Development and implementation of a dynamic and 4D GIS based on semantic location model

ABSTRACT

In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.

KEYWORDS:

• Place-based pan-information system (P2S)
• big data
• semantic location model
• ubiquitous information
• 4D map

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (grant number 41301517, 41271401, 41329001, 41401524, 1416509, and 1535031), the National Key Research and Development Program (grant number 2016YFB0502204), the Fundamental Research Funds for the Central Universities (grant number 413000010) and National Science and Technology Support Plan, the National Key Technology R&D Program (grant number 2012BAH35B03), Guangxi Natural Science Foundation (grant number 2015GXNSFBA139191); Scientific Project of Guangxi Education Department (grant number KY2015YB189).

Notes on contributors

Xinyan Zhu

Xinyan Zhu is a professor at Wuhan University. He received his Ph.D. in cartography and geographical information system at Wuhan University. His research interests are spatial-temporal data analysis and spatial databases.

Tao Hu

Tao Hu received his Ph.D. in cartography and geographical information system at Wuhan University in 2015. Currently, he is the post-doctoral researcher at the center for geographical analysis in Harvard University. His research interests include spatial data analysis and innovation studies.

Xinyue Ye

Xinyue Ye received his Ph.D. at the University of California at Santa Barbara. Currently, he is the associate professor at College of Computing in New Jersey Institute of Technology. He integrates social science and computational science towards information visualization, urban informatics and spatial social network analysis.

Wei Guo

Wei Guo received his Ph.D. in remote sensing. His research interests include spatial data analysis, natural language processing, and indoor real time mapping and navigation.

Liang Huang

Liang Huang received his Ph.D. in cartography and geographical information system. He is interested in the researches of spatial analysis, geoinformation, and traffic flow analysis.

Hanjiang Xiong

Hanjiang Xiong is a professor at Wuhan University. He received his Ph.D. in cartography and geographical information system. His is interested in the researches of distributed spatial database organization, web-based 3D model visualization, and 3D model reconstructions.

Haojun Ai

Haojun Ai is a professor at Computer Science School in Wuhan University. His research interests include signal processing and content analysis of audio and video, network of things system design and application.

Bing She

Bing She receeived Ph.D. in cartography and geographic information engineering from Wuhan Unviersity. His research interests include spatial data integration and CyberGIS.

Qing Xiong

Qing Xiong received his Ph.D. in photogrammetry at Wuhan University. His research topics include 3D model construction and reconstruction.

Lian Duan

Lian Duan received Ph.D. in cartography and geographic information engineering from Wuhan Unviersity. He has the expertise in urban crime patterns in space and time with geo-spatial analytics and machine learning methodologies. His academic interests focus on the understanding of geographical environments of local crimes, as well as offenders mobility predictions.