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Journal articles on the topic 'Aggregated data'

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Published: 4 June 2021

Last updated: 28 January 2023

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1

HASSANI, HOSSEIN, ABDOL SOOFI, and MOHAMMAD SADEGH AVAZALIPOUR. "FORECASTING GDP WITH AGGREGATED AND SECTORAL DATA." Fluctuation and Noise Letters 10, no. 03 (September 2011): 249–65. http://dx.doi.org/10.1142/s0219477511000533.

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We use the Singular Spectrum Analysis (SSA), a forecasting method which is based on the noise reduction procedure, in prediction of the Iranian gross domestic product (GDP). Two different approaches are considered in forecasting the series. In the first approach, we apply SSA to the aggregate GDP series. In the second approach, we predict the GDP by first forecasting the GDP of the sectors of the economy, and then sum the predicted values as the forecast of the aggregate GDP. We measured the prediction accuracy of both approaches using various criteria, and found that predictions based on the disaggregated, sectoral GDP tend to outperform the predictions based on the aggregated data.

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2

Jarjoura, D. "Inferences from Aggregated Data." Academic Emergency Medicine 10, no. 8 (August 1, 2003): 881–82. http://dx.doi.org/10.1197/aemj.10.8.881.

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3

Aadland, David. "Detrending time-aggregated data." Economics Letters 89, no. 3 (December 2005): 287–93. http://dx.doi.org/10.1016/j.econlet.2005.06.001.

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4

Toth, Daniell. "Data Smearing: An Approach to Disclosure Limitation for Tabular Data." Journal of Official Statistics 30, no. 4 (December 1, 2014): 839–57. http://dx.doi.org/10.2478/jos-2014-0050.

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Abstract Statistical agencies often collect sensitive data for release to the public at aggregated levels in the form of tables. To protect confidential data, some cells are suppressed in the publicly released data. One problem with this method is that many cells of interest must be suppressed in order to protect a much smaller number of sensitive cells. Another problem is that the covariates used to aggregate and level of aggregation must be fixed before the data is released. Both of these restrictions can severely limit the utility of the data. We propose a new disclosure limitation method that replaces the full set of microdata with synthetic data for use in producing released data in tabular form. This synthetic data set is obtained by replacing each unit’s values with a weighted average of sampled values from the surrounding area. The synthetic data is produced in a way to give asymptotically unbiased estimates for aggregate cells as the number of units in the cell increases. The method is applied to the U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages data, which is released to the public quarterly in tabular form and aggregated across varying scales of time, area, and economic sector.

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5

Seater, John J. "TESTING THE PERMANENT-INCOME/LIFE-CYCLE HYPOTHESIS WITH AGGREGATE DATA." Macroeconomic Dynamics 2, no. 3 (September 1998): 401–25. http://dx.doi.org/10.1017/s1365100598008062.

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The aggregate implications of the permanent-income/life-cycle hypothesis (PILCH) are derived rigorously. Virtually all empirical rejections of PILCH based on aggregated data are shown to result from misspecifications or from characteristics of aggregate data that have been overlooked. Valid aggregate tests are proposed. Those based on a properly formulated aggregate consumption function may be superior to those based on Euler-equation methods.

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6

Gelsema, Tjalling. "The Logic of Aggregated Data." Acta Cybernetica 24, no. 2 (November 3, 2019): 211–48. http://dx.doi.org/10.14232/actacyb.24.2.2019.4.

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A notion of generalization-specialization is introduced that is more expressive than the usual notion from, e.g., the UML or RDF-based languages. This notion is incorporated in a typed formal language for modeling aggregated data. Soundness with respect to a sets-and-functions semantics is shown subsequently. Finally, a notion of congruence is introduced. With it terms in the language that have identical semantics, i.e., synonyms, can be discovered. The resulting formal language is well-suited for capturing faithfully aggregated data in such a way that it can serve as the foundation for corporate metadata management in a statistical office.

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7

Linton, Oliver, and Yoon-Jae Whang. "NONPARAMETRIC ESTIMATION WITH AGGREGATED DATA." Econometric Theory 18, no. 2 (April 2002): 420–68. http://dx.doi.org/10.1017/s0266466602182089.

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We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intrafamily component but require that observations from different families be independent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behavior of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experiment.

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8

Bowman, K. O., and M. A. Kastenbaum. "Overdispersion of aggregated genetic data." Mutation Research/Environmental Mutagenesis and Related Subjects 272, no. 2 (October 1992): 133–37. http://dx.doi.org/10.1016/0165-1161(92)90041-j.

