spatial data mining

AM-97 - An Introduction to Spatial Data Mining

The goal of spatial data mining is to discover potentially useful, interesting, and non-trivial patterns from spatial data-sets (e.g., GPS trajectory of smartphones). Spatial data mining is societally important having applications in public health, public safety, climate science, etc. For example, in epidemiology, spatial data mining helps to nd areas with a high concentration of disease incidents to manage disease outbreaks. Computational methods are needed to discover spatial patterns since the volume and velocity of spatial data exceed the ability of human experts to analyze it. Spatial data has unique characteristics like spatial autocorrelation and spatial heterogeneity which violate the i.i.d (Independent and Identically Distributed) assumption of traditional statistic and data mining methods. Therefore, using traditional methods may miss patterns or may yield spurious patterns, which are costly in societal applications. Further, there are additional challenges such as MAUP (Modiable Areal Unit Problem) as illustrated by a recent court case debating gerrymandering in elections. In this article, we discuss tools and computational methods of spatial data mining, focusing on the primary spatial pattern families: hotspot detection, collocation detection, spatial prediction, and spatial outlier detection. Hotspot detection methods use domain information to accurately model more active and high-density areas. Collocation detection methods find objects whose instances are in proximity to each other in a location. Spatial prediction approaches explicitly model the neighborhood relationship of locations to predict target variables from input features. Finally, spatial outlier detection methods find data that differ from their neighbors. Lastly, we describe future research and trends in spatial data mining.

AM-68 - Rule Learning for Spatial Data Mining

Recent research has identified rule learning as a promising technique for geographic pattern mining and knowledge discovery to make sense of the big spatial data avalanche (Koperski & Han, 1995; Shekhar et al., 2003). Rules conveying associative implications regarding locations, as well as semantic and spatial characteristics of analyzed spatial features, are especially of interest. This overview considers fundamentals and recent advancements in two approaches applied on spatial data: spatial association rule learning and co-location rule learning.

AM-97 - An Introduction to Spatial Data Mining

The goal of spatial data mining is to discover potentially useful, interesting, and non-trivial patterns from spatial data-sets (e.g., GPS trajectory of smartphones). Spatial data mining is societally important having applications in public health, public safety, climate science, etc. For example, in epidemiology, spatial data mining helps to nd areas with a high concentration of disease incidents to manage disease outbreaks. Computational methods are needed to discover spatial patterns since the volume and velocity of spatial data exceed the ability of human experts to analyze it. Spatial data has unique characteristics like spatial autocorrelation and spatial heterogeneity which violate the i.i.d (Independent and Identically Distributed) assumption of traditional statistic and data mining methods. Therefore, using traditional methods may miss patterns or may yield spurious patterns, which are costly in societal applications. Further, there are additional challenges such as MAUP (Modiable Areal Unit Problem) as illustrated by a recent court case debating gerrymandering in elections. In this article, we discuss tools and computational methods of spatial data mining, focusing on the primary spatial pattern families: hotspot detection, collocation detection, spatial prediction, and spatial outlier detection. Hotspot detection methods use domain information to accurately model more active and high-density areas. Collocation detection methods find objects whose instances are in proximity to each other in a location. Spatial prediction approaches explicitly model the neighborhood relationship of locations to predict target variables from input features. Finally, spatial outlier detection methods find data that differ from their neighbors. Lastly, we describe future research and trends in spatial data mining.

CV-36 - Geovisual Analytics

Geovisual analytics refers to the science of analytical reasoning with spatial information as facilitated by interactive visual interfaces. It is distinguished by its focus on novel approaches to analysis rather than novel approaches to visualization or computational methods alone. As a result, geovisual analytics is usually grounded in real-world problem solving contexts. Research in geovisual analytics may focus on the development of new computational approaches to identify or predict patterns, new visual interfaces to geographic data, or new insights into the cognitive and perceptual processes that users apply to solve complex analytical problems. Systems for geovisual analytics typically feature a high-degree of user-driven interactivity and multiple visual representation types for spatial data. Geovisual analytics tools have been developed for a variety of problem scenarios, such as crisis management and disease epidemiology. Looking ahead, the emergence of new spatial data sources and display formats is expected to spur an expanding set of research and application needs for the foreseeable future. 

