Skip to main content
SpringerLink
Log in

Detecting Attribute-Based Homogeneous Patches Using Spatial Clustering: A Comparison Test

  • Conference paper
  • First Online:
Information Fusion and Intelligent Geographic Information Systems (IF&IGIS'17)

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

In spatial and urban sciences, it is customary to partition study areas into sub-areas—so-called regions, zones, neighborhoods, or communities—to carry out analyses of the spatial patterns and processes of socio-spatial phenomena. The purposeful delineation of these sub-areas is critical because of the potential biases associated with MAUP and UGCoP. If one agrees to characterize these sub-areas based on their homogeneity on a certain attribute, these homogeneous areas (patches) can be detected by data-driven algorithms. This study aims to evaluate the effectiveness and performance of five popular spatial clustering and regionalization algorithms (AZP, ARISeL, max-p-regions, AMOEBA, and SOM) for detecting attribute-based homogeneous patches of different sizes, shapes, and those with homogeneous values. The evaluation follows a quasi-experimental approach: It is based on 68 simulated data sets where the true distribution of patches is known and focuses purely on the capability of algorithms to successfully detect patches rather than computational costs. It is the most comprehensive assessment to-date thanks to a systematic control of various conditions so that a true baseline is available for comparison purposes. Among the tested algorithms, SOM and AMOEBA were found to perform very well in detecting patches of different sizes, different shapes, including those with holes, and different homogeneous values.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Openshaw S (1983) The modifiable areal unit problem. Geo Books, Norwich

    Google Scholar 

  2. Kwan M-P (2012) The uncertain geographic context problem. Ann Assoc Am Geogr 102:958–968

    Article  Google Scholar 

  3. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, University of California Press, pp 281–297

    Google Scholar 

  4. Kaufman L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley, Hoboken

    Google Scholar 

  5. Yu D, Chatterjee S, Sheikholeslami G, Zhang A (1998) Efficiently detecting arbitrary-shaped clusters in very large datasets with high dimensions. Technical Report 98-8, State University of New York at Buffalo, Department of Computer Science and Engineering

    Google Scholar 

  6. Openshaw S (1977) Algorithm 3: a procedure to generate pseudo random aggregations of N zones into M zones where M is less than N. Environ Plan A 9:1423–1428

    Article  Google Scholar 

  7. Duque JC, Church RL (2004) A new heuristic model for designing analytical regions. In: North American meeting of the regional science association international, Seattle, WA, November

    Google Scholar 

  8. Duque JC, Church RL, Middleton RS (2011) The p-regions problem. Geogr Anal 43(1):104–126

    Article  Google Scholar 

  9. Guo D, Peuquet D, Gahegan M (2003) ICEAGE: interactive clustering and exploration of large and high-dimensional geodata. Geoinformatica 7:229–253

    Article  Google Scholar 

  10. Guo D (2008) Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). Int J Geogr Inf Sci 22:801–823

    Article  Google Scholar 

  11. Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115

    Article  Google Scholar 

  12. Getis A, Ord J (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24(3):189–206

    Article  Google Scholar 

  13. Getis A, Aldstadt J (2004) Constructing the spatial weights matrix using a local statistic. Geogr Anal 36:90–104

    Article  Google Scholar 

  14. Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, Berlin

    Book  Google Scholar 

  15. Bação F, Lobo V, Painho M (2004) Geo-self-organizing map (Geo-SOM) for building and exploring homogeneous regions. In: Egenhofer M, Miller H, Freksa C (eds) Geographic information science. Lecture Notes in Computer Science. Springer, Berlin, pp 22–37

    Chapter  Google Scholar 

  16. Hagenauer J, Helbich M (2013) Contextual neural gas for spatial clustering and analysis. Int J Geogr Inf Sci 27:251–266

    Article  Google Scholar 

  17. Han J, Kamber M, Tung AKH (2001) Spatial clustering methods in data mining: a survey. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery. Taylor & Francis, London, pp 188–217

