@inbook{3469d1a2fca746a2911ea86c04623b53,
title = "The application of the SPAWNN toolkit to the socioeconomic analysis of Chicago, Illinois",
abstract = "The SPAWNN toolbox is an innovative toolkit for spatial analysis with self-organizing neural networks. It implements several self-organizing neural networks and so-called spatial context models which can be combined with the networks to incorporate spatial dependence. The SPAWNN toolkit interactively links the networks and data visualizations in an intuitive manner to support a better understanding of data and implements clustering algorithms for identifying clusters in the trained networks. These properties make it particularly useful for analyzing large amounts of complex and high-dimensional data. This chapter investigates the application of the SPAWNN toolkit to the socioeconomic analysis of the city of Chicago, Illinois. For this purpose, 2010 Census data, consisting of numerous indicators that describe the socioeconomic status of the US population in detail, is used. The results highlight the features of the toolkit and reveal important insights into the socioeconomic characteristics of the US.",
keywords = "Self-organizing neural networks, Spatial clustering , Spatial analysis ",
author = "J Hagenauer and M. Helbich",
year = "2016",
doi = "10.1007/978-3-319-52522-8_5",
language = "English",
isbn = "978-3-319-52520-4",
series = "Geotechnologies and the Environment",
publisher = "Springer",
pages = "75--90",
editor = "M Behnisch and G Meinel",
booktitle = "Trends in Spatial Analysis and Modelling",
}