Spatial microsimulation with R /
Lovelace, Robin
Spatial microsimulation with R / Robin Lovelace and Morgane Dumont. - First editon - 1 online resource : illustrations (black and white). 26 centimeters. - Chapman & Hall/CRC the R series .
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.
Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time;
Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
9781498711562 (PDF ebook) : £57.99 £57.99
TANDF_387630 Ingram Content Group
(WlAbNL)99964850002419
GBB653374 bnb
Spatial analysis (Statistics)--Data processing.
R (Computer program language)
HA30.6 LOV
519.50285
Spatial microsimulation with R / Robin Lovelace and Morgane Dumont. - First editon - 1 online resource : illustrations (black and white). 26 centimeters. - Chapman & Hall/CRC the R series .
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.
Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time;
Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
9781498711562 (PDF ebook) : £57.99 £57.99
TANDF_387630 Ingram Content Group
(WlAbNL)99964850002419
GBB653374 bnb
Spatial analysis (Statistics)--Data processing.
R (Computer program language)
HA30.6 LOV
519.50285