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WIREs Data Mining Knowl Discov
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Identifying patterns in spatial information: A survey of methods

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Abstract Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and implicit spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this paper, we explore the emerging field of spatial data mining, focusing on different methods to extract patterns from spatial information. We conclude with a look at future research needs. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 193–214 DOI: 10.1002/widm.25 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining

Attribute values in space with independent identical distribution and spatial autocorrelation.

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Cascading spatio‐temporal patterns from public safety.

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Flow anomaly example.

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Comparison between geometric and network based hotspot for requests during the Haiti earthquake (Best viewed in color).

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Spatial crime hotspots from the city of Lincoln, NE89(Best viewed in color).

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Complete spatial random (CSR) and spatially clustered patterns.

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(a) The actual locations of nests. (b) Pixels with actual nests. (c) Location predicted by a model. (d) Location predicted by another model. Prediction (d) is spatially more accurate than (c).

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Example to illustrate different approaches to discovering co‐location patterns: (a) Example data set. (b) Reference feature‐centric model. (c) Data partition approach. Support measure is ill‐defined and order sensitive. (d) Event‐centric model.

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Illustration of point spatial co‐location patterns.

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Scatterplot and spatial statistic Zs(x) to detect spatial outliers.

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Variogram cloud and Moran scatterplot to detect spatial outliers.

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A data set for outlier detection.

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A spatial framework and its four‐neighborhood contiguity matrix.

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Algorithmic Development > Spatial and Temporal Data Mining

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