On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Sequential, Bayesian geostatistics:a principled method for large data sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.

Classification and contextual enhancement of remotely sensed data

The aims of the project were twofold: 1) To investigate classification procedures for remotely sensed digital data, in order to develop modifications to existing algorithms and propose novel classification procedures; and 2) To investigate and develop algorithms for contextual enhancement of classified imagery in order to increase classification accuracy. The following classifiers were examined: box, decision tree, minimum distance, maximum likelihood. In addition to these the following algorithms were developed during the course of the research: deviant distance, look up table and an automated decision tree classifier using expert systems technology. Clustering techniques for unsupervised classification were also investigated. Contextual enhancements investigated were: mode filters, small area replacement and Wharton's CONAN algorithm. Additionally methods for noise and edge based declassification and contextual reclassification, non-probabilitic relaxation and relaxation based on Markov chain theory were developed. The advantages of per-field classifiers and Geographical Information Systems were investigated. The conclusions presented suggest suitable combinations of classifier and contextual enhancement, given user accuracy requirements and time constraints. These were then tested for validity using a different data set. A brief examination of the utility of the recommended contextual algorithms for reducing the effects of data noise was also carried out.

AstroWeb – A Graphical Representation of an Astronomical WFPDB Database Using a Web-Based Open Source Geographical Information System

ACM Computing Classification System (1998): H.5.2, H.2.8, J.2, H.5.3.

Comparing Several Methods of Discriminant Analysis on the Case of Wine Data

2000 Mathematics Subject Classification: 62H30, 62J20, 62P12, 68T99

Investigating the effect of geographical distances and cultural proximity on the Hungarian wine trade

Wine is a very special product from an economic, cultural, and sociological point of view. Wine culture and wine trade play an important role in Hungary. The effect of cultural and geographical proximity on international trade has already been proven in the international trade literature. The size of bilateral trade flows between any two countries can be approximated by the gravity theory of trade. The gravity model provides empirical evidence of the relationship between the size of the economies, the distances between them, and their trade. This paper seeks to analyse the effect of cultural and geographical proximity on Hungary’s bilateral wine trade between 2000 and 2012, employing the gravity equation. The analysis is based on data from the World Bank WITS, WDI, as well as CEPII, and WTO databases. I apply OLS, Random Effects, Poisson, Pseudo-Poisson-Maximum-Likelihood and Heckman two stage estimators to calculate the gravity regression. The results show that in the case of Hungary, cultural similarity and trade liberalisation have a positive impact, while geographical distance, landlockedness, and contiguity have a negative impact on Hungarian wine exports.

Data partitioning and replication management in distributed geographic information system (GIS) database

This research presents several components encompassing the scope of the objective of Data Partitioning and Replication Management in Distributed GIS Database. Modern Geographic Information Systems (GIS) databases are often large and complicated. Therefore data partitioning and replication management problems need to be addresses in development of an efficient and scalable solution. ^ Part of the research is to study the patterns of geographical raster data processing and to propose the algorithms to improve availability of such data. These algorithms and approaches are targeting granularity of geographic data objects as well as data partitioning in geographic databases to achieve high data availability and Quality of Service(QoS) considering distributed data delivery and processing. To achieve this goal a dynamic, real-time approach for mosaicking digital images of different temporal and spatial characteristics into tiles is proposed. This dynamic approach reuses digital images upon demand and generates mosaicked tiles only for the required region according to user's requirements such as resolution, temporal range, and target bands to reduce redundancy in storage and to utilize available computing and storage resources more efficiently. ^ Another part of the research pursued methods for efficient acquiring of GIS data from external heterogeneous databases and Web services as well as end-user GIS data delivery enhancements, automation and 3D virtual reality presentation. ^ There are vast numbers of computing, network, and storage resources idling or not fully utilized available on the Internet. Proposed "Crawling Distributed Operating System "(CDOS) approach employs such resources and creates benefits for the hosts that lend their CPU, network, and storage resources to be used in GIS database context. ^ The results of this dissertation demonstrate effective ways to develop a highly scalable GIS database. The approach developed in this dissertation has resulted in creation of TerraFly GIS database that is used by US government, researchers, and general public to facilitate Web access to remotely-sensed imagery and GIS vector information. ^

