ToToPI (Topographie de Tours Pré-Industriel), a GIS for understanding urban dynamics based on the OH_FET model (Social Use, Space and Time)
Résumé
ToToPI, for Topography of Tours Pre-Industrial, is a GIS for studying of the city of Tours (France) in large time span. The concepts for understanding the urban dynamic is based on considering the town as a set of complex objects, taking a systemic approach. The town system used to study the urban fabric over large time spans is composed of three sub-systems relating to historical objects from the level of the excavation to that of the former urban space: function (social use), space (location, surface area and morphology) and time (dating, duration and chronology). The historical object is the analytical unit of the studied space. It is the Cartesian product of the three sets, Social use, Space and Time, from which it stems. The OH_FET model is based on this process. The Historical Object (OH) is broken down into three types of simple object, functional (EF), spatial (ES) and temporal (ET). The relationships between these three sets each characterize an interaction (social use-space, social use-time, time-space, or function-space-time). In addition to reconstructing the OH, they allow urban changes to be observed by analyzing the distributions and mapping of each of the entities singly or two-by-two. The originality of this procedure lies in its approach whereby it is possible to start not from the mapping of a phenomenon at a time t1 and comparing it to that at a time t2, but to look at it in the same way whether its input is social use, space or time. The heuristic value of this modelling lies in the shift from description (what, where, when) to understanding the phenomena of change (how, why). The Implementation of OH_FET model in ToToPI, with classical GIS software (ArcGIS from ESRI), makes possible on one hand the analysis of data sets in large time span and on the other hand the creation of new analyses (and of new products which result from it as the temporal mappings). The paper will explain how GIS is used for historical data processing to understand time-space dynamics.