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CrowdZIP: A System to Improve Reverse ZIP Code Geocoding using Spatial and Crowdsourced Data (Demo Paper)

Published: 05 November 2019 Publication History

Abstract

Zoning Improvement Plan (ZIP) Codes provide a sub-division of space. Interestingly, the ZIP code area polygons for different data sources do not match, resulting in uncertainty for a range of services that rely on such data. This paper presents a system that employs traditional classification methods to map a given spatial coordinate to a distribution of ZIP-codes using various public available ZIP-code maps as predictors, and using the (not publicly available) United States Postal Service (USPS) map as an authoritative ground truth. We show that large sets of microblog data, from which we extract potential ZIP-codes, can significantly improve classification accuracy despite the noise of such data. The demonstrator allows users to select locations on a map of Orlando, FL, view the resulting distribution of ZIP-codes predicted for this location, compare the results to the ground-truth, and view the microblogs that have enriched the result. A focus will be on showing that the signal present in large, noisy, and 99.99% unrelated microblog data can indeed be used to improve reverse ZIP code geo-coding.

References

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"Usa zip code areas," http://www.arcgis.com/home/item.html?id=8d2012a2016e484dafaac0451f9aea24, accessed: 2017-12-6.
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"docs," https://developer.twitter.com/en/docs, accessed: 2017-12-6.
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T. Khan and A. Kabir, "Zipcode-classification-demo," https://github.com/subrina0013/ZIPCode-Classification-Demo, 2019.
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  1. CrowdZIP: A System to Improve Reverse ZIP Code Geocoding using Spatial and Crowdsourced Data (Demo Paper)

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      cover image ACM Conferences
      SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
      November 2019
      648 pages
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      Published: 05 November 2019

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      Author Tags

      1. Geocoding
      2. Location Based Services
      3. Microblog Data
      4. Reverse Geocoding
      5. ZIP Code Classification
      6. ZIP Codes

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      SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
      Overall Acceptance Rate 257 of 1,238 submissions, 21%

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