Abstract
As the volume, variety, and veracity of spatio-temporal datasets increase, traditional statistical methods for dealing with such data are becoming overwhelmed. Nevertheless, spatio-temporal data are rich sources of information and knowledge, waiting to be discovered. The field of spatio-temporal data mining emerged out of a need to create effective and efficient techniques in order to turn big spatio-temporal data into meaningful information and knowledge. This chapter reviews the state of the art in spatio-temporal data mining research and applications, from conventional statistical methods to machine learning approaches in the big data era, with emphasis placed on three key areas: prediction, clustering/classification, and visualization.
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References
Abu Awad Y, Koutrakis P, Coull BA, Schwartz J (2017) A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States. Environ Res 159:427–434
Andrienko G, Andrienko N, Jankowski P, Keim D, Kraak M-J, MacEachren A, Wrobel S (2007) Geovisual analytics for spatial decision support: setting the research agenda. Int J Geogr Inf Sci 21(8):839–857
Anselin L (1988) Spatial econometrics: methods and models. Springer, Dordrecht
Atluri G, Karpatne A, Kumar V (2018) Spatio-temporal data mining: a survey of problems and methods. ACM Comput Surv 51(4):83:1–83:41
Birant D, Kut A (2007) ST-DBSCAN: an algorithm for clustering spatial–temporal data. Data Knowl Eng 60(1):208–221
Bishop C (2006) Pattern recognition and machine learning. Springer, New York
Cheng T, Adepeju M (2014) Modifiable temporal unit problem (MTUP) and its effect on space-time cluster detection. PLoS ONE 9(6):e100465
Cheng T, Wang J, Li X (2011) A hybrid framework for space–time modeling of environmental data. 环境数据时空建模的混合框架. Geogr Anal 43(2):188–210
Cheng T, Haworth J, Wang J (2012) Spatio-temporal autocorrelation of road network data. J Geogr Syst 14(4):389–413
Cheng T, Tanaksaranond G, Brunsdon C, Haworth J (2013) Exploratory visualisation of congestion evolutions on urban transport networks. Transp Res C Emerg Technol 36:296–306
Cheng T, Wang J, Haworth J, Heydecker B, Chow A (2014) A dynamic spatial weight matrix and localized space–time autoregressive integrated moving average for network modeling. Geogr Anal 46(1):75–97
Elhorst JP (2010) Spatial panel data models. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Software, tools, methods and applications. Springer, Berlin/Heidelberg, pp 172–192
Fischer MM (2015) Neural networks. A class of flexible non-linear models for regression and classification. In: Karlsson C, Andersson M, Norman T (eds) Handbook of research methods and applications in economic geography. Elgar, Cheltenham, pp 172–192
Fotheringham AS, Crespo R, Yao J (2015) Geographical and temporal weighted regression (GTWR). Geogr Anal 47(4):431–452
González JA, Rodríguez-Cortés FJ, Cronie O, Mateu J (2016) Spatio-temporal point process statistics: a review. Spat Stat 18(Part B):505–544
Hägerstrand T (1970) What about people in regional science? Pap Reg Sci 24(1):7–24
Haworth J, Shawe-Taylor J, Cheng T, Wang J (2014) Local online kernel ridge regression for forecasting of urban travel times. Transp Res Part C Emerg Technol 46:151–178
Heuvelink GBM, Pebesma E, Gräler B (2015) Space-time geostatistics. In: Shekhar S, Xiong H, Zhou X (eds) Encyclopedia of GIS. Springer, Cham, pp 1–7
Huang B, Wu BM, Barry M (2010) Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int J Geogr Inf Sci 24(3):383–401
Kanevski M, Timonin V, Pozdnukhov A (2009) Machine learning for spatial environmental data: theory, applications, and software, Har/Cdr. EFPL Press, Lausanne
LeSage JP, Pace RK (2011) Pitfalls in higher order model extensions of basic spatial regression methodology. Rev Reg Stud 41(1):13–26
Li S, Dragicevic S, Castro FA, Sester M, Winter S, Coltekin A, Pettit C, Jiang B, Haworth J, Stein A, Cheng T (2016) Geospatial big data handling theory and methods: a review and research challenges. ISPRS J Photogramm Remote Sens 115:119–133
MacEachren AM, Gahegan M, Pike W, Brewer I, Cai G, Lengerich E, Hardisty F (2004) Geovisualization for knowledge construction and decision support. IEEE Comput Graph Appl 24(1):13–17
Miller HJ (2005) A measurement theory for time geography. Geogr Anal 37(1):17–45
Miller HJ, Han J (2009) Geographic data mining and knowledge discovery, second edition. CRC Press, Boca Raton
Monmonier M (1990) Strategies for the visualization of geographic time-series data. Cartographica 27(1):30–45
Neill DB (2009) Expectation-based scan statistics for monitoring spatial time series data. Int J Forecast 25(2009):498–517
Pfeifer PE, Deutsch SJ (1980) A three-stage iterative procedure for space-time modelling. Technometrics 22(1):35–47
Pfeifer PE, Deutsch SJ (1981) Variance of the sample space-time autocorrelation function. J R Stat Soc Ser B Methodol 43(1):28–33
Shekhar S, Jiang Z, Ali RY et al (2015) Spatiotemporal data mining: a computational perspective. ISPRS Int J Geo-Inf 4(4):2306–2338
Thomas JJ, Cook KA (2005) Illuminating the path: the research and development agenda for visual analytics. National Visualization and Analytics Center, Lausanne. https://ils.unc.edu/courses/2017_fall/inls641_001/books/RD_Agenda_VisualAnalytics.pdf
Wang M, Wang A, Li A (2006) Mining spatial-temporal clusters from geo-databases. In: Li X, Zaïane OR, Li Z (eds) Advanced data mining and applications. Springer, Berlin/Heidelberg, pp 263–270
Wood J, Dykes J (2008) Spatially ordered treemaps. IEEE Trans Vis Comput Graph 14(6):1348–1355
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Cheng, T., Haworth, J., Anbaroglu, B., Tanaksaranond, G., Wang, J. (2021). Spatio-temporal Data Mining. In: Fischer, M.M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60723-7_68
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DOI: https://doi.org/10.1007/978-3-662-60723-7_68
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