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Journal articles on the topic 'Spatial correlation'

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Author: Grafiati
Published: 4 June 2021
Last updated: 1 June 2024

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1

Dürre, Alexander, Daniel Vogel, and Roland Fried. "Spatial sign correlation." Journal of Multivariate Analysis 135 (March 2015): 89–105. http://dx.doi.org/10.1016/j.jmva.2014.12.002.

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2

Müller, Ulrich K., and Mark W. Watson. "Spatial Correlation Robust Inference." Econometrica 90, no. 6 (2022): 2901–35. http://dx.doi.org/10.3982/ecta19465.

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We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.
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3

Takahashi, Taro, Takeshi Sato, Hideo Aizaki, Na Guo, Yasuhiro Nakashima, Shigeo Ogawa, Nanae Yamada, and Xiaoyun Zheng. "Three-dimensional spatial correlation." Letters in Spatial and Resource Sciences 6, no. 3 (April 25, 2013): 163–75. http://dx.doi.org/10.1007/s12076-013-0095-6.

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4

Martellosio, Federico. "THE CORRELATION STRUCTURE OF SPATIAL AUTOREGRESSIONS." Econometric Theory 28, no. 6 (April 27, 2012): 1373–91. http://dx.doi.org/10.1017/s0266466612000175.

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This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In particular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on graph distance, and the behavior of the correlations at the extremes of the parameter space. Throughout the analysis differences between directed and undirected networks are emphasized. The graph theoretic representation also clarifies why it is difficult to relate properties of W to correlation properties of SAR(1) models defined on irregular lattices.
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5

Kerscher, Martin. "Spatial range of conformity." Astronomy & Astrophysics 615 (July 2018): A109. http://dx.doi.org/10.1051/0004-6361/201731212.

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Context. Properties of galaxies, such as their absolute magnitude and stellar mass content, are correlated. These correlations are tighter for close pairs of galaxies, which is called galactic conformity. In hierarchical structure formation scenarios, galaxies form within dark matter haloes. To explain the amplitude and spatial range of galactic conformity two-halo terms or assembly bias become important. Aims. With the scale dependent correlation coefficients, the amplitude and spatial range of conformity are determined from galaxy and halo samples. Methods. The scale dependent correlation coefficients are introduced as a new descriptive statistic to quantify the correlations between properties of galaxies or haloes, depending on the distances to other galaxies or haloes. These scale dependent correlation coefficients can be applied to the galaxy distribution directly. Neither a splitting of the sample into subsamples, nor an a priori clustering is needed. Results. This new descriptive statistic is applied to galaxy catalogues derived from the Sloan Digital Sky Survey III and to halo catalogues from the MultiDark simulations. In the galaxy sample the correlations between absolute magnitude, velocity dispersion, ellipticity, and stellar mass content are investigated. The correlations of mass, spin, and ellipticity are explored in the halo samples. Both for galaxies and haloes a scale dependent conformity is confirmed. Moreover the scale dependent correlation coefficients reveal a signal of conformity out to 40 Mpc and beyond. The halo and galaxy samples show a differing amplitude and range of conformity.
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Nashriyah, Siti Fadhilatun, Martya Rahmaniati Makhful, and Yuli Puspita Devi. "GAMBARAN SPASIAL HUBUNGAN ANTARA FAKTOR LINGKUNGAN DAN EKONOMI DENGAN STUNTING BALITA DI PROVINSI NUSA TENGGARA TIMUR." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 23, no. 2 (March 20, 2023): 1–8. http://dx.doi.org/10.21009/spatial.232.01.

