Matilde Trevisani

This study compares and contrasts the results of two lexical-based methods aimed at identifying content temporal trends in diachronic text corpora. A corpus of end-of-year addresses of the presidents of the Italian Republic constitutes a relevant case of political speech useful to understand how the temporal evolution of topics can be represented and whether a downward (ex post) or an upward (ex ante) extraction of topics is more effective for the identification of presidents’ distinctive traits and trends. The first method is a knowledge-based system (KBS), which identifies clusters of words sharing a similar temporal pattern through a three-step statistical learning procedure. The second is a structural topic model (STM), which identifies main topics by probing the possible effect of the year and president factors on the speech-topic and the topic-word distributions. In KBS clusters, the individual trait of the president stands out as one of the most relevant elements and determines the contents of speeches; moreover, topic trends can also be discerned ex post while interpreting the results. On the other hand, STM directly achieves the whole topic structure but seems not as powerful as expected in portraying the life cycle of words and detecting groups of words that distinguish the speeches of a specific president. As most presidential speeches are rich and cover a wide range of topics, the results suggest that, in this case, the interpretative tool offered by STM brings out more challenges than strengths. Conversely, direct observation of the temporal trajectory of individual words allows for more detailed analyses and meaningful results, thanks to the flexible and adaptive KBS approach.

The Bangladesh Arsenic Mitigation and Water Supply Program (BAMWSP) has compiled field-kit measurements of the arsenic content of groundwater for nearly five million wells. By comparing the spatial distribution of arsenic inferred from these field-kit measurements with geo-referenced laboratory data in a portion of Araihazar upazila, it is shown here that the BAMWSP data could be used for targeting safe aquifers for the installation of community wells in many villages of Bangladesh. Recent experiences with mobile-phone technology to access and update the BAMWSP data in the field are also described. It is shown that the technology, without guaranteeing success, could optimize interventions by guiding the choice of the drilling method that is likely to reach a safe aquifer and identifying those villages where exploratory drilling is needed.

A wide variety of tools are available, both parametric and nonparametric, for analyzing spatial data. However, it is not always clear how to translate statistical inferences into decision recommendations. This article explores the possibilities of estimating the effects of decision options using very direct manipulation of data, bypassing formal statistical analysis. We illustrate with the application that motivated this research, a study of arsenic in drinking water in nearly 5,000 wells in a small area in rural Bangladesh. We estimate the potential benefits of two possible remedial actions: (1) recommendations that people switch to nearby wells with lower arsenic levels; and (2) drilling new community wells. We use simple nonparametric clustering methods and estimate uncertainties using cross‐validation.