1. Summary of Work Packages 2-3: A Theoretical Assessment and A Practical Assessment
Marja Tammilehto-Luode, Statistics Finland
Philippe Guiblin, Office of National Statistics, United Kingdom
GISCO Working Party 25 October 2001

2. Plan for the presentation
Point of departure
The two parallel approaches
Methods
Tests with empirical data
Results
GISCO Working Party 25 October 2001

3. Point of departure: A need for more comparable territorial divisions for statistics Why?
to visualise data more effectively
to combine and compare data on different spatial units
to combine or compare data on different spatial scales
to make better statistical/spatial analysis of data (to test spatial patterns and trends
GISCO Working Party 25 October 2001

4. Finland 20 NUTS3-areas
Belgium 45 NUTS3-areas
GISCO Working Party 25 October 2001

5. Objectives
to find alternative comparable building blocks/territorial division for European system of small area statistics
to summarise studies of theoretical assessments of systems of statistics by regular and irregular tessellation
to present a practical pilot study -to product grids and blobs -to test their capabilities -to test both systems with the same user case
GISCO Working Party 25 October 2001

6. The study of two parallel approaches
Regular tessellation approach
Statistics Finland’s responsibility
study of methods to construct grid-based statistics
tests of candidate methods with empirical data
construction of a prototype
tests on a prototype with a user case
Irregular tessellation approach
Office of National Statistics in UK’s responsibility
study of methods of zoning design
tests with ZDES (Zone Design System) by Leeds University with empirical data
tests on a prototype with a user case
GISCO Working Party 25 October 2001

7. Irregular tessellation
From diversified input areas to harmonised building blocks and comparative output areas
Regular tessellation
Irregular tessellation
Input areas
Building blocks
Output areas
GISCO Working Party 25 October 2001

8. Towards a system of grid-based statistics
Methods -Points to grid cells - need for best practices -Polygons to grid cells - different methods give different results - need for standardisation
Size of a grid cell dependent on -quality of data -confidential grid cells -scale of the study -compatibility of different kind of source data -disk space, processing speed
Map projection -rectangular -common geo-reference system - with a same origin
GISCO Working Party 25 October 2001

9. Towards a system of polygon-based statistics
Optimum grouping of the source areas -equal values of selected variable (population) -similar degree of heterogeneity (homogeneity) -weighted accessibility on certain input variable (shape)
The automated zone design program (Openshaw 1977) -optimising an objective function -optimising under constraints
Design functions -equality population zoning -shape design function -homogeneity design function -correlation -, distance -, spatial autocorrelation functions
GISCO Working Party 25 October 2001

10. Test areas and data sets
The Finnish data sets cover the Helsinki region -56 municipalities, 168 postal code areas and 168 grid-squares (10 km x 10 km) and grid-squares (1 km x 1 km) census population counts of each area level
The British data set covers the region of Wales and sub set of the county of the South Glamorgan EDs for the Wales and 817 EDs for the South Glamorgan census population counts by EDs
GISCO Working Party 25 October 2001

11. Tests: Data from two countries, different types of geo-references, different types of variables
grids
tests of candidate algorithms to convert polygon-based data to grids
construction of prototype
visualisation of results
delineation of urban areas
blobs (polygons)
tests of zone function with constraints of equal population and a single weighted shape
construction of prototypes
visualisation of results
delineation of urban areas
GISCO Working Party 25 October 2001

12. Building up a system of grids - Results
From points to grids -Description of best practices
From polygons to grids -Regiongrid-algorithm maintains the original data structure - similar statistics to ‘real grids’ -Larger grids keep the structure better than smaller -Polygrid and Pointgrids algorithms overestimate data to small grids and underestimate data in larger grids
Delineation of Urban areas -The urban area by 1km x 1km grids is 45% smaller than that defined using the data on NUTS5 areas in Finland - The urban area of the UK test area, South Glamorgan is a little bit larger by 1km x 1km building blocks than by NUTS5 areas
GISCO Working Party 25 October 2001

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18. Building up a system of blobs
Software -Visual Basic program, AZM, developed by David Martin (Southampton University)
Constraints -only equal population zoning and a shape constraints - perimeter squared/area were used -no other source of heterogeneity was considered such as geography and/or social class
Delineation of Urban areas - Production of an ‘optimised’ boundary system
GISCO Working Party 25 October 2001

19. AZP Case Study: Cardiff Area
Input Areas : 818 ED’s
min. pop: 64
max. pop: 1030
Design Constraints
Thresholds:
Target pop.: 500
Min. pop. : 1500,
Homogeneity : off
Shape: on
Automated Output
Area Design
(AZP)
Building Blocks
min.pop: 500
max. pop: 1909
Delineation of
Urban / Rural Areas
GISCO Working Party 25 October 2001

20. AZP on South Glamorgan Enumeration District Input Areas
GISCO Working Party 25 October 2001

21. AZP on South Glamorgan Enumeration District Building Blocks
GISCO Working Party 25 October 2001

22. Enumeration Districts Urban / Rural Delimitation
AZP on South Glamorgan
Enumeration Districts
Urban / Rural Delimitation
GISCO Working Party 25 October 2001

23. Urban / Rural Delimitation
AZP on Helsinki
post-codes
Urban / Rural Delimitation
GISCO Working Party 25 October 2001

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25. Results and deliverables
Reports of theoretical and technical assessment of two alternative building blocks for a system of geo-statistics of Europe
Prototypes of grid (regular)-based and polygon(irregular)-based statistical system
Results of delineation of urban areas with the building blocks
Recommendations
GISCO Working Party 25 October 2001

26. Conclusions - grid-based statistics
A lot of advantages - a relevant alternative
The critical points - characteristics of input data - what is a optimum/minimum size of the grid - what is a projection system
Harmonised methods for converting grids needed
The resultant grid size should be the same or coarser than the one for the input data
The common georeferenced system needed - UTM applicable projection system
Confidentiality problems - special disclosure control methods needed
GISCO Working Party 25 October 2001

27. Conclusions - polygon-based statistics
Advantages of the zoning design approach
Software available and technical feasible for the whole of Europe
Theoretically can be used for any level of geography
During the tests some problems with the great numbers of input areas - the more areas the greater the number of possible combinations and thus the more time needed for processing
Face the definition of optimality (optimisation of an objective function)
Using multiple objective functions makes it more difficult to find a good optimum
GISCO Working Party 25 October 2001

28. Conclusions - both approaches
Both methods propose their own way to harmonise data production
To perform well both approaches: - Need the provision of a ‘good’ initial set of areal units - Need to incorporate confidentiality restrictions (for the dissemination of statistics) - Face the definition of optimality (e.g. optimal grid-size, optimisation of an objective function)
Need to deal with limitations due to processing speed and disk space
Need a good GIS software environment
GISCO Working Party 25 October 2001

29. Conclusions - future studies
Tests with more /different data sets -different variables -different kind of georeferences -different scale of analysis
Development of methods -polygons to grids -AZM
Case studies -to compare results with those by administrative areas -to make analysis which are not possible by administrative areas
GISCO Working Party 25 October 2001

30. marja.tammilehto-luode@stat.fi philippe.guiblin@ons.gov.uk
GISCO Working Party 25 October 2001