A strategy to fill gaps in soil survey over large spatial extents: an example from the Murray-Darling basin of Australia

We re-mapped the soils of the Murray-Darling Basin (MDB) in 1995-1998 with a minimum of new fieldwork, making the most out of existing data. We collated existing digital soil maps and used inductive spatial modelling to predict soil types from those maps combined with environmental predictor variables. Lithology, Landsat Multi Spectral Scanner (Landsat MSS), the 9-s digital elevation model (DEM) of Australia and derived terrain attributes, all gridded to 250-m pixels, were the predictor variables. Because the basin-wide datasets were very large data mining software was used for modelling. Rule induction by data mining was also used to define the spatial domain of extrapolation for the extension of soil-landscape models from existing soil maps. Procedures to estimate the uncertainty associated with the predictions and quality of information for the new soil-landforms map of the MDB are described. (C) 2002 Elsevier Science B.V. All rights reserved.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Using GIS to map the spatial and temporal occurrence of cholera epidemic in Camaroon

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

The multipoint Morisita index for the analysis of spatial patterns

In many fields, the spatial clustering of sampled data points has many consequences. Therefore, several indices have been proposed to assess the level of clustering affecting datasets (e.g. the Morisita index, Ripley's Kfunction and Rényi's generalized entropy). The classical Morisita index measures how many times it is more likely to select two measurement points from the same quadrats (the data set is covered by a regular grid of changing size) than it would be in the case of a random distribution generated from a Poisson process. The multipoint version (k-Morisita) takes into account k points with k >= 2. The present research deals with a new development of the k-Morisita index for (1) monitoring network characterization and for (2) detection of patterns in monitored phenomena. From a theoretical perspective, a connection between the k-Morisita index and multifractality has also been found and highlighted on a mathematical multifractal set.

Spatial analysis of corresponding fingerprint features from match and close non-match populations

The development of statistical models for forensic fingerprint identification purposes has been the subject of increasing research attention in recent years. This can be partly seen as a response to a number of commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. In addition, key forensic identification bodies such as ENFSI [1] and IAI [2] have recently endorsed and acknowledged the potential benefits of using statistical models as an important tool in support of the fingerprint identification process within the ACE-V framework. In this paper, we introduce a new Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via morphometric and spatial analyses of corresponding minutiae configurations for both match and close non-match populations often found in AFIS candidate lists. Computed LR values are derived from a probabilistic framework based on SVMs that discover the intrinsic spatial differences of match and close non-match populations. Lastly, experimentation performed on a set of over 120,000 publicly available fingerprint images (mostly sourced from the National Institute of Standards and Technology (NIST) datasets) and a distortion set of approximately 40,000 images, is presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment of match and close non-match populations. Results further indicate that the proposed model is a promising tool for fingerprint practitioners to use for analysing the spatial consistency of corresponding minutiae configurations.

Sensitivity of Spring Phenology to Warming Across Temporal and Spatial Climate Gradients in Two Independent Databases

Disparate ecological datasets are often organized into databases post hoc and then analyzed and interpreted in ways that may diverge from the purposes of the original data collections. Few studies, however, have attempted to quantify how biases inherent in these data (for example, species richness, replication, climate) affect their suitability for addressing broad scientific questions, especially in under-represented systems (for example, deserts, tropical forests) and wild communities. Here, we quantitatively compare the sensitivity of species first flowering and leafing dates to spring warmth in two phenological databases from the Northern Hemisphere. One-PEP725-has high replication within and across sites, but has low species diversity and spans a limited climate gradient. The other-NECTAR-includes many more species and a wider range of climates, but has fewer sites and low replication of species across sites. PEP725, despite low species diversity and relatively low seasonality, accurately captures the magnitude and seasonality of warming responses at climatically similar NECTAR sites, with most species showing earlier phenological events in response to warming. In NECTAR, the prevalence of temperature responders significantly declines with increasing mean annual temperature, a pattern that cannot be detected across the limited climate gradient spanned by the PEP725 flowering and leafing data. Our results showcase broad areas of agreement between the two databases, despite significant differences in species richness and geographic coverage, while also noting areas where including data across broader climate gradients may provide added value. Such comparisons help to identify gaps in our observations and knowledge base that can be addressed by ongoing monitoring and research efforts. Resolving these issues will be critical for improving predictions in understudied and under-sampled systems outside of the temperature seasonal mid-latitudes.

