Journal articles: 'Spatial variation'

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Kemp, C. D., and Bertil Matern. "Spatial Variation." Statistician 37, no. 1 (1988): 84. http://dx.doi.org/10.2307/2348387.

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Brown, James H., David W. Mehlman, and George C. Stevens. "Spatial Variation in Abundance." Ecology 76, no. 7 (October 1995): 2028–43. http://dx.doi.org/10.2307/1941678.

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Xu, Qin, and Li Wei. "Formulations for Estimating Spatial Variations of Analysis Error Variance to Improve Multiscale and Multistep Variational Data Assimilation." Advances in Meteorology 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/7931964.

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When the coarse-resolution observations used in the first step of multiscale and multistep variational data assimilation become increasingly nonuniform and/or sparse, the error variance of the first-step analysis tends to have increasingly large spatial variations. However, the analysis error variance computed from the previously developed spectral formulations is constant and thus limited to represent only the spatially averaged error variance. To overcome this limitation, analytic formulations are constructed to efficiently estimate the spatial variation of analysis error variance and associated spatial variation in analysis error covariance. First, a suite of formulations is constructed to efficiently estimate the error variance reduction produced by analyzing the coarse-resolution observations in one- and two-dimensional spaces with increased complexity and generality (from uniformly distributed observations with periodic extension to nonuniformly distributed observations without periodic extension). Then, three different formulations are constructed for using the estimated analysis error variance to modify the analysis error covariance computed from the spectral formulations. The successively improved accuracies of these three formulations and their increasingly positive impacts on the two-step variational analysis (or multistep variational analysis in first two steps) are demonstrated by idealized experiments.

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Itoh, M., Y. Kosugi, S. Takanashi, Y. Hayashi, S. Kanemitsu, K. Osaka, M. Tani, and A. R. Nik. "Temporal and spatial variations of soil carbon dioxide, methane, and nitrous oxide fluxes in a Southeast Asian tropical rainforest." Biogeosciences Discussions 7, no. 5 (September 9, 2010): 6847–87. http://dx.doi.org/10.5194/bgd-7-6847-2010.

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Abstract. To clarify the factors controlling temporal and spatial variations of soil carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes, we investigated these gas fluxes and environmental factors in a tropical rainforest in Peninsular Malaysia. Temporal variation of CO2 flux in a 2-ha plot was positively related to soil water condition and rainfall history. Spatially, CO2 flux was negatively related to soil water condition. When CO2 flux hotspots were included, no other environmental factors such as soil C or N concentrations showed any significant correlation. Although the larger area sampled in the present study complicates explanations of spatial variation of CO2 flux, our results support a previously reported bipolar relationship between the temporal and spatial patterns of CO2 flux and soil water condition observed at the study site in a smaller study plot. Flux of CH4 was usually negative with little variation, resulting in the soil at our study site functioning as a CH4 sink. Both temporal and spatial variations of CH4 flux were positively related to the soil water condition. Soil N concentration was also related to the spatial distribution of CH4 flux. Some hotspots were observed, probably due to CH4 production by termites, and these hotspots obscured the relationship between both temporal and spatial variations of CH4 flux and environmental factors. Temporal variation of N2O flux and soil N2O concentration was large and significantly related to the soil water condition, or in a strict sense, to rainfall history. Thus, the rainfall pattern controlled wet season N2O production in soil and its soil surface flux. Spatially, large N2O emissions were detected in wet periods at wetter and anaerobic locations, and were thus determined by soil physical properties. Our results showed that, even in Southeast Asian rainforests where distinct dry and wet seasons do not exist, variation in the soil water condition related to rainfall history controlled the temporal variations of soil CO2 flux, CH4 uptake, and N2O emission. The soil water condition associated with soil hydraulic properties was also the important controlling factor of the spatial distributions of these gas fluxes.

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Park, Yonghwan, Taewoong Jang, Jongkuk Kim, Su-Kyung Kim, Il-Kwon Kim, Chang-Jun Kim, and Yasuoki Takami. "Temporal Variation Dominates in Local Carabid Beetle Communities in Korean Mountains." Insects 12, no. 11 (November 12, 2021): 1019. http://dx.doi.org/10.3390/insects12111019.

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Spatial and temporal variation in ecological environments may result in spatial and temporal variation in communities. Temporal studies of biodiversity are essential for forecasting future changes in community structure and ecosystem function. Therefore, determining the mechanisms that drive temporal change in communities remains an important and interesting challenge in ecology. We quantified spatial and temporal variations in carabid beetle communities and site-specific environmental factors for 5 years at nine study sites on three mountains in the Baekdudaegan Mountain Range, Korea. Carabid beetle communities exhibited significant temporal variation, which was larger than spatial variations between and within mountains. Environmental factors mostly varied between sites within mountains. Community variation was only weakly associated with environmental factors at wide scales, i.e., between sites on three mountains, but was strongly associated at narrow spatial scales, i.e., between sites within one mountain. Our results indicate that temporal variation in communities occurs in response to variations in the local climate, and that the patterns of temporal variation differ between mountains. Thus, temporal surveys of insect communities and climates at local scales are important for predicting temporal changes in the communities.

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Praat, J. P., A. F. Bollen, and A. D. Mowat. "CHARACTERISING SPATIAL VARIATION IN QUALITY." Acta Horticulturae, no. 753 (October 2007): 305–16. http://dx.doi.org/10.17660/actahortic.2007.753.38.

