Latent trajectory models for space‐time analysis: An application in deciphering spatial panel data

This article introduces latent trajectory models (LTMs), an approach often employed in social sciences to handle longitudinal data, to the arena of GIScience, particularly space‐time analysis. Using the space‐time data collected at county level for the whole United States through webpage search on the keyword “climate change,” we show that LTMs, when combined with eigenvector filtering of spatial dependence in data, are very useful in unveiling temporal trends hidden in such data: the webpage‐data derived popularity measure for climate change has been increasing from December 2011 to March 2013, but the increase rate has been slowing down. In addition, LTMs help reveal potential mechanisms behind observed space‐time trajectories through linking the webpage‐data derived popularity measure about climate change to a set of socio‐demographic covariates. Our analysis shows that controlling for population density, greater drought exposure, higher percent of people who are 16 years old or above, and higher household income are positively predictive of the trajectory slopes. Higher percentages of Republicans and number of hot days in summer are negatively related to the trajectory slopes. Implications of these results are examined, concluding with consideration of the potential utility of LTMs in space‐time analysis and more generally in GIScience.     

Characterization of a Historical Cannonball from the Fortress of San Juan De Ulúa Exposed to a Marine Environment

Metallurgical analyses and chemical characterizations were carried out on historical cannonballs from the Fortress of San Juan de Ulúa, Veracruz, México. Cannonballs dating from the 18th and 19th centuries share metallurgical characteristics similar to those of material coming from a shipment of ammunition found in the wreck of a sunken French ship from the battle of Trafalgar. The analyses show that the base material is grey cast iron with a carbon equivalent of 4.94 and a ferritic–perlitic matrix, in which the high phosphorus content has led to the formation of iron phosphide compounds in conjunction with a homogeneous distribution of carbon graphite flakes of Type C. In addition, corrosion products from samples revealed the presence of various crystalline iron compounds (X‐ray diffraction), mostly highly chlorinated iron compounds identified as akaganeite. X‐ray fluorescence identified various characteristics of the corrosion products as a function of the sampling depth. FT–IR spectroscopy revealed that the main difference between the corrosion products (internal and external) is determined by the number of organic species. Differential scanning calorimetry corroborated that these corrosion products are thermally stable compounds at elevated temperatures.     

A Box Containing Carpenter's Accessories from The Akko 1 Shipwreck,Israel: Archaeometallurgical Analysis of Surviving Ironwork

The Akko 1 shipwreck was an Egyptian armed vessel, built at the beginning of the 19th century. A wooden saw handle and a box containing iron nails and two split pins were discovered towards the stern. Given their function, location and context, these were part of the ship's carpenter's tools and accessories. A methodology was developed for conducting systematic metallurgical analysis in order to understand the manufacturing process of the surviving ironwork items, as well as to enlarge our knowledge regarding ironworking technologies during the early 19th century. Such methodology may assist in the future understanding of the technological evolution of similar wrought‐iron objects. The results demonstrated that the artefacts have a wrought‐iron heterogeneous microstructure and were manufactured by hot‐working prior to surface hardening by pack carburization.     

LOCALIZATION AND INDUSTRY CLUSTERING ECONOMETRICS: AN ASSESSMENT OF GIBBS MODELS FOR SPATIAL POINT PROCESSES

The objective of this paper is to assess an approach to statistical modeling of point referenced establishment data that permit inclusion of “environmental” or establishment‐specific covariates and specific forms of interestablishment interaction. Gibbs models are used to decompose the conditional intensity of the spatial point process into trend and interaction components. The trend is composed of access measures (primarily different classes of roads) and three different interaction processes are tested: Geyer, area interaction, and Strauss hard core. While the models used have proved to be useful in ecology, we are unaware of any applications to establishment or firm data. In empirical application, the models yield intuitively appealing results for the trend component, and the ability to specify the interaction component gives deeper insights into interestablishment spatial dynamics than any previously published methods.