A spatio-temporal data model for road network in data center based on incremental updating in vehicle navigation system

Abstract

The technique of incremental updating, which can better guarantee the real-time situation of navigational map, is the developing orientation of navigational road network updating. The data center of vehicle navigation system is in charge of storing incremental data, and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal. According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model, the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request. RMOM supports the data center to store not only the current complete road network data, but also the overlays of incremental data from the time when each road network changed to the current moment. Moreover, the storage mechanism and index structure of the incremental data were designed, and the implementation algorithm of RMOM was developed. Taking navigational road network in Guangzhou City as an example, the simulation test was conducted to validate the efficiency of RMOM. Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM, and costs less time. Compared with the base map with overlay model, the data center does not need to temporarily overlay incremental data with RMOM, so time-consuming of response is significantly reduced. RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Debris flow events of 4000 a BP and its resulting archaeological site destruction in Qian River gorge, the upper reach of Wei River, central China

“…a BP. This dating result is consistent with the chronological records of many ancient floods in the Wei River Basin around 4000 a BP (Gu et al, 2012;Huang, Pang, Zha, Su, & Jia, 2011a;Huang, Pang, Zha, & Zhou, 2011b;Huang, 2002;Jia et al, 2012;Wang et al, 2011;Wang et al, 2012).…”

“…Through investigation and analysis, we believe that the debris flow incident on the south bank of the Qian River was also triggered by high-intensity rainfall and earthquakes. Around 4 ka BP, floods extensively occurred in the Wei River and its major tributaries in the middle latitude monsoonal climate zone in China, such as the Jing River, Qishui River, Luo River, and so on (Figures 1a and 6;Huang et al, 2010Huang et al, , 2012Huang et al, , 2017Huang, Pang, Zha, Su, & Jia, 2011a;Huang, Pang, Zha, & Zhou, 2011b;Wang et al, 2011;Zhang, Huang, Pang, Zhou, & Wang, 2015;Zhao, Huang, Pang, Zha, & Yao, 2011). The peaks of these ancient floods were much larger than the modern measured floods, and even the largest flood event since the Holocene (Li et al, 2009b;Wang et al, 2011Wang et al, , 2012Zhao et al, 2011;Zhang et al, 2013Zhang et al, , 2015.…”

“…In addition, the extreme flood is also a common response to the abrupt climatic change, especially to the cooling event (Knox, 2000;Wu & Ge, 2014). Around 4.0 ka BP, a series of extreme floods also occurred in the major tributaries of the Weihe River in the middle reach of the Yellow River, and the middle and lower reaches of the Yangtze River (Huang et al, 2010(Huang et al, , 2012(Huang et al, , 2017Huang, Pang, Zha, Su, & Jia, 2011a;Huang, Pang, Zha, & Zhou, 2011b;Li et al, 2009b;Wang et al, 2011;Wu et al, 2013;Zhang et al, 2013). These imply that the extraordinary floods and related disasters resulting from intense precipitation in the middle latitude monsoonal climate zone in China are universal F I G U R E 6 Pedostratigraphic and magnetic susceptibility (Xlf) correlation between the QJS sites and the Holocene loess-soil profiles at the Guanzhong Basin.…”

Spatial Data Mining and Big Data Analysis of Tourist Travel Behavior

“…Through the analysis on SMG data, it is possible to disclose the interaction among time, space and attribute(s), and determine the state and variation of geographic elements [3]. Moreover, the SMG data analysis can also reveal the spatiotemporal relationship of human behavior, identify the spatial distribution of people at different time points, and display the spatiotemporal features of the daily activities of a specific group of people.…”

Spatio-Temporal Database of Places Located in the Border Area

“…At the same time, the limitations of traditional solutions in this area resulted in the development of spatio-temporal databases, which are a source of data used for storytelling. There are a lot of considerations and research on possible spatio-temporal changes a geographic object [8,9], spatio-temporal data integration and querying [10,11], spatio-temporal data models [12,13], as well as spatio-temporal database management [14].…”