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Background: The President through Presidential Regulation Number 72 of 2021 is committed to accelerating the reduction of stunting in Indonesia. The stunting priority areas also continue to be increased from year to year. In Indonesia, East Nusa Tenggara Province is the province with the highest prevalence of stunting, namely 37.8% in 2021. Objective: To find out the spatial picture and the correlation between environmental factors and economic factors with the prevalence of stunting in East Nusa Tenggara Province in 2021. Methods: The research design is an ecological study with a spatial approach. This study uses secondary data in the form of reports issued by the Indonesian Ministry of Health (SSGI: Indonesian Nutritional Status Study) and the BKKBN (PK: Family Data Collection) in 2021 with district/city-level analysis units in East Nusa Tenggara Province. Data analysis used a correlation test and mapping was carried out using the QGIS 2.8.1 application. Results: The prevalence of stunting in NTT Province tends to be high in the eastern part while the risk factors for stunting (unfavorable environment and economy) tend to be high in the western part. The correlation test showed that the correlation between the prevalence of stunting and the poor was 0.165 (p = 0.463); with inadequate latrines of 0.420 (p = 0.052); and with inadequate drinking water sources of 0.426 (p = 0.048).
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7

Gautam, Sneha, Jaydev Teraiya, and Aditya Kuma Patra. "Spatial statistics, spatial correlation and spatial graph theory in air pollution." Environmental Technology & Innovation 11 (August 2018): 384–89. http://dx.doi.org/10.1016/j.eti.2018.07.002.

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8

Adutwum, Jerry Oppong, and Junji Matsumura. "Spatiotemporal variation and covariation of heartwood color in planted teak wood from Ghana." BioResources 17, no. 4 (September 19, 2022): 6178–90. http://dx.doi.org/10.15376/biores.17.4.6178-6190.

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Heartwood color is a complex trait that affects the economic and aesthetic value of the wood but is highly variable. How the color of the heartwood varies spatially and temporally is poorly understood. To illustrate how heartwood color varies within a tree, two opposite aspects of wood within the same tree, representing differential growth rate, were used to model the long-short axis system jointly. The color of the heartwood on the long and the short axis was considered to be two different traits. By jointly modeling the long and short axes, the correlation was examined between aspect (spatial) and contemporaneous correlations (within aspect). Spatial and temporal correlations and their interactions describe the indirect physiological, genetic, and environmental changes in wood formation with time and position in the trunk. Spatial correlations were consistently lower than temporal correlations but were positive and significant. Between the heartwood color parameters, b* showed a relatively higher spatial correlation. The results suggest that there is a spatial correlation in the long-short axis for all color parameters and in the two surfaces. Variations between aspects were not statistically significant for any color parameter. The bivariate mixed model method revealed hidden physics behind heartwood color formation. Models need to be developed to account for both spatial and temporal dependence in studies of wood property change.
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9

Lei Zhang and Paul Bao. "Denoising by spatial correlation thresholding." IEEE Transactions on Circuits and Systems for Video Technology 13, no. 6 (June 2003): 535–38. http://dx.doi.org/10.1109/tcsvt.2003.813426.

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10

Xiong, Jinjun, Vladimir Zolotov, and Lei He. "Robust Extraction of Spatial Correlation." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 26, no. 4 (April 2007): 619–31. http://dx.doi.org/10.1109/tcad.2006.884403.

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11

KUZUHA, Yasuhisa, Kunio TOMOSUGI, and Tokuo KISHII. "SPATIAL CORRELATION STRUCTURE OF PRECIPITATION." PROCEEDINGS OF HYDRAULIC ENGINEERING 46 (2002): 127–32. http://dx.doi.org/10.2208/prohe.46.127.

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12

CLAYTON, D. G., L. BERNARDINELLI, and C. MONTOMOLI. "Spatial Correlation in Ecological Analysis." International Journal of Epidemiology 22, no. 6 (1993): 1193–202. http://dx.doi.org/10.1093/ije/22.6.1193.

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13

Jaffiol, Rodolphe, Yoann Blancquaert, Antoine Delon, and Jacques Derouard. "Spatial fluorescence cross-correlation spectroscopy." Applied Optics 45, no. 6 (February 20, 2006): 1225. http://dx.doi.org/10.1364/ao.45.001225.

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14

Robinson, Peter M., and Francesca Rossi. "REFINED TESTS FOR SPATIAL CORRELATION." Econometric Theory 31, no. 6 (November 4, 2014): 1249–80. http://dx.doi.org/10.1017/s0266466614000498.

