Agent-Based Extensions to a Spatial Microsimulation Model of Demographic Change

Wu, Belinda; Birkin, Mark

doi:10.1007/978-90-481-8927-4_16

Cited by 21 publications

(12 citation statements)

References 9 publications

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“…Similar methods of optimization of demographic forecasts are presented in a significant amount of the scientific research relying on agent-oriented approach (Silverman et al, 2013;Wu, Birkin, 2012;Ма-каров et al, 2014). This technique has been evaluated by us during preparation of the demographic forecast for the Central and Eastern Europe countries (Манаков, Суворков, 2017).…”

Section: Source Base and Methodology Of The Researchmentioning

confidence: 97%

Analysis of demographic processes in Poland and the Baltic States since the end of the XVIII century and forecast up to 2096

Манаков

Суворков

2017

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Section: Source Base and Methodology Of The Researchmentioning

confidence: 97%

Analysis of demographic processes in Poland and the Baltic States since the end of the XVIII century and forecast up to 2096

Манаков

Суворков

2017

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“…Previous geographical research has employed this approach for studying student migration patterns in Leeds, 56 demonstrating that a combined model utilizing the two methods was able to model migration patterns more accurately than a microsimulation model on its own. Additionally, this combination has been used to investigate mortality data in the same city, 87 interactions which can be coded into models. 88 Clearly, combining the two techniques presents a considerable array of opportunities.…”

Section: Wh Ere To N Ext?mentioning

confidence: 99%

Application of geographic information systems and simulation modelling to dental public health: Where next?

Broomhead

Ballas

Baker

2018

Comm Dent Oral Epid

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Public health research in dentistry has used geographic information systems since the 1960s. Since then, the methods used in the field have matured, moving beyond simple spatial associations to the use of complex spatial statistics and, on occasions, simulation modelling. Many analyses are often descriptive in nature; however, and the use of more advanced spatial simulation methods within dental public health remains rare, despite the potential they offer the field. This review introduces a new approach to geographical analysis of oral health outcomes in neighbourhoods and small area geographies through two novel simulation methods—spatial microsimulation and agent‐based modelling. Spatial microsimulation is a population synthesis technique, used to combine survey data with Census population totals to create representative individual‐level population datasets, allowing for the use of individual‐level data previously unavailable at small spatial scales. Agent‐based models are computer simulations capable of capturing interactions and feedback mechanisms, both of which are key to understanding health outcomes. Due to these dynamic and interactive processes, the method has an advantage over traditional statistical techniques such as regression analysis, which often isolate elements from each other when testing for statistical significance. This article discusses the current state of spatial analysis within the dental public health field, before reviewing each of the methods, their applications, as well as their advantages and limitations. Directions and topics for future research are also discussed, before addressing the potential to combine the two methods in order to further utilize their advantages. Overall, this review highlights the promise these methods offer, not just for making methodological advances, but also for adding to our ability to test and better understand theoretical concepts and pathways.

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“…Also Benenson et al (2003) took the same approach to understand residential dynamics in Yaffo (Israel). Among recent works in this area, agent-based extensions of a spatial microsimulation model of demographic change have been proposed by Wu & Birkin (2012) for projecting the student migration and mortality in Leeds (UK). Geard et al (2013) showed an example of how agent-based modelling can be applied to create a synthetic population able to describe basic demographic processes and explore their interaction with patterns of infection and immunity.…”

Section: Related Workmentioning

confidence: 99%

Approaching parallel computing to simulating population dynamics in demography

et al. 2016

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Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model.

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Agent-Based Extensions to a Spatial Microsimulation Model of Demographic Change

Cited by 21 publications

References 9 publications

Analysis of demographic processes in Poland and the Baltic States since the end of the XVIII century and forecast up to 2096

Analysis of demographic processes in Poland and the Baltic States since the end of the XVIII century and forecast up to 2096

Application of geographic information systems and simulation modelling to dental public health: Where next?

Approaching parallel computing to simulating population dynamics in demography

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