CP-10 - Social Media Analytics

Social media streams have emerged as new sources to support various geospatial applications. However, traditional geospatial tools and systems lack the capacities to process such data streams, which are generated dynamically in extremely large volumes and with versatile contents. Therefore, innovative approaches and frameworks should be developed to detect an emerging event discussed over the social media, understand the extent, consequences of the event, as well as it time-evolving nature, and eventually discover useful patterns. In order to harness social media for geospatial applications, this entry introduces social media analytics technologies for harvesting, managing, mining, analyzing and visualizing the spatial, temporal, text, and network information of social media data.

AM-68 - Rule Learning for Spatial Data Mining

Recent research has identified rule learning as a promising technique for geographic pattern mining and knowledge discovery to make sense of the big spatial data avalanche (Koperski & Han, 1995; Shekhar et al., 2003). Rules conveying associative implications regarding locations, as well as semantic and spatial characteristics of analyzed spatial features, are especially of interest. This overview considers fundamentals and recent advancements in two approaches applied on spatial data: spatial association rule learning and co-location rule learning.

CV-36 - Geovisual Analytics

Geovisual analytics refers to the science of analytical reasoning with spatial information as facilitated by interactive visual interfaces. It is distinguished by its focus on novel approaches to analysis rather than novel approaches to visualization or computational methods alone. As a result, geovisual analytics is usually grounded in real-world problem solving contexts. Research in geovisual analytics may focus on the development of new computational approaches to identify or predict patterns, new visual interfaces to geographic data, or new insights into the cognitive and perceptual processes that users apply to solve complex analytical problems. Systems for geovisual analytics typically feature a high-degree of user-driven interactivity and multiple visual representation types for spatial data. Geovisual analytics tools have been developed for a variety of problem scenarios, such as crisis management and disease epidemiology. Looking ahead, the emergence of new spatial data sources and display formats is expected to spur an expanding set of research and application needs for the foreseeable future. 

CP-10 - Social Media Analytics

Social media streams have emerged as new sources to support various geospatial applications. However, traditional geospatial tools and systems lack the capacities to process such data streams, which are generated dynamically in extremely large volumes and with versatile contents. Therefore, innovative approaches and frameworks should be developed to detect an emerging event discussed over the social media, understand the extent, consequences of the event, as well as it time-evolving nature, and eventually discover useful patterns. In order to harness social media for geospatial applications, this entry introduces social media analytics technologies for harvesting, managing, mining, analyzing and visualizing the spatial, temporal, text, and network information of social media data.

AM-68 - Rule Learning for Spatial Data Mining

Recent research has identified rule learning as a promising technique for geographic pattern mining and knowledge discovery to make sense of the big spatial data avalanche (Koperski & Han, 1995; Shekhar et al., 2003). Rules conveying associative implications regarding locations, as well as semantic and spatial characteristics of analyzed spatial features, are especially of interest. This overview considers fundamentals and recent advancements in two approaches applied on spatial data: spatial association rule learning and co-location rule learning.

CV-36 - Geovisual Analytics

Geovisual analytics refers to the science of analytical reasoning with spatial information as facilitated by interactive visual interfaces. It is distinguished by its focus on novel approaches to analysis rather than novel approaches to visualization or computational methods alone. As a result, geovisual analytics is usually grounded in real-world problem solving contexts. Research in geovisual analytics may focus on the development of new computational approaches to identify or predict patterns, new visual interfaces to geographic data, or new insights into the cognitive and perceptual processes that users apply to solve complex analytical problems. Systems for geovisual analytics typically feature a high-degree of user-driven interactivity and multiple visual representation types for spatial data. Geovisual analytics tools have been developed for a variety of problem scenarios, such as crisis management and disease epidemiology. Looking ahead, the emergence of new spatial data sources and display formats is expected to spur an expanding set of research and application needs for the foreseeable future. 

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