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AbstractRegional scientists have increasingly been playing a very important role in the development and application of spatial microsimulation models for policy analysis. It has long been argued that spatial microsimulation modelling has enormous potential for the evaluation of the socio‐economic and spatial effects of major developments in the regional or local economy. This paper aims to add to this rapidly expanding work, by presenting a new spatial microsimulation model (SIMALBA) for Scotland (the development of which was co‐funded by the Scottish Government) and by demonstrating how it can be used to perform what‐if policy analysis in Scotland. The focus of the paper is on economic aspects of social and spatial inequality in the capital of Scotland, Edinburgh. The paper shows how spatial microsimulation modelling can address previously unanswered research questions in Scotland, particularly those relating to fiscal policy. The SIMALBA model has estimated income data for Scotland at output area level geography and this is the focus of the various 'what‐if' policy scenarios. Simulated data has been created using a deterministic reweighing algorithm to build a spatial microsimulation model by combining UK Census data for 2001 and Scottish Health Survey (SHS) data for 2003. The analysis demonstrates the importance of geography by examining trends at OA level in Scotland. The paper concludes with a discussion of the simulated data and resulting policy scenarios as well as the impact of this analysis for policy formation in Scotland.

The aim of this paper is to provide a new framework for the analysis and the evaluation of national social policies at the small-area level. In particular, the paper shows how microsimulation modelling can be employed to shed new light on the local impacts of major national policy changes such as taxes, regulations, government consumption, unemployment benefits, job seekers', and housing allowances, etc. Microsimulation modelling provides the possibility of defining the desired effects of economic and social policy, the instruments employed, and also the structural changes of those affected by socioeconomic policy measures. This paper builds on traditional economic microsimulation frameworks by adding a geographical dimension. More specifically, we seek to model national social policy impacts at a microspatial scale. First, spatial microsimulation modelling is used to synthesise a household micropopulation geographical database for an entire city. This micropopulation database has a wide range of demographic and socioeconomic attributes that are relevant to national social and economic policies and which play a major role in the determination of eligibility of households for various benefits and allowances. GIS software is used to identify the size and spatial location of particular groups such as the unskilled, low-waged, and undereducated. Finally, we explore potential social policies and demonstrate how microsimulation modelling can be used to perform what-if social policy analysis at the small-area level.

AbstractSpatial microsimulation is a powerful tool for creating large‐scale population datasets that can be used to assess spatial phenomena in health‐related outcomes. Despite this, it remains underutilized within dental public health. This paper outlines the development of an oral health focused microsimulation model for Sheffield (UK, SimSheffield), and how this can be used to assess potential socio‐spatial impacts of a sugar tax which was introduced in the United Kingdom in 2016 and is known as the Soft Drink Industry Levy (SDIL). Exploratory analysis showed areas paying more SDIL were not those with the highest tooth decay or deprivation scores as might be hoped (in the first case) and expected from the literature (in the second).

AbstractThe Philippines is one of the most populous countries in the world. In terms of population, it ranks twelfth globally and seventh in Asia behind China, India, Indonesia, Pakistan, Bangladesh and Japan. The estimated population of the country in 2010 was 94 million people. Using data from the Philippines 2000 Census, this paper presents a discussion of the creation of a 3‐tier hierarchical geodemographic system for the country at Barangay scale. Barangays are the smallest spatial entities in the structure of the administrative geography of the country. Most popular geodemographic systems are typically developed from continuous datasets. In this paper, we discuss how a geodemographic classification system can be created by combining categorical and continuous datasets. The first level of the Philippines geodemographic hierarchy ensures the population can be profiled broadly at Barangay level into seven super‐groups. The super‐groups are further subdivided into 24 groups and finally into 66 subgroups.