Skip to main content
SpringerLink
Log in

Inferring region significance by using multi-source spatial data

  • Multi-Source Data Understanding (MSDU)
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

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

References

  1. Bakalov P, Hadjieleftheriou M, Keogh EJ, Tsotras VJ (2005) Efficient trajectory joins using symbolic representations. In: MDM, pp 86–93

  2. Bakalov P, Tsotras VJ (2006) Continuous spatiotemporal trajectory joins. In: GSN, pp 109–128

  3. Brakatsoulas S, Pfoser D, Salas R, Wenk C (2005) On map-matching vehicle tracking data. In: VLDB, pp 853–864

  4. Chen Y, Patel JM (2009) Design and evaluation of trajectory join algorithms. In: ACM-GIS, pp 266–275

  5. Dijkstra EW (1959) A note on two problems in connection with graphs. Numerische Math 1:269–271

    MathSciNet  MATH  Google Scholar 

  6. Ding H, Trajcevski G, Scheuermann P (2008) Efficient similarity join of large sets of moving object trajectories. In: TIME, pp 79–87

  7. Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: SIGMOD, pp 47–57

  8. Lei C, Zhu X (2017) Unsupervised feature selection via local structure learning and sparse learning. Multimed Tools Appl 77(22):29605–29622

    Google Scholar 

  9. Li J, Wang Y, Guo YZD, Zhu S (2018) Aggregate location recommendation in dynamic transportation networks. World Wide Web 21(6):1637–1653

    Google Scholar 

  10. Luo W, Tan H, Chen L, Ni LM (2013) Finding time period-based most frequent path in big trajectory data. In: SIGMOD, pp 713–724

  11. Papadias D, Shen Q, Tao Y, Mouratidis K (2004) Group nearest neighbor queries. In: ICDE, pp 301–312

  12. Papadias D, Tao Y, Mouratidis K, Hui CK (2005) Aggregate nearest neighbor queries in spatial databases. TODS 30(2):529–576

    Google Scholar 

  13. Shang S, Chen L, Jensen CS, Wen J-R, Kalnis P (2017) Searching trajectories by regions of interest. IEEE Trans Knowl Data Eng 29(7):1549–1562

    Google Scholar 

  14. Shang S, Chen L, Wei Z, Jensen CS, Wen J, Kalnis P (2016) Collective travel planning in spatial networks. IEEE Trans Knowl Data Eng 28(5):1132–1146

    Google Scholar 

  15. Shang S, Chen L, Wei Z, Jensen CS, Zheng K, Kalnis P (2017) Trajectory similarity join in spatial networks. PVLDB 10(11):1178–1189

    Google Scholar 

  16. Shang S, Chen L, Wei Z, Jensen CS, Zheng K, Kalnis P (2018) Parallel trajectory similarity joins in spatial networks. VLDB J 27(3):395–420

    Google Scholar 

  17. Shang S, Deng K, Xie K (2010) Best point detour query in road networks. In: ACM GIS, pp 71–80

  18. Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P (2012) User oriented trajectory search for trip recommendation. In: EDBT, pp 156–167

  19. Shang S, Ding R, Zheng K, Jensen CS, Kalnis P, Zhou X (2014) Personalized trajectory matching in spatial networks. VLDB J 23(3):449–468

    Google Scholar 

  20. Shang S, Guo D, Liu J, Liu K (2014) Human mobility prediction and unobstructed route planning in public transport networks. In: MDM, pp 43–48

  21. Shang S, Lu H, Pedersen TB, Xie X (2013) Finding traffic-aware fastest paths in spatial networks. In: SSTD, pp 128–145

  22. Shang S, Lu H, Pedersen TB, Xie X (2013) Modeling of traffic-aware travel time in spatial networks. In: MDM, pp 247–250

  23. Shang S, Yuan B, Deng K, Xie K, Zhou X (2011) Finding the most accessible locations: reverse path nearest neighbor query in road networks. In: ACM GIS, pp 181–190

  24. Shang S, Zheng K, Jensen CS, Yang B, Kalnis P, Li G, Wen J (2015) Discovery of path nearby clusters in spatial networks. IEEE Trans Knowl Data Eng 27(6):1505–1518

    Google Scholar 

  25. Ta N, Li G, Feng J (2017) Signature-based trajectory similarity join. IEEE Trans Knowl Data Eng 29(4):870–883

    Google Scholar 

  26. Wang Y, Li J, Zhong Y, Zhu S, Guo D, Shang S (2018) Discovery of accessible locations using region-based geo-social data. World Wide Web. https://doi.org/10.1007/s11280-018-0538-5

    Google Scholar 

  27. Wenk C, Salas R, Pfoser D (2006) Addressing the need for map-matching speed: localizing global curve-matching algorithms. In: SSDBM, pp 379–388

  28. Yao B, Chen Z, Gao X, Shang S, Ma S, Guo M (2018) Flexible aggregate nearest neighbor queries in road networks. In: ICDE, pp 1–12

  29. Zheng W, Zhu X, Wen G, Zhu Y, Yu H, Gan J (2018) Unsupervised feature selection by self-paced learning regularization. Pattern Recognit Lett. https://doi.org/10.1016/j.patrec.2018.06.029

    Google Scholar 

  30. Zheng W, Zhu X, Zhu Y, Hu R, Lei C (2017) Dynamic graph learning for spectral feature selection. Multimed Tools Appl 77(22):29739–29755

    Google Scholar 

  31. Zhu S, Wang Y, Shang S, Zhao G, Wang J (2017) Probabilistic routing using multimodal data. Neurocomputing 253:49–55

    Google Scholar 

  32. Zhu X, Zhang S, Hu R, Zhu Y, Song J (2018) Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans Knowl Data Eng 30(3):517–529

    Google Scholar 

  33. Zhu X, Zhang S, Li Y, Zhang J, Yang L, Fang Y (2018) Low-rank sparse subspace for spectral clustering. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2018.2858782

    Google Scholar 

Download references

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).

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dahan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lijuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Danhuai Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dahan Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04070-7

Keywords

Search

Navigation