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9

Nicoletti, Cheti, and Nicky Best. "Quantile regression with aggregated data." Economics Letters 117, no. 2 (November 2012): 401–4. http://dx.doi.org/10.1016/j.econlet.2012.06.011.

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10

Hu, Jinyu, Juan Luo, Yuxi Zhang, Panwu Wang, and Yu Liu. "Location-Based Data Aggregation in 6LoWPAN." International Journal of Distributed Sensor Networks 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/912926.

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Location-based information has recently been exploited to assist the aggregated process of data, thereby reducing the spatial redundancy efficiently. The constraints nature in 6LoWPAN becomes one of the major concerns in data aggregation methods. However, traditional CSMA/CA in MAC layer may cause significant transmission and control overhead as well as delay on listening and competing for channels. It is a low efficient way to transfer IPv6 packet due to the big packet header. To overcome these shortages, in this paper, we propose LDAA, a location-based novel data aggregation model that aggregates data from the network layer according to the MAC layer queuing delay. When the queuing delay becomes larger, more packets will be dynamically aggregated into one packet to increase the proportion of application data. Otherwise, the amount of packets involved in aggregation will decrease to improve channels utilization. Simulation results show that our approach could provide better real-time guarantees and reduce data spatial redundancy and energy consumption efficiently.

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11

Song, Youtao, Yue-xuan Wu, Giman Jung, Yusuf Tutar, Evan Eisenberg, Lois E. Greene, and Daniel C. Masison. "Role for Hsp70 Chaperone in Saccharomyces cerevisiae Prion Seed Replication." Eukaryotic Cell 4, no. 2 (February 2005): 289–97. http://dx.doi.org/10.1128/ec.4.2.289-297.2005.

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ABSTRACT The Saccharomyces cerevisiae [PSI + ] prion is a misfolded form of Sup35p that propagates as self-replicating cytoplasmic aggregates. Replication is believed to occur through breakage of transmissible [PSI + ] prion particles, or seeds, into more numerous pieces. In [PSI + ] cells, large Sup35p aggregates are formed by coalescence of smaller sodium dodecyl sulfate-insoluble polymers. It is uncertain if polymers or higher-order aggregates or both act as prion seeds. A mutant Hsp70 chaperone, Ssa1-21p, reduces the number of transmissible [PSI + ] seeds per cell by 10-fold but the overall amount of aggregated Sup35p by only two- to threefold. This discrepancy could be explained if, in SSA1-21 cells, [PSI + ] seeds are larger or more of the aggregated Sup35p does not function as a seed. To visualize differences in aggregate size, we constructed a Sup35-green fluorescent protein (GFP) fusion (NGMC) that has normal Sup35p function and can propagate like [PSI + ]. Unlike GFP fusions lacking Sup35p's essential C-terminal domain, NGMC did not form fluorescent foci in log-phase [PSI + ] cells. However, using fluorescence recovery after photobleaching and size fractionation techniques, we find evidence that NGMC is aggregated in these cells. Furthermore, the aggregates were larger in SSA1-21 cells, but the size of NGMC polymers was unchanged. Possibly, NGMC aggregates are bigger in SSA1-21 cells because they contain more polymers. Our data suggest that Ssa1-21p interferes with disruption of large Sup35p aggregates, which lack or have limited capacity to function as seed, into polymers that function more efficiently as [PSI + ] seeds.

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12

Broniatowski, Michel, Jana Jurečková, Ashok Moses, and Emilie Miranda. "Composite Tests under Corrupted Data." Entropy 21, no. 1 (January 14, 2019): 63. http://dx.doi.org/10.3390/e21010063.

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This paper focuses on test procedures under corrupted data. We assume that the observations Z i are mismeasured, due to the presence of measurement errors. Thus, instead of Z i for i = 1 , … , n, we observe X i = Z i + δ V i, with an unknown parameter δ and an unobservable random variable V i. It is assumed that the random variables Z i are i.i.d., as are the X i and the V i. The test procedure aims at deciding between two simple hyptheses pertaining to the density of the variable Z i, namely f 0 and g 0. In this setting, the density of the V i is supposed to be known. The procedure which we propose aggregates likelihood ratios for a collection of values of δ. A new definition of least-favorable hypotheses for the aggregate family of tests is presented, and a relation with the Kullback-Leibler divergence between the sets f δ δ and g δ δ is presented. Finite-sample lower bounds for the power of these tests are presented, both through analytical inequalities and through simulation under the least-favorable hypotheses. Since no optimality holds for the aggregation of likelihood ratio tests, a similar procedure is proposed, replacing the individual likelihood ratio by some divergence based test statistics. It is shown and discussed that the resulting aggregated test may perform better than the aggregate likelihood ratio procedure.

