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4A_4_Disaggregating the nigerian postcode

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  • 1. DISAGGREGATING THE NIGERIAN POSTCODE A STEP TO CREATING AN ENVIRONMENT FOR GEOMARKETING IN NIGERIA RESEARCH TOPIC EXPLORING THE POTENTIALS OF GEOMARKETING TOOL FOR DEVELOPING COUNTRIES: NIGERIA AS A CASE STUDY PRESENTATION BY NICHOLAS ALLO | KINGSTON UNIVERSITY
  • 2. PRESENTATION OUTLINE
    • QUICK REVIEW OF THE CASE STUDY: NIGERIA
    • APPLICATION OF POSTCODES
    • COMPARISON OF POSTCODES - UK v NIGERIA
    • CONSIDERATIONS FOR DISAGGREGATING THE NIGERIAN
    • POSTCODE
    • METHOD DISCUSSIONS
    • FINAL DISCUSSIONS
  • 3. CASE STUDY: NIGERIA 1 . 2006 Census figures disputed 2 . Access to EA census data denied 3 . Non-availability of accurate administrative or small area vector data for Nigeria 4 . Areal inconsistency in boundaries seen 1 2 3 4
  • 4. POSTCODES
    • Labels that define a set of mail delivery points [Raper et al., 1992]
    • Smallest indivisible part of a postman walk [Martin, 1991]
    • Advocated use of postcodes as Basic Spatial Units in Britain [Chorley Report, DOE 1987]
    DESCRIPTION (UK) MARKETING GEODEMOGRAPHY Group G Municipal Dependency GEOMARKETING Group F Suburban Mindsets
  • 5. POSTCODES Routing hierarchy Spatial resolution Smallest unit Internal count Predominant uses 2 LEVELS POSTCODE ZONE; POSTCODE AREA BOUNDARY OF LOCAL AUTHORITY BASIC SPATIAL UNIT POSTCODE AREA UNIT POSTCODE 14 PROPERTIES PER UNIT POSTCODE APPROX 115 STREETS PER POSTCODE AREA MAIL DIRECTION DATA COLLECTION LIMITED MAIL DIRECTION VS 4 LEVELS POSTCODE AREAS; POSTCODE DISTRICT; POSTCODE SECTOR; UNIT POSTCODE
  • 6. DISAGGREGATING THE NIGERIAN POSTCODE Dasymetric Mapping -Intelligent Dasymetric Mapping [IDM]
    • Kuipers TOUR Model
    • Path
    • Place
    Modifiable Area Unit Problem [MAUP]
    • Street Network Topology Model
    • Semantic Networks
    1. SOURCE 2. EXTRACTION 3. CONCEPTS 4. THEORIES
  • 7. DISAGGREGATING THE NIGERIAN POSTCODE APPROACH
    • Determining likely building type or occupation using size of roof outline [Neufert, 1980; Perez and Perez, 2008]
    • Identify roof outlines with potential vertical addresses by size
    • Create building classifications based on type assigned by size
    • Apply Kupiers ‘PATH’ and ‘PLACE’ models as well as semantic topology to determine spatial extent of groupings used
    • Group building feature outlines to BSU, based on classification; omit non- residential buildings
    OBJECTIVE
    • Create Basic Spatial Units [BSU] for Nigeria
    • Provide an approach with considerable flexibility
  • 8. Search: 250 ≤ X ≤ 400 SQM Feature Count: 1196 Classification: Vertical Addresses Search: 100 ≤ X ≤ 250 SQM Feature Count: 1750 Classification: 2 Storey Addresses Search : 400 ≤ X ≤ 999 SQM Feature Count: 221 Classification: Mixed and Commercial Search: 50 ≤ X ≤ 100 SQM Feature Count: 104 Classification: Bungalow Addresses
  • 9.
    • CONSIDERATIONS
    • Classification of buildings into typologies using vector data is possible [Triantakonstantis and Barr, 2009]
    • Imagery is often used in conjunction with vector data for typology classification [Eidenbenz et al., 2000; Wu et al., 2008 and Eguchi, et al., 2000]; however, imagery over Nigeria is often with extensive cloud cover or of low quality for the period in question, i.e. 2006
    • Concepts of size and space allocation are borrowed from architecture to determine possible typology [Neufert, 1980]; however, tests and assessment for accuracy are needed
    • Roof outlines and area have been applied in the process of analysis where structured data sources are absent [Perez and Perez, 2008]
  • 10. DISCUSSIONS
    • NIGERIAN TOPOGRAPHIC DATASET STRUCTURE
    • Classified Building Outlines
    • BSU Structure for multipurpose use

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