Monitoring spatial and temporal dynamics of bird communities in Mediterranean landscapes affected by largewildfires = Seguimiento de la dinámica espacial y temporal de comunidades de aves en paisajes mediterráneos afectados por grandes fuegos

We presented a bird-monitoring database inMediterranean landscapes (Catalonia, NE Spain) affected by wildfires and we evaluated: 1) the spatial and temporal variability in the bird community composition and 2) the influence of pre-fire habitat configuration in the composition of bird communities. The DINDIS database results fromthemonitoring of bird communities occupying all areas affected by large wildfires in Catalonia since 2000.We used bird surveys conducted from 2006 to 2009 and performed a principal components analysis to describe two main gradients of variation in the composition of bird communities, which were used as descriptors of bird communities in subsequent analyses. We then analysed the relationships of these community descriptors with bioclimatic regions within Catalonia, time since fire and pre-fire vegetation (forest or shrubland).We have conducted 1,918 bird surveys in 567 transects distributed in 56 burnt areas. Eight out of the twenty most common detected species have an unfavourable conservation status, most of them being associated to open-habitats. Both bird communities’ descriptors had a strong regional component and were related to pre-fire vegetation, and to a lesser extent to the time since fire.We came to the conclusion that the responses of bird communities to wildfires are heterogeneous, complex and context dependent. Large-scale monitoring datasets, such as DINDIS, might allow identifying factors acting at different spatial and temporal scales that affect the dynamics of species and communities, giving additional information on the causes under general trends observed using other monitoring systems

Self-deposit, discovery, and delivery of scientific GIS datasets using GeoHydra

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.

A comparison of recent reanalysis datasets using objective feature tracking: Storm tracks and tropical easterly waves

Data from four recent reanalysis projects [ECMWF, NCEP-NCAR, NCEP - Department of Energy ( DOE), NASA] have been diagnosed at the scale of synoptic weather systems using an objective feature tracking method. The tracking statistics indicate that, overall, the reanalyses correspond very well in the Northern Hemisphere (NH) lower troposphere, although differences for the spatial distribution of mean intensities show that the ECMWF reanalysis is systematically stronger in the main storm track regions but weaker around major orographic features. A direct comparison of the track ensembles indicates a number of systems with a broad range of intensities that compare well among the reanalyses. In addition, a number of small-scale weak systems are found that have no correspondence among the reanalyses or that only correspond upon relaxing the matching criteria, indicating possible differences in location and/or temporal coherence. These are distributed throughout the storm tracks, particularly in the regions known for small-scale activity, such as secondary development regions and the Mediterranean. For the Southern Hemisphere (SH), agreement is found to be generally less consistent in the lower troposphere with significant differences in both track density and mean intensity. The systems that correspond between the various reanalyses are considerably reduced and those that do not match span a broad range of storm intensities. Relaxing the matching criteria indicates that there is a larger degree of uncertainty in both the location of systems and their intensities compared with the NH. At upper-tropospheric levels, significant differences in the level of activity occur between the ECMWF reanalysis and the other reanalyses in both the NH and SH winters. This occurs due to a lack of coherence in the apparent propagation of the systems in ERA15 and appears most acute above 500 hPa. This is probably due to the use of optimal interpolation data assimilation in ERA15. Also shown are results based on using the same techniques to diagnose the tropical easterly wave activity. Results indicate that the wave activity is sensitive not only to the resolution and assimilation methods used but also to the model formulation.

Spatial and temporal scale issues in determining biomass burning regimes in Bolivia and Peru