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13

Noh, Geontae, Ji Young Chun, and Ik Rae Jeong. "Sharing Privacy Protected and Statistically Sound Clinical Research Data Using Outsourced Data Storage." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/381361.

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It is critical to scientific progress to share clinical research data stored in outsourced generally available cloud computing services. Researchers are able to obtain valuable information that they would not otherwise be able to access; however, privacy concerns arise when sharing clinical data in these outsourced publicly available data storage services. HIPAA requires researchers to deidentify private information when disclosing clinical data for research purposes and describes two available methods for doing so. Unfortunately, both techniques degrade statistical accuracy. Therefore, the need to protect privacy presents a significant problem for data sharing between hospitals and researchers. In this paper, we propose a controlled secure aggregation protocol to secure both privacy and accuracy when researchers outsource their clinical research data for sharing. Since clinical data must remain private beyond a patient’s lifetime, we take advantage of lattice-based homomorphic encryption to guarantee long-term security against quantum computing attacks. Using lattice-based homomorphic encryption, we design an aggregation protocol that aggregates outsourced ciphertexts under distinct public keys. It enables researchers to get aggregated results from outsourced ciphertexts of distinct researchers. To the best of our knowledge, our protocol is the first aggregation protocol which can aggregate ciphertexts which are encrypted with distinct public keys.

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14

Rayward-Smith, V. J. "Measure based metrics for aggregated data." Intelligent Data Analysis 15, no. 2 (March 11, 2011): 109–30. http://dx.doi.org/10.3233/ida-2010-0459.

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15

McCoun, Paula Criswell, and Derald Walling. "Least squares analysis of aggregated data." International Journal of Mathematical Education in Science and Technology 19, no. 4 (July 1988): 561–66. http://dx.doi.org/10.1080/0020739880190407.

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16

Zadelaar, Jacqueline N., Joost A. Agelink van Rentergem, and Hilde M. Huizenga. "Univariate comparisons given aggregated normative data." Clinical Neuropsychologist 31, no. 6-7 (July 5, 2017): 1155–72. http://dx.doi.org/10.1080/13854046.2017.1348542.

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17

Berube, Sarah, and Karl-Dieter Crisman. "Decomposition behavior in aggregated data sets." Mathematical Social Sciences 61, no. 1 (January 2011): 12–19. http://dx.doi.org/10.1016/j.mathsocsci.2010.08.005.

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18

Palmonari, Matteo, Antonio Sala, Andrea Maurino, Francesco Guerra, Gabriella Pasi, and Giuseppe Frisoni. "Aggregated search of data and services." Information Systems 36, no. 2 (April 2011): 134–50. http://dx.doi.org/10.1016/j.is.2010.09.003.

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19

van Dijk, Bram, and Richard Paap. "Explaining individual response using aggregated data." Journal of Econometrics 146, no. 1 (September 2008): 1–9. http://dx.doi.org/10.1016/j.jeconom.2008.05.008.

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20

Edwards, D. A., Z. Liu, and R. G. Luthy. "Enhancing Polynuclear Aromatic Uptake into Bulk Solution with Amphiphilic Colloidal Aggregates." Water Science and Technology 26, no. 9-11 (November 1, 1992): 2341–44. http://dx.doi.org/10.2166/wst.1992.0732.

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Aggregated amphiphiles in an aqueous or solid/aqueous system can substantially enhance the uptake of polynuclear aromatic (PNA)compound into a bulk solution. The extent of PNA compound incorporated in an amphiphilic aggregate solution in the absence of solids is linearly dependent on the bulk solution concentration of the aggregated form of the amphiphile. In a system in which solids are in contact with a solution, however, the relationship is nonlinear as a result of the adherence of both amphiphile and PNA compound to the solids. The formation of amphiphile aggregates in the bulk solution of a system containing solids occurs only after a much greater amount of amphiphile has been added to the system than would be required for a similar system containing only solution. The partitioning of PNA compound between the solid, the colloidal amphiphilic aggregates in bulk solution, and the rest of the bulk solution can be characterized with two different partition coefficients and a number of other parameters, all of which are obtainable from independent experiments. The total fraction of PNA compound incorporated into bulk solution can be estimated with a mathematical model. Model results for the uptake of pyrene into a C8PE9.5 aggregate solution are shown plotted with experimental data. The effect of amphiphile aggregates on PNA compound transport in porous media may in some cases be substantial.