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Fenton, G. A., and E. H. Vanmarcke. "Spatial variation in liquefaction risk." Géotechnique 48, no. 6 (December 1998): 819–31. http://dx.doi.org/10.1680/geot.1998.48.6.819.

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Congdon, Peter. "Spatial variation in attributable risks." Spatial and Spatio-temporal Epidemiology 12 (January 2015): 39–52. http://dx.doi.org/10.1016/j.sste.2015.02.002.

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Warshaw, Christopher. "Spatial variation in messaging effects." Nature Climate Change 8, no. 5 (April 16, 2018): 360–61. http://dx.doi.org/10.1038/s41558-018-0143-8.

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Mulligan, Gordon F., and Timothy J. Fik. "Price Variation in Spatial Oligopolies." Geographical Analysis 21, no. 1 (September 3, 2010): 32–46. http://dx.doi.org/10.1111/j.1538-4632.1989.tb00875.x.

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Frank, Steven A. "Spatial variation in coevolutionary dynamics." Evolutionary Ecology 5, no. 2 (April 1991): 193–217. http://dx.doi.org/10.1007/bf02270834.

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Stewart-Koster, Ben, Mark J. Kennard, Bronwyn D. Harch, Fran Sheldon, Angela H. Arthington, and Bradley J. Pusey. "Partitioning the variation in stream fish assemblages within a spatio-temporal hierarchy." Marine and Freshwater Research 58, no. 7 (2007): 675. http://dx.doi.org/10.1071/mf06183.

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This paper describes the relative influence of (i) landscape scale environmental and hydrological factors, (ii) local scale environmental conditions including recent flow history, and (iii) spatial effects (proximity of sites to one another), on the spatial and temporal variation in local freshwater fish assemblages in the Mary River, south-eastern Queensland, Australia. Using canonical correspondence analysis, each of the three sets of variables explained similar amounts of variation in fish assemblages (ranging from 44 to 52%). Variation in fish assemblages was partitioned into eight unique components: pure environmental, pure spatial, pure temporal, spatially structured environmental variation, temporally structured environmental variation, spatially structured temporal variation, the combined spatial/temporal component of environmental variation and unexplained variation. The total variation explained by these components was 65%. The combined spatial/temporal/environmental component explained the largest component (30%) of the total variation in fish assemblages, whereas pure environmental (6%), temporal (9%) and spatial (2%) effects were relatively unimportant. The high degree of intercorrelation between the three different groups of explanatory variables indicates that our understanding of the importance to fish assemblages of hydrological variation (often highlighted as the major structuring force in river systems) is dependent on the environmental context in which this role is examined.

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Ives, Anthony R., and Eric D. Klopfer. "SPATIAL VARIATION IN ABUNDANCE CREATED BY STOCHASTIC TEMPORAL VARIATION." Ecology 78, no. 6 (September 1997): 1907–13. http://dx.doi.org/10.1890/0012-9658(1997)078[1907:sviacb]2.0.co;2.

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Sharon, Chelsea E., Andrew J. Baker, Andrew I. Harris, Dieter Lutz, and Linda J. Tacconi. "Spatial Variation of CO Excitation in High-z Galaxies." Proceedings of the International Astronomical Union 8, S292 (August 2012): 253. http://dx.doi.org/10.1017/s1743921313001282.

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AbstractPrevious studies of the molecular gas excitation in high-redshift galaxies have focused on galaxy-wide averages of CO line ratios. However, it is possible that these averages hide spatial variation on sub-galactic scales, disguising the true distribution and conditions of the molecular gas within star-forming galaxies. Even in the pre-ALMA era we have begun to see evidence for spatial variation of CO excitation in both rest-UV selected and submillimeter-selected galaxies at z > 2, aided both by the increased frequency coverage of the Jansky Very Large Array (allowing high-resolution observations of the CO(1–0) line, the best tracer for the coldest molecular gas) and by the benefits of gravitational lensing for spatially extended sources. We show new results for multiple high-redshift systems that reveal spatial and/or spectral variations in CO excitation, including an early-stage merger that has different conditions in its two components, thereby illustrating the need for high spatial and spectral resolution mapping in order to accurately characterize the molecular ISM in high-z galaxies.

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Caldararu, Silvia, Drew W. Purves, and Matthew J. Smith. "The effect of using the plant functional type paradigm on a data-constrained global phenology model." Biogeosciences 13, no. 4 (February 18, 2016): 925–41. http://dx.doi.org/10.5194/bg-13-925-2016.

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Abstract. Leaf seasonality impacts a variety of important biological, chemical, and physical Earth system processes, which makes it essential to represent leaf phenology in ecosystem and climate models. However, we are still lacking a general, robust parametrisation of phenology at global scales. In this study, we use a simple process-based model, which describes phenology as a strategy for carbon optimality, to test the effects of the common simplification in global modelling studies that plant species within the same plant functional type (PFT) have the same parameter values, implying they are assumed to have the same species traits. In a previous study this model was shown to predict spatial and temporal dynamics of leaf area index (LAI) well across the entire global land surface provided local grid cell parameters were used, and is able to explain 96 % of the spatial variation in average LAI and 87 % of the variation in amplitude. In contrast, we find here that a PFT level parametrisation is unable to capture the spatial variability in seasonal cycles, explaining on average only 28 % of the spatial variation in mean leaf area index and 12 % of the variation in seasonal amplitude. However, we also show that allowing only two parameters, light compensation point and leaf age, to be spatially variable dramatically improves the model predictions, increasing the model's capability of explaining spatial variations in leaf seasonality to 70 and 57 % of the variation in LAI average and amplitude, respectively. This highlights the importance of identifying the spatial scale of variation of plant traits and the necessity to critically analyse the use of the plant functional type assumption in Earth system models.

