Kirk Harland

Improving the Synthetic Data Generation Process in Spatial Microsimulation Models

Simulation models are increasingly used in applied research to create synthetic micro-populations and predict possible individual-level outcomes of policy intervention. Previous research highlights the relevance of simulation techniques in estimating the potential outcomes of changes in areas such as taxation and child benefit policy, crime, education, or health inequalities. To date, however, there is very little published research on the creation, calibration, and testing of such micro-populations and models, and little on the issue of how well synthetic data can fit locally as opposed to globally in such models. This paper discusses the process of improving the process of synthetic micropopulation generation with the aim of improving and extending existing spatial microsimulation models. Experiments using different variable configurations to constrain the models are undertaken with the emphasis on producing a suite of models to match the different sociodemographic conditions found within a typical city. The results show that creating processes to generate area-specific synthetic populations, which reflect the diverse populations within the study area, provides more accurate population estimates for future policy work than the traditional global model configurations.

Can a deterministic spatial microsimulation model provide reliable small-area estimates of health behaviours? An example of smoking prevalence in New Zealand.

Models created to estimate neighbourhood level health outcomes and behaviours can be difficult to validate as prevalence is often unknown at the local level. This paper tests the reliability of a spatial microsimulation model, using a deterministic reweighting method, to predict smoking prevalence in small areas across New Zealand. The difference in the prevalence of smoking between those estimated by the model and those calculated from census data is less than 20% in 1745 out of 1760 areas. The accuracy of these results provides users with greater confidence to utilize similar approaches in countries where local-level smoking prevalence is unknown.

Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing

Developing low carbon cities is a key goal of 21st century planning, and one that can be supported by a better understanding of the factors that shape travel behaviour, and resulting carbon emissions. Understanding travel based carbon emissions in mega-cities is vital, but city size and often a lack of required data, limits the ability to apply linked land use, transport and tactical transport models to investigate the impact of policy and planning interventions on travel and emissions. Here, we adopt an alternative approach, through the development of a static spatial microsimulation of people’s daily travel behaviour. Using Beijing as a case study, we first derive complete activity-travel records for 1026 residents from an activity diary survey. Then, using the 2000 population census data at the sub-district level, we apply a simulated annealing algorithm to create a synthetic population at fine spatial scale for Beijing and spatially simulate the population’s daily travel, including trip distance and mode choice at the sub-district scale. Finally, we estimate transport CO2 emission from daily urban travel at the disaggregate level in urban Beijing.

Using Agent-Based Models for Education Planning: Is the UK Education System Agent Based?

Agent-Based Modelling (ABM) is a relatively new spatial modelling technique. The ability of ABM to simulate a real world system, the UK education market, is explored in this chapter. It is shown how a simple ABM incorporating common sense rules can provide acceptable results with over 60% of pupils being allocated to the correct schools and 75% of schools containing at least 50% of correct pupils when compared to observed data. The exploration outlined here highlights that the education has a good deal to offer researchers in the ABM field. Possibly more importantly, the real potential of ABM as a technique for simulating real world systems and delivering appreciable benefits to the general population is demonstrated.

Simulating Spatial Dynamics and Processes in a Retail Gasoline Market: An Agent-Based Modeling Approach

Simulating the dynamics and processes within a spatially influenced retail market, such as the retail gasoline market, is a highly challenging research area. Current approaches are limited through their inability to model the impact of supplier or consumer behavior over both time and space. Agent-based models (ABMs) provide an alternative approach that overcomes these problems. We demonstrate how knowledge of retail pricing is extended by using a ‘hybrid’ model approach: an agent model for retailers and a spatial interaction model for consumers. This allows the issue of spatial competition between individual retailers to be examined in a way only accessible to agent-based models, allowing each model retailer autonomous control over optimizing their price. The hybrid model is shown to be successful at recreating spatial pricing dynamics at a national scale, simulating the effects of a rise in crude oil prices as well as accurately predicting which retailers were most susceptible to closure over a 10-year period.

The classification of space-time behaviour patterns in a British city from crowd-sourced data

The use of social messaging as a means to represent activity and behaviour patterns across small geographical areas is explored. A large corpus of messages provides the source from which a range of interesting marker words are identified. Profiles of the variations in language across neighbourhoods can then be constructed. Areas are classified on the basis of the types of messages which they tend to generate. The resulting patterns are interpreted as suggesting that variations in behaviour and activity over time within an urban area are an important adjunct to well-established spatial variations. It is asserted that further elaboration of these promising investigations within appropriate analytic frameworks could extend our understanding of movement and behaviour patterns in cities in important ways.

Identifying populations and areas at greatest risk of household food insecurity in England

Despite the increasing recognition of household food insecurity as a policy issue, there is currently no routine measurement of food insecurity in the UK. There is nothing to suggest that Government will address this in the near future for all parts of the UK. In which case, policy makers and campaigners might instead seek out consistent and robust measures of the population-level factors which are known to contribute to food insecurity. However, no systematic measures exist, meaning that resources may not be targeted at those areas most in need. This paper presents the first objective estimate of high population-level risk of household food insecurity in English neighbourhoods (4.09% of the population, 95%CI 4.08-4.10) using public data. Estimated geographic distribution of factors contributing to household food insecurity is customisable to local pressures and is adaptable to settings outside of England.