Many uses of open data rely on being mapped – that is, provided with a spatial reference to their particular location on Earth. For example, various socio-economic indicators can be mapped on top of residential districts. Soil quality data can be linked to digital elevation models to model erosion potential, and the same soil data can be compared to geographically overlapping land ownership and land subsidy data. Because human activities are largely confined to the near-surface of the Earth, many open datasets would have far less impact without their spatial component. By applying different visualisation techniques and tools, various socio-environmental problems can be analysed more deeply, because many sets of data can be observed together, regardless of whether they refer to the physical or social world. For example, to illustrate the effect of a green infrastructure development program, instead of images and tables "locked" in a PDF document, the visualisation of this data on a map can provide a common basis for experts from different domains in researching public policy approaches to mitigate the effects of heat islands in urban environments.