Introducing SpatialGridBuilder: A new system for creating geo-coded datasets

Researchers in the conflict research community have become increasingly aware that we can no longer depend on state-aggregated data. Numerous factors at the substate level affect the nature of human interactions, so if we really want to understand conflict, we need to find more appropriate units of analysis. However, while many conflict researchers have realized this, actually taking the next step and performing data analysis on spatial data grids has remained a rather elusive goal for many because of the difficulty of learning the new techniques to perform such analyses. This paper introduces SpatialGridBuilder, a new, freely available, open-source system with the goal of empowering conflict researchers with no background in GIS methods to start their own spatial analyses. SpatialGridBuilder allows the researcher to: (a) create entirely new spatial datasets, based on the needs of their own research; (b) import their own spatial data; (c) easily add a range of important variables to the datasets, including commonly used conflict variables, plus new variables that have not been presented before; and (d) visualize graphical renderings of this data. Having done this, SpatialGridBuilder will then export the dataset for the researcher to analyse using conventional statistical methods. This article introduces the new program, and demonstrates how it can be used to set up such a statistical analysis. It also shows how different results can be achieved by building grids of different resolutions, thereby encouraging researchers to choose grid resolutions appropriate to their research questions and data. The article also introduces a novel means of determining infrastructure complexity, using Google maps

Soft Image Segmentation: On the Clustering of Irregular, Weighted, Multivariate Marked Networks

The contribution exposes and illustrates a general, flexible formalism, together with an associated iterative procedure, aimed at determining soft memberships of marked nodes in a weighted network. Gathering together spatial entities which are both spatially close and similar regarding their features is an issue relevant in image segmentation, spatial clustering, and data analysis in general. Unoriented weighted networks are specified by an ``exchange matrix", determining the probability to select a pair of neighbors. We present a family of membership-dependent free energies, whose local minimization specifies soft clusterings. The free energy additively combines a mutual information, as well as various energy terms, concave or convex in the memberships: within-group inertia, generalized cuts (extending weighted Ncut and modularity), and membership discontinuities (generalizing Dirichlet forms). The framework is closely related to discrete Markov models, random walks, label propagation and spatial autocorrelation (Moran's I), and can express the Mumford-Shah approach. Four small datasets illustrate the theory

Local spatial regression models : a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis

Anonymisation of geographical distance matrices via Lipschitz embedding

BACKGROUND: Anonymisation of spatially referenced data has received increasing attention in recent years. Whereas the research focus has been on the anonymisation of point locations, the disclosure risk arising from the publishing of inter-point distances and corresponding anonymisation methods have not been studied systematically. METHODS: We propose a new anonymisation method for the release of geographical distances between records of a microdata file-for example patients in a medical database. We discuss a data release scheme in which microdata without coordinates and an additional distance matrix between the corresponding rows of the microdata set are released. In contrast to most other approaches this method preserves small distances better than larger distances. The distances are modified by a variant of Lipschitz embedding. RESULTS: The effects of the embedding parameters on the risk of data disclosure are evaluated by linkage experiments using simulated data. The results indicate small disclosure risks for appropriate embedding parameters. CONCLUSION: The proposed method is useful if published distance information might be misused for the re-identification of records. The method can be used for publishing scientific-use-files and as an additional tool for record-linkage studies

Spatio-Temporal Magnitude and Direction of Highly Pathogenic Avian Influenza (H5N1) Outbreaks in Bangladesh

BACKGROUND: The number of outbreaks of HPAI-H5N1 reported by Bangladesh from 2007 through 2011 placed the country among the highest reported numbers worldwide. However, so far, the understanding of the epidemic progression, direction, intensity, persistence and risk variation of HPAI-H5N1 outbreaks over space and time in Bangladesh remains limited. METHODOLOGY/PRINCIPAL FINDINGS: To determine the magnitude and spatial pattern of the highly pathogenic avian influenza A subtype H5N1 virus outbreaks over space and time in poultry from 2007 to 2009 in Bangladesh, we applied descriptive and analytical spatial statistics. Temporal distribution of the outbreaks revealed three independent waves of outbreaks that were clustered during winter and spring. The descriptive analyses revealed that the magnitude of the second wave was the highest as compared to the first and third waves. Exploratory mapping of the infected flocks revealed that the highest intensity and magnitude of the outbreaks was systematic and persistent in an oblique line that connects south-east to north-west through the central part of the country. The line follows the Brahmaputra-Meghna river system, the junction between Central Asian and East Asian flyways, and the major poultry trading route in Bangladesh. Moreover, several important migratory bird areas were identified along the line. Geostatistical analysis revealed significant latitudinal directions of outbreak progressions that have similarity to the detected line of intensity and magnitude. CONCLUSION/SIGNIFICANCE: The line of magnitude and direction indicate the necessity of mobilizing maximum resources on this line to strengthen the existing surveillance