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Caldararu, S., D. W. Purves, and M. J. Smith. "The effect of using the plant functional type paradigm on a data-constrained global phenology model." Biogeosciences Discussions 12, no. 20 (October 19, 2015): 16847–84. http://dx.doi.org/10.5194/bgd-12-16847-2015.

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Abstract. Leaf seasonality impacts a variety of important biological, chemical and physical Earth system processes, which makes it essential to represent leaf phenology in ecosystem and climate models. However, we are still lacking a general, robust parametrisation of phenology at global scales. In this study, we use a simple process-based model, which describes phenology as a strategy for carbon optimality, to test the effects of the common assumption in global modelling studies that plant species within the same plant functional type have the same parameter values, implying they are assumed to have the same species traits. In a previous study this model was shown to predict spatial and temporal dynamics of leaf area index (LAI) well across the entire global land surface provided local grid cell parameters were used, and is able to explain 96 % of the spatial variation in average LAI and 87 % of the variation in amplitude. In contrast, we find here that a PFT level parametrisation is unable to capture the spatial variability in seasonal cycles, explaining on average only 28 % of the spatial variation in mean leaf area index and 12 % of the variation in seasonal amplitude. However we also show that allowing only two parameters, light compensation point and leaf age, to be spatially variable dramatically improves the model predictions, increasing the model's capability of explaining spatial variations in leaf seasonality to 70 and 57 % of the variation in LAI average and amplitude respectively. This highlights the importance of identifying the spatial scale of variation of plant traits and the necessity to critically analyse the use of the plant functional type assumption in Earth system models.

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Kodama, Taketoshi, Taku Wagawa, Naoki Iguchi, Yoshitake Takada, Takashi Takahashi, Ken-Ichi Fukudome, Haruyuki Morimoto, and Tsuneo Goto. "Spatial variations in zooplankton community structure along the Japanese coastline in the Japan Sea: influence of the coastal current." Ocean Science 14, no. 3 (June 1, 2018): 355–69. http://dx.doi.org/10.5194/os-14-355-2018.

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Abstract. This study evaluates spatial variations in zooplankton community structure and potential controlling factors along the Japanese coast under the influence of the coastal branch of the Tsushima Warm Current (CBTWC). Variations in the density of morphologically identified zooplankton in the surface layer in May were investigated for a 15-year period. The density of zooplankton (individuals per cubic meter) varied between sampling stations, but there was no consistent west–east trend. Instead, there were different zooplankton community structures in the west and east, with that in Toyama Bay particularly distinct: Corycaeus affinis and Calanus sinicus were dominant in the west and Oithona atlantica was dominant in Toyama Bay. Distance-based redundancy analysis (db-RDA) was used to characterize the variation in zooplankton community structure, and four axes (RD1–4) provided significant explanation. RD2–4 only explained < 4.8 % of variation in the zooplankton community and did not show significant spatial difference; however, RD1, which explained 89.9 % of variation, did vary spatially. Positive and negative species scores on RD1 represent warm- and cold-water species, respectively, and their variation was mainly explained by water column mean temperature, and it is considered to vary spatially with the CBTWC. The CBTWC intrusion to the cold Toyama Bay is weak and occasional due to the submarine canyon structure of the bay. Therefore, the varying bathymetric characteristics along the Japanese coast of the Japan Sea generate the spatial variation in zooplankton community structure, and dominance of warm-water species can be considered an indicator of the CBTWC.

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Laughner, Joshua L., Azimeh Zare, and Ronald C. Cohen. "Effects of daily meteorology on the interpretation of space-based remote sensing of NO<sub>2</sub>." Atmospheric Chemistry and Physics 16, no. 23 (December 9, 2016): 15247–64. http://dx.doi.org/10.5194/acp-16-15247-2016.

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Abstract. Retrievals of tropospheric NO2 columns from UV–visible observations of reflected sunlight require a priori vertical profiles to account for the variation in sensitivity of the observations to NO2 at different altitudes. These profiles vary in space and time but are usually approximated using models that do not resolve the full details of this variation. Currently, no operational retrieval simulates these a priori profiles at both high spatial and high temporal resolution. Here we examine the additional benefits of daily variations in a priori profiles for retrievals already simulating a priori NO2 profiles at sufficiently high spatial resolution to identify variations of NO2 within urban plumes. We show the effects of introducing daily variation into a priori profiles can be as large as 40 % and 3 × 1015 molec. cm−2 for an individual day and lead to corrections as large as −13 % for a monthly average in a case study of Atlanta, GA, USA. Additionally, we show that NOx emissions estimated from space-based remote sensing using daily, high-spatial-resolution a priori profiles are ∼ 100 % greater than those of a retrieval using spatially coarse a priori profiles, and 26–40 % less than those of a retrieval using monthly averaged high-spatial-resolution profiles.

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Zerva, Aspasia, and Vassilios Zervas. "Spatial variation of seismic ground motions: An overview." Applied Mechanics Reviews 55, no. 3 (May 1, 2002): 271–97. http://dx.doi.org/10.1115/1.1458013.

