Authors: Zhengcong Yin*, Department of Geography, Texas A&M University, Daniel W. Goldberg, Department of Geography, Texas A&M University
Topics: Geographic Information Science and Systems
Keywords: Spatial Data, Platform, NoSQL
Session Type: Poster
Start / End Time: 1:20 PM / 3:00 PM
Room: Napoleon Foyer/Common St. Corridor, Sheraton, 3rd Floor
Presentation File: No File Uploaded
Sensors, web technologies, GPS-enable mobile devices largely increases the availability of data. People find it becomes easier to collect data and become more informative to make decisions; more interesting knowledge about the interactions between environment and human could be discovered. These ubiquitous data acquisition technologies also introduce some emerging concepts such as “Smart City”, “Volunteered Geographic Information” and “Internet of Things”. Behind the scenes, it is the proliferation of data with huge volume and heterogeneity. Thus, there is a demand for an efficient and scalable data storage and retrieval system. NoSQL with its document-oriented and easy data partition design attracts increasing attentions in the era of “Big Data”. Recent development in NoSQL databases encourages us to investigate its application to the geographic information retrieval field. In this work, we proposed a platform that enables near real-time spatial and the associated contextual data search and analytics by leveraging NoSQL data warehouse, geocoding, Geohash and visualization technologies. Our aim is to assess how well these technologies could be applied to geographic information search domain. Address point look-up, K-Nearest Neighbor, containment query were benchmarked by real-world search scenarios to evaluate the system’s performance.