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We consider testing the null hypothesis of no spatial correlation against the alternative of pure first order spatial autoregression. A test statistic based on the least squares estimate has good first-order asymptotic properties, but these may not be relevant in small- or moderate-sized samples, especially as (depending on properties of the spatial weight matrix) the usual parametric rate of convergence may not be attained. We thus develop tests with more accurate size properties, by means of Edgeworth expansions and the bootstrap. Although the least squares estimate is inconsistent for the correlation parameter, we show that under quite general conditions its probability limit has the correct sign, and that least squares testing is consistent; we also establish asymptotic local power properties. The finite-sample performance of our tests is compared with others in Monte Carlo simulations.
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15

Malik, W. Q. "Spatial correlation in ultrawideband channels." IEEE Transactions on Wireless Communications 7, no. 2 (February 2008): 604–10. http://dx.doi.org/10.1109/twc.2008.060547.

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16

Holdsworth, David A., and Iain M. Reid. "The spatial correlation analysis revisited." Advances in Space Research 20, no. 6 (January 1997): 1281–84. http://dx.doi.org/10.1016/s0273-1177(97)00787-4.

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17

Elhorst, J. Paul. "Serial and spatial error correlation." Economics Letters 100, no. 3 (September 2008): 422–24. http://dx.doi.org/10.1016/j.econlet.2008.03.009.

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18

Gori, F., and M. Santarsiero. "Devising genuine spatial correlation functions." Optics Letters 32, no. 24 (December 10, 2007): 3531. http://dx.doi.org/10.1364/ol.32.003531.

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19

Maleev, I. D., and G. A. Swartzlander, Jr. "Propagation of spatial correlation vortices." Journal of the Optical Society of America B 25, no. 6 (May 14, 2008): 915. http://dx.doi.org/10.1364/josab.25.000915.

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20

Haining, Robert. "Bivariate Correlation with Spatial Data." Geographical Analysis 23, no. 3 (September 3, 2010): 210–27. http://dx.doi.org/10.1111/j.1538-4632.1991.tb00235.x.

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21

Liang, Ke, Sihang Zhou, Meng Liu, Yue Liu, Wenxuan Tu, Yi Zhang, Liming Fang, Zhe Liu, and Xinwang Liu. "Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (March 24, 2024): 8733–41. http://dx.doi.org/10.1609/aaai.v38i8.28719.

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Crime prediction is a crucial yet challenging task within urban computing, which benefits public safety and resource optimization. Over the years, various models have been proposed, and spatial-temporal hypergraph learning models have recently shown outstanding performances. However, three correlations underlying crime are ignored, thus hindering the performance of previous models. Specifically, there are two spatial correlations and one temporal correlation, i.e., (1) co-occurrence of different types of crimes (type spatial correlation), (2) the closer to the crime center, the more dangerous it is around the neighborhood area (neighbor spatial correlation), and (3) the closer between two timestamps, the more relevant events are (hawkes temporal correlation). To this end, we propose Hawkes-enhanced Spatial-Temporal Hypergraph Contrastive Learning framework (HCL), which mines the aforementioned correlations via two specific strategies. Concretely, contrastive learning strategies are designed for two spatial correlations, and hawkes process modeling is adopted for temporal correlations. Extensive experiments demonstrate the promising capacities of HCL from four aspects, i.e., superiority, transferability, effectiveness, and sensitivity.
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22

Takeda, Mitsuo, Wei Wang, Dinesh N. Naik, and Rakesh K. Singh. "Spatial statistical optics and spatial correlation holography: A review." Optical Review 21, no. 6 (November 2014): 849–61. http://dx.doi.org/10.1007/s10043-014-0138-2.

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23

Song, Jiekun, Huisheng Xiao, and Zhicheng Liu. "Analysis of the Driving Mechanism of Urban Carbon Emission Correlation Network in Shandong Province Based on TERGM." Sustainability 16, no. 10 (May 17, 2024): 4233. http://dx.doi.org/10.3390/su16104233.

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Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.
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24

Glenny, R. W. "Spatial correlation of regional pulmonary perfusion." Journal of Applied Physiology 72, no. 6 (June 1, 1992): 2378–86. http://dx.doi.org/10.1152/jappl.1992.72.6.2378.

