Socio-demographic determinants of place attachment in Gauteng,South Africa,using partial proportional odds models

GeoJournal - Determinants of place attachment have been extensively explored in the world now characterised by increased globalisation and mobility. Apart from some studies analysing attachment to...     

Lightning fatalities in Swaziland: 2000–2007

An investigation is undertaken to analyze the human lightning fatalities in Swaziland. A total of 123 victims of lightning-related death were identified from the records of the Royal Swaziland Police Service and the local printed media for the period 2000–2007. An annual average fatality rate of 15.5 people per million, the highest recorded rate in the world, was obtained. The results also reveal that 66% were male, most (67%) of them were within the 10–39 age group with an average age of 28 years. Lightning fatalities occurred from September to May mainly in the afternoon (1400–1800 h). Deaths most commonly occurred indoors inside rural houses (17%), whilst walking (16%) and under a tree (14%). The incidents resulted in multiple fatalities in 22% of the cases with an average of 1.4 casualties per incident. The need for awareness campaigns, protection measures and detailed investigation is highlighted.     

Application of Bayesian networks for fire risk mapping using GIS and remote sensing data

This study estimates fire risk in Swaziland using geographic information system (GIS) and remote sensing data. Fire locations were identified in the study area from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) active fire and burned area data for the period between April 2000 to December 2008 and January 2001 and December 2008, respectively. A total of thirteen biophysical and socio-economic explanatory variables were analyzed and processed using a Bayesian network (BN) and GIS to generate the fire risk maps. The interdependence of each of the factors was probabilistically determined using the expectation-maximization (EM) learning algorithm. The final probabilistic outputs were then used to classify the country into five fire risk zones for mitigation and management. Accuracy assessments and comparison of the fire risk maps indicate that the risk maps derived from the active fire and burned area data were 93.14 and 96.64% accurate, respectively, demonstrating sufficient agreement between the risk maps and the existing data. High fire risk areas are observed in the Highveld particularly plantation forests and grasslands and within the Lowveld sugarcane plantations. Land tenure and land cover are the dominant determinants of fire risk, the implications of which are discussed for fire management in Swaziland. Limitations of the data used and the modeling approach are also discussed including suggestions for improvements and future research.     

Characterization of the July 2007 Swaziland fire disaster using satellite remote sensing and GIS

Data from the NASA'S MODIS (Aqua and Terra) and EUMETSAT'S MSG-SEVIRI satellite sensors is analysed to characterise the geographic and temporal (including diurnal) evolution of the July 2007 fire disaster in the Kingdom of Swaziland using a geographic information system (GIS). Significant fire activity was observed during a three-day period beginning on the 27th July 2007. A total of 1358 and 4365 active fire hotpots were detected by MODIS and MSG-SEVIRI, respectively, mainly concentrated in the Highveld (70.91% for MODIS, 89.89% for MSG) and Middleveld (11.27% for MODIS, 5.23% for MSG) with MSG/MODIS active fire count ratio ranging from a high of 3.69 in the Highveld to a low of 0.06 in the Lubombo Plateau. The results indicate complex differences in spatial fire distribution, behaviour and risk within the country and the effect of sensor differences. A pronounced fire diurnal cycle with a broad afternoon peak centred on 14:00 local time is observed, in general agreement with observations from the region. Despite their limitations, the study demonstrates the importance and usefulness of remotely sensed data and GIS technology for fire disaster and risk assessment for a developing country, where fire monitoring resources are scarce.