Big data brings new trend for both IT as well as GIS industry, the concept of spatial big data also emerged, however, this is a lack in the unified understanding of spatial big data. In some articles, massive spatial data are called spatial big data, which leads confusion and misunderstanding.
What is spatial big data? Spatial big data is embedded with location, with the features of big volume, quick transformation, large variety, low value density, etc., which cannot be processed by routine software tools, big data technology can transform the data into digital assets with more powerful decision, insight and optimized process.
Maybe this definition is still very hard to comprehend, so which data are spatial big data? The big data listed below are all spatial big data.
Big data has low value density, therefore, labeling existing classic massive spatial data as big data will not create more value. For classic massive spatial data, even using big data technologies based on Spark, Haddop, MongoDB will not make them big data.
In addition, from DIKW model, big data is the first layer data of the model, which is the primitive material and classic mapping 4D product is the second layer information, which is the logical data after processing. From this meaning, massive mapping 4D products being labeled as big data is downgrading.
In order to let the readers know the extension of spatial big data, some examples are given in the article, not all of them. With the development of spatial big data, we will find more spatial big data. Correctly understanding the intension and extension of spatial big data will advance the development and application of spatial big data.
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