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Despite the heterogeneous distribution of pulmonary blood flow, perfusion appears to be spatially ordered, with neighboring regions of lung having similar magnitudes of flow. This premise was tested by determining the spatial correlation of regional flow [rho(d)] as a function of distance (d) between regions. Regional pulmonary perfusion was measured in both supine and prone positions in seven anesthetized mechanically ventilated dogs with radiolabeled microspheres. After excision and drying, the lungs were cubed into pieces 1.2 cm on a side, with a three-dimensional coordinate assigned to each piece. The microsphere-determined flow to each piece was measured by radioactive counts, and rho(d) was calculated for all paired pieces within the same lobe. rho(d) was greatest for adjacent pieces (d = 1.2 cm) and decreased with increasing d, becoming negative at large distances in all dogs and positions. The spatial correlation of flow between adjacent pieces, rho(1.2 cm), was greater in the supine than in the prone position (0.66 vs. 0.72, P less than 0.05). The observations for each dog and position were fit to the equation rho(d) = d(a)+b.d+c, and the coefficients were used to compare rho(d) in the supine and prone positions. rho(d) differed in the two positions (P less than 0.05), with rho(d) falling off more rapidly with distance in the supine position. When trends in flow due to gravity were mathematically removed, differences between supine and prone positions were no longer observed. The spatial correlation of regional pulmonary perfusion was anisotropic in both supine and prone positions. The observation that regional pulmonary perfusion is highly correlated over large spatial distances has important implications for models of flow distribution.
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25

Schiappapietra, Erika, and Chiara Smerzini. "Spatial correlation of broadband earthquake ground motion in Norcia (Central Italy) from physics-based simulations." Bulletin of Earthquake Engineering 19, no. 12 (June 24, 2021): 4693–717. http://dx.doi.org/10.1007/s10518-021-01160-7.

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AbstractThis paper investigates the spatial correlation of response spectral accelerations from a set of broadband physics-based ground motion simulations generated for the Norcia (Central Italy) area by means of the SPEED software. We produce several ground-motion scenarios by varying either the slip distribution or the hypocentral location as well as the magnitude to systematically explore the impact of such physical parameters on spatial correlations. We extend our analysis to other ground-motion components (vertical, fault-parallel, fault-normal) in addition to the more classic geometric mean to highlight possible ground-motion directionality and therefore identify specific spatial correlation features. Our analyses provide useful insights on the role of slip heterogeneities as well as the relative position between hypocentre and slip asperities on the spatial correlation. Indeed, we found a significant variability in terms of both range and sill among the considered case studies, suggesting that the spatial correlation is not only period-dependent, but also scenario-dependent. Finally, our results reveal that the isotropy assumption may represent an oversimplification especially in the near-field and thus it may be unsuitable for assessing the seismic risk of spatially-distributed infrastructures and portfolios of buildings.
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26

Safavi, Shervin, Abhilash Dwarakanath, Vishal Kapoor, Joachim Werner, Nicholas G. Hatsopoulos, Nikos K. Logothetis, and Theofanis I. Panagiotaropoulos. "Nonmonotonic spatial structure of interneuronal correlations in prefrontal microcircuits." Proceedings of the National Academy of Sciences 115, no. 15 (March 27, 2018): E3539—E3548. http://dx.doi.org/10.1073/pnas.1802356115.

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Correlated fluctuations of single neuron discharges, on a mesoscopic scale, decrease as a function of lateral distance in early sensory cortices, reflecting a rapid spatial decay of lateral connection probability and excitation. However, spatial periodicities in horizontal connectivity and associational input as well as an enhanced probability of lateral excitatory connections in the association cortex could theoretically result in nonmonotonic correlation structures. Here, we show such a spatially nonmonotonic correlation structure, characterized by significantly positive long-range correlations, in the inferior convexity of the macaque prefrontal cortex. This functional connectivity kernel was more pronounced during wakefulness than anesthesia and could be largely attributed to the spatial pattern of correlated variability between functionally similar neurons during structured visual stimulation. These results suggest that the spatial decay of lateral functional connectivity is not a common organizational principle of neocortical microcircuits. A nonmonotonic correlation structure could reflect a critical topological feature of prefrontal microcircuits, facilitating their role in integrative processes.
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27

Zhang, Yining, and Zhong Wu. "Research on the spatial association network structure for innovation efficiency of China’s new energy vehicle industry and its influencing factors." PLOS ONE 16, no. 8 (August 26, 2021): e0255516. http://dx.doi.org/10.1371/journal.pone.0255516.