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This study addresses the topic of the spatial variation of seismic ground motions as evaluated from data recorded at dense instrument arrays. It concentrates on the stochastic description of the spatial variation, and focuses on spatial coherency. The estimation of coherency from recorded data and its interpretation are presented. Some empirical and semi-empirical coherency models are described, and their validity and limitations in terms of physical causes discussed. An alternative approach that views the spatial variation of seismic motions as deviations in amplitudes and phases of the recorded data around a coherent approximation of the seismic motions is described. Simulation techniques for the generation of artificial spatially variable seismic ground motions are also presented and compared. The effect of coherency on the seismic response of extended structures is highlighted. This review article includes 133 references.

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Miller, William E. B., and Alan L. Kastengren. "Measurements of spatial variations in response of ionization chambers." Journal of Synchrotron Radiation 20, no. 1 (November 10, 2012): 160–65. http://dx.doi.org/10.1107/s0909049512041337.

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Measurements of the spatial variations in the response of three ionization chamber (IC) designs were tested as a function of chamber bias voltage, incident X-ray flux and fill gas. Two components of spatial variation are seen. When the ionization chambers are near saturation, spatial variations exist that are tied to the chamber geometry. While the response of some chambers is relatively flat, others show significant variation across the IC. These variations appear to be inherent in the response of each IC at saturation. When the chamber is far from saturation, large spatial variations in response are present when N2is used as a fill gas, but not when ambient air is used as a fill gas. These appear to be tied to space charge effects.

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Hu, Lan, Daniel Griffith, and Yongwan Chun. "Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000–2011." International Journal of Environmental Research and Public Health 15, no. 11 (October 30, 2018): 2406. http://dx.doi.org/10.3390/ijerph15112406.

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The geographic distribution of lung cancer rates tends to vary across a geographic landscape, and covariates (e.g., smoking rates, demographic factors, socio-economic indicators) commonly are employed in spatial analysis to explain the spatial heterogeneity of these cancer rates. However, such cancer risk factors often are not available, and conventional statistical models are unable to fully capture hidden spatial effects in cancer rates. Introducing random effects in the model specifications can furnish an efficient approach to account for variations that are unexplained due to omitted variables. Especially, a random effects model can be effective for a phenomenon that is static over time. The goal of this paper is to investigate geographic variation in Florida lung cancer incidence data for the time period 2000–2011 using random effects models. In doing so, a Moran eigenvector spatial filtering technique is utilized, which can allow a decomposition of random effects into spatially structured (SSRE) and spatially unstructured (SURE) components. Analysis results confirm that random effects models capture a substantial amount of variation in the cancer data. Furthermore, the results suggest that spatial pattern in the cancer data displays a mixture of positive and negative spatial autocorrelation, although the global map pattern of the random effects term may appear random.

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Prabhakaran, Rahul, Giovanni Bertotti, Janos Urai, and David Smeulders. "Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics." Solid Earth 12, no. 10 (September 30, 2021): 2159–209. http://dx.doi.org/10.5194/se-12-2159-2021.

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Abstract. Rock fractures organize as networks, exhibiting natural variation in their spatial arrangements. Therefore, identifying, quantifying, and comparing variations in spatial arrangements within network geometries are of interest when explicit fracture representations or discrete fracture network models are chosen to capture the influence of fractures on bulk rock behaviour. Treating fracture networks as spatial graphs, we introduce a novel approach to quantify spatial variation. The method combines graph similarity measures with hierarchical clustering and is applied to investigate the spatial variation within large-scale 2-D fracture networks digitized from the well-known Lilstock limestone pavements, Bristol Channel, UK. We consider three large, fractured regions, comprising nearly 300 000 fractures spread over 14 200 m2 from the Lilstock pavements. Using a moving-window sampling approach, we first subsample the large networks into subgraphs. Four graph similarity measures – fingerprint distance, D-measure, Network Laplacian spectral descriptor (NetLSD), and portrait divergence – that encapsulate topological relationships and geometry of fracture networks are then used to compute pair-wise subgraph distances serving as input for the statistical hierarchical clustering technique. In the form of hierarchical dendrograms and derived spatial variation maps, the results indicate spatial autocorrelation with localized spatial clusters that gradually vary over distances of tens of metres with visually discernable and quantifiable boundaries. Fractures within the identified clusters exhibit differences in fracture orientations and topology. The comparison of graph similarity-derived clusters with fracture persistence measures indicates an intra-network spatial variation that is not immediately obvious from the ubiquitous fracture intensity and density maps. The proposed method provides a quantitative way to identify spatial variations in fracture networks, guiding stochastic and geostatistical approaches to fracture network modelling.

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Morrisey, DJ, L. Howitt, AJ Underwood, and JS Stark. "Spatial variation in soft-sediment benthos." Marine Ecology Progress Series 81 (1992): 197–204. http://dx.doi.org/10.3354/meps081197.

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Reid, Bell, and Copeland. "MODELLING THE SPATIAL VARIATION OF WIND." Weather and Climate 18, no. 1 (1998): 21. http://dx.doi.org/10.2307/44280025.

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Andris, Clio, David Cowen, and Jason Wittenbach. "Support Vector Machine for Spatial Variation." Transactions in GIS 17, no. 1 (October 9, 2012): 41–61. http://dx.doi.org/10.1111/j.1467-9671.2012.01354.x.

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Kelsall, Julia, and Jonathan Wakefield. "Modeling Spatial Variation in Disease Risk." Journal of the American Statistical Association 97, no. 459 (September 2002): 692–701. http://dx.doi.org/10.1198/016214502388618438.

