Efficient Proximity Computation Techniques Using ZIP Code Data for Smart Cities ^†.

Area of Science:

• Geographic Information Systems (GIS)
• Computational Geography
• Spatial Analysis

Background:

• ZIP code proximity analysis is crucial for applications like targeted marketing and smart city initiatives.
• Existing methods often rely on simple centroid distances, which may not accurately reflect real-world connectivity.
• The need for more sophisticated proximity metrics that consider network infrastructure is evident.

Purpose of the Study:

• To develop and validate novel methods for computing ZIP code proximity.
• To address two types of proximity: Ad-Hoc (between two specific ZIP codes) and Top-K (neighborhood proximity).
• To enhance the efficiency and accuracy of spatial computations for marketing and smart city applications.

Main Methods:

• A redefined distance metric combining centroid distance and intersecting road networks using a weighted sum.
• Development of a general and heuristic approach for Ad-Hoc ZIP code proximity.
• Proposal of a general approach for Top-K ZIP code proximity.

Main Results:

• The proposed combined distance metric satisfies the properties of a distance measurement.
• Experimental results demonstrate the effectiveness of the developed approaches.
• Significant reduction in execution time and search space compared to naive methods.

Conclusions:

• The novel approaches for computing ZIP code proximity are verifiable and effective.
• The refined distance metric provides a more accurate representation of spatial relationships.
• This work contributes to advancing smart city applications through improved spatial analysis techniques.