This book explores a little-studied arena that exists between science and technology, an arena in which a singular and important variety of open-ended, multi-purpose instrumentation is developed by practitioners (neither scientist nor engineer, call them research-technologists) for use in academia, industry, state metrology and technical services, and considerably beyond. The generic instrumentation designed in this almost subterraneously institutionalized/professionalized, interstitial arena fuels both science and engineering work. This involves intermittent crossings of the boundaries that demarcate and protect the conventional cognitive and artefact cultures familiar to many historians and sociologists. Research-technologists thereby comprise a distinctive (but never distinct) transverse science and technology culture that generates a species of pragmatic universality, which in turn provides multiple and diversified audiences with a common repertory of vocabularies, notational systems, images, and perhaps even paradigms. Research-technology practitioners deliver a lingua franca that contributes to cognitive, material, and social cohesion. Research-technology is about the complementarity between boundary-crossing and the stability/maintenance of boundaries.

Methods used for regional development analysis are employed mainly to make forecasts and comparisons. Forecasting models of various types (e.g. econometric models) are usually used for forecasting. Recently, vector-autoregressive models (VAR) have become popular. These models were proposed by Sims in 1980. On the contrary, taxonomic methods (that are in the center of attention as far as the present publication is concerned) are most often employed to make comparisons. Linear ordering methods, including standard methods, are the most popular among ta- nomic methods. They are based on different distance and similarity measures, which leads to the fact that they do not always provide reliable information. When, for example, one construes the standard for a base year and then compares it with data for other years, it may turn out that the measure determined will have worse values than the standard for a real object (region, micro region) although this object is better from the standard. Hence, one must look for new methods employed in regional development analysis or improve hitherto existing ones in such a way so that information obtained re?ects the reality to a larger extent. The main aim of the present publication is to work out methodological basis for regional development analysis based on vector calculus together with assumptions about computer system supporting the implementation of the method suggested.