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21

Lee, Yong-Woo, and David B. Sampson. "Monte Carlo evaluation of a nonlinear regression estimator for aggregated lengthweight data." Canadian Journal of Fisheries and Aquatic Sciences 62, no. 2 (February 1, 2005): 362–73. http://dx.doi.org/10.1139/f04-194.

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Parameters of fish lengthweight relationships (W = aLb) are usually estimated by applying linear regression to log-transformed length and weight values, but measuring individual weights is time-consuming and expensive. Often, length and weight data are available as sets of length measurements and aggregated sample weights, and the aggregate average weight of a sample can be expressed as the average of the weights predicted for the individual fish lengths. This study evaluated the feasibility of applying nonlinear regression to aggregated lengthweight data. Experiments with simulated random lengthweight data demonstrated that the estimates of parameter b appear to be unbiased and the estimates of a are right-skewed and biased. Further, the estimates of ln(a) and b are almost perfectly correlated. The precision and accuracy of the estimates were greatly influenced by the number of aggregate samples but were relatively unaffected by the number of fish in each sample. An additional experiment showed that the residuals from the regression can be used to detect small changes in the lengthweight parameters.

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22

Jiang, Shiyan, and Jennifer Kahn. "Data wrangling practices and collaborative interactions with aggregated data." International Journal of Computer-Supported Collaborative Learning 15, no. 3 (August 26, 2020): 257–81. http://dx.doi.org/10.1007/s11412-020-09327-1.

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23

Bass, Frank M., and Robert P. Leone. "Estimating Micro Relationships from Macro Data: A Comparative Study of two Approximations of the Brand Loyal Model under Temporal Aggregation." Journal of Marketing Research 23, no. 3 (August 1986): 291–97. http://dx.doi.org/10.1177/002224378602300310.

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The data interval bias issue in econometric studies of the relationship between advertising and sales first was discussed in JMR by Clarke in 1976. Within the last few years, several studies reported in the marketing literature have investigated the modeling of sales-advertising relationships with temporally aggregated data. Weiss, Weinberg, and Windal developed an aggregate form of the brand loyal model for data aggregated over time and compared their estimates of micro-level parameters with those derived from an alternative approximation developed by Bass and Leone. The authors use simulated data and empirical data to compare the performance of the two approximation approaches.

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24

Acebrón, Sergio P., Vanesa Fernández-Sáiz, Stefka G. Taneva, Fernando Moro, and Arturo Muga. "DnaJ Recruits DnaK to Protein Aggregates." Journal of Biological Chemistry 283, no. 3 (November 5, 2007): 1381–90. http://dx.doi.org/10.1074/jbc.m706189200.

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Thermal stress might lead to protein aggregation in the cell. Reactivation of protein aggregates depends on Hsp100 and Hsp70 chaperones. We focus in this study on the ability of DnaK, the bacterial representative of the Hsp70 family, to interact with different aggregated model substrates. Our data indicate that DnaK binding to large protein aggregates is mediated by DnaJ, and therefore it depends on its affinity for the cochaperone. Mutations in the structural region of DnaK known as the “latch” decrease the affinity of the chaperone for DnaJ, resulting in a defective activity as protein aggregate-removing agent. As expected, the chaperone activity is recovered when DnaJ concentration is raised to overcome the lower affinity of the mutant for the cochaperone, suggesting that a minimum number of aggregate-bound DnaK molecules is necessary for its efficient reactivation. Our results provide the first experimental evidence of DnaJ-mediated recruiting of ATP-DnaK molecules to the aggregate surface.

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25

Taylor, Benjamin M., Ricardo Andrade‐Pacheco, and Hugh J. W. Sturrock. "Continuous inference for aggregated point process data." Journal of the Royal Statistical Society: Series A (Statistics in Society) 181, no. 4 (January 6, 2018): 1125–50. http://dx.doi.org/10.1111/rssa.12347.

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26

Bosina, Ernst, and Ulrich Weidmann. "Estimating pedestrian speed using aggregated literature data." Physica A: Statistical Mechanics and its Applications 468 (February 2017): 1–29. http://dx.doi.org/10.1016/j.physa.2016.09.044.

