Error sensitive historical GIS: Identifying areal interpolation errors in time series data

Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.

Prices and Wages in Ireland, 1700-1850

The article presents primary research on rural wages and the prices of agricultural goods and draws conclusions concerning the trend in the living conditions of rural workers in the century before the Great Irish Famine of 1845-1850.

Politics, Patriotism and Women in Ireland, Britain and Colonial America, c.1700-c.1780

The use of the consumer boycott as a political tool is commonly associated with pre-revolutionary colonial America and has been identified by historians as an important means through which American women were politicized. This article argues that from the late seventeenth century, Irish political discourse advocated the non-consumption of imported goods and support for home manufactures by women in ways that were strikingly similar to those used later in North America. In Ireland and, subsequently in the American colonies, the virtuous woman consumer was given an active public role by political and social commentators. Rather than being a “brilliantly original American invention,” as T. H. Breen has argued, the political exploitation of a consumer boycott and the promotion of local industry were among what Bernard Bailyn has described as the “set of ideas, already in scattered ways familiar” to the revolutionary leaders through the Irish experience. The article also argues that a shared colonial environment gave Irish and American women a public patriotic role in the period, c. 1700–1780 that they did not have in the home countries of England and Scotland.

Rao-blackwellized particle filter for human appearance and position tracking

In human motion analysis, the joint estimation of appearance, body pose and location parameters is not always tractable due to its huge computational cost. In this paper, we propose a Rao-Blackwellized Particle Filter for addressing the problem of human pose estimation and tracking. The advantage of the proposed approach is that Rao-Blackwellization allows the state variables to be splitted into two sets, being one of them analytically calculated from the posterior probability of the remaining ones. This procedure reduces the dimensionality of the Particle Filter, thus requiring fewer particles to achieve a similar tracking performance. In this manner, location and size over the image are obtained stochastically using colour and motion clues, whereas body pose is solved analytically applying learned human Point Distribution Models.

Exploiting spatio-temporal constraints for robust 2D pose tracking

We present a Spatio-temporal 2D Models Framework (STMF) for 2D-Pose tracking. Space and time are discretized and a mixture of probabilistic "local models" is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatio-temporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the problem, we propose to apply a Rao-Blackwellized Particle Filter (RBPF) in the 2D-Pose eigenspace, thus interpolating unseen data between view-based clusters. The fitness to the images of the predicted 2D-Poses is evaluated combining our STMF with spatio-temporal constraints. A robust, fast and smooth human motion tracker is obtained by tracking only the few most important dimensions of the state space and by refining deterministically with our STMF.