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Most manufacturers and distributors face a critical challenge in ensuring the accuracy of GPS locations of their customers. To address this issue, we have developed an algorithm capable of detecting customers with questionable locations by utilizing network clusterization techniques.
What is Clusterization?
Clusterization involves the process of identifying clusters or groups of points in a manner where points within the same cluster exhibit greater similarity to each other than to those in different clusters. In our specific case, working with geospatial data, we utilize a density clusterization model designed to uncover high-density regions separated by low-density regions within our spatial data.
How Clusterization can help achieve better gps data accuracy?
Upon identifying the clusters within our network of customers, we can identify zones where all the points are geotagged with identical locations. These zones signify potentially suspicious points whose accuracy can be verified and, if necessary, re-geotagged through on-site control visits.
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