ZIP-Code Classification Using Spatial and Crowdsourced Data

Zoning Improvement Plan (ZIP) Code polygon maps, obtained from different data sources, do not match, thus creating uncertainty in spatial analysis. In this dissertation, we want to combine multiple source of ZIP-code polygon data into a coherent system that maps a given location to a ZIP-code. For this purpose, we want to harness the wisdom of the crowd by combining various polygon map data sets with various sources of volunteered geographical information. The system that we want to build will employ traditional classification methods to map a given spatial coordinate to a distribution of ZIP-codes using the (not publicly available) United States Postal Service (USPS) map as an authoritative ground truth. In our first studies, we train a Naïve Bayes classifier using multiple (publicly available) ZIP Code polygon maps. In addition, we enrich our classification by using a lazy K-Nearest Neighbor classifier to predict the ZIP Codes for a given location using Twitter. Feeding the result of this classifier to the Naïve Bayes classification our experimental evaluation shows an improvement in classification accuracy, compared to using only map data.