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Methods of analysing change over time and space

Methods of analysing change over time and space. Ian Gregory (University of Portsmouth) & Paul Ell (Queen’s University, Belfast). Advantages of temporal GIS data. 1. Integration Potentially any data with a spatial and a temporal reference can be integrated Allows new data to be created

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Methods of analysing change over time and space

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  1. Methods of analysing change overtime and space Ian Gregory (University of Portsmouth) & Paul Ell (Queen’s University, Belfast)

  2. Advantages of temporal GIS data • 1. Integration • Potentially any data with a spatial and a temporal reference can be integrated • Allows new data to be created • 2. Analysis • Need to spot broad trends and places/times that show different patterns • Only limited techniques available: • Multi-level modelling • Geographically Weighted Regression (GWR) • 3. Visualisation • Allows exploration of data and presentation of results • In all cases we want to make best use of all of the available detail in the data (attribute, spatial and temporal)

  3. Data integration: District-level net migration rates • Net migration from the “basic demographic equation” NMt,t+n = (pt+n – pt) - (Bt,t+n - Dt,t + n) • Age and sex specific population, fertility and mortality data have been published decennially in Britain since the 1850s • Net migration for women aged 5 to 14 at the start of the decade can be calculated as: • Females aged 15 to 24 at end of decade minus females aged 5 to 14 at start of decade minus number of deaths in the cohort through the decade • Problem: As net migration is the residual it is highly susceptible to error. In particular, the impact of any boundary changes will appear as migration. • Traditional studies: • Most studies of net migration use county-level data to avoid boundary change issues • Only use the census so are unable to sub-divide migrants by age/sex

  4. Net migration through areal interpolation • Standardise population and mortality data from many dates onto a single set of target units • Integrate data from census and Registrar Generals’ Decennial Supplement • Allows us to calculate net migration rates for males and females in ten-year cohorts from ages 5 to 14 to ages 55 to 64 (at start of decade).

  5. Standardised time-series Net migration rates among the 5 to 14 cohort Bristol Cheltenham Westbury

  6. Detailed attribute comparisons Net migration rates among different cohorts in the 1920s Bristol Cheltenham Westbury

  7. Net migration: strengths and weaknesses • Strengths: • From the census (comprehensive) • Can compute complete time-series from 1851-2001 • Can be integrated with other aggregate information: • Pop. density • Employment • Social class • Proximity to coast/areas of natural beauty, etc. • Weaknesses: • No information on flows • Low net mig. can be caused by high in and out mig. cancelling each other out • Ecological fallacy when analysing data

  8. Other sources • Pooley & Turnbull (1996) • Sample of 75,000 migrations by 16,000 people born 1750-1930 created using genealogical societies. • Gives: • Where each move was to and from (including grid references) • When the move occurred • Large amounts of attribute information on employment, family structure, etc. • Strengths: • Detailed individual-level info • Weaknesses: • Potentially biased sample • Doesn’t include the young up to the present

  9. Bringing them together • Both datasets are geo-referenced – can be integrated • Allows: • Comparison of individual-level and ecological data (use of multi-level modelling) • Tests whether ecological and individual level relationships are consistent • Evaluates the accuracy of the sample • Therefore: • Integrates different datasets • Makes full use of spatial, attribute and temporal information

  10. Spatial analysis with GWR • Global vs local analysis • Global analysis: • Gives a single summary statistic or equation for whole study area • Average relationship – implies spatial homogeneity • Local analysis: • Allows parameters to vary over space • Shows how relationships vary geographically • Allows spatial heterogeneity

  11. Geographically Weighted Regression • Descriptive: Allows the relationship between the variables to vary over space by providing separate intercept and regression coefficients for every location on the map • Test as to whether the model shows significant spatial variation • Conventional regression: yi=a0+a1x1i+a2x2i+εi • GWR: yi=a0(ui,vi)+a1(ui,vi)x1i+a2(ui,vi)x2i+εi • (ui,vi) represents the coordinates of the ith point and an(ui,vi) is the impact of an(u,v) at the ith point. This is implemented using a distance decay model

  12. Example • Global: LTLIi=3.8+96.6UNEMi+31.1CROWi-3.5SPFi-22.5SC1i-5.6DENSi Intercept SC1 UNEM DENS

  13. Mapping the R2i values Source: Fotheringham et al, 1998

  14. Uses in spatio-temporal analysis • In C19 young women migrated as much as men but the spatial pattern differed significantly because of the different employment opportunities (main employers: domestic service, textiles) • Conventional regression: • Mig proportional to DS and Text • GWR: • Textiles attract women in Lancs/W. York • DS attracts women to wealthy areas eg West London, Cheltenham, Leamington Spa • Over time this pattern will become more complex and the differences between men and women will reduce

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