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Fisher, Eileen L., R. William Field, Brian J. Smith, Charles F. Lynch, Daniel J. Steck, and John S. Neuberger. "Spatial Variation of Residential Radon Concentrations." Health Physics 75, no. 5 (November 1998): 506–13. http://dx.doi.org/10.1097/00004032-199811000-00007.

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Molless, Antoinette F. "Linewidth variation characterization by spatial decomposition*." Journal of Micro/Nanolithography, MEMS, and MOEMS 1, no. 2 (July 1, 2002): 106. http://dx.doi.org/10.1117/1.1488159.

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Derungs, Curdin, and Ross S. Purves. "Characterising landscape variation through spatial folksonomies." Applied Geography 75 (October 2016): 60–70. http://dx.doi.org/10.1016/j.apgeog.2016.08.005.

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Morrison, Kathryn T., Trisalyn A. Nelson, and Aleck S. Ostry. "Mapping spatial variation in food consumption." Applied Geography 31, no. 4 (October 2011): 1262–67. http://dx.doi.org/10.1016/j.apgeog.2010.11.020.

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Ambrose, Brent W., and Thomas M. Springer. "Spatial variation of nonmetropolitan industrial location." Journal of Real Estate Finance and Economics 7, no. 1 (July 1993): 17–27. http://dx.doi.org/10.1007/bf01096933.

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Thorson, James T., James N. Ianelli, Stephan B. Munch, Kotaro Ono, and Paul D. Spencer. "Spatial delay-difference models for estimating spatiotemporal variation in juvenile production and population abundance." Canadian Journal of Fisheries and Aquatic Sciences 72, no. 12 (December 2015): 1897–915. http://dx.doi.org/10.1139/cjfas-2014-0543.

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Many important ecological questions require accounting for spatial variation in demographic rates (e.g., survival) and population variables (e.g., abundance per unit area). However, ecologists have few spatial modelling approaches that (i) fit directly to spatially referenced data, (ii) represent population dynamics explicitly and mechanistically, and (iii) estimate parameters using rigorous statistical methods. We therefore demonstrate a new and computationally efficient approach to spatial modelling that uses random fields in place of the random variables typically used in spatially aggregated models. We adapt this approach to delay-difference dynamics to estimate the impact of fishing and natural mortality, recruitment, and individual growth on spatial population dynamics for a fish population. In particular, we develop this approach to estimate spatial variation in average production of juvenile fishes (termed recruitment), as well as annual variation in the spatial distribution of recruitment. We first use a simulation experiment to demonstrate that the spatial delay-difference model can, in some cases, explain over 50% of spatial variance in recruitment. We also apply the spatial delay-difference model to data for rex sole (Glyptocephalus zachirus) in the Gulf of Alaska and show that average recruitment (across all years) is greatest near Kodiak Island but that some years show greatest recruitment in Southeast Alaska or the western Gulf of Alaska. Using model developments and software advances presented here, we argue that future research can develop models to approximate adult movement, incorporate spatial covariates to explain annual variation in recruitment, and evaluate management procedures that use spatially explicit estimates of population abundance.

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Ghazali, Anis Kausar, Thomas Keegan, and Benjamin M. Taylor. "Spatial Variation of Survival for Colorectal Cancer in Malaysia." International Journal of Environmental Research and Public Health 18, no. 3 (January 25, 2021): 1052. http://dx.doi.org/10.3390/ijerph18031052.

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A patient’s survival may depend on several known and unknown factors and it may also vary spatially across a region. Socioeconomic status, accessibility to healthcare and other environmental factors are likely to contribute to survival rates. The aim of the study was to model the spatial variation in survival for colorectal cancer patients in Malaysia, accounting for individual and socioeconomic risk factors. We conducted a retrospective study of 4412 colorectal cancer (ICD-10, C18-C20) patients diagnosed from 2008 to 2013 to model survival in CRC patients. We used the data recorded in the database of the Malaysian National Cancer Patient Registry-Colorectal Cancer (NCPR-CRC). Spatial location was assigned based on the patients’ central district location, which involves 144 administrative districts of Malaysia. We fitted a parametric proportional hazards model in which the spatially correlated frailties were modelled by a log-Gaussian stochastic process to analyse the spatially referenced survival data, which is also known as a spatial survival model. After controlling for individual and area level characteristics, our findings indicate wide spatial variation in colorectal cancer survival across Malaysia. Better healthcare provision and higher socioeconomic index in the districts where patients live decreased the risk of death from colorectal cancer, but these associations were not statistically significant. Reliable measurement of environmental factors is needed to provide good insight into the effects of potential risk factors for the disease. For example, a better metric is needed to measure socioeconomic status and accessibility to healthcare in the country. The findings provide new information that might be of use to the Ministry of Health in identifying populations with an increased risk of poor survival, and for planning and providing cancer control services.

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Liu, Yanchun, Qing Shang, Lei Wang, and Shirong Liu. "Effects of Understory Shrub Biomass on Variation of Soil Respiration in a Temperate-Subtropical Transitional Oak Forest." Forests 10, no. 2 (January 23, 2019): 88. http://dx.doi.org/10.3390/f10020088.

