This paper addresses new trends in quantitative geography research. Modern social science research--including economic and social geography--has in the past decades shown an increasing interest in micro-oriented behav...This paper addresses new trends in quantitative geography research. Modern social science research--including economic and social geography--has in the past decades shown an increasing interest in micro-oriented behaviour of actors. This is inter alia clearly reflected in SIMs （spatial interaction models）, where discrete choice approaches have assumed a powerful position. This paper aims to provide in particular a concise review of micro-based research, with the aim to review the potential--but also the caveats---of micro models to map out human behaviour. In particular, attention will be devoted to interactive learning principles that shape individual decisions. Lessons from cognitive sciences will be put forward and illustrated, amongst others on the basis of computational neural networks or spatial econometric approaches. Particular attention will be paid to non-linear dynamic spatial models, amongst others, in the context of chaos theory and complexity science. The methodology of deductive reasoning under conditions of large data bases in studying human mobility will be questioned as well. In this context more extensive attention is given to ceteris paribus conditions and evolutionary thinking. The relevance of the paper will be illustrated by referring to various spatial applications in different disciplines and different application areas, e.g. in geography, regional science or urban economics.展开更多
文摘This paper addresses new trends in quantitative geography research. Modern social science research--including economic and social geography--has in the past decades shown an increasing interest in micro-oriented behaviour of actors. This is inter alia clearly reflected in SIMs （spatial interaction models）, where discrete choice approaches have assumed a powerful position. This paper aims to provide in particular a concise review of micro-based research, with the aim to review the potential--but also the caveats---of micro models to map out human behaviour. In particular, attention will be devoted to interactive learning principles that shape individual decisions. Lessons from cognitive sciences will be put forward and illustrated, amongst others on the basis of computational neural networks or spatial econometric approaches. Particular attention will be paid to non-linear dynamic spatial models, amongst others, in the context of chaos theory and complexity science. The methodology of deductive reasoning under conditions of large data bases in studying human mobility will be questioned as well. In this context more extensive attention is given to ceteris paribus conditions and evolutionary thinking. The relevance of the paper will be illustrated by referring to various spatial applications in different disciplines and different application areas, e.g. in geography, regional science or urban economics.