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It is of great significance to study the spatial network of the new energy vehicle (NEV) industry innovation efficiency and its factors to promote the rational allocation of innovative resources and the coordinated development of Chinese NEV industry. First, the Super Efficiency Data Envelope Analysis model is used to measure innovation efficiency in the NEV industry in Chinese provinces, and based on the results, the improved gravity model is applied to construct a spatial correlation network. Then, by applying social network analysis (SNA) to study NEV industry development node spatial correlations, we conclude that there is no overall hierarchical structure. The SNA are applied to examine spatial correlations with respect to NEV industry innovation efficiency in each province, and to analyze the role and position of each province in the spatial correlation network. Finally, the influencing factors of spatial correlation of the innovation efficiency of China’s NEV industry has been discussed. The result shows that the difference in spatial distance and R&D investment has a significant impact on the spatial correlation of the NEV industry.
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Yani, Ahmad, Asep Mulyadi, and Mamat Ruhimat. "CONTEXTUALIZATION OF SPATIAL INTELLIGENCE: CORRELATION BETWEEN SPATIAL INTELLIGENCE, SPATIAL ABILITY, AND GEOGRAPHY SKILLS." Journal of Baltic Science Education 17, no. 4 (August 20, 2018): 564–75. http://dx.doi.org/10.33225/jbse/18.17.564.

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This research attempted to find whether spatial intelligence (without context) has a correlation to (contextual) spatial ability and affects geographic skills as crystallized ability (Gc). It employed the descriptive method with subjects including students at the primary, junior high, and senior high schools. The data were collected through two instruments, namely test and questionnaire. Data of test results were processed by tabulation technique and correlated for the scores of spatial intelligence, spatial ability, and geographic skills. The results show that spatial intelligence (Gf) tended to increase from the level of primary to junior high and to senior high school level, whereas spatial ability (Gc) and geographic skills (Geo-s) tended to decrease. Despite the decline, all three (Gf, Gc, and Geo-s) had the potentials for improvement. Thus, geography teachers are encouraged to participate in improving students’ spatial and geographic skills, so students can develop their potentials optimally for geographic skills and future career development. Keywords: contextualization, crystallized intelligence, geographic skills, spatial intelligence, intelligence and skill correlation.
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29

Holl, Berry, David Hobbs, and Lennart Lindegren. "Spatial correlations in the Gaia astrometric solution." Proceedings of the International Astronomical Union 5, S261 (April 2009): 320–24. http://dx.doi.org/10.1017/s1743921309990573.

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AbstractAccurate characterization of the astrometric errors in the forthcoming Gaia catalogue is essential for making optimal use of the data. Using small-scale numerical simulations of the astrometric solution, we investigate the expected spatial correlation between the astrometric errors of stars as function of their angular separation. Extrapolating to the full-scale solution for the final Gaia catalogue, we find that the expected correlations are generally very small, but could reach some fraction of a percent for angular separations smaller than about one degree. The spatial correlation length is related to the size of the field of view of Gaia, while the maximum correlation coefficient is related to the mean number of stars present in the field at any time. Our scalable simulation tool (AGISLab) makes it possible to characterize the astrometric errors and correlations, e.g., as functions of position and magnitude.
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Binion-Rock, Samantha M., Brian J. Reich, and Jeffrey A. Buckel. "A spatial kernel density method to estimate the diet composition of fish." Canadian Journal of Fisheries and Aquatic Sciences 76, no. 2 (February 2019): 249–67. http://dx.doi.org/10.1139/cjfas-2017-0306.

