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Networks represent connections between people, things, ideas, and events in physical, digital, or hybrid spaces. The topic has long piqued geographers' curiosity in regard to enduring fundamental questions in our field: How and why do we perceive and represent geographic phenomena as networks? Do networked spaces challenge established geographic thoughts? What kinds of new knowledge inform or transform how we view geographic phenomena as networks? Might this network thinking collectively foster new policies or approaches to social and spatial problems? The collection of twenty-three articles included in this special issue addresses these questions by showcasing theoretical, epistemological, ontological, and methodological innovation in contemporary geography scholarship on networks. Insights gained from the discussion advance our discipline forward and meet societal needs. [ABSTRACT FROM AUTHOR]

Although many statistical approaches have been developed to quantify and assess spatial point patterns, the challenge to analyze complicated patterns has yet to be met. Statistics that describe the level of clustering usually assume that point events are isotropic. Many point events are influenced by linear features and clusters of these point events often have an elongated shape. Existing statistical cluster detection approaches often ignore these types of processes. This study proposes a new method, termed an L-function analysis for clusters influenced by linear features (L-Function-l) to test anisotropic point patterns with respect to the orientation of nearby linear features. To explicitly account for the influence of the underlying linear features on the point events, a number of ellipses with varying lengths, orientations and eccentricities are used to replace the circles that are drawn in the original L-function analysis. A case study of testing anisotropically clustered patterns of mosquito larval sites is used to illustrate the application of this method. The results indicate that the proposed approach provides a more flexible and comprehensive description of point patterns than the original L-function analysis.

The increasing spread of multi-drug resistant malaria in African highlands has highlighted the importance of malaria suppression through vector control. Its historical success has meant that larval control has been proposed as part of an integrated malaria vector control program. Due to high operation costs, larval control activities would benefit greatly if the locations of mosquito habitats could be identified quickly and easily, allowing for focal habitat source suppression. Several mosquito habitat models have been developed to predict the location of mosquito habitats. However, to what extent these models can be generalised across time and space to predict the distribution of dynamic mosquito habitats remains largely unexplored. This study used mosquito habitat data collected in six different time periods and four different modelling approaches to establish 24 mosquito habitat models. We systematically tested the generality of these 24 mosquito habitat models. We found that although habitat--environment relationships change temporally, a modest level of performance was attained when validating the models using data collected from different time periods. We also describe flexible approaches to the predictive modelling of mosquito habitats, that provide novel modelling architecture for future research efforts.

Abstract Background Localized mosquito larval habitat management and the use of larvicides have been proposed as important control tools in integrated malaria vector management programs. In order to optimize the utility of these tools, detailed knowledge of the spatial distribution patterns of mosquito larval habitats is crucial. However, the spatial and temporal changes of habitat distribution patterns under different climatic conditions are rarely quantified and their implications to larval control are unknown. Results Using larval habitat data collected in western Kenya highlands during both dry and rainy seasons of 2003-2005, this study analyzed the seasonal and inter-annual changes in the spatial patterns in mosquito larval habitat distributions. We found that the spatial patterns of larval habitats had significant temporal variability both seasonally and inter-annually. Conclusions The pattern of larval habitats is extremely important to the epidemiology of malaria because it results in spatial heterogeneity in the adult mosquito population and, subsequently, the spatial distribution of clinical malaria cases. Results from this study suggest that larval habitat management activities need to consider the dynamic nature of malaria vector habitats.

Background: A sharp rise in the malaria mortality rate has been observed recently in western Kenya. Malaria is transmitted by mosquito vectors. Malaria control strategies can be more successful if the distribution and abundance of mosquito vectors is predicted. However, how mosquito vectors are distributed in space remain poor understood, and this question is rarely studied using spatial methods. This study aims to provide a better understanding of the distribution and abundance of mosquito vectors. To achieve this objective, spatial and non-spatial methods were employed. The data on the distribution of adult mosquitoes, and mosquito breeding habitats in a study area in western Kenya, and environmental variables were analyzed. Results: The models developed using spatial methods outperformed the models developed using non-spatial methods. Houses close to locations where mosquito breeding habitats were repeatedly observed had more abundant adult female mosquitoes. Distance to high-order streams was identified as an effective predictor for the distribution of adult mosquitoes. Conclusion: The spatial method is more effective in modeling the distribution of adult mosquitoes than the non-spatial method. The results of this study can be used to facilitate decision-making related to mosquito surveillance and malaria prevention.

