What is the mining of spatial data?
spatial data mining is the process of attempting to find patterns in geographical data. They are most commonly used in retail, grew from data mining, which originally focused on searching for formulas in text and numerical electronic information. Spatial data mining is considered to be a more complicated challenge than traditional mining due to difficulties associated with analyzing objects with specific existence in space and time.
As with standard data mining, spatial mining is mainly used in the world of marketing and retail. It is a technique for deciding where to open, what kind of trade. It can help inform these decisions by processing already existing data about what factors motivate consumers to go to one place and not the other.
say that Ashley wants to open a night club in a certain city block. If she had access to the relevant data, she could use spatial data mining to see what spatial factors make night clubs successful. CouldWE Like: Will more people come to the club if there is public transport nearby? What distance from other places of night life maximizes patrons? Is the proximity of the pumping stations plus or minus?
Ashley might also want to ensure that people who come to her night club arrive in an even division during an individual night. It could also use spatial data mining - perhaps more precisely, spatial data mining - to find out how people move through the city at certain times. The same process can be used for sponsorship at different nights of the week.
The difficulty of spatial data mining is the result of the complexity of the world outside the Internet. While the previous data mining efforts usually had a database mature for analysis, inputs available for spatial data mining are not grids of information but maps. These maps have different types of LIKE roads, populations, businesses, etc.
determination whether something is "closerThe "something else, is from discrete to continuous variables. This massively increases the complexity needed for analysis. It is incredible that it is one of the simpler types of relationships that have available to try to make spatial data.
spatial data mining also faces the problem of false positives. In the process of searching for data looking for relationships, many apparent trends appear as a result of statistical false positives. This problem also exists for the role of mining a simpler database, but is amplified by the size of the data available to the upper miner. Finally, the trend should be identified by the mining data mining data by the process of explanation and further research.