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We present a novel spatially explicit kernel density approach to estimate the proportional contribution of a prey to a predator’s diet by mass. First, we compared the spatial estimator to a traditional cluster-based approach using a Monte Carlo simulation study. Next, we compared the diet composition of three predators from Pamlico Sound, North Carolina, to evaluate how ignoring spatial correlation affects diet estimates. The spatial estimator had lower mean squared error values compared with the traditional cluster-based estimator for all Monte Carlo simulations. Incorporating spatial correlation when estimating the predator’s diet resulted in a consistent increase in precision across multiple levels of spatial correlation. Bias was often similar between the two estimators; however, when it differed it mostly favored the spatial estimator. The two estimators produced different estimates of proportional contribution of prey to the diets of the three field-collected predator species, especially when spatial correlation was strong and prey were consumed in patchy areas. Our simulation and empirical data provide strong evidence that data on food habits should be modeled using spatial approaches and not treated as spatially independent.
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31

Seaton, N. A., and E. D. Glandt. "Spatial correlation functions from computer simulations." Journal of Chemical Physics 85, no. 9 (November 1986): 5262–68. http://dx.doi.org/10.1063/1.451667.

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32

Chia-Wen Lin, Er-Yin Fei, and Yung-Chang Chen. "Hierarchical disparity estimation using spatial correlation." IEEE Transactions on Consumer Electronics 44, no. 3 (1998): 630–37. http://dx.doi.org/10.1109/30.713174.

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33

Grochová, Ladislava, and Luboš Střelec. "Heteroskedasticity, temporal and spatial correlation matter." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2151–55. http://dx.doi.org/10.11118/actaun201361072151.

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As economic time series or cross sectional data are typically affected by serial correlation and/or heteroskedasticity of unknown form, panel data typically contains some form of heteroskedasticity, serial correlation and/or spatial correlation. Therefore, robust inference in the presence of heteroskedasticity and spatial dependence is an important problem in spatial data analysis. In this paper we study the standard errors based on the HAC of cross-section averages that follows Vogelsang’s (2012) fixed-b asymptotic theory, i.e. we continue with Driscoll and Kraay approach (1998). The Monte Carlo simulations are used to investigate the finite sample properties of commonly used estimators both not accounting and accounting for heteroskedasticity and spatiotemporal dependence (OLS, GLS) in comparison to brand new estimator based on Vogelsang’s (2012) fixed-b asymptotic theory in the presence of cross-sectional heteroskedasticity and serial and spatial correlation in panel data with fixed effects. Our Monte Carlo experiment shows that the OLS exhibits an important downward bias in all of the cases and almost always has the worst performance when compared to the other estimators. The GLS corrected for HACSC performs well if time dimension is greater than cross-sectional dimension. The best performance can be attributed to the Vogelsang’s estimator with fixed-b version of Driscoll-Kraay standard errors.
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34

Kagan, Yan Y. "Earthquake spatial distribution: the correlation dimension." Geophysical Journal International 168, no. 3 (March 2007): 1175–94. http://dx.doi.org/10.1111/j.1365-246x.2006.03251.x.

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35

Fowler, Erin E. E., Cassandra Hathaway, Fabryann Tillman, Robert Weinfurtner, Thomas A. Sellers, and John Heine. "Spatial correlation and breast cancer risk." Biomedical Physics & Engineering Express 5, no. 4 (May 22, 2019): 045007. http://dx.doi.org/10.1088/2057-1976/ab1dad.

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36

Takabayashi, Masanori, Hassaan Majeed, Andre Kajdacsy-Balla, and Gabriel Popescu. "Tissue spatial correlation as cancer marker." Journal of Biomedical Optics 24, no. 01 (January 21, 2019): 1. http://dx.doi.org/10.1117/1.jbo.24.1.016502.

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37

Agarwal, Ashish, and S. Chopra. "Spatial correlation effects in a laser." Physical Review A 54, no. 3 (September 1, 1996): 2503–5. http://dx.doi.org/10.1103/physreva.54.2503.

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38

Chin, David A. "Spatial Correlation of Hydrologic Time Series." Journal of Water Resources Planning and Management 114, no. 5 (September 1988): 578–93. http://dx.doi.org/10.1061/(asce)0733-9496(1988)114:5(578).

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39

Glick, Barry J. "A Spatial Rank-Order Correlation Measure." Geographical Analysis 14, no. 2 (September 3, 2010): 177–81. http://dx.doi.org/10.1111/j.1538-4632.1982.tb00066.x.

