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Open AccessArticle

Efficient Proximity Computation Techniques Using ZIP Code Data for Smart Cities

School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea
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Author to whom correspondence should be addressed.
Current address: Wijaya Putra University, Surabaya, Indonesia.
This paper is an extended version of our paper published in Murdani, M.H.; Kwon, J. Measuring Proximity in a Graph of Spatial Data (ZIP Codes). In Proceedings of the First International Workshop on Big Data Management and Analytics, Wuhan, China, 22–25 April 2013; pp. 79–85.
Sensors 2018, 18(4), 965; https://doi.org/10.3390/s18040965
Received: 15 February 2018 / Revised: 16 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
(This article belongs to the Special Issue Smart Cities)
In this paper, we are interested in computing ZIP code proximity from two perspectives, proximity between two ZIP codes (Ad-Hoc) and neighborhood proximity (Top-K). Such a computation can be used for ZIP code-based target marketing as one of the smart city applications. A naïve approach to this computation is the usage of the distance between ZIP codes. We redefine a distance metric combining the centroid distance with the intersecting road network between ZIP codes by using a weighted sum method. Furthermore, we prove that the results of our combined approach conform to the characteristics of distance measurement. We have proposed a general and heuristic approach for computing Ad-Hoc proximity, while for computing Top-K proximity, we have proposed a general approach only. Our experimental results indicate that our approaches are verifiable and effective in reducing the execution time and search space. View Full-Text
Keywords: proximity computation; data models; ZIP code data; smart city proximity computation; data models; ZIP code data; smart city
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Murdani, M.H.; Kwon, J.; Choi, Y.-H.; Hong, B. Efficient Proximity Computation Techniques Using ZIP Code Data for Smart Cities. Sensors 2018, 18, 965.

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