Urban vulnerability in Sydney: Policy and institutional ambiguities in bushfire protection

Despite the inevitability of the bushfires hazard across the Sydney region, a mismatch exists between reactive technological fixes and proactive social programs which have far-reaching vulnerability and governance consequences. This paper questions the adequacy of current policy and action, revealing contradictions and tensions that expose Sydney's vulnerability and have implications for other Australian cities.

Efficiently computing weighted proximity relationships in spatial databases

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.