Geo-tagged tweets provide useful implications for studies in human geography, urban science, location-based services, targeted advertising, and social network. This research aims to discover the patterns and mobility of Twitter users by analyzing the spatial and temporal dynamics in their tweets. Geo-tagged tweets are collected over a period of six months for four US Midwestern college cities: (1) West Lafayette, IN; (2) Bloomington, IN; (3) Ann Arbor, MI; (4) Columbus, OH. Various analytical and statistical methods are used to reveal the spatial and temporal patterns of tweets, and the tweeting behaviors of Twitter users. It is discovered that Twitter users are most active between 9:00 pm and 11:00 pm. In smaller cities, tweets aggregate at campuses and apartment complexes, while tweets in residential areas of bigger cities make up the majority of tweets. We also found that most Twitter users have two to four places of frequent visits. The mean mobility range of frequent Twitter users is linearly correlated to the size of the city, specifically, about 40% of the city radius. The research therefore confirms the feasibility and promising future for using geo-tagged microblogging services such as Twitter to understand human behavior patterns and carry out other geo-social related studies.

One of the intrinsic properties of conventional terrestrial laser scanning technology is the unevenness of its point density over the scene where objects rendered closer to the scanner are more densely covered than the ones far away. This uneven distribution can be amplified as the working range of a laser scanner gets longer. In such case a higher pulse repetition rate (PRR) is applied to the whole scanning area and the scanning time will be dramatically increased. To improve the efficiency of the conventional laser scanning technology, a prototype of adaptive scanning technology, the HRS3D-AS scanner has been developed by Blackmore Sensors and Analytics, Inc. This paper briefly describes the working principles of the adaptive scanner and presents a thorough evaluation on the distributions of the point density in comparison to the conventional scanning. Based on this study, we show that such a new technology can produce a point cloud of more uniform density and less data volume. The overall field scanning time can be reduced by several times compared to the conventional, PRR-fixed scanning. Such properties are expected to significantly simplify the algorithmic development and increase the productivity in data acquisition and processing. The limitations of this new adaptive scanning technology are also discussed in terms of redundant and unresolved details. Finally, recommendations related to the practicing of such adaptive scan are discussed.