Big geodata aggregation: Connotation, classification, and framework

Abstract

Big geodata can be regarded as the "footprints" generated by geographic objects, which are divided into two types: big earth observation data and big social behavior data. Big earth observation data refer to remote sensing data and all kinds of monitoring data from ground observation stations; big social behavior data are defined as the data generated by humans, such as mobile phone data, traffic card data, social media data, and trajectory data of floating cars. Big earth observation data contain the information of land surface, and big social behavior data record the social behavior of humans. The essence of big geodata mining is to reveal the man-land relationship via the retrieval of the "footprint" of geographic objects. Magnificent progresses in recent years demonstrate that most of them have been made through the aggregation of multiple types of big geodata. The significance of big geodata aggregation can be summarized into three aspects: to represent a new research paradigm, to produce a new research angle, and to improve the research quality by combining different types of information. In contrast to image fusion, which is regarded as the combination of imageries with the same data structure, big geodata aggregation is defined as the merging of different types of big geodata into a multidimensional dataset through transformation in terms of structure, content, and representation. In general, big geodata aggregation includes big earth observation data and big social behavior data. Given the differences in data source, data structure, connotation, and granularity, the aggregation procedure is various and complicated. Considering the characteristics and the classification of big geodata, we classify big geodata aggregation into four types: spatiotemporal aggregation, object-oriented aggregation, topic-oriented aggregation, and model-oriented aggregation. In spatiotemporal aggregation, data are merged via their common locations, such as the aggregation between POI and remote sensing data. In object-oriented aggregation, data are connected through the same object, such as the aggregation of indoor and outdoor trajectory data via the same phone carrier. In topic-oriented aggregation, data with the same topic are integrated, such as the aggregation of different types of information sourced from the same earthquake event. In model-oriented aggregation, data are assimilated via the same model, such as the aggregation of street view data and POI data when using a deep learning model for the recognition of building style. As an important part of big geodata mining, the procedure of big geodata aggregation can be divided into four steps: the determination of the aggregation core, information back tracking, retrieving, and converging. The key issues in big geodata aggregation include unifying spatiotemporal benchmarks, uniting data representation and storage, matching multisource big geodata from the same geo-object, centralizing the spatiotemporal scope of multisource big geodata, settling the changing velocities of different objects, solving data-sharing and privacy protection problems, and aggregating big and small geodata. With the development of methods and techniques of big geodata aggregation, some research areas, including the sensing of places, the identification of land functions, the detection of crowd activities, the determination of the relationship between urban functions and multisource flow, and the introduction of a land surface complex giant system, may be substantially expanded.