Mingshu Wang

Outside the ivory tower: visualizing university students’ top transit-trip destinations and popular corridors

Universities are where innovations, face-to-face interactions and social capital are commonplace. Nevertheless, often regarded as ‘the ivory tower’, universities cannot be separated from the social and economic transformations outside of them. Traffic, information and financial flows between universities and other locations can be used to reveal connections between the ivory tower and other locales. Therefore, this paper uses the weekday public transit smartcard records from 6 to 9 April 2010 (158,262 transit trips in total, including bus-only, bus plus subway and subway-only trips) to identify and profile the most popular destinations of student riders from the ‘985 universities’ (a short list of top universities designated by the Chinese Central Government in 1999) and associated transit trip flows in Beijing. It identifies destination hotspots for the 985 universities’ students in Beijing, allocates traffic volume to major roads and delineates the transit trips of students from each campus. The results indicate that there exist only weak ties and little movement between the top universities and the most disadvantaged areas.

A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things

The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities.

What geomorphological characteristics accommodate emergent herbaceous wetlands in North Georgia? – geographic knowledge discovery from the NLCD and DEM

When we examine the National Land Cover Database 2006 (NLCD 2006) in a small scale, we find out that there are only 4.5 ha (50 pixels) of emergent herbaceous wetlands in Athens-Clarke County, Georgia. In order to testify that it is not simply due to mapping error or uncertainty, we propose a geographic knowledge discovery (GKD) process based on NLCD. The GKD process consists of data preparation, data preprocessing, feature extraction and knowledge consolidation. A case study ‘What geomorphological characteristics accommodate emergent herbaceous wetlands in North Georgia’ is presented to illustrate the process. Geomorphological characteristics refer to digital elevation model (DEM) and eight DEM-derived variables, which are proxies to geomorphological conditions. Geographic data are inherently spatial dependent and heterogeneous and such properties are considered in data preparation. In feature extraction, the goal of the study and the nature of the data are taken into consideration to select a suitable algorithm. In knowledge consolidation, three steps of validation – with statistics, cross-validation and field survey – are presented. The proposed methods can be extended to GKD from NLCD for other purposes. The GKD results testify that the small area of emergent herbaceous wetlands in Athens-Clarke County, Georgia, is not due to mapping error or uncertainty. The case study also proves that See5 algorithm performs very well to extract the learned knowledge. The procedure that we propose with NLCD can be applied to other environmental monitoring purpose.