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Quantification of the temporal and spatial variations of soil respiration is an essential step in modeling soil carbon (C) emission associated with the spatial distribution of plants. To examine the temporal and spatial variations of soil respiration and its driving factors, we investigated soil respiration, microclimate, and understory vegetation in a 50 m × 70 m plot in a climatic transitional zone oak forest in Central China. The temporal variation of soil respiration based on the 21 measurements ranged from 15.01% to 30.21% across the 48 subplots. Structural equation modeling showed that soil temperature and understory shrub biomass had greater positive effects on the seasonal variability of soil respiration. The spatial variation of soil respiration of the 48 subplots varied from 3.61% to 6.99% during the 21 measurement campaigns. Understory shrub biomass and belowground fine root biomass positively regulated the spatial variation of soil respiration. Soil respiration displayed strong spatial autocorrelation, with an average spatial correlation length of 20.1 m. The findings highlight the importance of understory shrub and belowground biomass in regulating the temporal and spatial heterogeneity of soil respiration in forest ecosystems, and the need to carefully address it to robustly estimate the contribution of soil C emission in terrestrial C cycling.

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Ndzi, David L., Nick Savage, and Boris Gremont. "Spatial and Temporal Variation of Wideband Indoor Channels." International Journal of Antennas and Propagation 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/735434.

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Extensive studies of the impact of temporal variations induced by people on the characteristics of indoor wideband channels are reported. Singular Value Decomposition Prony algorithm has been used to compute the impulse response from measured channel transfer functions. The high multipath resolution of the algorithm has allowed a detailed assessment of the shapes of individual multipath clusters and their variation in time and space in indoor channels. Large- and small-scale analyses show that there is a significant dependency of the channel response on room size. The presence of people in the channel has been found to induce both signal enhancements and fading with short-term dynamic variations of up to 30 dB, depending on the number of people and their positions within the room. A joint amplitude and time of arrival model has been used to successfully model measured impulse response clusters.

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Osei, Frank, Alfred Stein, and Anthony Ofosu. "Poisson-Gamma Mixture Spatially Varying Coefficient Modeling of Small-Area Intestinal Parasites Infection." International Journal of Environmental Research and Public Health 16, no. 3 (January 26, 2019): 339. http://dx.doi.org/10.3390/ijerph16030339.

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Understanding the spatially varying effects of demographic factors on the spatio-temporal variation of intestinal parasites infections is important for public health intervention and monitoring. This paper presents a hierarchical Bayesian spatially varying coefficient model to evaluate the effects demographic factors on intestinal parasites morbidities in Ghana. The modeling relied on morbidity data collected by the District Health Information Management Systems. We developed Poisson and Poisson-gamma spatially varying coefficient models. We used the demographic factors, unsafe drinking water, unsafe toilet, and unsafe liquid waste disposal as model covariates. The models were fitted using the integrated nested Laplace approximations (INLA). The overall risk of intestinal parasites infection was estimated to be 10.9 per 100 people with a wide spatial variation in the district-specific posterior risk estimates. Substantial spatial variation of increasing multiplicative effects of unsafe drinking water, unsafe toilet, and unsafe liquid waste disposal occurs on the variation of intestinal parasites risk. The structured residual spatial variation widely dominates the unstructured component, suggesting that the unaccounted-for risk factors are spatially continuous in nature. The study concludes that both the spatial distribution of the posterior risk and the associated exceedance probability maps are essential for monitoring and control of intestinal parasites.

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Zhang, Cheng, and Gao. "Permutation Entropy-Based Analysis of Temperature Complexity Spatial-Temporal Variation and Its Driving Factors in China." Entropy 21, no. 10 (October 13, 2019): 1001. http://dx.doi.org/10.3390/e21101001.

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Air temperature fluctuation complexity (TFC) describes the uncertainty of temperature changes. The analysis of its spatial and temporal variation is of great significance to evaluate prediction uncertainty of the regional temperature trends and the climate change. In this study, annual-TFC from 1979–2017 and seasonal-TFC from 1983–2017 in China were calculated by permutation entropy (PE). Their temporal trend is described by the Mann-Kendall method. Driving factors of their spatial variations are explored through GeoDetector. The results show that: (1). TFC shows a downward trend generally, with obvious time variation. (2). The spatial variation of TFC is mainly manifested in the differences among the five sub-regions in China. There is low uncertainty in the short-term temperature trends in the northwest and southeast. The northeastern and southwestern regions show high uncertainties. TFC in the central region is moderate. (3). The vegetation is the main factor of spatial variation, followed by the climate and altitude, and the latitude and terrain display the lowest impact. The interactions of vegetation-altitude, vegetation-climate and altitude-latitude can interpret more than 50% of the spatial variations. These results provide insights into causes and mechanisms of the complexity of the climate system. They can help to determine the influencing process of various factors.

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Huang, Chen, Jun Hong Bai, Jun Jing Wang, Qiong Qiong Lu, Qing Qing Zhao, Xiao Fei Ye, and Haifeng Gao. "Spatial Variability of Nitrogen and Phosphorus in Wetland Soils of the Yellow River Delta, China." Advanced Materials Research 610-613 (December 2012): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.1028.

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Spatial variability of soil nitrogen and phosphorus in the Yellow River Delta was investigated using geostatistical method. Our results showed moderate variation in TN and TP and high variations in NH4+-N and AP. The best semi-variogram model for each nutrient was identified. The model parameters suggested that the structure variance dominated the total variance of TN, TP and NH4+-N, while the spatial variability of AP was relatively random. The spatial variation scales of soil nitrogen and phosphorus are similar.

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Bianda, M., S. Berdyugina, D. Gisler, R. Ramelli, L. Belluzzi, E. S. Carlin, J. O. Stenflo, and T. Berkefeld. "Spatial variations of the Sr I 4607 Å scattering polarization peak." Astronomy & Astrophysics 614 (June 2018): A89. http://dx.doi.org/10.1051/0004-6361/201731887.