ATSR-2 active fire data from 1996 to 2000, TRMM VIRS fire counts from 1998 to 2000 and burn scars derived from SPOT VEGETATION ( the Global Burnt Area 2000 product) were mapped for Peru and Bolivia to analyse the spatial distribution of burning and its intra- and inter-annual variability. The fire season in the region mainly occurs between May and October; though some variation was found between the six broad habitat types analysed: desert, grassland, savanna, dry forest, moist forest and yungas (the forested valleys on the eastern slope of the Andes). Increased levels of burning were generally recorded in ATSR-2 and TRMM VIRS fire data in response to the 1997/1998 El Nino, but in some areas the El Nino effect was masked by the more marked influences of socio-economic change on land use and land cover. There were differences between the three global datasets: ATSR-2 under-recorded fires in ecosystems with low net primary productivities. This was because fires are set during the day in this region and, when fuel loads are low, burn out before the ATSR-2 overpass in the region which is between 02.45 h and 03.30 h. TRMM VIRS was able to detect these fires because its overpasses cover the entire diurnal range on a monthly basis. The GBA2000 product has significant errors of commission (particularly areas of shadow in the well-dissected eastern Andes) and omission (in the agricultural zone around Santa Cruz, Bolivia and in north-west Peru). Particular attention was paid to biomass burning in high-altitude grasslands, where fire is an important pastoral management technique. Fires and burn scars from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data for a range of years between 1987 and 2000 were mapped for areas around Parque Nacional Rio Abiseo (Peru) and Parque Nacional Carrasco (Bolivia). Burn scars mapped in the grasslands of these two areas indicate far more burning had taken place than either the fires or the burn scars derived from global datasets. Mean scar sizes are smaller and have a smaller range in size between years the in the study area in Peru (6.6-7.1 ha) than Bolivia (16.9-162.5 ha). Trends in biomass burning in the two highland areas can be explained in terms of the changing socio-economic environments and impacts of conservation. The mismatch between the spatial scale of biomass burning in the high-altitude grasslands and the sensors used to derive global fire products means that an entire component of the fire regime in the region studied is omitted, despite its importance in the farming systems on the Andes.

Fast regridding of large, complex geospatial datasets

In the earth sciences, data are commonly cast on complex grids in order to model irregular domains such as coastlines, or to evenly distribute grid points over the globe. It is common for a scientist to wish to re-cast such data onto a grid that is more amenable to manipulation, visualization, or comparison with other data sources. The complexity of the grids presents a significant technical difficulty to the regridding process. In particular, the regridding of complex grids may suffer from severe performance issues, in the worst case scaling with the product of the sizes of the source and destination grids. We present a mechanism for the fast regridding of such datasets, based upon the construction of a spatial index that allows fast searching of the source grid. We discover that the most efficient spatial index under test (in terms of memory usage and query time) is a simple look-up table. A kd-tree implementation was found to be faster to build and to give similar query performance at the expense of a larger memory footprint. Using our approach, we demonstrate that regridding of complex data may proceed at speeds sufficient to permit regridding on-the-fly in an interactive visualization application, or in a Web Map Service implementation. For large datasets with complex grids the new mechanism is shown to significantly outperform algorithms used in many scientific visualization packages.

The benefits of a compute cluster approach to high spatial resolution biodiversity richness modelling: projecting the impact of climate change on Mediterranean flora

High spatial resolution environmental data gives us a better understanding of the environmental factors affecting plant distributions at fine spatial scales. However, large environmental datasets dramatically increase compute times and output species model size stimulating the need for an alternative computing solution. Cluster computing offers such a solution, by allowing both multiple plant species Environmental Niche Models (ENMs) and individual tiles of high spatial resolution models to be computed concurrently on the same compute cluster. We apply our methodology to a case study of 4,209 species of Mediterranean flora (around 17% of species believed present in the biome). We demonstrate a 16 times speed-up of ENM computation time when 16 CPUs were used on the compute cluster. Our custom Java ‘Merge’ and ‘Downsize’ programs reduce ENM output files sizes by 94%. The median 0.98 test AUC score of species ENMs is aided by various species occurrence data filtering techniques. Finally, by calculating the percentage change of individual grid cell values, we map the projected percentages of plant species vulnerable to climate change in the Mediterranean region between 1950–2000 and 2020.

The distribution of London residential property prices and the role of spatial lock-in

Much of mainstream economic analysis assumes that markets adjust smoothly, through prices, to changes in economic conditions. However, this is not necessarily the case for local housing markets, whose spatial structures may exhibit persistence, so that conditions may not be those most suited to the requirements of modern-day living. Persistence can arise from the existence of transaction costs. The paper tests the proposition that housing markets in Inner London exhibit a degree of path dependence, through the construction of a three-equation model, and examines the impact of variables constructed for the 19th and early 20th centuries on modern house prices. These include 19th-century social structures, slum clearance programmes and the 1908 underground network. Each is found to be significant. The tests require the construction of novel historical datasets, which are also described in the paper.