Abstract
Data sets that represent observational units that are located at different points on a map often exhibit spatial dependence. Symptoms of such spatial dependence include clustering of similar values by location (e.g., house prices and incomes are similar in a subdivision, wetlands usually linear other wetlands, pollution gradually dissapates away from the source). This chapter discusses alternative models and methods that can be used to estimate regression relationships for this type of sample data.
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LeSage, J.P., Pace, R.K. (2001). Spatial Dependence in Data Mining. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_24
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DOI: https://doi.org/10.1007/978-1-4615-1733-7_24
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-0114-7
Online ISBN: 978-1-4615-1733-7
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