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Spatial microsimulation with R / Robin Lovelace and Morgane Dumont.

By: Contributor(s): Material type: TextSeries: Chapman & Hall/CRC the R seriesPublisher: Boca Raton : Chapman & Hall/CRC, 2016Edition: First editonDescription: 1 online resource : illustrations (black and white). 26 centimetersContent type:
  • text
  • still image
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781498711562 (PDF ebook) :
Subject(s): Additional physical formats: Print version :: No titleDDC classification:
  • 519.50285 23
LOC classification:
  • HA30.6 LOV
Contents:
Introducing spatial microsimulation with R; Introduction; Who this book is for and how to use it; Motivations; A definition of spatial microsimulation; Learning by doing; Why spatial microsimulation with R?; Learning the R language; Typographic conventions; An overview of the book SimpleWorld: A worked example of spatial microsimulation; Getting setup with the RStudio environment; SimpleWorld data; Generating a weight matrix; Spatial microdata; SimpleWorld in context What is spatial microsimulation?; Terminology; What spatial microsimulation is not; Applications; Assumptions Generating spatial microdata ; Data preparation; Accessing the input data; Target and constraint variables; Loading input data; Subsetting to remove excess information; Re-categorising individual level variables; Matching
individual and aggregate level data names; ‘Flattening’ the individual level data Population synthesis; Weighting algorithms; Iterative Proportional Fitting; Integerisation; Expansion; Integerisation and expansion; Comparing ipfp with mipfp Alternative approaches to population synthesis; GREGWT; Population synthesis as an optimization problem; simPop; The Urban Data Science Toolkit (UDST) Spatial microsimulation in the wild; Selection of constraint variables; Preparing the input data; Using the ipfp package; Using the mipfp package; Comparing methods of reweighting large datasets Model checking and evaluation; Internal validation; Empty cells; External validation Population synthesis without microdata; Global cross-tables and local marginal distributions; Two level aggregated data
Household allocation; Independent data (individuals and households); Cross data: individual and household level information Modelling spatial microdata ; The TRESIS approach to spatial microsimulation; Overview of TRESIS modelling system; Synthetic households; Using demand models to allocate synthetic households to zones using R; Conclusions Spatial microsimulation for agent-based models; ABM software; Setting up SimpleWorld in NetLogo; Allocating attributes to agents; Running SimpleWorld; Control the ABM from R Appendix: Getting up to speed with R Glossary Bibliography A summary appears at the end of each chapter.
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Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Books GHU Library QA278.2 LOV (Browse shelf(Opens below)) 2 Available GHU4774116940
Books GHU Library QA278.2 LOV (Browse shelf(Opens below)) 1 Available copy to be changed class number to: QA278.2 LOV GHU5711976733

Introducing spatial microsimulation with R; Introduction; Who this book is for and how to use it; Motivations; A definition of spatial microsimulation; Learning by doing; Why spatial microsimulation with R?; Learning the R language; Typographic conventions; An overview of the book SimpleWorld: A worked example of spatial microsimulation; Getting setup with the RStudio environment; SimpleWorld data; Generating a weight matrix; Spatial microdata; SimpleWorld in context What is spatial microsimulation?; Terminology; What spatial microsimulation is not; Applications; Assumptions Generating spatial microdata ; Data preparation; Accessing the input data; Target and constraint variables; Loading input data; Subsetting to remove excess information; Re-categorising individual level variables; Matching

individual and aggregate level data names; ‘Flattening’ the individual level data Population synthesis; Weighting algorithms; Iterative Proportional Fitting; Integerisation; Expansion; Integerisation and expansion; Comparing ipfp with mipfp Alternative approaches to population synthesis; GREGWT; Population synthesis as an optimization problem; simPop; The Urban Data Science Toolkit (UDST) Spatial microsimulation in the wild; Selection of constraint variables; Preparing the input data; Using the ipfp package; Using the mipfp package; Comparing methods of reweighting large datasets Model checking and evaluation; Internal validation; Empty cells; External validation Population synthesis without microdata; Global cross-tables and local marginal distributions; Two level aggregated data

Household allocation; Independent data (individuals and households); Cross data: individual and household level information Modelling spatial microdata ; The TRESIS approach to spatial microsimulation; Overview of TRESIS modelling system; Synthetic households; Using demand models to allocate synthetic households to zones using R; Conclusions Spatial microsimulation for agent-based models; ABM software; Setting up SimpleWorld in NetLogo; Allocating attributes to agents; Running SimpleWorld; Control the ABM from R Appendix: Getting up to speed with R Glossary Bibliography A summary appears at the end of each chapter.

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