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27

Dias, Ronaldo, Nancy L. Garcia, and Alexandra M. Schmidt. "A Hierarchical Model for Aggregated Functional Data." Technometrics 55, no. 3 (August 2013): 321–34. http://dx.doi.org/10.1080/00401706.2013.765316.

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28

Michaud, Clayton P., and Thomas W. Sproul. "Bayesian Downscaling Methods for Aggregated Count Data." Agricultural and Resource Economics Review 47, no. 1 (December 17, 2017): 178–94. http://dx.doi.org/10.1017/age.2017.26.

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Policy-critical, micro-level statistical data are often unavailable at the desired level of disaggregation. We present a Bayesian methodology for “downscaling” aggregated count data to the micro level, using an outside statistical sample. Our procedure combines numerical simulation with exact calculation of combinatorial probabilities. We motivate our approach with an application estimating the number of farms in a region, using count totals at higher levels of aggregation. In a simulation analysis over varying population sizes, we demonstrate both robustness to sampling variability and outperformance relative to maximum likelihood. Spatial considerations, implementation of “informative” priors, non-spatial classification problems, and best practices are discussed.

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29

Leeflang, Peter S. H., and Dick R. Wittink. "Diagnosing competitive reactions using (aggregated) scanner data." International Journal of Research in Marketing 9, no. 1 (March 1992): 39–57. http://dx.doi.org/10.1016/0167-8116(92)90028-j.

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30

Tütüncü, G. Yazgı, and Necla Kayaalp. "An Aggregated Fuzzy Naive Bayes Data Classifier." Journal of Computational and Applied Mathematics 286 (October 2015): 17–27. http://dx.doi.org/10.1016/j.cam.2015.02.004.

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31

Kvamsdal, Sturla Furunes. "Indexing of Technical Change in Aggregated Data." Computational Economics 53, no. 3 (November 14, 2017): 901–20. http://dx.doi.org/10.1007/s10614-017-9771-8.

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32

Toke, Ioane Muni. "Reconstruction of Order Flows using Aggregated Data." Market Microstructure and Liquidity 02, no. 02 (September 2016): 1650007. http://dx.doi.org/10.1142/s2382626616500076.

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We investigate TRTH tick-by-tick data on three exchanges (Paris, London and Frankfurt) and on a five-year span. A simple algorithm helps the synchronization of the trades and quotes data, enhancing the basic procedure. The analysis of the performance of this algorithm turns out to be a forensic tool assessing the quality of the database: significant technical changes affecting the exchanges are tracked through the data. Moreover, the choices made when reconstructing order flows have consequences on the quantitative models that are calibrated afterwards on such data. Finally, this order flow reconstruction provides a refined look at the Lee–Ready procedure and its optimal lags. Findings are in line with both financial reasoning and the analysis of an illustrative Poisson model.

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33

Rao, A. Ramachandra, Srinivasa G. Rao, and R. L. Kashyap. "STOCHASTIC ANALYSIS OF TIME-AGGREGATED HYDROLOGIC DATA." Journal of the American Water Resources Association 21, no. 5 (October 1985): 757–70. http://dx.doi.org/10.1111/j.1752-1688.1985.tb00170.x.

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34

Damgaard, Christian. "Hierarchical and spatially aggregated plant cover data." Ecological Informatics 18 (November 2013): 35–39. http://dx.doi.org/10.1016/j.ecoinf.2013.06.001.

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35

Boulware, L. E., G. B. Harris, P. Harewood, F. F. Johnson, P. Maxson, N. Bhavsar, S. S. Blackwelder, et al. "Democratizing health system data to impact social and environmental health contexts: a novel collaborative community data-sharing model." Journal of Public Health 42, no. 4 (January 9, 2020): 784–92. http://dx.doi.org/10.1093/pubmed/fdz171.

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Abstract Background Community health data are infrequently viewed in the context of social and environmental health determinants. We developed a novel data-sharing model to democratize health system data and to facilitate community and population health improvement. Methods Durham County, the City of Durham in North Carolina, Durham health systems and other stakeholders have developed a data-sharing model to inform local community health efforts. Aggregated health system data obtained through clinical encounters are shared publicly, providing data on the prevalence of health conditions of interest to the community. Results A community-owned web platform called the Durham Neighborhood Compass provides aggregate health data (e.g. on diabetes, heart disease, stroke and other conditions of interest) in the context of neighborhood social (e.g. income distribution, education level, demographics) and environmental (e.g. housing prices, crime rates, travel routes, school quality, grocery store proximity) contexts. Health data are aggregated annually to help community stakeholders track changes in health and health contexts over time. Conclusions The Durham Neighborhood Compass is among the first collaborative public efforts to democratize health system data in the context of social and environmental health determinants. This model could be adapted elsewhere to support local community and population health improvement initiatives.

