Abstract
Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting co-occurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus, it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance.
Similar content being viewed by others
References
Agarwal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceeding of the 20th International Conference on Very Large Data Bases (VLDB). pp 487–499
Akbari M, Samadzadegan F (2014) New regional co-location pattern mining method using fuzzy definition of neighborhood. Adv in Comput Sci: An Int J (ACSIJ) 3(3):32–37
Azizi MH (2011) Impact of traffic-related air pollution on public health: a real challenge. Arch Iran Med 14(2):139–143
Beelen R, Hoek G, Vienneau D, Eeftens M, Dimakopoulou K, Pedeli X, de Hoogh K et al (2013) Development of NO 2 and NO x land use regression models for estimating air pollution exposure in 36 study areas in Europe–the ESCAPE project. Atmos Env 72:10–23. doi:10.1016/j.atmosenv.2013.02.037
Celik M (2011) Discovering partial spatio-temporal co-occurrence patterns. In: Proceeding of the 1st international conference on spatial data mining and geographical knowledge services, Fuzhou, China, 116–120. doi:10.1109/ICSDM.2011.5969016
Celik M, Kang JM, Shekhar S (2007) Zonal co-location pattern discovery with dynamic parameters. In: Proceeding of the seventh IEEE international conference on data mining, Omaha, NE, 433–438. doi:10.1109/ICDM.2007.102
Celik M, Shekhar S, Rogers JP, Shine JA (2008) Mixed-drove spatiotemporal co-occurrence pattern mining. IEEE Trans Knowl Data Eng 20(10):1322–1335. doi:10.1109/TKDE.2008.97
Celik M, Azginoglu N, Terzi R (2012) Mining periodic spatio-temporal co-occurrence patterns: a summary of results. In: Proceeding of the international symposium on innovations in intelligent systems and applications (INISTA), Trabzon, Turkey, 411–415. doi:10.1109/INISTA.2012.6247044
Champendal A, Kanevski M, Huguenot PE (2014) Air pollution mapping using nonlinear land use regression models. In: Murgante B et al (eds) Computational science and its applications–ICCSA 2014. Part III, LNCS 8581, Springer, Switzerland, pp 682–690. doi:10.1007/978-3-319-09150-1_50
Desmier E, Flouvat F, Gay D, Selmaoui-Folcher N (2011) A clustering-based visualization of colocation patterns. In: Proceedings of the 15th Symposium on international database engineering & applications. 70–78. ACM. doi:10.1145/2076623.2076633
Ding W, Jiamthapthaksinl R, Parmar R, Jiang D, Stepinski TF, Eick CF (2008) Towards region discovery in spatial datasets. In: Proceeding of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD). Osaka, Japan 88–99. doi:10.1007/978-3-540-68125-0_10
Goodchild MF (2003) The fundamental laws of GIScience. invited talk at university consortium for geographic information science. University of California, Santa Barbara
Gudmundsson J, Kreveld MV (2006) Computing longest duration flocks in trajectory data. In: Proceeding of the ACM international symposium on geographic information systems. Virginia, USA. 35–42. doi:10.1145/1183471.1183479
Huang Y, Shekhar S, Xiong H (2004) Discovering co-location patterns from spatial datasets: a general approach. IEEE Trans Knowl Data Eng 16(12):1472–1485
Huang Y, Zhang L, Zhang P (2008) A framework for mining sequential patterns from spatio-temporal event datasets. IEEE Trans Knowl Data Eng 20(4):433–448. doi:10.1109/TKDE.2007.190712
Icking C, Klein R, Kollner P, Ma L (2003) Java applets for the dynamic visualization of voronoi diagrams. comput. sci. in perspect. Lect Notes Comput Sci 2598:191–205. doi:10.1007/3-540-36477-3_14
Kanaroglou PS, Adams MD, De Luca PF, Corr D, Sohel N (2013) Estimation of sulfur dioxide air pollution concentrations with a spatial autoregressive model. Atmos Env 79:421–427. doi:10.1016/j.atmosenv.2013.07.014
Kavousi A, Sefidkar R, Alavimajd H, Rashidi Y, Khonbi ZA (2013) Spatial analysis of CO and PM10 pollutants in Tehran city. J Paramed Sci (JPS) 4(3):41–50 (ISSN: 2008-4978)
