Aoristic analysis: the spatial interpretation of unspecific temporal events

Abstract

Temporal limitations of GIS databases are never more apparent than when the time of a change to any spatial object is unknown. This paper examines an unusual type of spatiotemporal imprecision where an event occurs at a known location but at an unknown time. Aoristic analysis can provide a temporal weight and give an indication of the probability that the event occurred within a dee ned period. Visualisation of temporal weights can be enhanced by modii cations to existing surface generation algorithms and a temporal intensity surface can be created. An example from burglaries in Central Nottingham (UK) shows that aoristic analysis can smooth irregularities arising from poor database interrogation , and provide an alternative conceptualisation of space and time that is both comprehensible and meaningful. 1. Introduction Recognition of the importance of time within many GIS has brought with it a recognition of the limitations of many GIS to cope with this extra variable. There now exists a considerable literature on spatiotemporal databases and their design (Al-Taha et al. 1994) and this is perhaps in response to the criticism levelled at some GIS that the database is often perceived to be one of the weaker aspects of the system. For example, advances in spatial display and analysis have not been matched by improvements in database functionality, and 'the rigid spatio-temporal framework embedded in the current generation of GIS is too restrictive to capture the current urban reality' (Sui 1998, p. 661). Temporal variables relating to spatial objects are stored in the database. Previously; 'the conceptual and practical di culties in representing and analysing complex spatial patterns within GIS have caused the representation and analysis of the temporal dynamics of those spatial patterns to be ignored' (Peuquet 1994, p. 442). With limited analytical capability within the GIS database, often accompanied by the assumption that changes occur at known times with a xed duration, the opportunities to examine the changing spatial distributions and patterns of objects over time are limited. The picture is more complicated when the times of changes are unknown or have variable time spans and it is only possible to estimate a probability that a change occurred at a given time.

DOI: 10.1080/136588100424963

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