Detecting clusters in spatially repetitive point event data sets
The analysis of point event patterns has a long tradition. The patterns 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 ten...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
Unité Mixte de Recherche 8504 Géographie-cités
2007-07-01
|
| Series: | Cybergeo |
| Subjects: | |
| Online Access: | https://journals.openedition.org/cybergeo/8462 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849236404628881408 |
|---|---|
| author | Allan Brimicombe |
| author_facet | Allan Brimicombe |
| author_sort | Allan Brimicombe |
| collection | DOAJ |
| description | The analysis of point event patterns has a long tradition. The patterns 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’. |
| format | Article |
| id | doaj-art-e1d9104226e9458ab59b1a4a0b2df3be |
| institution | Kabale University |
| issn | 1278-3366 |
| language | deu |
| publishDate | 2007-07-01 |
| publisher | Unité Mixte de Recherche 8504 Géographie-cités |
| record_format | Article |
| series | Cybergeo |
| spelling | doaj-art-e1d9104226e9458ab59b1a4a0b2df3be2025-08-20T04:02:14ZdeuUnité 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. The patterns 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’.https://journals.openedition.org/cybergeo/8462spatial clusteringpoint event dataspatial repetitionGeo-ProZone analysisrobust normalization/normalisation |
| spellingShingle | Allan Brimicombe Detecting clusters in spatially repetitive point event data sets Cybergeo spatial clustering point event data spatial repetition Geo-ProZone analysis robust normalization/normalisation |
| title | 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_short | Detecting clusters in spatially repetitive point event data sets |
| title_sort | detecting clusters in spatially repetitive point event data sets |
| topic | spatial clustering point event data spatial repetition Geo-ProZone analysis robust normalization/normalisation |
| url | https://journals.openedition.org/cybergeo/8462 |
| work_keys_str_mv | AT allanbrimicombe detectingclustersinspatiallyrepetitivepointeventdatasets |