Songnian Li

Predicting human mobility with activity changes

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R2) derived from the data with and without implementing the activity change detection. The results show that the R2 is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.

A Web GIS for Sea Ice Information and an Ice Service Archive

Sea ice data has significant scientific value for climate, environmental impact and engineering studies leading to the construction of facilities in Arctic waters, as well as to support tourism and fishing planning. Large collections of such data are acquired, compiled, produced and maintained by national and international agencies such as the Canadian Ice Service (CIS). Some of these data collections have been made available online. However, current Internet-based sea ice data dissemination practices do not foster easy access to and use of the data, especially given the amount of the archived sea ice data and the nature of their spatial changes and high temporal variations. This article reports a research effort in developing a web-based geographical information system (GIS) that facilitates the access and use of the historical sea ice data. The system provides online access, exploration, visualization, and analysis of the archived data, mostly in the form of ice charts, within a web-based GIS. The results from a prototype development indicate that web GIS, developed using Rich Internet Application (RIA) technologies, provides added values in serving sea ice data and suggests that such a system can better accommodate more advanced sea ice data access and analysis tools.

Developing a groupware-based prototype to support geomatics production management

Abstract Digital map and chart providers are under increasing pressure to bring their original and updated products to market in shorter time frames than ever before. As the contract production model has come to play an increasingly significant role in digital mapping and charting programs, workflow processes and production tasks have at least been extended, if not redefined. At the same time, through the refinement and integration of groupware tools and Intranet/Extranet software, applications of computer-supported cooperative work have emerged in support of both project-driven research and corporate information management. The Geographical Engineering Group at University of New Brunswick has undertaken a program of research to: (1) investigate the applicability of groupware and database technologies to support collaborative geomatics production in networked environments; (2) develop Extranet-based prototypes; and (3) evaluate the effects of these prototypes on project management and organizational culture. A summary of the results from the preliminary works is presented in this paper. Focus has been placed on the collaboration prototype developed in terms of quality control inspection project in mapping and charting productions. The paper concludes with a discussion of key design and implementation issues.

Modeling distributed GIS data production workflow

Real-time access to status of project progress, control of production processes and activities, and automation of production procedures are major hurdles in further improving production throughput and project management in distributed GIS data production projects. Workflow technology promises to address these issues by facilitating, automating or controlling GIS data production processes. The success of applying workflow technologies to distributed GIS data production requires comprehensive understanding of the underlying production processes. This paper presents the first research effort on rigorously modeling distributed GIS data production workflows, with particular attention to a GIS data quality control process using the Internet-based workflow management technologies. Existing practices of distributed GIS data production projects are used as the essential sources for modeling the required workflow. The paper discusses some issues faced and concepts such as workflow process repository and GIS data QC testing program repository.

Road extraction using smart phones GPS

GPS data crowd-sourced through smart phones is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions. The same type of data can be very useful for cost-effective, fast updating of road network databases due to its rich spatial and temporal coverage and high data volume. This paper presents the results of a study that extracts road geometry data using GPS data received from smart phones. The focus is on the method for road centerlines and the study presents some promising results. It is expected the method can supplement, if not replace, the current practices of acquiring road network data using traditional expensive and time consuming survey or remote sensing approaches.

Towards GIS-enabled virtual public meeting space for public participation

The by-laws of many municipalities and local governments require certain level of public participation in their planning, decision-making processes. One of the common practices in Canada or elsewhere around the world is to hold public meetings, during which the proposed developments are explained and discussed, and public opinions are collected. The problems associated with existing practices are threefold: insufficient access to the information required for public input, lack of effective means of participation other than attending public meetings, and difficulties in explaining spatial concepts during the debate. Using the municipal environmental assessment (EA) process as the application context, this paper presents our effort on developing an integrated online GIS-enabled virtual public meeting space. The system provides a new way of engaging the public and addressing their concerns by facilitating information access, understanding of planning study, and proactive participation in public meetings and in overall municipal class EA process.

A 3D GIS-based interactive registration mechanism for outdoor augmented reality system

An interactive method is proposed for registration between virtual and real objects.We propose an outdoor augmented reality (AR) system, named ARGIS.The proposed ARGIS combines 3DGIS, HMD, GPS, IMU and interactive devices.ARGIS overcomes the drawback of traditional vision tracking method for registration.We developed a prototype and tested it on the campus of Peking University, China. Registering virtual objects with reference to their corresponding real-world objects plays a key role in augmented reality (AR) system. Although there have been a lot of work on using vision-based method to perform registration for indoor AR system, it is very difficult to apply such registration method for outdoor AR system due to the inability to modify the objects in outdoor environment and the huge range of working area. 3D Geographic Information System (GIS) is capable of providing an outdoor virtual geographic environment where users are located at, which may provide users with a corresponding virtual object for the one in the physical world. In this study, a 3D GIS-based registration mechanism is proposed for outdoor AR system. Specifically, an easy-use interactive method for precise registration was developed to improve the performance of the registration. To implement the registration mechanism, an outdoor AR system built upon 3D GIS was developed, named Augmented Reality Geographical Information System (ARGIS). ARGIS has the capability of performing precise registration in outdoor environment without using traditional vision tracking method, which thus enables users to arbitrarily manipulate the system. A prototype was developed to conduct experiment on the campus of Peking University, Beijing, China to test the proposed registration mechanism. The experiment shows that the developed registration mechanism is feasible and efficient in the outdoor environment. The ARGIS is expected to enrich the applications of outdoor AR system, including but not limited to underground facility mapping, emergency rescue and urban planning.

Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts

Sample data plays an important role in land cover (LC) map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they contain valuable information for LC map validation. However, this kind of special textual data has seldom been analyzed and used for supporting LC map validation. This paper examines the potential of geo-tagged web texts as a new cost-free sample data source to assist LC map validation and proposes an active data collection approach. The proposed approach uses a customized deep web crawler to search for geo-tagged web texts based on land cover-related keywords and string-based rules matching. A data transformation based on buffer analysis is then performed to convert the collected web texts into LC sample data. Using three provinces and three municipalities directly under the Central Government in China as study areas, geo-tagged web texts were collected to validate artificial surface class of China’s 30-meter global land cover datasets (GlobeLand30-2010). A total of 6283 geo-tagged web texts were collected at a speed of 0.58 texts per second. The collected texts about built-up areas were transformed into sample data. User’s accuracy of 82.2% was achieved, which is close to that derived from formal expert validation. The preliminary results show that geo-tagged web texts are valuable ancillary data for LC map validation and the proposed approach can improve the efficiency of sample data collection.

A new burn severity index based on land surface temperature and enhanced vegetation index

Abstract Remotely sensed data have already become one of the major resources for estimating the burn severity of forest fires. Recently, Land Surface Temperature (LST) calculated from remote sensing data has been considered as a potential indicator for estimating burn severity. However, using the LST-based index alone may not be sufficient for estimating burn severity in the areas that has unburned trees and vegetation. In this paper, a new index is proposed by considering LST and enhanced vegetation index (EVI) together. The accuracy of the proposed index was evaluated by using 264 composite burn index (CBI) field sample data of the five fires across different regional eco-type areas in the Western United States. Results show that the proposed index performed equally well for post-fire areas covered with both sparse vegetation and dense vegetation and relatively better than some commonly-used burn severity indices. This index also has high potential of estimating burn severity if more accurate surface temperatures can be obtained in the future.

A hybrid spatio-temporal data indexing method for trajectory databases.

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.