually indirect via home address elements (night-time/day-time) • Point pattern valuable, but mostly reflects population distribution • Many analysis concepts require aggregation (rates, denominators etc.)

  7. Increasing spatial resolution • Early census data – large zones • Smaller zones, service delivery areas • GIS manipulation of zonal data • Point referencing – indirect and direct • Which is the ‘correct’ spatial object for the representation of population-related phenomena?

  8. Representational issues • Spatial representation is a process, not a single technical decision • Decisions ‘frozen’ in conventional cartography • Digital datasets make possible remodelling of the data, and linkage between datasets • Possible to think of the same entities as different types of spatial object

  9. Disease incidence As points As lines As areas What kind of spatial object? As surface

  10. Representation as a process

  11. Some UK address lists... • Postcode Address File (Royal Mail) • Council Tax Registers (local government/Valuation Office Agency) • National Land and Property Gazetteer (local government/Intelligent Addressing) • ADDRESS-POINT (Ordnance Survey)

  12. Points: address referencing • ~25m postal addresses • Increasing use of address-level referencing but difficulties achieving national standards (BS7666, Acacia) • 2001 census difficulties in Manchester and Westminster • 14000 ‘missed’ addresses added to Manchester 2003 mid-year estimate

  13. St. Andrews Rd SO17 1BJ Flat 3, 9 Winn Road Caerdydd The Haven, Chalk Dr 1 Church Hill Drive Lawn Place, SE15 Saint Andrew’s Road S017IBJ 9c Winn Road Cardiff 379 Chalk Drive 1 Churchill Drive Haslam Street, SE15 Address matching problems…

  14. Ambiguous addresses Photo: Dave Martin <West Park, BS8 or Cotham Hill, BS6?>

  15. Lookup tables • Giving (population or household weighted) entity-to-entity relationships e.g. postcode to census output area • Independent from direct effects of imposed areal unit boundaries • May include coordinate references • Tools for cross-matching geographies • May be generated administratively or analytically

  16. UK Lookup tables tool

  17. County District Ward Output Area

  18. Lookup tables… • No sliver polygon or spatial mis-matching issues • May be the only alternative for modelling change over time for detailed geographies • Source of ancillary variable for areal interpolation (e.g. redistribute unemployment pro rata population counts)

  19. Modifiable areal unit problem • Openshaw (1984) • Applicable to all imposed areal units • Scale and aggregation effects • Always impacts on spatial analysis • Effects often ignored

  20. MAUP illustrated Scale Aggregation

  21. MAUP • If we change the boundaries of an areal unit, would we alter the population ‘captured’ to the extent that we would change • Its geodemographic classification • Its position in a deprivation league table

  22. Ecological fallacy • Relationships observed in ecological data would not necessarily hold at other scales of aggregation • Relationships observed in ecological data do not necessarily apply at the individual level

  23. Ecological data issues • Are all the people living in a deprived ward ‘deprived’? • Are all the people living in a ‘well-off retirees’ neighbourhood either well-off or retired? • Almost certainly not!

  24. Lecture summary • Ambiguity of geographical referencing • Different spatial data types • Multiple address referencing options • Lookup tables, particularly based on postcode geographies • Modifiable areal unit and ecological fallacy issues

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