Three challenges: social applications I cannot achieve without GIS Ludi Simpson, Bradford Council and CCSR, University of Manchester

net migration within UK, % of population 2001, LAs White Not White Manchester Which map is different and by how much?

How to keep data confidential?

Targeting areas: which 20 areas of population 25,000 have most unemployment?

Questions Does the toolkit include a solution? Does the toolkit include a solution? Has that solution been applied to this problem? Has that solution been applied to this problem?

(1) Change between maps - different groups or different times Rates based on small numbers Rates based on small numbers Spatial averages Spatial averages Empirical Bayes, shrinkage to spatial average Empirical Bayes, shrinkage to spatial average Correlation within and between maps Correlation within and between maps Correlation with neighbours average Correlation with neighbours average Correlation with same point on another map Correlation with same point on another map What makes an outlier What makes an outlier

Luc Anselin, Illinois Data vs local spatial average -> outliers Simulation: statistical significance Correlation between maps GeoDa, after GeoTools

(2) Keeping confidential data

Keeping confidential data Addressed geo-coded administrative data Addressed geo-coded administrative data Aggregate to any area – point in polygon Aggregate to any area – point in polygon Restrict access to personal data Restrict access to personal data 1. Restrict enquiries 1. Restrict enquiries Minimum area, eg. Population. Differencing. Minimum area, eg. Population. Differencing. 2. Perturb data 2. Perturb data Aggregate to non-confidential areas Aggregate to non-confidential areas Output Areas; Super-Output AreasOutput Areas; Super-Output Areas Perturb data Perturb data Re-distribute aggregated data (Community Statistics Project)Re-distribute aggregated data (Community Statistics Project) Spatial smoothingSpatial smoothing Spatial perturbationSpatial perturbation Respect multivariate relationshipsRespect multivariate relationships

Community Statistics Project: 1.Aggregate to EDs 2.Spread back to all addresses 3.Spreading variable spatially correlated to data, but not perfectly 4.Maintains correct values for each ED

(3) Targeting areas of need Indices of deprivation Data for much smaller areas Automatic zoning, homogenous with respect to deprivation No need for complete zoning Different from redistricting Target size X from policy purpose

Targeting areas of need Brushing – a moving window on the map Brushing – a moving window on the map Spatial sum around all points, with constraints: Spatial sum around all points, with constraints: Population total Population total Natural area – compactness Natural area – compactness Select seed points rather than all points Select seed points rather than all points Automate Automate