A Review of Microsimulation and Hybrid Agent-Based Approaches

Birkin, Mark; Wu, Belinda

doi:10.1007/978-90-481-8927-4_3

Cited by 42 publications

(25 citation statements)

References 18 publications

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“…This is particularly important for modelling school children and their carers (this group was noted to be the most likely contributor to the difference between the model and real data). Data for households could be estimated from the census through spatial microsimulation [40,41]. Doing activities with other household members would have an impact on the activity intensities of the agents.…”

Section: Future Workmentioning

confidence: 99%

Quantifying the ambient population using hourly population footfall data and an agent-based model of daily mobility

Crols

Malleson

2019

Geoinformatica

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The ambient population, i.e. the demographics and volume of people in a particular location throughout the day, has been studied less than the night-time residential population. Although the spatio-temporal behaviour of some groups, such as commuters, are captured in sources such as population censuses, much less is known about groups such as retired people who have less documented behaviour patterns. This paper uses agent-based modelling to disaggregate some ambient population data to estimate the size and demographics of the constituent populations during the day. This is accomplished by first building a model of commuters to model typical 9-5 workday patterns. The differences between the model outputs and real footfall data (the error) can be an indication of the contributions that other groups make to the overall footfall. The research then iteratively simulates a wider range of demographic groups, maximising the correspondence between the model and data at each stage. An application of this methodology to the town centre of Otley, West Yorkshire, UK, is presented. Ultimately this approach could lead to a better understanding about how town-and city-centres are used by residents and visitors, contributing useful information in a situation where raw data on the populations do not exist.

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Section: Future Workmentioning

confidence: 99%

Quantifying the ambient population using hourly population footfall data and an agent-based model of daily mobility

Crols

Malleson

2019

Geoinformatica

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“…Spatial microsimulation, for example, would benefit from the inclusion of an interactive element to the modelling of its populations, while agent‐based models can be made more representative with the inclusion of accurate population microdata. Indeed, these two bottom‐up methods are complementary, and address some of the limitations of the other . Spatial microsimulation models are able to process large‐scale data through list processing power, and provide numerical methods of reweighting population data, while agent‐based models include interactions and behaviours, not being restricted by statistical approaches.…”

Section: Where To Next?mentioning

confidence: 99%

“…Indeed, these two bottom-up methods are complementary, and address some of the limitations of the other. 55,86 Spatial microsimulation models are able to process large-scale data through list processing power, and provide numerical methods of reweighting population data, while agent-based models include interactions and behaviours, not being restricted by statistical approaches. This partnership has been used only once before within dental public health research.…”

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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“….it is more constructive to view the relationship of ABM to MSM as one of complementarity rather than supremacy." [3] 2.3 Methods for Developing a New Simulation There are three broad strategies for constructing a simulation. First, the simulation can be created from scratch.…”

Section: Observations On the State Of The Artmentioning

confidence: 99%