Generate and Analyze Multi-Level Data Spatial microsimulation involves the generation, analysis, and modeling of individual-level data allocated to geographical zones. Spatial Microsimulation with R is the first practical book to illustrate this approach in a modern statistical programming language. Get Insight into Complex Behaviors The book progresses from the principles underlying population synthesis toward more complex issues such as household allocation and using the results of spatial microsimulation for agent-based modeling. This equips you with the skills needed to apply the techniques to real-world situations. The book demonstrates methods for population synthesis by combining individual and geographically aggregated datasets using the recent R packages ipfp and mipfp. This approach represents the "best of both worlds" in terms of spatial resolution and person-level detail, overcoming issues of data confidentiality and reproducibility. Implement the Methods on Your Own Data Full of reproducible examples using code and data, the book is suitable for students and applied researchers in health, economics, transport, geography, and other fields that require individual-level data allocated to small geographic zones. By explaining how to use tools for modeling phenomena that vary over space, the book enhances your knowledge of complex systems and empowers you to provide evidence-based policy guidance.
This book is a practical guide on how to design, create and validate a spatial microsimulation model. These models are becoming more popular as academics and policy makers recognise the value of place in research and policy making.
Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to ...
Small Area Estimation and Microsimulation Modeling is the first practical handbook that comprehensively presents modern statistical SAE methods in the framework of ultramodern spatial microsimulation modeling while providing the novel ...
... H. Mwambi, J. Githure, A. Toure, and F. E. McKenzie. Malaria Journal, 3(1):29, 2004. 149. D. Dery, C. Brown, K. Asante, M. Adams, D. Dosoo, S. Amenga-Etego, M. Wilson, D. Chandramohan, B. Greenwood, and S. Owusu-Agyei.
Throughout the second half of the last century, the farm-level simulation approach 'slipped out of fashion' as agricultural policy modellers tended to favour macro models, either partial equilibrium models of the agriculture sector or ...
The widespread popularity of geographic information systems (GIS) has led to new insights in countless areas of application. It has facilitated not only the collection and storage of geographic data, but also the display of such data.
Mapping social networks, spatial data & hidden populations. Thousand Oaks: Sage. Simon, H. (1955). On a class of skew distribution functions. Biometrika, 42(3–4), 425–440. Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. (2010).
This is the applied primer for Geocomputation in the social sciences.
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis.
26 Big data, privacy and the policy process in the United States In regional economic development Roger Stough and Dennis ... The ways big data can potentially contribute to enhancing the quality of public policy at each step are then ...