Malaria, transmitted by mosquitoes, is the leading cause of mortality in many African countries. This study assesses the spatial distribution of mosquito larval habitats on the highlands of western Kenya. Environmental factors such as terrain, land use, and surface waters are evaluated for their influence on the habitats. Logistic regression is used to model the global trend in the variation of larval habitats, while kriging is used to model the spatially dependent local variation. Results show notable improvement in modeling accuracy by combining the regression and kriging estimates over using the regression estimate alone. Copyright © 2006 by V.H. Winston & Son, Inc. All rights reserved.

α-l-arabinofuranosidase is a subfamily of glycosidases involved in the hydrolysis of l-arabinofuranosidic bonds, especially in those of the terminal non-reducing arabinofuranosyl residues of glycosides, from which efficient glycoside hydrolases can be screened for the transformation of ginsenosides. In this study, the ginsenoside Rc-hydrolyzing α-l-arabinofuranosidase gene, BsAbfA, was cloned from Bacilus subtilis, and its codons were optimized for efficient expression in E. coli BL21 (DE3). The recombinant protein BsAbfA fused with an N-terminal His-tag was overexpressed and purified, and then subjected to enzymatic characterization. Site-directed mutagenesis of BsAbfA was performed to verify the catalytic site, and the molecular mechanism of BsAbfA catalyzing ginsenoside Rc was analyzed by molecular docking, using the homology model of sequence alignment with other β-glycosidases. The results show that the purified BsAbfA had a specific activity of 32.6 U/mg. Under optimal conditions (pH 5, 40 °C), the kinetic parameters Km of BsAbfA for pNP-α-Araf and ginsenoside Rc were 0.6 mM and 0.4 mM, while the Kcat/Km were 181.5 s−1 mM−1 and 197.8 s−1 mM−1, respectively. More than 90% of ginsenoside Rc could be transformed by 12 U/mL purified BsAbfA at 40 °C and pH 5 in 24 h. The results of molecular docking and site-directed mutagenesis suggested that the E173 and E292 variants for BsAbfA are important in recognizing ginsenoside Rc effectively, and to make it enter the active pocket to hydrolyze the outer arabinofuranosyl moieties at C20 position. These remarkable properties and the catalytic mechanism of BsAbfA provide a good alternative for the effective biotransformation of the major ginsenoside Rc into Rd.

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1467-8306.2007.00535.x Byline: Ling Bian (*) Keywords: environmental modeling; object-orientation; spatial representation Abstract: A geographic space must be partitioned into a finite number of discrete pieces, such as points, lines, polygons, and grid cells, to accommodate the finite computing environment. Because these discrete geometric primitives can be implemented as software objects, the object-oriented computing paradigm might have encouraged the tendency to 'objectify' spatial phenomena, regardless of whether they are best represented as objects. A critical review is necessary to assess whether object-orientation, a concept first developed outside geography, is suitable for spatial representation. This article reviews object-oriented spatial representation in the context of environmental modeling. The review is organized into four topics: (1) the principles underlying object-orientation, (2) the categorization of environmental phenomena, (3) GIS data models and their object-oriented implementation, and (4) the compatibility between these three sets of concepts. The discussion argues that spatial objects, regions, and fields represent three categories of phenomena that are well represented, reasonably represented, and not well represented by the objects, respectively. Author Affiliation: (*)Department of Geography, University at Buffalo, State University of New York Article History: Initial submission, February 2004; revised submissions, January and August 2006; final acceptance, August 2006 Article note: Correspondence: Ling Bian, Department of Geography, University at Buffalo, State University of New York, Amherst, NY 14261, email: lbian@buffalo.edu.

This article reports preliminary results of using a spatial dependence approach to retrieving industrial building complexes from digital aerial photographs. Because image retrieval was originally developed outside geography, this paper first discusses the principle of image retrieval in the context of geographic studies, the basic types of geographic features for retrieval, and the spatially dependent nature of geographic features. Based on these discussions, the spatial dependence approach is presented for the intended retrieval. Semivariogram is used to represent the spatial dependence of geographic features and for the subsequent retrieval of these features. Results show the effectiveness of this approach and warrant further investigations. Key Words: image retrieval, spatial dependence, semivariogram.