1. Generalisation for statistical thematic maps
A proposal
Against the background of the seven hypotheses presented in this document, the European Forum for Geostatistics (EFGS), under a generous grant from EUROSTAT, have from the beginning of this year started work on the GEOSTAT ESSnet project. The project consortium of nine NSI’s is led by Statistics Norway. The need to take action for the integration of geography and statistics should not be limited to the European scale.
We therefore propose:
The establishment of a Global Forum For Geostatistics (GFGS) to serve as a professional reference group (PRG) for Geostatistics and provide a professional link between UN and the NSI’s in issues related to this field.. This PRG will primarily be concerned with questions related to the integration of geography (GSDI projects) and statistics (GEOSTAT)
The initiation of a joint UN spatial data infrastructure (SDI) project for geostatistics. This is the first step towards a GSDI based Global Integrated Information System (GGIISS) and should be developed in close cooperation with the national mapping agency (NMA) -led GSDI and INSPIRE projects.
The initiation of the GEOSTAT GSSgrid project to provide a high-resolution harmonised infrastructure for geostatistics liberated from administrative areas and demonstrating its power and utility through a UN project to plot the global population on km grids.
Keywords: GSDI (Global Spatial Data Infrastructure), GSS (Global Statistical System), Small area statistics, GIS (Geographical Information System), GGIISS (Global Geographic Integrated Information Systems of Systems)
Mrs Diana Makarenko-Piirsalu
MSc in Landscape Ecology and Environmental Protection
Geolytics OÜ
Mere tee 15, Saviranna,
Jõelähtme vald, Harjumaa, ESTONIA
Mob
ESTP Course: Use of GIS in NSIs – Representing statistics on thematic maps,
Kongsvinger, Norway, 5th to 7th of March, 2013
Attribution (by) Licensees may copy, distribute, display and perform the work and make derivative works based on it only if they give the author or licensor the credits in the manner specified by these.

2. Generalisation level and map scales
Map scale is defined as the ratio of distance on the map to the actual distances on the Earth ground.
Generalisation is a selection and simplified representation of detail appropriate to the scale and/or the purpose of a map.
Large scale maps
Medium scale maps
Small scale maps
1:5 000
1:25 000
1:50 000 1: 1: 1: 1: 1:
Smaller area
Part of the town
Bigger area
Europe, World
More details
Single house
Less details
Country names
Less generalized
More generalized

3. What is a goal of generalization?
Reduce complexity
Maintain spatial accuracy
Maintain attribute accuracy
Maintain aesthetic quality
Maintain a logical hierarchy
Due to a scale change generalization goal is to avoid
that too many object will be compressed into too small area
that features will collide
noise on the map and provide readable maps
Source: Slocum et al,Thematic Cartography and Geovisualization 2009

4. Generalisation of thematic maps
Generalisation of attribute data (for example data aggregation into mapped unit level)
Generalisation of mapped units (country borders, NUTS, LAU etc.)
Generalisation of symbols
Depends on the output map format and scale

5. Example of generalized regions1: 1: 1: 1:

6. Dilemma
Improving display quality vs geometrical or statistical calculations accuracy.
Area calculation , length and distance etc calculations error will have higher error on small scale maps.
In general it is advisable to generalize at the source map scale and then to reduce to the desired scale.
Data which are already generalized by others is difficult to generalize as the assumptions made and methods used are not necessarily known.

7. Generalisation operations
Simplification – reducing the number of coordinates required to replace an object
Smoothing – Reducing angularity of angels between the lines
Aggregation – Grouping point locations and representing them as areal objects
Amalgation – grouping several areal features into a larger element
Collapse – replacing a feature with symbol
Merging – grouping of line features
Refinement – Selecting specific portions of an pbject to represent the entire objects
Exaggregation – to amplify specific portion of an object
Enhancement – to elevate the message impaired by the object
Displacement – separating objects

8. The most used simplifications algorithm
Douglas – Peucker
process lines holistically
considers the entire line
selects critical points iteratively
Users can specify tolerance value, which determines how much details the algorithm eliminates

10. Different algorithms – each have + and -

11. THANK YOU!
Mrs Diana Makarenko-Piirsalu
MSc in Landscape Ecology and Environmental Protection
Geolytics OÜ
Mere tee 15, Saviranna,
Jõelähtme vald, Harjumaa, ESTONIA
Mob
ESTP Course: Use of GIS in NSIs – Representing statistics on thematic maps,
Kongsvinger, Norway, 5th to 7th of March, 2013