COMPARISON OF URBAN HUMAN MOVEMENTS INFERRING FROM MULTI-SOURCE SPATIAL-TEMPORAL DATA

被引:0
|
作者
Cao, Rui [1 ,2 ]
Tu, Wei [1 ,2 ]
Cao, Jinzhou [3 ]
Li, Qingquan [1 ,2 ,3 ]
机构
[1] Shenzhen Univ, Coll Civil Engn, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China
[2] Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone, Natl Adm Surveying Mapping & GeoInformat, Shenzhen 518060, Peoples R China
[3] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
来源
XXIII ISPRS Congress, Commission II | 2016年 / 41卷 / B2期
基金
美国国家科学基金会;
关键词
human mobility; smart card data; mobile phone data; space-time GIS; big data; HUMAN MOBILITY PATTERNS; NETWORKS;
D O I
10.5194/isprsarchives-XLI-B2-471-2016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.
引用
收藏
页码:471 / 476
页数:6
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