Young and Vulnerable: Spatial-temporal Trends and Risk Factors for Infant Mortality in Rural South Africa (Agincourt), 1992-2007

BMC Public Health 2010, 10:645, Published 26 October 2010

Benn KD Sartorius, Kathleen Kahnl, Penelope Vounatsou, Mark A Collinson, and Stephen M Tollman

“Background: Infant mortality is an important indicator of population health in a country. It is associated with several health determinants, such as maternal health, access to high-quality health care, socioeconomic conditions, and public health policy and practices.

“Methods: A spatial-temporal analysis was performed to assess changes in infant mortality patterns between 1992-2007 and to identify factors associated with infant mortality risk in the Agincourt sub-district, rural northeast South Africa. Period, sex, refugee status, maternal and fertility-related factors, household mortality experience, distance to nearest primary health care facility, and socio-economic status were examined as possible risk factors. All-cause and cause-specific mortality maps were developed to identify high risk areas within the study site. The analysis was carried out by fitting Bayesian hierarchical geostatistical negative binomial autoregressive models using Markov chain Monte Carlo simulation. Simulation-based Bayesian kriging was used to produce maps of all-cause and cause-specific mortality risk.

“Results: Infant mortality increased significantly over the study period, largely due to the impact of the HIV epidemic. There was a high burden of neonatal mortality (especially perinatal) with several hot spots observed in close proximity to health facilities. Significant risk factors for all-cause infant mortality were mother’s death in first year (most commonly due to HIV), death of previous sibling and increasing number of household deaths. Being born to a Mozambican mother posed a significant risk for infectious and parasitic deaths, particularly acute diarrhoea and malnutrition.

“Conclusions: This study demonstrates the use of Bayesian geostatistical models in assessing risk factors and producing smooth maps of infant mortality risk in a health and socio-demographic surveillance system. Results showed marked geographical differences in mortality risk across a relatively small area. Prevention of vertical transmission of HIV and survival of mothers during the infants’ first year in high prevalence villages needs to be urgently addressed, including expanded antenatal testing, prevention of mother-to-child transmission, and improved access to antiretroviral therapy. There is also need to assess and improve the capacity of district hospitals for emergency obstetric and newborn care. Persisting risk factors, including inadequate provision of clean water and sanitation, are yet to be fully addressed.”

From Doves to Hawks: A Spatial Analysis of Voting in the Monetary Policy Committee of the Bank of England

European Journal of Political Research, 49 (6). pp. 731-758 (2010)

Hix, Simon; Holyland, Bjorn; and Vivyan, Nick

“This article examines the making of monetary policy in the United Kingdom between 1997 and 2008 by analysing voting behaviour in the Bank of England’s Monetary Policy Committee (MPC). It provides a new set of measures for the monetary policy preferences of individual MPC members by estimating a Bayesian item response model. The article demonstrates the usefulness of these measures by comparing the ideal points of outgoing MPC members with their successors and by looking at changes over time in the median ideal point on the MPC. The analysis indicates that the British Government has been able to move the position of the median voter on the MPC through its appointments to the Committee. This highlights the importance of central bank appointments for monetary policy.”

Application of Spatial Analysis Methods for Understanding Geographic Variation of Prices, Demand and Market Success

A dissertation submitted to ETH ZURICH for the degree of Doctor of Sciences, 2010

Michael Löchl

“Spatial analysis is a general term to describe mathematical methods that use locational information in order to better understand processes generating observed attribute values (Fotheringham and Rogerson, 2009, 1). Such techniques are applied in many fields, including biology, epidemiology, ethnology, geography, sociology and statistics. Nevertheless, the nature of the spatial variation of interest is sometimes not well understood and the patterns of spatial dependence and heterogeneity are disregarded. Therefore, this dissertation brings together different applications of spatial analysis methods for understanding geographic variation of certain entities while considering those spatial effects. Additionally, certain spatial analysis, modelling and simulation techniques are examined for solving location problems and informing spatial allocation and deployment of resources. Namely, regression techniques, integrated land use and transport simulation and agent-based modelling approaches are applied in four examples, whereas all those techniques are grounded in spatial analysis.”

Space–time Density of Trajectories: Exploring Spatio-temporal Patterns in Movement Data

International Journal of Geographical Information Science, Volume 24 Issue 10 2010, Pages 1527 – 1542: Geospatial Visual Analytics: Focus on Time Special Issue of the ICA Commission on GeoVisualization

Urška Demšar; Kirsi Virrantaus

“Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space-time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. The space-time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space-time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation.”