Calcium carbonate global LGM Burial rate data

Global databases of calcium carbonate concentrations and mass accumulation rates in Holocene and last glacial maximum sediments were used to estimate the deep-sea sedimentary calcium carbonate burial rate during these two time intervals. Sparse calcite mass accumulation rate data were extrapolated across regions of varying calcium carbonate concentration using a gridded map of calcium carbonate concentrations and the assumption that accumulation of noncarbonate material is uncorrelated with calcite concentration within some geographical region. Mean noncarbonate accumulation rates were estimated within each of nine regions, determined by the distribution and nature of the accumulation rate data. For core-top sediments the regions of reasonable data coverage encompass 67% of the high-calcite (>75%) sediments globally, and within these regions we estimate an accumulation rate of 55.9 ± 3.6 x 10**11 mol/yr. The same regions cover 48% of glacial high-CaCO3 sediments (the smaller fraction is due to a shift of calcite deposition to the poorly sampled South Pacific) and total 44.1 ± 6.0 x 10**11 mol/yr. Projecting both estimates to 100 % coverage yields accumulation estimates of 8.3 x 10**12 mol/yr today and 9.2 x 10**12 mol/yr during glacial time. This is little better than a guess given the incomplete data coverage, but it suggests that glacial deep sea calcite burial rate was probably not considerably faster than today in spite of a presumed decrease in shallow water burial during glacial time.

Data compilation on the biological response to ocean acidification: environmental and experimental context of data sets and related literature

The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

Gene expression under thermal stress varies across a geographical range expansion front

Acknowledgements: We would like to thank Hanna Bensch and Hannes Weise for assistance with the collection of samples in the field. This work was supported by the Biodiversity and Ecosystem Services in a Changing Climate (BECC; a joint Lund-Gothenburg University initiative), the Swedish Research Council (EIS, BH), the Crafoord Foundation (EIS, BH), the Swedish Royal Society (EIS), ‘Gyllenstiernska Krapperupstiftelsen (EIS), the Wenner-Gren Foundations (postdoctoral stipend to RYD), EU FP7 (Marie Curie International Incoming Fellowship to RYD), the Kungliga Fysiografiska Sällskapet i Lund (MW) and the Helge Ax:son Johnson Stiftelse (MW). B.H. and E.I.S. conceived of the study. L.L. developed the hypotheses to be tested. L.L. and R.D. collected the field data and samples. All six authors contributed to planning RNA-seq analyses. P.C. and L.L. analysed the data. L.L. wrote the manuscript, which all six authors edited.

(Table 1) Geographical location and nutrient composition of saline lake sediments (SLS) and nearby adjacent biological soil crusts (BSC) identified as potential dust sources

While microbial communities of aerosols have been examined, little is known about their sources. Nutrient composition and microbial communities of potential dust sources, saline lake sediments (SLS) and adjacent biological soil crusts (BSC), from Southern Australia were determined and compared with a previously analyzed dust sample. Multivariate analyses of fingerprinting profiles indicated that the bacterial communities of SLS and BSC were different, and these differences were mainly explained by salinity. Nutrient concentrations varied among the sites but could not explain the differences in microbial diversity patterns. Comparison of microbial communities with dust samples showed that deflation selects against filamentous cyanobacteria, such as the Nostocales group. This could be attributed to the firm attachment of cyanobacterial filaments to soil particles and/or because deflation occurs mainly in disturbed BSC, where cyanobacterial diversity is often low. Other bacterial groups, such as Actinobacteria and the spore-forming Firmicutes, were found in both dust and its sources. While Firmicutes-related sequences were mostly detected in the SLS bacterial communities (10% of total sequences), the actinobacterial sequences were retrieved from both (11-13%). In conclusion, the potential dust sources examined here show highly diverse bacterial communities and contain nutrients that can be transported with aerosols. The obtained fingerprinting and sequencing data may enable back tracking of dust plumes and their microorganisms.

Talos Dome Ice Core Deuterium Data

During the 1996 Programma Nazionale di Ricerche in Antartide-International Trans-Antarctic Scientific Expedition traverse, two firn cores were retrieved from the Talos Dome area (East Antarctica) at elevations of 2316 m (TD, 89 m long) and 2246 m (ST556, 19 m long). Cores were dated by using seasonal variations in non-sea-salt (nss) SO42- concentrations coupled with the recognition of tritium marker level (1965-1966) and nss SO42- spikes due to the most important volcanic events in the past (Pinatubo 1991, Agung 1963, Krakatoa 1883, Tambora 1815, Kuwae 1452, Unknown 1259). The number of annual layers recognized in the TD and ST556 cores was 779 and 97, respectively. The dD record obtained from the TD core has been compared with other East Antarctic isotope ice core records (Dome C EPICA, South Pole, Taylor Dome). These records suggest cooler climate conditions between the middle of 16th and the beginning of 19th centuries, which might be related to the Little Ice Age (LIA) cold period. Because of the high degree of geographical variability, the strongest LIA cooling was not temporally synchronous over East Antarctica, and the analyzed records do not provide a coherent picture for East Antarctica. The accumulation rate record presented for the TD core shows a decrease during part of the LIA followed by an increment of about 11% in accumulation during the 20th century. At the ST556 site, the accumulation rate observed during the 20th century was quite stable.