Detecting Clusters in Spatially Repetitive Point Event Data Sets
The analysis of point event patterns has a long tradition. Of particular interest are patterns of clustering or ‘hot spots’ and such cluster detection lies at the heart of spatial data mining. Certain classes of point event patterns have a significant proportion of the data having a tendency towards...
Main Author: | |
---|---|
Format: | Article |
Language: | deu |
Published: |
Unité Mixte de Recherche 8504 Géographie-cités
2007-07-01
|
Series: | Cybergeo |
Subjects: | |
Online Access: | http://journals.openedition.org/cybergeo/8462 |
id |
doaj-18c4b91600ad4f64895c0375bc71f750 |
---|---|
record_format |
Article |
spelling |
doaj-18c4b91600ad4f64895c0375bc71f7502020-11-25T01:30:55ZdeuUnité Mixte de Recherche 8504 Géographie-citésCybergeo1278-33662007-07-0110.4000/cybergeo.8462Detecting Clusters in Spatially Repetitive Point Event Data SetsAllan BrimicombeThe analysis of point event patterns has a long tradition. Of particular interest are patterns of clustering or ‘hot spots’ and such cluster detection lies at the heart of spatial data mining. Certain classes of point event patterns have a significant proportion of the data having a tendency towards exact spatial repetitiveness. Examples are crime and traffic accidents. Spatial superimposition of point events challenges many existing approaches to cluster detection. In this paper a variable resolution approach, Geo-ProZones, is applied to residential burglary data exhibiting a high level of repeat victimisation. This is coupled with robust normalisation as a means of consistently defining and visualising the ‘hot spots’.http://journals.openedition.org/cybergeo/8462Geo-ProZone analysispoint event datarobust normalisationspatial clusteringspatial repetition |
collection |
DOAJ |
language |
deu |
format |
Article |
sources |
DOAJ |
author |
Allan Brimicombe |
spellingShingle |
Allan Brimicombe Detecting Clusters in Spatially Repetitive Point Event Data Sets Cybergeo Geo-ProZone analysis point event data robust normalisation spatial clustering spatial repetition |
author_facet |
Allan Brimicombe |
author_sort |
Allan Brimicombe |
title |
Detecting Clusters in Spatially Repetitive Point Event Data Sets |
title_short |
Detecting Clusters in Spatially Repetitive Point Event Data Sets |
title_full |
Detecting Clusters in Spatially Repetitive Point Event Data Sets |
title_fullStr |
Detecting Clusters in Spatially Repetitive Point Event Data Sets |
title_full_unstemmed |
Detecting Clusters in Spatially Repetitive Point Event Data Sets |
title_sort |
detecting clusters in spatially repetitive point event data sets |
publisher |
Unité Mixte de Recherche 8504 Géographie-cités |
series |
Cybergeo |
issn |
1278-3366 |
publishDate |
2007-07-01 |
description |
The analysis of point event patterns has a long tradition. Of particular interest are patterns of clustering or ‘hot spots’ and such cluster detection lies at the heart of spatial data mining. Certain classes of point event patterns have a significant proportion of the data having a tendency towards exact spatial repetitiveness. Examples are crime and traffic accidents. Spatial superimposition of point events challenges many existing approaches to cluster detection. In this paper a variable resolution approach, Geo-ProZones, is applied to residential burglary data exhibiting a high level of repeat victimisation. This is coupled with robust normalisation as a means of consistently defining and visualising the ‘hot spots’. |
topic |
Geo-ProZone analysis point event data robust normalisation spatial clustering spatial repetition |
url |
http://journals.openedition.org/cybergeo/8462 |
work_keys_str_mv |
AT allanbrimicombe detectingclustersinspatiallyrepetitivepointeventdatasets |
_version_ |
1725088924608495616 |