Geographical Influences on Long-Run Development

We show that geographical factors influence long-run per capita income directly, as well as indirectly, through the quality of institutions. The direct influence of geography on per capita incomes is robust to the inclusion of a sub-Saharan Africa dummy and other tests. We obtain our results by replacing the usual instrument (settlers’ mortality (SM)) by stronger instruments for institutional quality (latitude, the share of the country in the temperate climatic zone). We also provide evidence that SM suffers from endogeneity with respect to institutional quality for early colonies, because of its dependence on nineteenth-century mortality data.

Red-shifts and red herrings in geographical ecology

I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated - the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables "significant" much more frequently than it should. The chances of mistakenly identifying a "significant" slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red-shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods.

Geographical variation in total and inorganic arsenic content of polished (white) rice

An extensive data set of total arsenic analysis for 901 polished (white) grain samples, originating from 10 countries from 4 continents, was compiled. The samples represented the baseline (i.e., notspecifically collected from arsenic contaminated areas), and all were for market sale in major conurbations. Median total arsenic contents of rice varied 7-fold, with Egypt (0.04 mg/kg) and India (0.07 mg/kg) having the lowest arsenic content while the U.S. (0.25 mg/kg) and France (0.28 mg/kg) had the highest content. Global distribution of total arsenic in rice was modeled by weighting each country's arsenic distribution by that country's contribution to global production. A subset of 63 samples from Bangladesh, China, India, Italy, and the U.S. was analyzed for arsenic species. The relationship between inorganic arsenic contentversus total arsenic contentsignificantly differed among countries, with Bangladesh and India having the steepest slope in linear regression, and the U.S. having the shallowest slope. Using country-specific rice consumption data, daily intake of inorganic arsenic was estimated and the associated internal cancer risk was calculated using the U.S. Environmental Protection Agency (EPA) cancer slope. Median excess internal cancer risks posed by inorganic arsenic ranged 30-fold for the 5 countries examined, being 0.7 per 10,000 for Italians to 22 per 10,000 for Bangladeshis, when a 60 kg person was considered.

Temporal and geographical distribution of measles virus genotypes

The nucleotide sequence encoding the C terminus of the nucleocapsid protein of measles virus (MV) is the most variable in the genome. The sequence of this region is reported for 21 new MV strains and for virus RNA obtained from cases of subacute panencephalitis (SSPE) tissue. The nucleotide sequence of a total of 65 MV strains has been analysed using the CLUSTAL program to determine the relationships between the strains. An unrooted tree shows that eight different genotypes can be discerned amongst the sequences analysed so far. The data show that the C-terminal coding sequence of the nucleocapsid gene, although highly variable between strains, is stable in a given strain and does not appear to diverge in tissue culture. It therefore provides a good 'signature' sequence for specific genotypes. The sequence of this region can be used to discriminate new imported viruses from old 'endemic' strains of MV in a geographical area. The different genotypes are not geographically restricted although some appear to be the mainly 'endemic' types in large areas of the world. In global terms there appears to be at least four co-circulating genotypes of MV. The low level of divergence in the Edmonston lineage group isolated before 1970 indicates that some isolates are probably laboratory contaminants. This applies to some SSPE isolates such as the Halle, Mantooth and Horta-Barbosa strains as well as some wild-type isolates from that period.

Mapping Medieval Geographies: Geographical Encounters in the Latin West and Beyond, 300–1600

'Mapping Medieval Geographies' explores the ways in which geographical knowledge, ideas and traditions were formed in Europe during the Middle Ages. Leading scholars reveal the connections between Islamic, Christian, Biblical, and Classical geographical traditions from Antiquity to the later Middle Ages and Renaissance. The book is divided into two parts: Part I focuses on the notion of geographical tradition and charts the evolution of celestial and earthly geography in terms of its intellectual, visual and textual representations; whilst Part II explores geographical imaginations; that is to say, those 'imagined geographies' that came into being as a result of everyday spatial and spiritual experience. Bringing together approaches from art, literary studies, intellectual history and historical geography, this pioneering volume will be essential reading for scholars concerned with visual and textual modes of geographical representation and transmission, as well as the spaces and places of knowledge creation and consumption.

Profiling the ionome of rice and its use in discriminating geographical origins at the regional scale, China

Element profile was investigated for their use to trace the geographical origin of rice (Oryza sativa L.) samples. The concentrations of 13 elements (calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), boron (B), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), molybdenum (Mo), and cadmium (Cd)) were determined in the rice samples by inductively coupled plasma optical emission and mass spectrometry. Most of the essential elements for human health in rice were within normal ranges except for Mo and Se. Mo concentrations were twice as high as those in rice from Vietnam and Spain. Meanwhile, Se concentrations were three times lower in the whole province compared to the Chinese average level of 0.088 mg/kg. About 12% of the rice samples failed the Chinese national food safety standard of 0.2 mg/kg for Cd. Combined with the multi-elemental profile in rice, the principal component analysis (PCA), discriminant function analysis (DFA) and Fibonacci index analysis (FIA) were applied to discriminate geographical origins of the samples. Results indicated that the FIA method could achieve a more effective geographical origin classification compared with PCA and DFA, due to its efficiency in making the grouping even when the elemental variability was so high that PCA and DFA showed little discriminatory power. Furthermore, some elements were identified as the most powerful indicators of geographical origin: Ca, Ni, Fe and Cd. This suggests that the newly established methodology of FIA based on the ionome profile can be applied to determine the geographical origin of rice.