Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. Presidential Election

Pearson, Paul; Cooper, Cameron I.

doi:10.1007/978-3-642-33212-8_19

Cited by 7 publications

(4 citation statements)

References 11 publications

(11 reference statements)

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“…They are used for the classification of the most diverse data types. Their application, since their introduction by Kohonen [47], has been experimented in different sectors, like gene expression analysis [48], financial diagnosis [49], synoptic climatology [50], microbial community dynamics [51], bankrupt prediction [52] and political science [53]. SOMs have been widely applied to remote sensing since the '90s [54] as well, and new applications are still studied and proposed today.…”

Section: A Scenario 1: Monitoring Two-year Agricultural Activitiesmentioning

confidence: 99%

Multitemporal Level- $1\beta$ Products: Definitions, Interpretation, and Applications

Amitrano

Cecinati

Martino

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal SAR data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1β products are particularly oriented toward non-expert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of the paper is the design of a suitable RGB representation of data aiming to enhance the information content of time-series. The physical rationale of the products is presented through examples in which we show their robustness with respect to sensor, acquisition mode and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new ESA sensor. Finally, we propose two applications based on the usage of Kohonen's self-organizing maps dealing with classification problems.

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Section: A Scenario 1: Monitoring Two-year Agricultural Activitiesmentioning

confidence: 99%

Multitemporal Level- $1\beta$ Products: Definitions, Interpretation, and Applications

Amitrano

Cecinati

Martino

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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“…Since Openshaw (1994), a geographical interest in dimensionality reduction using the Self-Organizing Map (Kohonen 1990) has sought to reduce high-dimensional structures in geographical processes and proven extensively useful. In an exploratory mode of analysis, the Self-Organizing Map and its variants (Bação et al 2004;Xu et al 2017;Clark et al 2017) have been consistently used in the analysis of complex, non-linear demographic relationships (Skupin and Fabrikant 2003;Agarwal and Skupin 2008;Pearson and Cooper 2012;Arribas-Bel, Nijkamp, et al 2011;Delmelle et al 2013;Spielman and Logan 2013;Psyllidis et al 2018). Another consistent interest is the use of Self-Organizing map for data-driven map reprojection (Skupin 2003;Henriques, Bação, et al 2009;Skupin and Esperbé 2011), which exploits the Self-Organizing Map's distinctive properties in order to build new or better map projections and transformations.…”

Section: Past Explorations and Prior Concerns For Geographic Dimension mentioning

confidence: 99%

Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

Wolf¹,

Knaap²

2019

Preprint

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Dimension reduction is one of the oldest concerns in geographical analysis. Despite significant, longstanding attention in geographical problems, recent advances in non-linear techniques for dimension reduction, called manifold learning, have not been adopted in classic data-intensive geographical problems. More generally, machine learning methods for geographical problems often focus more on applying standard machine learning algorithms to geographic data, rather than applying true "spatially-correlated learning," in the words of Kohonen. As such, we suggest a general way to incentivize geographical learning in machine learning algorithms, and link it to many past methods that introduced geography into statistical techniques. We develop a specific instance of this by specifying two geographical variants of Isomap, a non-linear dimension reduction, or "manifold learning," technique. We also provide a method for assessing what is added by incorporating geography and estimate the manifold's intrinsic geographic scale. To illustrate the concepts and provide interpretable results, we conducting a dimension reduction on geographical and high-dimensional structure of social and economic data on Brooklyn, New York. Overall, this paper's main endeavor--defining and explaining a way to "geographize" many machine learning methods--yields interesting and novel results for manifold learning the estimation of intrinsic geographical scale in unsupervised learning.

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“…SOM, is a machine-learning technique of the artificial neural network (ANN) family. It has been exploited to classify the most diverse data types in different sectors, from climatology [23] to political science [24], finance [25], and remote sensing [26]. This widespread use of SOM is due to its high flexibility and adaptability.…”

Section: Modified Som Clusteringmentioning

confidence: 99%

Feature Extraction From Multitemporal SAR Images Using Selforganizing Map Clustering and Object-Based Image Analysis

Amitrano¹,

Cecinati

Martino³

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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We introduce a new architecture for feature extraction from multitemporal synthetic aperture radar data. Its purpose is to combine classic synthetic aperture radar processing and geographical object-based image analysis to provide a robust unsupervised tool for information extraction from time series images. The architecture takes advantage from the characteristics of the recently introduced RGB products of the Level-1α and Level-1β families, and employs self-organizing map clustering and object-based image analysis. In particular, the input products are clustered using color homogeneity and automatically enriched with a semantic attribute referring to clusters' color, providing a pre-classification mask. Then, in the frame of an applicationoriented object-based image analysis, opportune layers measuring scattering and geometric properties of candidate objects are evaluated, and an appropriate rule-set is implemented in a fuzzy system to extract the feature of interest. The obtained results have been compared with those given by existing techniques and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semi-arid environment.

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Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. Presidential Election

Cited by 7 publications

References 11 publications

Multitemporal Level- $1\beta$ Products: Definitions, Interpretation, and Applications

Multitemporal Level- $1\beta$ Products: Definitions, Interpretation, and Applications

Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

Feature Extraction From Multitemporal SAR Images Using Selforganizing Map Clustering and Object-Based Image Analysis

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