Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (2): 233-250.doi: 10.1007/s11442-020-1725-8
• Research Articles • Previous Articles Next Articles
LI Deren1,2, GUO Wei1,2,*(), CHANG Xiaomeng3, LI Xi1,2
Received:
2018-12-16
Accepted:
2019-04-15
Online:
2020-02-25
Published:
2020-04-21
Contact:
GUO Wei
E-mail:guowei98032@gmail.com
About author:
Li Deren (1939-), Professor and Academician of Chinese Academy of Sciences, Academician of Chinese Academy of Engineering, Academician of Euro-Asia International Academy of Sciences, specialized in the research and education on spatial information science and technology represented by RS, GPS and GIS, and promoting the construction of geographic national monitoring, digital city, digital China, smart city and smart China. E-mail: drli@whu.edu.cn
Supported by:
LI Deren, GUO Wei, CHANG Xiaomeng, LI Xi. From earth observation to human observation: Geocomputation for social science[J].Journal of Geographical Sciences, 2020, 30(2): 233-250.
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Table 1
Research foci of geocomputation in social science"
Research topics | Data type | Main results and conclusions |
---|---|---|
Assessment of social and economic development | Nighttime light image | Investigation on the spatial patterns of economic recessions ( |
Identification and evolution analysis of urban agglomeration and urban system ( | ||
Analyses of the impact of urbanization on ecological environment ( | ||
Mobile phone metadata | Socioeconomic status and socioeconomic characteristic of people were inferred ( | |
Remote sensing image | Population consumption and asset changes were predicted ( | |
Remote sensing image and online rental information | Poverty measurement of urban internal space ( | |
Street view image | The demographics and socioeconomic characteristics were estimated and voting trends in presidential elections were predicted (Fei-Fei L, 2017 ) | |
Quantifying the street-visible greenery and estimating the economic benefits that the neighbor visible greenery would have on residential developments ( | ||
High-speed railway and airline networks | The influence of high-speed railway and air networks on urban system was analyzed ( | |
Causal analysis of major social events | Nighttime light image | The impact of war was assessed ( |
Monitoring humanitarian crises (Li et al., 2011) | ||
The correlation between night light change and disaster loss in earthquake-stricken areas was analyzed ( | ||
Assessing the impact of three types of natural disasters: earthquakes, floods, and storms ( | ||
Crowd activity in large cities | Mobile phone data | The taxi demand characteristics and potential land use patterns between two places were revealed ( |
Human mobility was speculated to improve traffic planning and urban planning management ( | ||
The disparities in park access were explored ( | ||
Nighttime light image | The house vacancy rate was estimated. ( | |
Nighttime light image and cancer registry data | There is a significant correlation between the intensity of light at night and the incidence of breast cancer. ( | |
Nighttime light image and taxi trajectories data | The nighttime light and taxi trajectory data were integrated to estimate population at micro levels. ( | |
Research topics | Data type | Main results and conclusions |
Crowd activity in large cities | Taxi trajectories data | The demand-supply of healthcare services was analyzed ( |
Sharing bikes’ trajectories | Illegal parking behaviors were detected to ease traffic congestion ( | |
Transit smart card data | To discuss the influence of housing burden pressure on housing spatial distribution pattern ( | |
Social media data | The development trend and spatial distribution law of emergency events are mined to provide decision-making basis for disaster emergency response ( | |
Street view image | Image detection methods are used to determine the presence of pedestrian and extract pedestrian count data ( | |
Examining associations between exposure to green and blue spaces as well as geriatric depression ( | ||
Crime data | It reveals the spatial-temporal characteristics and influences of crimes, and predicts of space crimes (Liu et al., 2018) | |
Analysis of human activity in virtual space | Social media data | It extracts the public interest and attention to the event and predicts the reported disease level ( |
It reveals the users who made political comments onsocial networking sites were mostly urban males ( | ||
The traveler’s family and workplace were estimated and the characteristics of human travel were depicted (Chang et al., 2017) |
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