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Context. The scattering polarization signal observed in the photospheric Sr I 4607 Å line is expected to vary at granular spatial scales. This variation can be due to changes in the magnetic field intensity and orientation (Hanle effect), but also to spatial and temporal variations in the plasma properties. Measuring the spatial variation of such polarization signal would allow us to study the properties of the magnetic fields at subgranular scales, but observations are challenging since both high spatial resolution and high spectropolarimetric sensitivity are required. Aims. We aim to provide observational evidence of the polarization peak spatial variations, and to analyze the correlation they might have with granulation. Methods. Observations conjugating high spatial resolution and high spectropolarimetric precision were performed with the Zurich IMaging POLarimeter, ZIMPOL, at the GREGOR solar telescope, taking advantage of the adaptive optics system and the newly installed image derotator. Results. Spatial variations of the scattering polarization in the Sr I 4607 Å line are clearly observed. The spatial scale of these variations is comparable with the granular size. Small correlations between the polarization signal amplitude and the continuum intensity indicate that the polarization is higher at the center of granules than in the intergranular lanes.

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40

Smock, Leonard A. "Spatial and temporal variation in organic matter storage in low-gradient, headwater streams." Archiv für Hydrobiologie 118, no. 2 (April 27, 1990): 169–84. http://dx.doi.org/10.1127/archiv-hydrobiol/118/1990/169.

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Hatton-Ellis, Tristan W., Andrew Hope, Leslie R. Noble, and Beth Okamura. "Genetic variation in a freshwater bryozoan: II: The effect of increasing spatial scale." Fundamental and Applied Limnology 154, no. 2 (May 29, 2002): 293–309. http://dx.doi.org/10.1127/archiv-hydrobiol/154/2002/293.

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Khamis, Faisal G. "The Relationship between Health and Household Economic Status Using Spatial Measures in Iraq, 2004." International Journal of Quality, Statistics, and Reliability 2012 (April 5, 2012): 1–8. http://dx.doi.org/10.1155/2012/481538.

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This study addresses spatial effects by applying spatial analysis in studying whether household economic status (HES) is related to health across governorates in Iraq. The aim is to assess variation in health and whether this variation is accounted for by variation in HES. A spatial univariate and bivariate autocorrelation measures were applied to cross-sectional data from census conducted in 2004. The hypothesis of spatial clustering for HES was confirmed by a positive global Moran’s I of 0.28 with P= 0.010, while for health was not confirmed by a negative global Moran’s I of −0.03. Based on local Moran’s Ii, two and seven significant clusters in health and in HES were found respectively. Bivariate spatial correlation between health and HES wasn’t found significant (Ixy=-0.08) with P=0.80. In conclusion, geographical variation was found in each of health and HES. Based on visual inspection, the patterns formed by governorates with lowest health and those with lowest HES were partly identical. However, this study cannot support the hypothesis that variation in HES may spatially explain variation in health. Further research is needed to understand mechanisms underlying the influence of neighbourhood context.

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Schneider, Max, Gilles Blanchard, Christian Levers, and Tobias Kuemmerle. "Spatial Variation of Drivers of Agricultural Abandonment with Spatially Boosted Models." Procedia Environmental Sciences 27 (2015): 53–57. http://dx.doi.org/10.1016/j.proenv.2015.07.114.

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Liu, Guangping, Weihong He, and Shuqun Cai. "Seasonal Variation of Dissolved Oxygen in the Southeast of the Pearl River Estuary." Water 12, no. 9 (September 3, 2020): 2475. http://dx.doi.org/10.3390/w12092475.

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Dissolved oxygen (DO) concentration in estuaries is highly variable at different spatial and temporal scales, which is affected by physical, chemical and biological processes. This study analyzed the spatial–temporal distributions of dissolved oxygen concentration and bottom hypoxia in the southeast of the Pearl River Estuary (PRE) using monthly water quality monitoring and hydrographic data covering the period 2000–2017. The seasonal spatial–temporal variation of DO concentration was studied using various methods, such as rotated empirical orthogonal functions, harmonic analysis, and correlation analysis. The results showed that DO stratification was significant in summer, but it was not distinct in winter, during which DO concentration peaked. DO stratification exhibited a significantly positive correlation with water stratification. In the south and west of Hong Kong (SHK and WHK, respectively), DO concentration fields exhibited distinct seasonal changes in the recent 18 years. In SHK, the main periods of the surface DO variation were 24, 12, and 6 months, whereas the main period was 12 months in WHK. The main period of the bottom DO variation was 12 months in both SHK and WHK. In SHK, the spatial–temporal variations in surface and bottom DO were highly related to the variations of salinity, dissolved inorganic nitrogen (DIN), and active phosphorus, and the variation of surface DO was also connected to the variation of temperature and chlorophyll a. In WHK, the variations in surface and bottom DO were highly related to the variations of salinity and temperature, and the variation of surface DO was also connected to the variation of DIN. The river discharge and wind had a different important influence on the temporal variability of DO in WHK and SHK. These findings suggested that the variations of DO may be controlled by coupled physical and biochemical processes in the southeast of PRE. From 2000 to 2017, bottom hypoxia in the southeast of PRE occurred in the summers of 7 years. SHK appeared to be more vulnerable to hypoxia than WHK.