    Chapter  Google Scholar 

  18. Duque J, Ramos R, Surinach J (2007) Supervised regionalization methods: a survey. Int Reg Sci Rev 30:195–220

    Article  Google Scholar 

  19. Grubesic TH, Wei R, Murray AT (2014) Spatial clustering overview and comparison: accuracy, sensitivity, and computational expense. Ann Assoc Am Geogr 104(6):1134–1156

    Article  Google Scholar 

  20. Rogerson P, Yamada I (2009) Statistical detection and surveillance of geographic clusters. Taylor & Francis Group, London and New York

    Google Scholar 

  21. Craglia M, Haining R, Wiles P (2000) A comparative evaluation of approaches to urban crime pattern analysis. Urban Stud 37(4):711–729

    Article  Google Scholar 

  22. Murray AT, Grubesic TH (2013) Exploring spatial patterns of crime using non-hierarchical cluster analysis. In: Leitner M (ed) Crime modeling and mapping using geospatial technologies. Springer, Berlin, pp 105–124

    Chapter  Google Scholar 

  23. Guo D, Wang H (2011) Automatic region building for spatial analysis. Trans GIS 15:29–45

    Article  Google Scholar 

  24. Openshaw S, Rao L (1995) Algorithms for reengineering 1991 census geography. Environ Plan A 27:425–446

    Article  Google Scholar 

  25. Duque JC, Dev B, Betancourt A, Franco JL (2011) ClusterPy: library of spatially constrained clustering algorithms, Version 0.9.9. RiSE-group (Research in Spatial Economics). EAFIT University. http://www.rise-group.org

  26. Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp 1027–1035

    Google Scholar 

  27. Glover F (1977) Heuristic for integer programming using surrogate constraints. Decis Sci 8:156–166

    Article  Google Scholar 

  28. Duque JC, Anselin L, Rey S (2010) The max-p region problem. Working paper, GeoDa Center for Geospatial Analysis and Computation, Arizona State University, Tempe, AZ

    Google Scholar 

  29. Aldstadt J, Getis A (2006) Using AMOEBA to create a spatial weights matrix and identify spatial clusters. Geogr Anal 38:327–343

    Article  Google Scholar 

  30. Ord J, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and application. Geogr Anal 27(4):286–306

    Article  Google Scholar 

  31. Duque JC, Aldstadt J, Velasquez E, Franco JL, Betancourt A (2011) A computationally efficient method for delineating irregularly shaped spatial clusters. J Geogr Syst 13:355–372

    Article  Google Scholar 

  32. Dao THD, Thill J-C (2016) The SpatialARMED framework: handling complex spatial components in spatial association rule mining. Geogr Anal 48:248–274

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography and Environmental Studies, University of Colorado-Colorado Springs, 1420 Austin Bluffs Pkwy, Colorado Springs, CO, 81918, USA

    Thi Hong Diep Dao

  2. Department of Geography and Earth Sciences, University of North Carolina-Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA

    Jean-Claude Thill

Authors
  1. Thi Hong Diep Dao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean-Claude Thill
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thi Hong Diep Dao .

Editor information

Editors and Affiliations

  1. SPIIRAS Hi Tech Research and Development Office Ltd., St. Petersburg, c.St-Peterburg, Russia

    Vasily Popovich

  2. Department for Urbanism, Transport, Environment & Information Society, Central European Institute of Technology - CEIT ALANOVA gemeinnĂĽtzige GmbH , Schwechat, Austria

    Manfred Schrenk

  3. Department of Geography and Earth Sciences, The University of North Carolina, Charlotte, North Carolina, USA

    Jean-Claude Thill

  4. Naval Academy Research Institute , Brest Naval, Finistère, France

    Christophe Claramunt

  5. Shanghai Maritime University, Shanghai, Shanghai, China

    Tianzhen Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Dao, T.H.D., Thill, JC. (2018). Detecting Attribute-Based Homogeneous Patches Using Spatial Clustering: A Comparison Test. In: Popovich, V., Schrenk, M., Thill, JC., Claramunt, C., Wang, T. (eds) Information Fusion and Intelligent Geographic Information Systems (IF&IGIS'17). Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-59539-9_4

Download citation

Publish with us

Policies and ethics

Search

Navigation