.png){kind=icon}
.png){kind=icon}
Return
'Truncate, replicate, sample': A method for creating integer weights for spatial microsimulation
.png){kind=icon}
OA
AI
DOI：10.1016/j.compenvurbsys.2013.03.004.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Iterative proportional fitting (IPF) is a widely used method for spatial microsimulation. The technique results in non-integer weights for individual rows of data. This is problematic for certain applications and has led many researchers to favour combinatorial optimisation approaches such as simulated annealing. An alternative to this is 'integerisation' of IPF weights: the translation of the continuous weight variable into a discrete number of unique or 'cloned' individuals. We describe four existing methods of integerisation and present a new one. Our method - 'truncate, replicate, sample' (TRS) - recognises that IPF weights consist of both 'replication weights' and 'conventional weights', the effects of which need to be separated. The procedure consists of three steps: (1) separate replication and conventional weights by truncation; (2) replication of individuals with positive integer weights; and (3) probabilistic sampling. The results, which are reproducible using supplementary code and data published alongside this paper, show that TRS is fast, and more accurate than alternative approaches to integerisation. (c) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Microsimulation
Integerisation
Iterative proportional fitting
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.3
Papers:
1.6K
Citations:
8.3K