Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

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The discovery of preferences in space and time is important in a variety of applications. In this paper we first establish the correspondence between a set of preferences in space and time and density estimates obtained from observations of spatial-temporal features recorded within large databases. We perform density estimation using both kernel methods and mixture models. The density estimates constitute a probabilistic representation of preferences. We then present a point process transition density model for space-time event prediction that hinges upon the density estimates from the preference discovery process. The added dimension of preference discovery through feature space analysis enables our model to outperform traditional preference modeling approaches. We demonstrate this performance improvement using a criminal incident database from Richmond, Virginia. Criminal incidents are humaninitiated events that may be governed by criminal preferences over space and time. We applied our modeling technique to breaking and entering crimes committed in both residential and commercial settings. Our approach effectively recovers the preference structure of the criminals and enables one-week ahead forecasts of threatened areas. This capability to accommodate all measurable features, identify the key features, and quantify their relationship with event occurrence over space and time makes this approach applicable to domains other than law enforcement.

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Crime analysis uses past crime data to predict future crime locations and times. Typically this analysis relies on hot spot models that show clusters of criminal events based on past locations of these events. It does not consider the decision making processes of criminals as human initiated events susceptible to analysis using spatial choice models. This paper analyzes criminal incidents as spatial choice processes. Spatial choice analysis can be used to discover the distribution of people's behaviors in space and time. Two adjusted spatial choice models that include models of decision making processes are presented. The comparison results show that adjusted spatial choice models provide efficient and accurate predictions of future crime patterns and can be used as the basis for a law enforcement decision support system. This paper also extends spatial choice modeling to include the class of problems where the decision makers' preferences are derived indirectly through incident reports rather than directly through survey instruments.