Sandra De Iaco

1.4k total citations
67 papers, 968 citations indexed

About

Sandra De Iaco is a scholar working on Environmental Engineering, Economics and Econometrics and Artificial Intelligence. According to data from OpenAlex, Sandra De Iaco has authored 67 papers receiving a total of 968 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Environmental Engineering, 26 papers in Economics and Econometrics and 20 papers in Artificial Intelligence. Recurrent topics in Sandra De Iaco's work include Soil Geostatistics and Mapping (44 papers), Spatial and Panel Data Analysis (23 papers) and Geochemistry and Geologic Mapping (16 papers). Sandra De Iaco is often cited by papers focused on Soil Geostatistics and Mapping (44 papers), Spatial and Panel Data Analysis (23 papers) and Geochemistry and Geologic Mapping (16 papers). Sandra De Iaco collaborates with scholars based in Italy, United States and Finland. Sandra De Iaco's co-authors include D. Posa, Donald E. Myers, Monica Palma, L. De Cesare, Dionissios T. Hristopulos, Guang Lin, Klaus Nordhausen, Julián González-Trinidad, Hugo Enrique Júnez-Ferreira and Reza Ghezelbash and has published in prestigious journals such as The Science of The Total Environment, Neural Networks and Journal of Statistical Software.

In The Last Decade

Sandra De Iaco

58 papers receiving 932 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sandra De Iaco Italy 18 632 272 232 223 111 67 968
D. Posa Italy 20 820 1.3× 345 1.3× 261 1.1× 292 1.3× 164 1.5× 54 1.2k
Gardar Johannesson United States 10 496 0.8× 230 0.8× 272 1.2× 310 1.4× 205 1.8× 19 1.1k
Joseph Guinness United States 12 314 0.5× 120 0.4× 135 0.6× 130 0.6× 95 0.9× 40 688
William Kleiber United States 19 531 0.8× 211 0.8× 241 1.0× 622 2.8× 439 4.0× 57 1.5k
Raphaël Huser Saudi Arabia 20 302 0.5× 230 0.8× 105 0.5× 782 3.5× 281 2.5× 71 1.5k
Alexander Kolovos United States 13 291 0.5× 98 0.4× 141 0.6× 160 0.7× 94 0.8× 24 622
Dorit Hammerling United States 16 356 0.6× 160 0.6× 211 0.9× 617 2.8× 436 3.9× 56 1.2k
Jo Eidsvik Norway 21 373 0.6× 133 0.5× 367 1.6× 119 0.5× 38 0.3× 93 1.4k
Surajit Chattopadhyay India 27 639 1.0× 128 0.5× 154 0.7× 612 2.7× 406 3.7× 219 2.5k
Henning Omre Norway 23 468 0.7× 96 0.4× 293 1.3× 98 0.4× 50 0.5× 79 2.6k

Countries citing papers authored by Sandra De Iaco

Since Specialization
Citations

This map shows the geographic impact of Sandra De Iaco's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sandra De Iaco with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sandra De Iaco more than expected).

Fields of papers citing papers by Sandra De Iaco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sandra De Iaco. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sandra De Iaco. The network helps show where Sandra De Iaco may publish in the future.

Co-authorship network of co-authors of Sandra De Iaco

This figure shows the co-authorship network connecting the top 25 collaborators of Sandra De Iaco. A scholar is included among the top collaborators of Sandra De Iaco based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sandra De Iaco. Sandra De Iaco is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Iaco, Sandra De, et al.. (2026). One-class support vector machine with compositional data analysis to recognize geochemical anomaly patterns related to mineralization. Stochastic Environmental Research and Risk Assessment. 40(1).
2.
Iaco, Sandra De, et al.. (2025). A term structure geostatistical model with correlated residuals: A comparative analysis. Spatial Statistics. 67. 100886–100886.
3.
Iaco, Sandra De, et al.. (2025). Traditional Prediction Techniques and Machine Learning Approaches for Financial Time Series Analysis. Mathematics. 13(3). 537–537. 5 indexed citations
4.
Iaco, Sandra De, et al.. (2025). Anisotropic local covariance matrices for spatial blind source separation. AStA Advances in Statistical Analysis. 109(4). 753–770. 1 indexed citations
5.
Iaco, Sandra De & D. Posa. (2025). Characteristics of some isotropic covariance models with negative values. Spatial Statistics. 68. 100905–100905.
6.
Iaco, Sandra De, et al.. (2024). Modelling multivariate spatio-temporal data with identifiable variational autoencoders. Neural Networks. 181. 106774–106774.
7.
Iaco, Sandra De, et al.. (2024). An Advanced Spatial Approach Based on Multi-criteria Analysis and Geostatistical Simulation for a Comprehensive Geogenic Radon Hazard Index Mapping. Journal of Agricultural Biological and Environmental Statistics. 30(2). 334–362. 2 indexed citations
8.
Iaco, Sandra De, et al.. (2024). Spatial multi-criteria approaches for estimating geogenic radon hazard index. The Science of The Total Environment. 956. 176419–176419. 7 indexed citations
9.
Iaco, Sandra De, et al.. (2023). Multilevel modeling for investigating the probability of digital innovation in museums. Annals of Operations Research. 342(3). 1737–1764. 2 indexed citations
10.
Palma, Monica, et al.. (2023). Tourism composite spatial indicators through variography and geographically weighted principal components analysis. Annals of Operations Research. 342(3). 1687–1705. 3 indexed citations
11.
Júnez-Ferreira, Hugo Enrique, et al.. (2023). Assessment of changes in regional groundwater levels through spatio-temporal kriging: application to the southern Basin of Mexico aquifer system. Hydrogeology Journal. 31(6). 1405–1423. 11 indexed citations
12.
Iaco, Sandra De. (2023). Families of complex‐valued covariance models through integration. Environmetrics. 34(3). 2 indexed citations
13.
Iaco, Sandra De, et al.. (2023). Multivariate Modeling for Spatio-Temporal Radon Flux Predictions. Entropy. 25(7). 1104–1104. 1 indexed citations
14.
Palma, Monica, et al.. (2023). Spatio-temporal modeling of groundwater quality deterioration and resource depletion. Hydrogeology Journal. 31(6). 1443–1461.
15.
Iaco, Sandra De, et al.. (2022). Blind recovery of sources for multivariate space-time random fields. Stochastic Environmental Research and Risk Assessment. 37(4). 1593–1613. 6 indexed citations
16.
Iaco, Sandra De, et al.. (2016). Radon Predictions withGeographical Information SystemCovariates: From Spatial Sampling to Modeling. Geographical Analysis. 49(2). 215–235. 5 indexed citations
17.
Iaco, Sandra De, et al.. (2011). Validation Techniques for Geological Patterns Simulations Based on Variogram and Multiple-Point Statistics. Mathematical Geosciences. 43(4). 483–500. 34 indexed citations
18.
Iaco, Sandra De, et al.. (2011). Towards an automatic procedure for modeling multivariate space–time data. Computers & Geosciences. 41. 1–11. 18 indexed citations
19.
Iaco, Sandra De. (2010). Space–time correlation analysis: a comparative study. Journal of Applied Statistics. 37(6). 1027–1041. 30 indexed citations
20.
Myers, Donald E., Sandra De Iaco, D. Posa, & L. De Cesare. (2002). Space-time radial basis functions. Computers & Mathematics with Applications. 43(3-5). 539–549. 33 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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