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Buchin, Kevin, Maike Buchin, Marc van Kreveld, Maarten Löffler, Jun Luo, and Rodrigo I. Silveira. "Processing aggregated data: the location of clusters in health data." GeoInformatica 16, no. 3 (October 18, 2011): 497–521. http://dx.doi.org/10.1007/s10707-011-0143-6.

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37

Zenor, Michael J., and Rajendra K. Srivastava. "Inferring Market Structure with Aggregate Data: A Latent Segment Logit Approach." Journal of Marketing Research 30, no. 3 (August 1993): 369–79. http://dx.doi.org/10.1177/002224379303000308.

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In this paper, the authors introduce a “latent segment logit” (LSL) model that allows the identification of latent market segments when only macro-level time-series data (e.g., market share or sales, not individual choices) are available. The proposed model provides a paramorphic representation of market structure, based on the notion that “structure” implies heterogeneity in preferences and/or response to marketing mix elements. It assumes that independence of irrelevant alternatives (IIA) holds within latent segments (i.e., segments are homogeneous) but allows for heterogeneity across segments. Estimates for segment characteristics (including size, brand preferences, and sensitivity to marketing mix variables) are obtained by applying the model to aggregated longitudinal panel data. Validation tests are conducted on both the aggregated and disaggregated panel data. Aggregate validation demonstrates that the model is superior to standard market share models in terms of calibration and predictive fit. Disaggregated validation demonstrates that the latent segments recovered by the model account for much of the variation across household purchase histories, even though these data were not utilized in the estimation.

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38

Dmowska, Anna. "Dasymetric Modelling of Population Distribution – Large Data Approach." Quaestiones Geographicae 38, no. 1 (March 29, 2019): 15–27. http://dx.doi.org/10.2478/quageo-2019-0008.

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Abstract Existing resources of population data, provided by national censuses in the form of areal aggregates, have usually insufficient resolution for many practical applications. Dasymetric modelling has been a standard technique to disaggregate census aggregates into finer grids. Although dasymetric modelling of population distribution is well-established, most literature focuses on proposing new variants of the technique, while only few are devoted to developing broad-scale population grids that could be used for real-life applications. This paper reviews literature on construction of broad-scale population grids using dasymetric modelling. It also describes an R implementation of fully automated framework to calculate such grids from aggregated data provided by national censuses. The presented implementation has been used to produce high resolution, multi-year comparable, U.S.-wide population datasets that are the part of the SocScape (Social Landscape) project.

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39

Larsen, Andreas Haahr, Jan Skov Pedersen, and Lise Arleth. "Assessment of structure factors for analysis of small-angle scattering data from desired or undesired aggregates." Journal of Applied Crystallography 53, no. 4 (July 16, 2020): 991–1005. http://dx.doi.org/10.1107/s1600576720006500.

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Aggregation processes are central features of many systems ranging from colloids and polymers to inorganic nanoparticles and biological systems. Some aggregated structures are controlled and desirable, e.g. in the design of size-controlled clustered nanoparticles or some protein-based drugs. In other cases, the aggregates are undesirable, e.g. protein aggregation involved in neurodegenerative diseases or in vitro studies of single protein structures. In either case, experimental and analytical tools are needed to cast light on the aggregation processes. Aggregation processes can be studied with small-angle scattering, but analytical descriptions of the aggregates are needed for detailed structural analysis. This paper presents a list of useful small-angle scattering structure factors, including a novel structure factor for a spherical cluster with local correlations between the constituent particles. Several of the structure factors were renormalized to get correct limit values in both the high-q and low-q limit, where q is the modulus of the scattering vector. The structure factors were critically evaluated against simulated data. Structure factors describing fractal aggregates provided approximate descriptions of the simulated data for all tested structures, from linear to globular aggregates. The addition of a correlation hole for the constituent particles in the fractal structure factors significantly improved the fits in all cases. Linear aggregates were best described by a linear structure factor and globular aggregates by the newly derived spherical cluster structure factor. As a central point, it is shown that the structure factors could be used to take aggregation contributions into account for samples of monomeric protein containing a minor fraction of aggregated protein. After applying structure factors in the analysis, the correct structure and oligomeric state of the protein were determined. Thus, by careful use of the presented structure factors, important structural information can be retrieved from small-angle scattering data, both when aggregates are desired and when they are undesired.

