It is always difficult to validate a spatial MSM due to the lack of appropriate empirical data. As the microdata used to estimate the transition probabilities in the dynamic MSM are often subject to sampling error or lack of critical characteristics to explain heterogeneous behaviours, it is not surprising that the predictions of unaligned dynamic MSMs can drift away from benchmark aggregates such as the official population projections. On the other hand, the decision makers and some researchers often have used the official datasets/models in the processes of decision making or a study. Such users are often reluctant to accept results/models drifting too far away from the official estimates. Due to the above reasons, recently the result alignment has emerged as a crucial component of many dynamic MSM.
Nowadays “almost all existing dynamic MSMs are adjusted to align to external projections of aggregate or group variables when used for policy analysis” (Anderson, 2001).
In an MSM little attention to changes in the underpinning transition probabilities while Macro models do. Therefore alignment exercises can be used to improve the MSM inputs. The transition probabilities and rates can also arguably better estimated for aggregate populations than disaggregate, as errors either way at disaggregate level cancel out at aggregate level. However, the problem with macro top-down approach is that it generates all sorts of consistencies, e.g. the outputs of the model are no longer a simple function of the inputs. The macroscopic model projections are less sensitive to sub-population disaggregation and often overlook the disaggregated characteristics.
Bækgaard (2002) in his thorough investigation of alignment identified the objective of alignment as to compensate for imperfectness of data and estimation techniques. As he pointed out: not only the total output, but also the distribution of base data and output can be aligned. Alignment not only includes the calibration of processes but also the adjustment of base data caused by, e.g. sample stratification. There are two major classes of alignment: alignment of outputs and alignment of inputs.
Through alignment exercises, macroscopic feedback can be imposed on MSM aggregate results. Therefore by aligning the aggregated results to ONS projection results, it enables incorporation of the recent and future population trends revealed in ONS assumptions that have significant impact on simulation results. The attempt is to find out the assumptions underlying the most up-to-date ONS SNPP projections on the basis of year 2006 population and modify the probabilities that have been used in various demographic transitions accordingly to align the model to the ONS projections.
There are a couple of steps in our alignment exercises. A framework has been designed for this exercise. The alignment exercises are composed of 2 87
assessments between 3 models. Model A is the ONS aggregate projection, Model B is a naïvely disaggregated hybrid MSM and Model C adopts a full disaggregation. The first assessment is through the alignments to the ONS projections by applying the naïvely disaggregated/averaged estimations that are used in ONS projection to all populations in Leeds wards. Then the results from the modified simulation are re-aggregated and compared against ONS’ aggregated results to test the consistency of the model. In the second assessment, the ONS estimations are fully disaggregated to the ward level and the results are re-aggregated and compared to test the robustness of the model. The analyses and framework specified here will be discussed in details in Chapter 7.
It should be noticed that in these alignment exercises this study only tried to match the assumptions/trends found in ONS’ SNPP. However this model is not trying to recreate the results of ONS. Due to the aggregate nature of the ONS SNPP projections, such projection results do not necessarily provide the best representation of Leeds population, especially at the scale of small areas. Previous studies have recognised the limitations in subnational projections (Smith and Shahidullah, 1995; Rees et al., 2001; Smith and Tayman 2003). The alignment exercises will be the first steps towards model validation. Further development of the validation method is also being considered, e.g. investigation through more empirical studies will be carried out and findings from this will be used to help refine our model so that the model provides a better representation of the Leeds population. More details of the alignment framework and model validation will be discussed in Chapter 7.
3.6 Conclusion
We discussed the methodology of the project in this chapter. The methods used for system design and system development, as well as how the two aspects link to each other have been explained.
The population projection method, demographic processes, main components of the system, the development and validation methods of this dynamic spatial MSM have been discussed in this chapter. The component- cohort projection method has been explained using the Lexis diagram. The six demographic processes of Ageing, Mortality, Marriage, Fertility, Migration, and Health Change and the sequence of the processes have also been introduced. The representation of individuals and the interdependency between the individual, household and environment has also been explained. To explain how such design has been implemented into the model, the development methods and data selection have been described. Also the proposed method for model validation has been described with a framework design for alignment to ONS projections. However, there is a noticeable difference: this study is only trying to align to the ONS assumption, not trying to recreate the ONS results. Due to the limitation of aggregate projections, ONS projections do not necessarily provide the best representation of the ward population in Leeds.
The hybrid modelling approach has also been described here. This is mainly because we are trying to resolve two issues that arise from the requirement of this model. This model would like to use the list processing power and the real data roots of the MSM to tackle the scale issue that this study is facing: 761 thousand individuals in Leeds (mid-2007). The scale challenge for the model arises not only from the size of the base population, but also from the richness of the attributes, probability generation and updating. At the same time, this study attempts to capture the individual movements, interactions and behaviours of people in Leeds in the model. It is found that MSM is not a very flexible instrument to accommodate modelling of such behaviours because of its statistical nature and lack of quality data on specific behaviours. ABM is an alternative social modelling approach, where individuals are modelled as agents that move around and interact with each other and the environment according to their built-in rules. Thus, it is very flexible to model heterogeneous individuals/sub-populations where there is a knowledge gap or data limitations.
After careful consideration, it is believed that a hybrid modelling approach can combine the strength of both MSM and ABM to accommodate both aspects of requirements of the model. Adopting the hybrid approach, we have achieved the goal of both the effective handling of a large-scale individual based system, as well as providing extra flexibility to model various movements, interactions and behaviours of sub-populations in different scenarios.