Perils of using speed zone data to assess real-world compliance to speed limits

Objective: Real-world driving studies, including those involving speeding alert devices and autonomous vehicles, can gauge an individual vehicle's speeding behavior by comparing measured speed with mapped speed zone data. However, there are complexities with developing and maintaining a database of mapped speed zones over a large geographic area that may lead to inaccuracies within the data set. When this approach is applied to large-scale real-world driving data or speeding alert device data to determine speeding behavior, these inaccuracies may result in invalid identification of speeding. We investigated speeding events based on service provider speed zone data.

Methods: We compared service provider speed zone data (Speed Alert by Smart Car Technologies Pty Ltd., Ultimo, NSW, Australia) against a second set of speed zone data (Google Maps Application Programming Interface [API] mapped speed zones).

Results: We found a systematic error in the zones where speed limits of 50–60 km/h, typical of local roads, were allocated to high-speed motorways, which produced false speed limits in the speed zone database. The result was detection of false-positive high-range speeding. Through comparison of the service provider speed zone data against a second set of speed zone data, we were able to identify and eliminate data most affected by this systematic error, thereby establishing a data set of speeding events with a high level of sensitivity (a true positive rate of 92% or 6,412/6,960).

Conclusions: Mapped speed zones can be a source of error in real-world driving when examining vehicle speed. We explored the types of inaccuracies found within speed zone data and recommend that a second set of speed zone data be utilized when investigating speeding behavior or developing mapped speed zone data to minimize inaccuracy in estimates of speeding.     

Gaining forests but losing ground: A GIS evaluation in a Himalayan watershed

GIS overlay techniques were used to provide a quantitative historic documentation of deforestation and land-use dynamics in the Middle Mountains of Nepal between 1947 and 1990. Deforestation was most critical in the 1960s, but active afforestation programs in the 1980s have reversed the process. In spite of these trends, the degradation problem is more complex. The GIS evaluation showed that 86% of the recently afforested land is now under pine plantations located primarily at lower elevations and moderately steep slopes. In contrast, rainfed agricultural expansion is most pronounced on acidic soils and steeper, upper elevation sites, suggesting marginalization of agriculture. Agricultural expansion coupled with major losses of grazing land to pine forests are the key processes pointing towards major animal feed deficits. An alternative animal feed source is suggested through GIS using a topographically based microclimatic classification to generate a tree-planting map where the optimum ecological conditions for selective native fodder tree species are identified.     

Gorlin syndrome (naevoid basal cell carcinoma syndrome): Prenatal detection in a fetus with macrocephaly and ventriculomegaly

The Gorlin (naevoid basal cell carcinoma) syndrome is an autosomal dominant disorder consisting principally of naevoid basal cell carcinomas, odontogenic keratocysts, skeletal abnormalities, and intracranial calcification. We report the prenatal detection of the Gorlin syndrome by ultrasonography in a fetus with macrocephaly and mild ventriculomegaly.     

Land-Use Change and Forestry in the Kyoto Protocol

Mitigation and Adaptation Strategies for Global Change -     

Growth of the Decision Tree: Advances in Bottom‐Up Climate Change Risk Management

There has recently been a return in climate change risk management practice to bottom‐up, robustness‐based planning paradigms introduced 40 years ago. The World Bank's decision tree framework (DTF) for “confronting climate uncertainty” is one incarnation of those paradigms. In order to better represent the state of the art in climate change risk assessment and evaluation techniques, this paper proposes: (1) an update to the DTF, replacing its “climate change stress test” with a multidimensional stress test; and (2) the addition of a Bayesian network framework that represents joint probabilistic behavior of uncertain parameters as sensitivity factors to aid in the weighting of scenarios of concern (the combination of conditions under which a water system fails to meet its performance targets). Using the updated DTF, water system planners and project managers would be better able to understand the relative magnitudes of the varied risks they face, and target investments in adaptation measures to best reduce their vulnerabilities to change. Next steps for the DTF include enhancements in: modeling of extreme event risks; coupling of human‐hydrologic systems; integration of surface water and groundwater systems; the generation of tradeoffs between economic, social, and ecological factors; incorporation of water quality considerations; and interactive data visualization.