Abstract
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.
Highlights
Proximity is a measure of closeness between two or more correlated objects
We find that the centroid distance for 95971 and 95981 is not large, but, because there is a mountain between these ZIP codes, we have to take a detour
All experiments use the ZIP code graph and the proximity measure between nodes is expressed by a linear combination of the centroid distance and the intersecting roads weight, as shown in Equation (4)
Summary
Proximity is a measure of closeness between two or more correlated objects. It is used for finding a nearby hotel, target marketing, disease outbreak analysis, social network analysis [1], and identification of a false insurance claim. A top-k spatio-textual preference query [7] considers the spatial location, and additional information such as ratings These systems do not exploit the ZIP code and variant weights of road types during the proximity computation. We need to formulate a new definition for distance and find other measurement to support the distance correctness To further address these challenging issues, we designed and implemented a system to support efficient proximity computation techniques for ZIP code graph data. We formally define and combine two proximity measures, which are the adjacent ZIP code distance and the weight of the intersecting roads. We combine the intersecting road weight with the centroid distance for computing the proximity in a graph of ZIP codes.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
