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Ko Ko Lwin Komei Sugiura Koji Zettsu 《International journal of geographical information science》2016,30(8):1579-1593
We can collect, store, and analyze a huge amount of information about human mobility and social interaction activities due to the emergence of information and communication technologies and location-enabled mobile devices under cyber physical system frameworks. The high spatial resolution of population data on a multi-temporal scale is required by transport planners, human geographers, social scientists, and emergency management teams. In this study, we build a space-time multiple regression model to estimate grid-based (500 m × 500 m) spatial resolution at multi-temporal scale (30-min intervals) population data based on the space-time relationship among geospatially enabled person trip (PT) survey data and incorporate both mobile call (MC) and geotagged Twitter (GT) data. Since using geospatially enabled PT survey data as dependent variables enables us to acquire actual population amounts, which strongly depend on MCs and social interaction activities. Although many grids have a strong correlation between PT and MC/GT, some show fewer correlation results, especially where the grids have factories, schools, and workshops in which fewer MCs are found but a large population is presented. Although GT data are sparser than MCs, people from amusement and tourist areas can be detected by GT data. The space-time multiple regression model can also estimate the different amounts of populations based on human travel behavior that changes over space and time. According to accuracy assessments, the night-time estimated results, especially between 00:00 and 06:30, strongly correlate with national census data except in places where the grids have railway and subway stations. 相似文献
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张留民 《测绘与空间地理信息》2015,(2):204-206
为适应河南经济发展对基础测绘成果的需求,实现测绘服务模式的转变,河南省对1∶10 000基础地理信息数据更新,采取了“人·县·年”的快速更新模式,较好地满足了经济快速发展对基础测绘成果的需求,取得了良好的社会效益和经济效益. 相似文献
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