3D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

A. Suhaibah, U. Uznir, Francesc/François Antón Castro, Darka Mioc, A. A. Rahman

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Abstract

Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

Conference

Conference23rd Congress of the International Society of Photogrammetry and Remote Sensing
Number23
CountryCzech Republic
CityPrague
Period12/07/201619/07/2016
Internet address

Bibliographical note

Since Volume XXXII-3/W14, 1999, the Archives are open access publications, they are published under the Creative Common Attribution 3.0 License, see publications.copernicus.org/for_authors/license_and_copyright.html for details.

Cite this

Suhaibah, A. ; Uznir, U. ; Antón Castro, Francesc/François ; Mioc, Darka ; Rahman, A. A. / 3D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016 ; Vol. Volume XLI-B2. pp. 87-93.
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abstract = "Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.",
author = "A. Suhaibah and U. Uznir and {Ant{\'o}n Castro}, Francesc/Fran{\cc}ois and Darka Mioc and Rahman, {A. A.}",
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3D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure. / Suhaibah, A.; Uznir, U.; Antón Castro, Francesc/François; Mioc, Darka; Rahman, A. A.

In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. Volume XLI-B2, 2016, p. 87-93.

Research output: Contribution to journalConference articleResearchpeer-review

TY - GEN

T1 - 3D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

AU - Suhaibah, A.

AU - Uznir, U.

AU - Antón Castro, Francesc/François

AU - Mioc, Darka

AU - Rahman, A. A.

N1 - Since Volume XXXII-3/W14, 1999, the Archives are open access publications, they are published under the Creative Common Attribution 3.0 License, see publications.copernicus.org/for_authors/license_and_copyright.html for details.

PY - 2016

Y1 - 2016

N2 - Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

AB - Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

U2 - 10.5194/isprs-archives-XLI-B2-87-2016

DO - 10.5194/isprs-archives-XLI-B2-87-2016

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EP - 93

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 1682-1750

ER -