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Inferring region significance by using multi-source spatial data

  • Multi-Source Data Understanding (MSDU)
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Abstract

The ranking and recommendation of regions of interest are increasingly important in recent years. In this light, we propose and study a novel and interesting problem of inferring region significance using multi-source spatiotemporal data. In our study, POIs, locations, regions, trajectories, and spatial networks are taken into account. Given a set of regions R and a set of trajectories T, we seek for the top-k most attractive regions to users, i.e., regions with the top-k highest spatial-density correlations to the trajectories of travelers. This study is useful in many mobile applications such as urban computing, region recommendation, and location-based service in general. This problem is challenging due to two reasons: (1) how to model the spatial-density correlation effectively and practically and (2) how to process the problem in interactive time. To overcome the challenges, we design a novel spatial-density correlation function to evaluate the relationship between regions and trajectories, and the density of POIs and network distance are taken into account. Then, we develop a series of optimization techniques to accelerate the query efficiency. Furthermore, we develop a parallel mechanism to support big spatial data. Finally, we conduct extensive experiments on real and synthetic spatial data sets to show the efficiency and effectiveness of developed algorithms.

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Notes

  1. https://www.bikely.com/.

  2. https://www.gps-waypoints.net.

  3. https://www.sharemyroutes.com/.

  4. https://www.facebook.com/.

  5. https://www.twitter.com/.

  6. https://www.foursquare.com/.

Abbreviations

\(G\cdot V\) :

The set of vertices in graph G

\(G\cdot E\) :

The set of edges in graph G

C :

The set of regions

T :

The set of trajectories

c :

A region

\(c\cdot o\) :

The center of region c

\(\tau\) :

A trajectory

\(\lambda\) :

A significance parameter of spatial and density domains

\(\theta\) :

A threshold

\({\text{sd}}()\) :

Network shortest path distance

\(d(c,\tau )\) :

Network distance between region c and trajectory \(\tau\)

\(d(c,\tau )\cdot {\text{lb}}\) :

The lower bound network distance between region c and trajectory \(\tau\)

\(C_{{\text{sd}}}()\) :

Spatial-density correlation

\(C_{{\text{sd}}}()\cdot {\text{ub}}\) :

The upper bound of spatial-density correlation

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61672442), the Science and Technology Planning Project of Fujian Province (No. 2016Y0079), and the Science and Technology Planning Project of Xiamen/Quanzhou City (Nos. 3502Z20183055, 2017G030).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Xiamen University Technology, Xiamen, China

    Shunzhi Zhu, Dahan Wang, Lijuan Liu & Yan Wang

  2. CNIC, Chinese Academy of Sciences, Beijing, China

    Danhuai Guo

Authors
  1. Shunzhi Zhu
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  2. Dahan Wang
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  3. Lijuan Liu
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  4. Yan Wang
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  5. Danhuai Guo
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Correspondence to Dahan Wang.

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Zhu, S., Wang, D., Liu, L. et al. Inferring region significance by using multi-source spatial data. Neural Comput & Applic 32, 6523–6531 (2020). https://doi.org/10.1007/s00521-019-04070-7

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