With the recent explosion of geographic data generated by smartphones, sensors, and satellites, a data storage that can handle the massive volume of data and support high-computational spatial queries is becoming essential. Although key-value stores efficiently handle large-scale data, they are not equipped with effective functions for supporting geographic data. To solve this problem, in this paper, we present G-HBase, a high-performance geographical database based on HBase, a standard key-value store. To index geographic data, we first use Geohash as the rowkey in HBase. Then, we present a novel partitioning method, namely binary Geohash rectangle partitioning, to support spatial queries. Our extensive experiments on real datasets have demonstrated an improved performance with k nearest neighbors and range query in G-HBase when compared with SpatialHadoop, a state-of-the-art framework with native support for spatial data. We also observed that performance of spatial join in G-HBase is on par with SpatialHadoop and outperforms SJMR algorithm in HBase.

Smart card payment systems provide a convenient billing mechanism for public transportation providers and passengers. In this paper, a smart card-based transit log is used to reveal functionally related regions in a city, which are called zones. To discover significant zones based on the transit log data, two algorithms, minimum spanning trees and agglomerative hierarchical clustering, are extended by considering the additional factors of geographical distance and adjacency. The hierarchical spatial geocoding system, called Geohash, is adopted to merge nearby bus stops to a region before zone discovery. We identify different urban zones that contain functionally interrelated regions based on passenger trip data stored in the smart card-based transit log by manipulating the level of abstraction and the adjustment parameters.