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40

Xiao, N. "HACKING SPATIAL DATA: AN EXAMPLE OF AGGREGATION PROBLEMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 231–32. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-231-2018.

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<p><strong>Abstract.</strong> Many applications using spatially aggregated data tend to treat the spatial units as given. For example, in the United States, analyses using the social and economic data often rely on the existing and fixed spatial units of census blocks or tracts. However, these spatial units are often aggregated arbitrarily. It is therefore important to ask this question: what if the spatial units are aggregated differently? Will the results obtained using the existing units still hold? This paper addresses questions like these. We first develop a search algorithm that can be used to find alternative aggregations with relatively equal total populations among the aggregated units. Then a number of experiments are conducted to test the algorithm and to demonstrate how alternative aggregations will affect the analysis. These experiments clearly suggest the significant effects of spatial aggregation on the analysis results.</p>

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Liu, Wenbin, Bo Yang, and Zhili Chen. "Minimum-latency data aggregation scheduling based on multi-path routing structures under physical interference model." International Journal of Distributed Sensor Networks 14, no. 5 (May 2018): 155014771877447. http://dx.doi.org/10.1177/1550147718774471.

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Minimum-Latency Aggregation Scheduling is a significant problem in wireless sensor networks. The key challenge is to find an effective solution to aggregate data from all sensors to the sink with minimum aggregation latency. In this article, we propose a novel data aggregation scheduling algorithm under the physical interference model. First, the algorithm partitions the network into square cells according to the communication range of a sensor. Second, a node is selected randomly as the aggregated node to receive the data from the other nodes in the same cell. Finally, a data aggregation tree, which consists of multiple disjoint paths, is constructed to aggregate data from all aggregated nodes to the sink. We empirically proved that the delay of the aggregation schedule generated by our algorithm is ( K+1)2Δ− K−1+2λ time-slots at most, where K is a constant depending on the sensors transmitting power, the signal-to-interference-plus-noise-ratio threshold, and the path-loss exponent; [Formula: see text] represents the maximal number of nodes in a cell; and [Formula: see text] denotes the number of cells at a row/column in a square network area. Simulation results also show that our algorithm achieves lower average latency than the previous works.

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Qu, Xu, Mofeng Yang, Junyi Ji, Linheng Li, and Bin Ran. "Analyzing the Safety Impacts of Variable Speed Limit Control on Aggregated Driving Behavior Based on Traffic Big Data." Journal of Advanced Transportation 2021 (March 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/8823292.

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Variable speed limit (VSL) control dynamically adjusts the displayed speed limit to harmonize traffic speed, prevent congestions, and reduce crash risks based on prevailing traffic stream and weather conditions. Previous research studies examine the impacts of VSL control on reducing corridor-level crash risks and improving bottleneck throughput. However, less attention focuses on utilizing real-world data to see how compliant the drivers are under different VSL values and how the aggregated driving behavior changes. This study aims to fill the gap. With the high-resolution lane-by-lane traffic big data collected from a European motorway, this study performs statistical analysis to measure the difference in driving behavior under different VSL values and analyze the safety impacts of VSL controls on aggregate driving behaviors (mean speed, average speed difference, and the percentage of small space headway). The data analytics show that VSL control can effectively decrease the mean speed, the speed difference, and the percentage of small space headways. The safety impacts of VSL control on aggregated driving behavior are also discussed. The aggregated driving behavior variables follow a trend of first decreasing and then increasing with the continuous decrease in VSL values, indicating that potential traffic safety benefits can be achieved by adopting suitable VSL values that match with prevailing traffic conditions.

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van der Hagen, Eline A. E., Cas Weykamp, Sverre Sandberg, Anne V. Stavelin, Finlay MacKenzie, and W. Greg Miller. "Feasibility for aggregation of commutable external quality assessment results to evaluate metrological traceability and agreement among results." Clinical Chemistry and Laboratory Medicine (CCLM) 59, no. 1 (January 26, 2021): 117–25. http://dx.doi.org/10.1515/cclm-2020-0736.

