Spatial Microsimulation with R

Spatial Microsimulation with R
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Artikel-Nr:
9781498711548
Veröffentl:
2016
Erscheinungsdatum:
21.03.2016
Seiten:
260
Autor:
Robin Lovelace
Gewicht:
423 g
Format:
228x151x20 mm
Sprache:
Englisch
Beschreibung:

Robin Lovelace is a University Academic Fellow at the University of Leeds specializing in methods of spatial data analysis and applied transport modeling. Creator of the stplanr package and a number of popular tutorials, he is an experienced R user, teacher, and developer. Robin uses open source software daily for spatial analysis, map making, statistics, and modeling. His current research focuses on online interactive mapping and modeling to provide the evidence base needed for a transition away from fossil fuels in the transport sector. Morgane Dumont is an applied mathematician currently undertaking a PhD at the University of Namur. She has a wealth of experience programming in R, Python, C, Fortran, and MATLAB®. Her research focuses on forecasting the health needs of the elderly in 2030 for Belgium. To achieve this aim, Morgane is developing a synthetic population for Belgium as an input to an agent-based model.
This is the first practical book on spatial microsimulation, an approach that involves the generation, analysis, and modeling of individual-level data allocated to geographical zones. Full of reproducible examples using code and data, the book demonstrates methods for population synthesis by combining individual and geographically aggregated datasets of administrative zones. This approach represents the "best of both worlds" in terms of spatial resolution and person-level detail, overcoming issues of data confidentiality and reproducibility.
Introducing spatial microsimulation with R. Introduction. SimpleWorld: A worked example of spatial microsimulation. What is spatial microsimulation? Generating spatial microdata. Data preparation. Population synthesis. Alternative approaches to population synthesis. Spatial microsimulation in the wild. Model checking and evaluation. Population synthesis without microdata. Household allocation. Modelling spatial microdata. The TRESIS approach to spatial microsimulation. Spatial microsimulation for agent-based models. Appendix. Glossary. Bibliography.

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