From spatial ecology to spatial epidemiology: Modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices

Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. Results: PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165thPCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r 2= 0.579). Conclusions: PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information

Regional Environmental Breadth Predicts Geographic Range and Longevity in Fossil Marine Genera

Geographic range is a good indicator of extinction susceptibility in fossil marine species and higher taxa. The widely-recognized positive correlation between geographic range and taxonomic duration is typically attributed to either accumulating geographic range with age or an extinction buffering effect, whereby cosmopolitan taxa persist longer because they are reintroduced by dispersal from remote source populations after local extinction. The former hypothesis predicts that all taxa within a region should have equal probabilities of extinction regardless of global distributions while the latter predicts that cosmopolitan genera will have greater survivorship within a region than endemics within the same region. Here we test the assumption that all taxa within a region have equal likelihoods of extinction.We use North American and European occurrences of marine genera from the Paleobiology Database and the areal extent of marine sedimentary cover in North America to show that endemic and cosmopolitan fossil marine genera have significantly different range-duration relationships and that broad geographic range and longevity are both predicted by regional environmental breadth. Specifically, genera that occur outside of the focal region are significantly longer lived and have larger geographic ranges and environmental breadths within the focal region than do their endemic counterparts, even after controlling for differences in sampling intensity. Analyses of the number of paleoenvironmental zones occupied by endemic and cosmopolitan genera suggest that the number of paleoenvironmental zones occupied is a key factor of geographic range that promotes genus survivorship.Wide environmental tolerances within a single region predict both broad geographic range and increased longevity in marine genera over evolutionary time. This result provides a specific driving mechanism for the spatial and temporal distributions of marine genera at regional and global scales and is consistent with the niche-breadth hypothesis operating on macroevolutionary timescales

Two new species of Odontostilbe historically hidden under O. microcephala (Characiformes: Cheirodontinae)

Specimens historically identified as Odontostilbe microcephala from the upper rio Paraná and Andean piedmont tributaries of the río Paraguay are reviewed and split in three species. We found that the distribution of O. microcephala is restricted to the Andean slope of the río Paraguay basin. The species is distinguished from congeners with subterminal mouth by the elongate body, usually 10-12 gill rakers on upper branch and smaller horizontal orbital diameter (24.6-32.8 % HL, mean 28.7%). Specimens from upper rio Paraná constitute two new species, diagnosed from other Cheirodontinae by the presence of mesopterygoid teeth, grouped on median portion and forming a continuous row. The new species are distinguished from each other by having premaxillary teeth with five cusps vs. nine cusps and by the number of lamellae in left and right sides of central median raphe of olfactory rosette with 20-21 vs. 11-12.Espécimes historicamente identificados com Odontostilbe microcephala do rio Paraná e tributários do río Paraguay, foram revisados e separados em três espécies. A distribuição de O. microcephala é restrita ao sopé andino da bacia do río Paraguay. A espécie é distinta das congêneres com boca subterminal pela forma alongada, geralmente 10-12 rastros branquiais no ramo superior e menor diâmetro horizontal da órbita (24,6-32,8 % CC, média 28,7%). Espécimes do alto rio Paraná constituem duas espécies novas diagnosticadas de outros Cheirodontinae pela presença de dentes no mesopterigoide, agrupados em sua porção média e formando uma fileira continua. As novas espécies distinguem-se por ter dentes premaxilares com cinco cúspides vs. nove cúspides e pelo número de lamelas nos lados esquerdo e direito da rafe central da roseta olfativa com 20-21 vs. 11-12