Introduction

Geographic information systems (GIS) use maps and spatial analysis as a way of investigating real-world problems. Although traditionally associated with the geographical disciplines, it has since been adopted and used throughout the social sciences, including urban and environmental planning, public and community health, political and social studies, crime prevention and transport to name a few (Longley et al., 2015). GIS originally developed out of computerised cartography and it still uses maps and mapping as a way of structuring, managing, visualising and analysing data. A GIS stores a map as a series of separate map layers, with each layer containing data on a particular theme. A theme could relate to the population of an area, such as population density or percentage of children of school age; physical infrastructure, for instance the road network or public transport routes; or the environmental quality of an area, such as the location of parks and green space or the concentration of air pollution. By layering these themes one on top of the other within the GIS digital mapping, it is possible to see how they relate to each other across an area and to produce composite maps that can reveal spatial patterns and relationships. GIS contains tools that allow the data to be manipulated and analysed spatially and statistically so it is possible to determine, for example, how many people live within five minutes’ walk of a bus stop or how many children in low-income households live in a particular school catchment area. This combination of mapping and spatial analysis makes GIS a powerful tool when researchers are investigating how social and economic problems vary across space, how spatial areas relate to one another, or how policy interventions may have different outcomes geographically. And as GIS can map and analyse data at a variety of spatial scales it is possible to investigate problems at the very local level through to the national and international level.

Traditionally, GIS has been a predominately static tool – it emphasises variation across space rather than change over time. This has partly been a reflection of data availability – socioeconomic data collection tends to be cross-sectional rather than longitudinal and in terms of mapping, data are usually only available aggregated into predefined geographical areas whose boundaries often change, hampering comparisons between time periods.