Abstract
This research focuses on examining point pattern distributions over a network, therefore abandoning the usual hypotheses of homogeneity and isotropy of space and considering network spaces as frameworks for the distribution of point patterns. Many human related point phenomena are distributed over a space that is usually not homogenous and that depend on a network-led configuration. Kernel Density Estimation (KDE) and K-functions are commonly used and allow analysis of first and second order properties of point phenomena. Here an extension of KDE, called Network Density Estimation (NDE) is proposed. The idea is to consider the kernel as a density function based on network distances rather than Euclidean ones. That should allow identification of ‘linear’ clusters along networks and the identification of a more precise surface pattern of network related phenomena.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Borchert, J.R.: The twin cities urbanized areas: past present and future. Geographical Review 51, 47–70 (1961)
Borruso, G.: Network Density and the Delimitation of Urban Areas. Transactions in GIS 7(2), 177–191 (2003)
Okabe, A., Yamada, I.: The K-function method on a network and its computational implementation. Geographical Analysis 33(3), 271–290 (2001)
Gatrell, A., Bailey, T., Diggle, P., Rowlingson, B.: Spatial Point Pattern Analysis and its Application in Geographical Epidemiology. Transactions of the Institute of British Geographers 21, 256–274 (1996)
Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Longman Scientific & Technical, Essex (1995)
Holm, T.: Using GIS in Mobility and Accessibility Analysis. In: ESRI Users Conference, San Diego (1997), http://www.esri.com/library/userconf/proc97/proc97/to450/pap440/p440.htm
Matti, W.: Gridsquare Network as a reference system for the analysis of small area data. Acta Geographica Lovaniensia 10, 147–163 (1972)
Chainey, S., Reid, S., Stuart, N.: When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime. In: Kidner, D., Higgs, G., White, S. (eds.) Socio-Economic Applications of Geographic Information Science, Innovations in GIS 9, pp. 21–36. Taylor and Francis, Abington (2002)
Ned, L.: CrimeStat II: A Spatial Statistics Program for the Analysis of Crime Incident Locations (version 2.0). Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC (May 2002)
Ratcliffe, J.H., McCullagh, M.J.: Hotbeds of crime and the search for spatial accuracy. Journal of Geographical Systems 1(4), 385–398 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Borruso, G. (2005). Network Density Estimation: Analysis of Point Patterns over a Network. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424857_14
Download citation
DOI: https://doi.org/10.1007/11424857_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25862-9
Online ISBN: 978-3-540-32045-6
eBook Packages: Computer ScienceComputer Science (R0)