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AbstractObjectivesExternal quality assessment (EQA) with commutable samples is used for assessing agreement of results for patients’ samples. We investigated the feasibility to aggregate results from four different EQA schemes to determine the bias between different measurement procedures and a reference target value.MethodsWe aggregated EQA results for creatinine from programs that used commutable EQA material by calculating the relative difference between individual participant results and the reference target value for each sample. The means and standard errors of the means were calculated for the relative differences. Results were partitioned by methods, manufacturers and instrument platforms to evaluate the biases for the measurement procedures.ResultsData aggregated for enzymatic methods had biases that varied from −8.2 to 3.8% among seven instrument platforms for creatinine at normal concentrations (61–85 μmol/L). EQA schemes differed in the evidence provided about the commutability of their samples, and in the amount of detail collected from participants regarding the measurement procedures which limited the ability to sub-divide aggregated data by instrument platforms and models.ConclusionsEQA data could be aggregated from four different programs using different commutable samples to determine bias among different measurement procedures. Criteria for commutability for EQA samples as well as standardization of reporting the measurement methods, reagents, instrument platforms and models used by participants are needed to improve the ability to aggregate the results for optimal assessment of performance of measurement procedures. Aggregating data from a larger number of EQA schemes is feasible to assess trueness on a global scale.

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Nutt, Perry J., Michael R. Reed, and Jerry R. Skees. "Farm Level Corn Acreage Response Estimation." Journal of Agricultural and Applied Economics 18, no. 2 (December 1986): 1–6. http://dx.doi.org/10.1017/s0081305200006051.

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AbstractThis study reports results of an acreage supply equation using individual farm data and develops implications for bias introduced by use of aggregate data. The analysis shows that use of aggregate acreage response models underestimates the absolute value of price elasticities and the lagged adjustment process. Further, aggregated analyses cannot incorporate some variables found to be important in explaining from level acreage response.

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Krassanakis, Vassilios. "Aggregated Gaze Data Visualization Using Contiguous Irregular Cartograms." Digital 1, no. 3 (June 30, 2021): 130–44. http://dx.doi.org/10.3390/digital1030010.

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Gaze data visualization constitutes one of the most critical processes during eye-tracking analysis. Considering that modern devices are able to collect gaze data in extremely high frequencies, the visualization of the collected aggregated gaze data is quite challenging. In the present study, contiguous irregular cartograms are used as a method to visualize eye-tracking data captured by several observers during the observation of a visual stimulus. The followed approach utilizes a statistical grayscale heatmap as the main input and, hence, it is independent of the total number of the recorded raw gaze data. Indicative examples, based on different parameters/conditions and heatmap grid sizes, are provided in order to highlight their influence on the final image of the produced visualization. Moreover, two analysis metrics, referred to as center displacement (CD) and area change (AC), are proposed and implemented in order to quantify the geometric changes (in both position and area) that accompany the topological transformation of the initial heatmap grids, as well as to deliver specific guidelines for the execution of the used algorithm. The provided visualizations are generated using open-source software in a geographic information system.

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Iwaoka, Koichiro, and Yuzo Hirotsu. "Failure Detection Method using Aggregated Vehicle Detector Data." IEEJ Transactions on Industry Applications 137, no. 4 (2017): 318–25. http://dx.doi.org/10.1541/ieejias.137.318.

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Iwaoka, Koichiro, and Yuzo Hirotsu. "Failure Detection Method using Aggregated Vehicle Detector Data." IEEJ Journal of Industry Applications 6, no. 4 (2017): 295–302. http://dx.doi.org/10.1541/ieejjia.6.295.

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Buckee, Caroline O., Satchit Balsari, Jennifer Chan, Mercè Crosas, Francesca Dominici, Urs Gasser, Yonatan H. Grad, et al. "Aggregated mobility data could help fight COVID-19." Science 368, no. 6487 (March 23, 2020): 145.2–146. http://dx.doi.org/10.1126/science.abb8021.

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Kennedy, Erika, and Leslie W. Kennedy. "Using Dasymetric Mapping for Spatially Aggregated Crime Data." Journal of Quantitative Criminology 20, no. 3 (September 2004): 243–62. http://dx.doi.org/10.1023/b:joqc.0000037733.74321.14.

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Xu, Ying, Paul S. F. Yip, and Richard M. Huggins. "A Generalized Chain Binomial Model with Aggregated Data." Communications in Statistics - Theory and Methods 41, no. 18 (September 15, 2012): 3325–38. http://dx.doi.org/10.1080/03610926.2011.563012.

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