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Arai, Takaomi, and Shogo Kimura. "Spatiotemporal Variability of Trace Elements Fingerprints in Otoliths of Japanese Eel (Anguilla japonica) and Its Use in Tracing Geographic Origin." Biology 11, no. 12 (November 29, 2022): 1733. http://dx.doi.org/10.3390/biology11121733.

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To secure traceability along supply chains of foodstuffs, the spatiotemporal variability of trace elements’ fingerprints (TEF) in fish otoliths provides a powerful tool to determine and discriminate the origin. Spatiotemporal variability of TEF was examined in a commercially important seafood, Japanese eel (Anguilla japonica), by means of laser ablation-inductively coupled plasma mass spectrometry (LA-ICPMS). Six elemental ratios (Na:Ca, Mg:Ca, P:Ca, K:Ca, Sr:Ca, and Ba:Ca) were determined in the otoliths of specimens originating from four aquaculture farms to examine the spatial variability and from one wild habitat over three years to examine the temporal variation. Significant temporal variation was found in Mg:Ca and Sr:Ca ratios; however, discriminant function analysis showed a lower temporal variation (50%) for the three years. Spatial variations were significant in Sr:Ca and Ba:Ca ratios, and discriminant function analysis showed high (80%) spatial variation among the four farms. Otolith TEF in the Japanese eel showed specific spatial variation among aquaculture farms but intangible temporal variation, suggesting the otolith TEF reflect each aquaculture environment. The present study shows that otolith TEF can be a reliable tool to discriminate the geographic origin of the Japanese eel.

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Irish, Jennifer L., Donald T. Resio, Taylor G. Asher, and Yi Liu. "CHARACTERIZATION OF SPATIAL VARIATION IN HURRICANE SURGE." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 51. http://dx.doi.org/10.9753/icce.v36.currents.51.

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Planning, engineering, and development along surgeprone coasts rely on probabilistic surge hazard assessments. Over the last decade, U.S. agencies have implemented the joint probability method with optimal sampling (JPM-OS) (e.g., Resio et al. 2009) to overcome shortcomings in probabilistic estimates developed from the limited set of observed surges alone. Here, optimal sampling is used to reduce the number of high-fidelity surge simulations needed, given computational resource limitations. In current practice, hazard assessments with the JPM-OS use discrete storm simulations (order of 200 to 1000 storms), where each is assigned a probability mass (e.g., Toro et al. 2010), rather than defining surges for the continuum of probability densities. Such an approach introduces uncertainty because it does not fully capture the natural structure inherent in surge response (meteorological and larger-scale bathymetric effects) (Resio et al. 2017). On the other hand, physically based surge response functions (SRFs) that capture natural structure in the surge response provide an accurateâ€”0.2 to 0.5 m rootmean- square error depending on topographic and geographic complexityâ€”and efficient means for continuously defining probability densities (e.g., Taylor et al. 2015). But, application of SRFs in JPM-OS (JPMOS- SRF) has not been widely used in practice due a lack of systematic methods for spatial interpolation along complex shorelines and throughout the floodplain. Herein, we explore the use of spatially derived empirical orthogonal functions (EOFs) to overcome this spatial interpolation challenge.

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Negrelli, C., L. Kraiselburd, S. Landau, and E. García-Berro. "Spatial variation of fundamental constants: Testing models with thermonuclear supernovae." International Journal of Modern Physics D 27, no. 09 (July 2018): 1850099. http://dx.doi.org/10.1142/s0218271818500992.

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Since Dirac stated his Large Number Hypothesis the space-time variation of fundamental constants has been an active subject of research. Here we analyze the possible spatial variation of two fundamental constants: the fine structure constant [Formula: see text] and the speed of light [Formula: see text]. We study the effects of such variations on the luminosity distance and on the peak luminosity of Type Ia supernovae (SNe Ia). For this, we consider the change of each fundamental constant separately and discuss a dipole model for its variation. Elaborating upon our previous work, we take into account the variation of the peak luminosity of Type Ia supernovae resulting from the variation of each of these fundamental constants. Furthermore, we also include the change of the energy release during the explosion, which was not studied before in the literature. We perform a statistical analysis to compare the predictions of the dipole model for [Formula: see text] and [Formula: see text] variation with the Union 2.1 and JLA compilations of SNe Ia. Allowing the nuisance parameters of the distance estimator [Formula: see text] and the cosmological density matter [Formula: see text] to vary. As a result of our analysis, we obtain a first estimate of the possible spatial variation of the speed of light [Formula: see text]. On the other hand, we find that there is no significant difference between the several phenomenological models studied here and the standard cosmological model, in which fundamental constants do not vary at all. Thus, we conclude that the actual set of data of Type Ia supernovae does not allow to verify the hypothetical spatial variation of fundamental constants.

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Dai, Jian, and David M. Rocke. "Modeling Spatial Variation in Area Source Emissions." Journal of Agricultural, Biological, and Environmental Statistics 5, no. 1 (March 2000): 7. http://dx.doi.org/10.2307/1400628.

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49

Starr, J. L. "Spatial and Temporal Variation of Ponded Infiltration." Soil Science Society of America Journal 54, no. 3 (May 1990): 629–36. http://dx.doi.org/10.2136/sssaj1990.03615995005400030001x.

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Low, B. K., S. Lacasse, and F. Nadim. "Slope reliability analysis accounting for spatial variation." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 1, no. 4 (December 2007): 177–89. http://dx.doi.org/10.1080/17499510701772089.

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Journal articles on the topic 'Spatial variation'

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