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Beausir, Benoît, Claude Fressengeas, Nilesh P. Gurao, László S. Tóth, and Satyam Suwas. "Spatial correlation in grain misorientation distribution." Acta Materialia 57, no. 18 (October 2009): 5382–95. http://dx.doi.org/10.1016/j.actamat.2009.07.035.

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Delgado, Miguel A., and Peter M. Robinson. "Non-nested testing of spatial correlation." Journal of Econometrics 187, no. 1 (July 2015): 385–401. http://dx.doi.org/10.1016/j.jeconom.2015.02.044.

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Shen, Jianqi, Ulrich Riebel, and Xiaoai Guo. "Transmission Fluctuation Spectrometry with Spatial Correlation." Particle & Particle Systems Characterization 22, no. 1 (June 2005): 24–37. http://dx.doi.org/10.1002/ppsc.200400896.

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43

March, N. H. "Spatial correlation of electrons in metals." International Journal of Quantum Chemistry 34, S22 (March 12, 1988): 655–64. http://dx.doi.org/10.1002/qua.560340867.

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44

Borůvka, L., H. Donátová, and K. Němeček. "Spatial distribution and correlation of soil properties in a field: a case study." Plant, Soil and Environment 48, No. 10 (December 22, 2011): 425–32. http://dx.doi.org/10.17221/4391-pse.

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Analysis of spatial distribution and correlation of soil properties represents an important outset for precision agriculture. This paper presents an analysis of spatial distribution and mutual correlations, both classical and spatial, of soil properties in an agricultural field in Klučov. Clay and fine silt content, pH, organic carbon content (C<sub>org</sub>), moisture (Q), total porosity (Pt), capillary porosity (P<sub>c</sub>), and coefficients of aggregate vulnerability to fast wetting (K<sub>v1</sub>), to slow wetting and drying (K<sub>v2</sub>), and to mechanical impacts (K<sub>v3</sub>) were determined. Semivariogram ranges from 206 m (clay content) to 1120 m (K<sub>v3</sub>) were detected. Many relationships between soil properties were spatially based. Fine silt content and Corg&nbsp;proved to be the most important soil properties controlling all the three aggregate vulnerability coefficients, which was not clear for K<sub>v2</sub>&nbsp;from classical correlation only. Determined spatial correlations and similarities in spatial distribution may serve as groundwork in delineation of different zones for site-specific management.
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45

Fujisawa, Toshiaki, Takahiko Horiuchi, and Hiroaki Kotera. "Image Coding Algorithm Using Luminance-Chrominance Correlation and Spatial Correlation." NIP & Digital Fabrication Conference 20, no. 1 (January 1, 2004): 617–21. http://dx.doi.org/10.2352/issn.2169-4451.2004.20.1.art00023_2.

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46

Wu, Jiansheng, Jingtian Liang, Liguo Zhou, Fei Yao, and Jian Peng. "Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China." Remote Sensing 11, no. 19 (September 24, 2019): 2223. http://dx.doi.org/10.3390/rs11192223.

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Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.
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Mu, He-Qing, Xin-Xiong Liang, Ji-Hui Shen, and Feng-Liang Zhang. "Analysis of Structural Health Monitoring Data with Correlated Measurement Error by Bayesian System Identification: Theory and Application." Sensors 22, no. 20 (October 19, 2022): 7981. http://dx.doi.org/10.3390/s22207981.

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Measurement error is non-negligible and crucial in SHM data analysis. In many applications of SHM, measurement errors are statistically correlated in space and/or in time for data from sensor networks. Existing works solely consider spatial correlation for measurement error. When both spatial and temporal correlation are considered simultaneously, the existing works collapse, as they do not possess a suitable form describing spatially and temporally correlated measurement error. In order to tackle this burden, this paper generalizes the form of correlated measurement error from spatial correlation only or temporal correlation only to spatial-temporal correlation. A new form of spatial-temporal correlation and the corresponding likelihood function are proposed, and multiple candidate model classes for the measurement error are constructed, including no correlation, spatial correlation, temporal correlation, and the proposed spatial-temporal correlation. Bayesian system identification is conducted to achieve not only the posterior probability density function (PDF) for the model parameters, but also the posterior probability of each candidate model class for selecting the most suitable/plausible model class for the measurement error. Examples are presented with applications to model updating and modal frequency prediction under varying environmental conditions, ensuring the necessity of considering correlated measurement error and the capability of the proposed Bayesian system identification in the uncertainty quantification at the parameter and model levels.
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Лесовой, Сергей, Sergey Lesovoi, Вероника Кобец, and Veronika Kobets. "Correlation plots of the Siberian Radioheliograph." Solar-Terrestrial Physics 3, no. 1 (May 5, 2017): 19–25. http://dx.doi.org/10.12737/article_58f96eeb8fa318.06122835.

