Abstract
Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.
Highlights
Many data are collected from peoples’ daily life, including daily travel, weather, and industries, which contain lots of information [1,2,3]
We propose a general framework for multi-source spatio-temporal data analysis aware knowledge graph embedding
In order to realistically analyze multi-source spatio-temporal data from the network structure and semantic, we divided the experiment into two parts
Summary
Many data are collected from peoples’ daily life, including daily travel, weather, and industries, which contain lots of information [1,2,3]. It is an important task to understand the potential laws behind multi-source spatio-temporal data. The target of data analysis is to examine potential laws behind active data and the many external-influence data of city residents, including predicting the possibility for future development [4] and the state of aggregation of the region [5], explaining their practical significance [6] and abnormal road surface recognition [7]. Urban multi-source spatio-temporal data analysis can understand the practical significance of data existence from the perspective of the human–land relationship, and provide a connective point for the construction of new smart cities and the integration of big data development strategies. The development of a smart city is inseparable from the support of resident activity data
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