结合手机信令数据和地理空间数据的居民职住信息识别
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摘要
基于手机信令数据和地理信息数据,融合地理信息空间运算能力,创新性地提出了一种识别居民职住信息的方法。在Spark平台上,首先运用地理信息空间分析服务将手机信令数据绑定至地理实体,再通过降噪算法完成定位校准和信令数据的清洗、加工,最后构建数学算法和模型得到居民的工作地和居住地。以成都市居民连续两周的手机信令数据为例,验证了该方法的可行性,该成果将会为商业选址、客群管理、城市规划等应用提供数据支撑。
Based on the mobile phone signaling data and geographical information data,integrated geographic information space computing,a method for identifying the occupation and residence of the residents is proposed innovatively. On the Spark platform,first the mobile information signaling data is bound to the geographic entities by using the geo spatial analysis service. Then the positioning calibration and the cleaning of the signaling data are completed by noise reduction algorithm. Finally,mathematical algorithms and models are constructed to obtain the occupation and residence of the residents. This paper takes two weeks' mobile phone signaling data of Chengdu residents as an example to verify the feasibility of the method. The result will provide data support for commercial site selection,customers management,city planning and other applications.
Based on the mobile phone signaling data and geographical information data,integrated geographic information space computing,a method for identifying the occupation and residence of the residents is proposed innovatively. On the Spark platform,first the mobile information signaling data is bound to the geographic entities by using the geo spatial analysis service. Then the positioning calibration and the cleaning of the signaling data are completed by noise reduction algorithm. Finally,mathematical algorithms and models are constructed to obtain the occupation and residence of the residents. This paper takes two weeks' mobile phone signaling data of Chengdu residents as an example to verify the feasibility of the method. The result will provide data support for commercial site selection,customers management,city planning and other applications.
引文
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[13]李庆君. Hadoop架构下海量空间数据存储与管理[D].武汉:武汉大学,2017.
[14]靳凤营,张丰,杜震洪,等.基于Spark的土地利用矢量数据空间叠加分析方法[J].浙江大学学报(理学版),2016,43(1):40-44.
[15]翟婷,宋文爱,富丽贞,等.基于路网感知的时空轨迹聚类[J].计算机工程与设计,2016,37(3):635-642.
[2]廖慧,李娜,王蓉.大数据时代下电信运营商应用模式研究[J].电信技术,2016(3):8-12.
[3]张天然.基于手机信令数据的上海市域职住空间分析[J].城市交通,2016,14(1):15-23.
[4] SCHNEIDER C M,BELIK V,COURONNT,et al.Unravelling daily human mobility motifs[J]. Journal of the Royal Society Interface,2103,10(84):246-253.
[5]张天然.基于手机信令数据的上海市域职住空间分析[J].城市交通,2016,14(1):15-23.
[6]钟炜菁,王德,谢栋灿,等.上海市人口分布与空间活动的动态特征研究———基于手机信令数据的探索[J].地理研究,2017,36(5):972-984.
[7]赖见辉.基于移动通信定位数据的交通信息提取及分析方法的研究[D].北京:北京工业大学,2014:35-52.
[8] ISAACMAN S,BECKER R,CACERES R,et al. Identifying important places in people’s lives from cellular network data[M].Berlin Heidelberg:Springer,2011:133-151.
[9] FRIAS-MARTINEZ V,SOGUERO C,FRIAS-MARTINEZ E. Estimation of urban commuting patterns using cellphone network data[C]∥Proceedings of the ACM Sigk DD International workshop on urban computing.Beijing:[s.n.],2012.
[10]丁奇.大话无线通信[M].北京:人民邮电出版社,2017:318-320.
[11]杜翠凤,余艺.大数据背景下基于手机信令的道路识别研究[J].移动通信,2015(13):21-24.
[12]钱琨.基于蜂窝信令数据的移动轨迹清洗和预测方法研究与实现[D].成都:西南交通大学,2016.
[13]李庆君. Hadoop架构下海量空间数据存储与管理[D].武汉:武汉大学,2017.
[14]靳凤营,张丰,杜震洪,等.基于Spark的土地利用矢量数据空间叠加分析方法[J].浙江大学学报(理学版),2016,43(1):40-44.
[15]翟婷,宋文爱,富丽贞,等.基于路网感知的时空轨迹聚类[J].计算机工程与设计,2016,37(3):635-642.