Synonyms
Definition
Given a definition of change and a dataset about spatiotemporal (ST) phenomena, ST change footprint discovery is the process of identifying the location and/or time of such changes from the dataset. As shown in Fig. 1, there are four main components of a change footprint pattern discovery process: ST data from an application is the input of the problem. A definition of a change pattern is given based on the underlying application. Finally, a method (e.g., statistical, computational) that discovers the pattern from the data will produce the ST footprints as output.
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References
Aach T, Kaup A (1995) Bayesian algorithms for adaptive change detection in image sequences using markov random fields. Signal Process Image Commun 7(2):147–160
Aach T, Kaup A, Mester R (1993) Statistical model-based change detection in moving video. Signal Process 31(2):165–180
Aronov B, Driemel A, Kreveld MV, Löffler M, Staals F (2015) Segmentation of trajectories on nonmonotone criteria. ACM Trans Algorithms (TALG) 12(2):26
Barry D, Hartigan J (1993) A bayesian analysis for change point problems. J Am Stat Assoc 88:309–319
Basseville M, Nikiforov IV et al (1993) Detection of abrupt changes: theory and application, vol 104. Prentice Hall, Englewood Cliffs
Berman, M. L. and GIS, C. H. (2003). A data model for historical gis: The chgis time series. Cambridge, MA, Harvard Yenching Institute Technical Report.
Bruzzone L, Prieto DF (2002) An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Trans Image Proces 11(4):452–466
Chen J, Gupta AK (2001) On change point detection and estimation. Commun Stat Simul Comput 30(3):665–697
Chen G, Hay GJ, Carvalho LM, Wulder MA (2012) Object-based change detection. Int J Remote Sens 33(14):4434–4457
Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25(9):1565–1596
Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartograph Int J Geograph Inf Geovis 10(2):112–122
Fagan W, Fortin M, Soykan C (2003) Integrating edge detection and dynamic modeling in quantitative analyses of ecological boundaries. BioScience 53(8):730–738
Gabriel E, Allard D (2008) Evaluating the sampling pattern when detecting zones of abrupt change. Environ Ecol Stat 15(4):469–489
Ge Y, Xiong H, Zhou Z-h, Ozdemir H, Yu J, Lee KC (2010) Top-eye: Top-k evolving trajectory outlier detection. In: Proceedings of the 19th ACM international conference on information and knowledge management, Toronto. ACM, pp 1733–1736
Gustafsson F, Gustafsson F (2000) Adaptive filtering and change detection, vol 1. Wiley, Londres
Im J, Jensen J (2005) A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens Environ 99(3):326–340
Keogh E, Chu S, Hart D, Pazzani M (2001) An online algorithm for segmenting time series. In: Proceedings of IEEE international conference on data mining (ICDM 2001), San Jose. IEEE, pp 289–296
Kulldorff M (1997) A spatial scan statistic. Commun Stat Theory Methods 26(6):1481–1496 (Xc912 Times Cited:716 Cited References Count:22)
Lee J-G, Han J, Li X (2008) Trajectory outlier detection: a partition-and-detect framework. In: IEEE 24th International Conference on Data engineering (ICDE 2008), Toronto, Cancun, Mexico. IEEE, pp 140–149
Lu H, Carlin B (2005) Bayesian areal wombling for geographical boundary analysis. Geograph Anal 37(3):265–285
Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401
Lunetta RS, Elvidge CD et al (1999) Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis, London
Neill D, Moore A (2004) Rapid detection of significant spatial clusters. ACM, ACM.
Neill D, Moore A, Sabhnani M, Daniel K (2005) Detection of emerging space-time clusters. ACM, ACM.
Page ES (1954) Continuous inspection schemes. Biometrika 41(1/2):100–115
Radke R, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307
Remote Sensing and Geospatial Analysis Laboratory (2013) University of Minnesota. Twin cities metro area impervious surface data 1998 and 2002
University of Nebraska-Lincoln (2011) Historical GIS: the 1840-1845-1850-1861-1870 railroad system in America, State and National shapefiles. http://railroads.unl.edu/resources/
Womble W (1951) Differential systematics. Science 114(2961):315
Wong W, Neill D (2009) Tutorial on event detection. Presentation in ACM SIGKDD conference on knowledge discovery and data mining. http://www.pptsearch.net/download.php?fid=109746
Zhou X, Shekhar S, Mohan P, Liess S, Snyder PK (2011) Discovering interesting sub-paths in spatiotemporal datasets: a summary of results. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, Chicago, IL, USA. ACM, pp 44–53
Zhou X, Shekhar S, Ali RY (2014) Spatiotemporal change footprint pattern discovery: an inter-disciplinary survey. Wiley Int Rev Data Mining Knowl Discov 4(1):1–23
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Zhou, X., Shekhar, S., Ali, R.Y. (2017). Spatiotemporal Change Footprint Pattern Discovery. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1512
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