Characterizing mixed-use buildings based on multi-source big data

ABSTRACT

To-date few research has successfully integrated big data from multiple sources to characterize urban mixed-use buildings. In this paper, we introduce a probabilistic model to integrate multi-source and geospatial big data (social network data, taxi trajectories, Points of Interest and remote sensing images) to characterize urban mixed-use buildings. The usefulness of our model is demonstrated with a case study of the Tianhe District in megacity Guangzhou, China. The model predicted building functions at 85% accuracy based on ground truth data from field surveys. We further explored the spatial patterns of the identified building functions. Most mixed-use buildings are located along major streets. Our proposed model can identify mixed-use buildings in a city; information is useful for planning evaluation and urban policymaking.

KEYWORDS:

• Mixed-use buildings
• geospatial big data
• Bayes’ theorem

Acknowledgment

The authors would like to thank Prof. Yuan May and Bo Huang, and reviewers who gave us so many useful comments and suggestions for the revision.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the National Key R&D Program of China [grant No. 2017YFA0604404] and the National Natural Science Foundation of China [grant No. 41671398].