Manikandan G, Srinivasan S (2012a) Mining of spatial co-location pattern implementation by FP growth. Indian J Comput Sci Eng (IJCSE) 3(2):344–348 (ISSN: 0976-5166)
Manikandan G, Srinivasan S (2012b) Mining spatially co-located objects from vehicle moving data. Eur J Sci Res 68(3):352–366 (ISSN: 1450-216X)
Miller HJ, Han J (2009) Geographic data mining and knowledge discovery, 2nd edn. CRC Press, published, London, p 486
Mohan P, Shekhar S, Shine JA, Rogers JP (2010) Cascading spatiotemporal pattern discovery: a summary of results. In: Proceeding of the SIAM international conference on data mining (SDM): pp 327–338
Priya G, Jaisankar N, Venkatesan M (2011) Mining co-location patterns from spatial data using rulebased approach. Int J Glob Res Comput Sci 2(7):58–61
Qian F, He Q, He J (2009a) Mining spread patterns of spatio-temporal co-occurrences over zones. In: Proceedings of the international conference on computational science and applications. 686–701. doi:10.1007/978-3-642-02457-3_57
Qian F, Yin L, He Q, He J (2009b) Mining spatio-temporal co-location patterns with weighted sliding window. In: Proceedings of the IEEE international conference on intelligent computing and intelligent systems ICIS 2009.181–185. doi:10.1109/ICICISYS.2009.5358192
Rahimi Ghoroghi N (2012) Evaluation of geographical factors on Tehran air pollution and its relation with temperature inversion. In: Proceedings of the first conference of air and noise pollution management. Tehran, Iran. http://www.civilica.com/Paper-CANPM01-CANPM01_039.html. (In Persian)
Saadatabadi AR, Mohammadian L, Vazifeh A (2012) Controls on air pollution over a semi-enclosed basin, Tehran: a synoptic climatological approach Iran. J Sci & Technol (IJST) 4:501–510
Safavi SY, Alijani B (2006) Evaluation of geographical parameters in Tehran air pollution. Geogr Res J 58:99–112 (In Persian)
Shad R, Mesgari MS, Shad A (2009) Predicting air pollution using fuzzy genetic linear membership kriging in GIS. Comput, Environ Urban Syst 33(6):472–481
Shekhar S, Huang Y, Xiong H (2001) Discovering spatial co-location patterns: a summary of results. In: Proceeding of the 7th international symposium on spatial and temporal databases (SSTD). Redondo Beach, CA, USA. doi:10.1007/3-540-47724-1_13
Wan Y, Zhou J (2008) KNFCOM-T: a k-nearest features-based co-location pattern mining algorithm for large spatial data sets by using T-trees. Int J Bus Intell Data Min 3(4):375–389. doi:10.150/IJBIDM.2008.022735
Xiao X, Xie X, Luo Q, Ma W (2008) Density based co-location pattern discovery. In: Proceeding of the ACM SIGSPATIAL international conference on advances in geographic information systems (ACM-GIS). Irvine, CA, USA. doi:10.1145/1463434.1463471
Xiong H, Shekhar S, Huang Y, Kumar V, Ma X, Yoo JS (2004) A framework for discovering co-location patterns in data sets with extended spatial objects. In: Proceeding of the 2004 SIAM international conference on data mining (SDM’04). Lake Buena Vista, FL.78–89
Yoo JS, Bow M (2011) Mining top-k closed co-location patterns. In: Proceeding of the IEEE international conference on spatial data mining and geographical knowledge services (ICSDM). Fuzhou 100–105. doi:10.1109/ICSDM.2011.5969013
Yoo JS, Shekhar S (2005) A partial join approach for mining co-location patterns. In: Proceeding of the ACM SIGSPATIAL international conference on advances in geographic information systems (ACM-GIS). 241–249. doi:10.1145/1032222.1032258
Yoo JS, Shekhar S (2006) A join-less approach for mining spatial co-location patterns. IEEE Trans Knowl Data Eng 18(10):1323–1337. doi:10.1109/ICDM.2005.8
Acknowledgments
We are grateful of the Iran Meteorological Organization, the Tehran Air Quality Control Center, the Tehran Traffic Control Center and the National Cartographic Center of Iran for providing our case study data.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Akbari, M., Samadzadegan, F. & Weibel, R. A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution. J Geogr Syst 17, 249–274 (2015). https://doi.org/10.1007/s10109-015-0216-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10109-015-0216-4