Study Of Spatial-Temporal Characteristics Of Students’ Campus Activity Based On WiFi Data

Big Data era, with the development of mobile positioning, wireless communications and Internet technology, the means to acquire the user’s location become more diverse. Using mobile data communications, GPS positioning data or social network data to study human’s spatial-temple behavior has become a hot topic of big data era, with new opportunities and challenges for the development of space-time GIS. With the development of higher education, more scholars focused on the behavioral characteristics’ study of community college students who as a special group. The buildings of University information system, improvement of network environment and explosive growth of the position data of students provide objective conditions for the study of spatial and temporal behavior of college students. This study conforms to the development of human behavior research trend in the era of big data. But different from the current study in administrative regions, the scale of research is the Asian city-scale campus community. Research of students’ spatial-temporal behavior characteristic mainly though the interviews, questionnaires and other traditional methods in the past, using a large data provides a new idea for the study of spatial and temporal behavior characteristics of college students which eliminating the limitations of small sample.This paper aims to use Big Data technologies to provide new ideas and technical support for the study of spatial-temporal behavior characteristics of college students. Central China Normal University students selected as empirical research object.1) Obtain university students’ campus WiFi login location history data, the big data pre-processing is filtered, washed and discrete data and so on to obtain valuable data for this study, then the data can be stored and managed.2) Set up B/S structure of the student campus distribution visualization platform.3) using temporal data classification, spatial analysis, data mining and inference method to analyze spatial-temporal college students’ campus activity characteristics from campus activity time, space, type of activity. Characterized by campus activities Time: ① Different grade students’ campus activities have different characteristic, higher grade students have cyclical convergence in time behavior with more options and higher stability; ② Campus activities of boys and girls have similar temporal behavior characteristics, and the stability of boys is greater than girls; ③ Science and engineering students compared to liberal arts students, the highest frequency of Internet sports students with most single behavior; ④ Comprehensive good scores, the more number of Internet users and more frequent activity time, integrated poor students Internet access with more irregular and uncertainty. Characterized by campus activities space: ⑤ high-campus access point density areas, mainly in the Eastern student dormitories and some of the main school building area; ② The increase in hot spot areas to reduce and then increase to reduce or even disappear finally, which is consistent with the lifestyle. ③ campus access point distribution polymerization something significantly different. The types of activities on campus have stability, periodicity, which characterized that significantly different. In addition mandatory behaviors are more than autonomous behaviors. Then the paper summarized that students have five characteristics of temporal behavior consistency, regularity, clustering, dependency and proximity, etc.4) Try to explore the characteristics of the driving force from two aspects of internal mechanism and environmental factors to provide decision support for the school management.