This paper is an attempt to explain the concept of emergence of spatial systems. It indicates basic features of this concept, such as: coevolution, selforganization, patterns, sudden changes, hierarchy. The emergence of complex systems is very fruitful approach to the theoretical reconstruction of the processes of space economy. It should be included in the discussion on evolutionary economics and geography. Recently, in both disciplines creative research is carried out on this subject matter.
Fifteen species of isopods, representing 10 families, were recorded on holdfasts of the brown alga Himantothallus grandifolius . Material was collected in the 15–75 m depth range during the austral summer of 1979/80. The isopod community was dominated by Caecognathia antarctica (mean density 12.4 ± 13.1 ind./100 ml) followed by Cymodocella tubicauda (mean density 0.7 ± 2.1 ind./100 ml). Mean total density of isopods reached the value of 16.1 ± 14.0 ind./100 ml. The comparison with the other studies showed that hold− fasts are inhabited by a distinctive isopod community that differs from the isopod fauna associated with soft bottom of Admiralty Bay.
This paper presents a set of concepts aiming at the reconstruction of mechanisms of the development of economic space. These concepts are ordered in the way that consecutive concepts add new pieces of knowledge increasing the degree of cognition of the mechanisms of economic space. This set includes among others: shift from one steady-state to the next steady-states, selforganization and the development out of equilibrium, multiple equilibrium, punctuated equilibrium, innovation in the phase transition, pulsative course of development process, emergence of complex spatial systems, development code of the system of regions.
Following the results presented in , we present an efficient approach to the Schur parametrization/modeling of a subclass of second-order time-series which we term p-stationary time-series, yielding a uniform hierarchy of algorithms suitable for efficient implementations and being a good starting point for nonlinear generalizations to higher-order non-Gaussian nearstationary time-series.