Emplacing Current Trends in Feminist Historical Geography

In this article, the authors assess some of the major trends within anglophonic feminist historical geography appearing in the decade since Rose & Ogborn called for the development of an explicitly feminist approach to the subfield. In examining the 'geography' of feminist historical geographies, three main categories of scholarship are evident: a 'new' historical geography of North America, portions of which are informed by feminist theories and methods; a British school of feminist historical geography with a focus on the discipline of geography, geographical knowledges and colonialism/imperialism; and feminist historical geography interventions in cultural politics of space and place. A diversity of feminist methods and epistemologies appears across the literature. In an attempt to avoid a reading of these trends as better or worse approximations of historical 'progress', the authors conceptualize them as emplaced within a number of specific social and spatial contexts. Most recent work is concerned with the production of gender differences as they are worked through economic, political, cultural and sexual differences in the creation of past geographies. The continued need simply to write women into historical narratives and geographies, however, is also evident. The work of feminist historical geography questions and challenges geography's masculinist historical record.     

Information Entropy of Non‐Probabilistic Processes

We derive an expression for the entropy of non‐probabilistic distributions encountered in spatial and mathematical mappings. The entropy of non‐probabilistic distributions can be formulated using probabilistic notions of the hypothetical random redistribution of finite information. We show that the discrete approximation to the information content of spatial maps can be based on the discrete hypergeometric distribution. The resultant “associative” entropy is distinct from the Shannon entropy for probability distributions and addresses several shortcomings of the current entropy paradigm as applied to spatial analysis. The associative entropy statistic is distributed approximately as a chi‐squared random variable under limitations of variation. We formulate a univariate logical equivalent of the associative entropy statistic, freeing the paradigm from the degrees of freedom constraint to which it has been traditionally shackled. This entropy has application in spatial analysis and fuzzy set theory. The associative entropy is based on the concept of proportional information and is related to the Getis G‐statistics of spatial association and the Chi‐squared statistics of sample means. We explore the utility of the theory when applied to spatial distribution of vegetation in New Brunswick, Canada. The limitations and implications of the entropy expression are discussed and suggestions are made for future applications of the theory. This work is part of the development of an information theory framework for the analysis of landscape patterns of animal habitat.