Using non-predetermined spatial disaggregation

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Non-predetermined spatial disaggregation is a method for utilising spatial information in a very flexible way in a situation where predetermined grid structures cannot be used. This method makes it possible to start with a coarse spatial disaggregation, and refine disaggregation in locations where detailed data becomes available. The method is based on an information structure where aggregation or disaggregation of information is performed based on a separate


Let there be an area A, which is being examined in a collaborative assessment. Some parts of A are known with higher detail than others. The level of information available depends on the people that happen to collaborate. Therefore, it is not known beforehand, what data becomes available and what the best way to divide the area A is. Still, there should be an efficient way to divide A into sub-areas so that the information that occurs during the assessment can be effectively used. A further complication is that the disaggregation may change in time during the assessment, or it may have differential needs for different variables. How should the spatial disaggregation be performed in such a situation?



The input data may be spatially distributed, or it may also be distributed along some other dimension. Different pieces of data may have very different spatial disaggregation.


The output should be such that it can be seamlessly utilised in other, relevant variables in the assessment.


The result is based on intuition and personal practical experience.



Area A is divided into n subareas Ai, i=1,2,...n. Ai must be exclusive and mutually exhaustive (i.e., they must not overlap, and together they must cover the whole area of A). Ai must be either

  • spatially defined areas, or
  • areas categorised based on another than spatial determinant. For example, Ai may be municipalities categorised into three groups based on the number of inhabitants: A1: under 50000, A2: 50000 - 250000, A3: above 250000 inhabitants. Also in this case, Ai must have a spatial interpretation, but for the assessment, there is no need to specify it within A, given i.
    • In this case, the description of each Ai is divided into two parts: a) the description of the actual property per each unit (e.g., municipality), and b) the number of units (municipalities) in each Ai.

Any of Ai can be further divided in the same way as A. However, if the Ai is based on a non-spatial determinant, it can be further divided into non-spatial sub-areas but not into spatial sub-areas.


Transforming between spatial indices

It is possible to transform the data from one spatial disaggregation index to another index. Disggregation and aggregation can be used for any part to make this transformation. However, the rules of exclusiveness and mutual exhaustiveness must always be fulfilled.

Utilising data from different levels

Typically, the following formulas apply for Q(A), where Q is some quantitative property of A:

Q(A) = sum(Q(Ai))

(if there is more information about Ai than A)

Q(Ai) = Q(A)*fi,

where fi are disaggregation weight factors (when there is more information about A than Ai).

There may also be cases where there is information about A and one of the Ai, say A1. Then, it might be practical to calculate the results in the following way

Q(A1) = Q(A) - Q(A1)

and for other Ai than A1

Q(Ai) = Q(A1)*fi,

where A1 is A without A1.