Fusing remote sensing with demographic data for synthetic population generation

When building agent-based models related to “real” world locations and people, the challenge is to build agents which resemble people on the ground. I have blogged about microsimulation approaches before and their utility with respect to agent-based models. A new paper in the International Journal of Geographical Information Science by alumnae from the Department of Computational Social Science here at GMU have developed a new algorithm which could prove useful. Below is abstract of the paper:

We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

Full Reference:

Rizi, S.M.M., Łatek, M.M. and Geller, A. (2012), ‘Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan’, International Journal of Geographical Information Science, DOI:10.1080/13658816.2012.734825.

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Fusing remote sensing with demographic data for synthetic population generation

When building agent-based models related to “real” world locations and people, the challenge is to build agents which resemble people on the ground. I have blogged about microsimulation approaches before and their utility with respect to agent-based models. A new paper in the International Journal of Geographical Information Science by alumnae from the Department of Computational Social Science here at GMU have developed a new algorithm which could prove useful. Below is abstract of the paper:

We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

Full Reference:

Rizi, S.M.M., Łatek, M.M. and Geller, A. (2012), ‘Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan’, International Journal of Geographical Information Science, DOI:10.1080/13658816.2012.734825.

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6 Fellowships £10,000 Each at The Centre for Spatial Analysis and Policy (CSAP) in Leeds or the Centre for Advanced Spatial Analysis (CASA) in London

We are pleased to announce the availability of six fellowships are available to support individuals in non-academic institutions to undertake defined research projects at the Centre for Spatial Analysis and Policy (CSAP) in Leeds or the Centr…

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