2012
DOI: 10.1016/j.physa.2011.11.005
|View full text |Cite
|
Sign up to set email alerts
|

Intra-urban human mobility patterns: An urban morphology perspective

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

7
145
0
1

Year Published

2014
2014
2023
2023

Publication Types

Select...
6
3
1

Relationship

1
9

Authors

Journals

citations
Cited by 210 publications
(153 citation statements)
references
References 30 publications
7
145
0
1
Order By: Relevance
“…After that, the patient spatial distribution demonstrates an obvious distance decay trend: the number of patients decreases with distance. The probability distribution fits well with an exponential distance decay function ( = 0.91) and is consistent with existing findings observed from taxi data [24] and mobile phone data [25], and accords with the general law of people's service utilization. Figure 6 depicts the overall distance decay of visits to all hospitals.…”
Section: Hospital Service Area and Patient Spatial Distribution Patternsupporting
confidence: 88%
“…After that, the patient spatial distribution demonstrates an obvious distance decay trend: the number of patients decreases with distance. The probability distribution fits well with an exponential distance decay function ( = 0.91) and is consistent with existing findings observed from taxi data [24] and mobile phone data [25], and accords with the general law of people's service utilization. Figure 6 depicts the overall distance decay of visits to all hospitals.…”
Section: Hospital Service Area and Patient Spatial Distribution Patternsupporting
confidence: 88%
“…Studies that investigate the characteristic spatio-temporal pattern of the collective human mobility from a more dynamic perspective, and make a comparison between different urban environments, are very rare. For example, previous work has shown a significant differences between cities (areas) along metrics such as: commute distances (Isaacman et al, 2010(Isaacman et al, , 2011a(Isaacman et al, , 2011bBecker et al, 2013); commuting patterns (Amini et al, 2014) mobility patterns (Liu et al, 2009;Isaacman et al 2011b;Calabrese et al, 2011a;Kang et al, 2012;Tanahashi et al, 2012;Amini et al, 2014); community structures (Eagle et al, 2009b;Amini et al, 2014), hotspots (Louail et al, 2014), and population density (Martino et al, 2010;Becker et al, 2013;Csáji et al, 2012;Isaacman et al, 2012;Sagle et al, 2012;Yuan and Raubal, 2012). The findings of such studies can be helpful for policy makers in understanding the characteristics and dynamic nature of different urban areas, as well as updating environmental and (public) transportation policies.…”
Section: Discussionmentioning
confidence: 99%
“…Since 2006, a number of mobile-phone data case studies have been initiated to analyse human mobility patterns (Eagle and Pentland, 2006;Mateos, 2006;Shoval, 2007;González et al, 2008;Liu et al, 2009;Song, et al, 2010aSong, et al, , 2010bHuang et al, 2010;Isaacman et al 2011bIsaacman et al , 2012Calabrese et al, 2011;Kang et al, 2012;Tanashia et al, 2012;Amini et al, 2014). Huang et al (2010) stated that these places and the routes between them are of significant value to effective network management, public transportation planning and city management.…”
Section: Human Dynamics Important Activity Places and Mobility Patternsmentioning
confidence: 99%