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The Siberian Solar Radio Telescope [Grechnev et al., 2011] is now being upgraded. The upgrading is aimed at providing the aperture synthesis imaging in the frequency range 4–8 GHz [Lesovoi et al., 2011, 2014] instead of the single-frequency direct imaging due to the Earth rotation. The first phase of the upgrading is a 48-antenna array — Siberian Radioheliograph. One type of radioheliograph data represents correlation plots [badary.iszf.irk.ru/srhCorrPlot.php]. In evaluating the covariation of two-level signals, these plots are sums of complex correlations, obtained for different antenna pairs. Bearing in mind that correlation of signals from an antenna pair is related to a spatial frequency, we can say that each value of the plot is an integral over a spatial spectrum. Limits of the integration are defined by a task. Only high spatial frequencies are integrated to obtain dynamics of compact sources. The whole spectrum is integrated to reach maximum sensitivity. We show that the covariation of two-level values accurate to Van Vleck correction is a correlation coefficient of these values.
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Wu, Xiaofeng, Song Yang, Di Zhang, and Liang Zhang. "Transformer based neural network for daily ground settlement prediction of foundation pit considering spatial correlation." PLOS ONE 18, no. 11 (November 20, 2023): e0294501. http://dx.doi.org/10.1371/journal.pone.0294501.

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Deep foundation pit settlement prediction based on machine learning is widely used for ensuring the safety of construction, but previous studies are limited to not fully considering the spatial correlation between monitoring points. This paper proposes a transformer-based deep learning method that considers both the spatial and temporal correlations among excavation monitoring points. The proposed method creates a dataset that collects all excavation monitoring points into a vector to consider all spatial correlations among monitoring points. The deep learning method is based on the transformer, which can handle the temporal correlations and spatial correlations. To verify the model’s accuracy, it was compared with an LSTM network and an RNN-LSTM hybrid model that only considers temporal correlations without considering spatial correlations, and quantitatively compared with previous research results. Experimental results show that the proposed method can predict excavation deformations more accurately. The main conclusions are that the spatial correlation and the transformer-based method are significant factors in excavation deformation prediction, leading to more accurate prediction results.
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Sivakumar, Bellie, Fitsum M. Woldemeskel, Rajendran Vignesh, and Vinayakam Jothiprakash. "A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall." Hydrology 6, no. 1 (January 23, 2019): 11. http://dx.doi.org/10.3390/hydrology6010011.

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Rainfall data at fine spatial resolutions are often required for various studies in hydrology and water resources. However, such data are not widely available, as their collection is normally expensive and time-consuming. A common practice to obtain fine-spatial-resolution rainfall data is to employ interpolation schemes to derive them based on data available at nearby locations. Such interpolation schemes are generally based on rainfall correlation or distance between stations. The present study proposes a combined rainfall correlation-spatial scale-correlation threshold method for representing spatial rainfall variability. The method is applied to monthly rainfall data at a resolution of 0.25 × 0.25 latitude/longitude across Australia, available from the Tropical Rainfall Measuring Mission (TRMM 3B43 version). The results indicate that rainfall dynamics in northern and northeastern Australia have far greater spatial correlations when compared to the other regions, especially in southern and southeastern Australia, suggesting that tropical climates generally have greater spatial rainfall correlations when compared to temperate, oceanic, and continental climates, subject to other influencing factors. The implications of the outcomes for rainfall data interpolation and the rain gauge monitoring network are also discussed, especially based on results obtained for ten major cities in Australia.
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