Microsimulation - Spatial Microsimulation

Spatial Microsimulation

Economic and health approaches to microsimulation provide insight into the impacts of changes in environmental, economic, or policy conditions on a given population of individuals. However, the impacts of many changes are context dependent, meaning that the same alteration (e.g. in income tax bands) may have desirable effects in some regions, but undesirable effects in others. This understanding lies at the root of spatial approaches to microsimulation. The term spatial microsimulation refers to a set of techniques that allow the characteristics of individuals living in a particular area to be approximated, based on a set of 'constraint variables' that are known about the area. As with econometric microsimulation, spatial microsimulation can be either dynamic or static, and can include interacting or passive units.

Guy Orcutt is widely cited as the originator of spatial microsimulation. Spatial microsimulation has high computational and data requirements and some degree of computer programming is a prerequisite to setting up models. For these reasons, the technique is not widely used. However, a number of factors have led to rapid growth in the number of publications on spatial microsimulation within academic geography and related disciplines. These include:

• The availability and low costs of powerful personal computers.
• The emergence of user friendly and low-cost computer software with which microsimulation models can be created. R, Java, and Python are examples, each of which can be classified as Free and open source software.
• Improving data collection activities by governments, corporations, and non-profit organisations.
• Improving data accessibility.