Spatial data mining finds patterns in geographic data, commonly used in retail to make decisions about store locations. It is more challenging due to analyzing objects in space and time. It can also address the problem of false positives, but trends should be confirmed through further research.
Spatial data mining is the process of trying to find patterns in geographic data. Most commonly used in retail, it grew out of the field of data mining, which initially focused on finding patterns in textual and numerical electronic information. Spatial data mining is considered to be more of a challenge than traditional mining due to the difficulties associated with analyzing objects with concrete existences in space and time.
As with standard data mining, spatial data mining is primarily used in the marketing and retail world. It’s a technique for making decisions about where to open what kind of store. It can help inform these decisions by building on pre-existing data on what factors motivate consumers to go one place and not another.
Let’s say Ashley wants to open a nightclub on a certain block. If she had access to the appropriate data, she could use spatial data mining to find out which spatial factors determine the success of nightclubs. She could ask questions like: Will more people come to the club if public transport is nearby? What distance from other nightclubs maximizes patronage? Is proximity to petrol stations an advantage or a disadvantage?
Ashley may also want to make sure that people who come to her nightclub arrive in an even distribution over the course of a single night. She could also use spatial data mining, perhaps more accurately, spacetime data mining, to find out how people move through the city at certain times. The same procedure could apply to advocacy on different nights of the week.
The difficulties of this method are a result of the complexity of the world beyond the Internet. While past data mining efforts usually had mature databases for analysis, the inputs available to spatial data mining are not grids of information but maps. These maps have different types of objects such as roads, populations, businesses, and so on.
Determining whether something is “close to” something else goes from being a discrete variable to a continuous variable. This greatly increases the complexity required for the analysis. Incredibly, this is one of the simplest types of relations available to someone attempting to mine spatial data.
Spatial data mining also addresses the problem of false positives. In the process of digging through data looking for relationships, many apparent trends will emerge as a result of statistical false positives. This problem also exists for the simpler database mining task, but is magnified by the size of data available to the dataminer. Ultimately, a trend identified by data mining should be confirmed through the process of explanation and further research.
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