Skip to main content

Spatiotemporal Change Footprint Pattern Discovery

  • Reference work entry
  • First Online:
Encyclopedia of GIS

Synonyms

Change detection

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.

Spatiotemporal Change Footprint Pattern Discovery, Fig. 1
figure 20110 figure 20110

The change footprint pattern discovery process

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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

    Article  Google Scholar 

  • Aach T, Kaup A, Mester R (1993) Statistical model-based change detection in moving video. Signal Process 31(2):165–180

    Article  MATH  Google Scholar 

  • Aronov B, Driemel A, Kreveld MV, Löffler M, Staals F (2015) Segmentation of trajectories on nonmonotone criteria. ACM Trans Algorithms (TALG) 12(2):26

    MathSciNet  Google Scholar 

  • Barry D, Hartigan J (1993) A bayesian analysis for change point problems. J Am Stat Assoc 88:309–319

    MathSciNet  MATH  Google Scholar 

  • Basseville M, Nikiforov IV et al (1993) Detection of abrupt changes: theory and application, vol 104. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • 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.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Chen J, Gupta AK (2001) On change point detection and estimation. Commun Stat Simul Comput 30(3):665–697

    Article  MathSciNet  MATH  Google Scholar 

  • Chen G, Hay GJ, Carvalho LM, Wulder MA (2012) Object-based change detection. Int J Remote Sens 33(14):4434–4457

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Fagan W, Fortin M, Soykan C (2003) Integrating edge detection and dynamic modeling in quantitative analyses of ecological boundaries. BioScience 53(8):730–738

    Article  Google Scholar 

  • Gabriel E, Allard D (2008) Evaluating the sampling pattern when detecting zones of abrupt change. Environ Ecol Stat 15(4):469–489

    Article  MathSciNet  Google Scholar 

  • 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

    Google Scholar 

  • Gustafsson F, Gustafsson F (2000) Adaptive filtering and change detection, vol 1. Wiley, Londres

    MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Kulldorff M (1997) A spatial scan statistic. Commun Stat Theory Methods 26(6):1481–1496 (Xc912 Times Cited:716 Cited References Count:22)

    Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Lu H, Carlin B (2005) Bayesian areal wombling for geographical boundary analysis. Geograph Anal 37(3):265–285

    Article  Google Scholar 

  • Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401

    Article  Google Scholar 

  • Lunetta RS, Elvidge CD et al (1999) Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis, London

    Google Scholar 

  • Neill D, Moore A (2004) Rapid detection of significant spatial clusters. ACM, ACM.

    Book  Google Scholar 

  • Neill D, Moore A, Sabhnani M, Daniel K (2005) Detection of emerging space-time clusters. ACM, ACM.

    Book  Google Scholar 

  • Page ES (1954) Continuous inspection schemes. Biometrika 41(1/2):100–115

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Remote Sensing and Geospatial Analysis Laboratory (2013) University of Minnesota. Twin cities metro area impervious surface data 1998 and 2002

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xun Zhou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this entry

Cite this entry

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

Download citation

Publish with us

Policies and ethics