From earth observation to human observation: Geocomputation for social science

Fig. 3. Monthly average nighttime light and its corresponding changes in all Syrian provinces between March 2011 and February 2014: (a) monthly average nighttime light in March 2011; (b) monthly average nighttime light in February 2014; (c) sum of nighttime light (SNL); (d) lit area (LA)

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Fig. 4. Class maps and centers derived from normalized time series nighttime light images: (a) two-class map; (b) class centers for the two-class map; (c) three-class map; (d) class centers for the three-class map

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Fig. 5. Spatiotemporal propagation paths and evolution of micro-blog topics: (a) tropical disturbance; (b) super typhoon; (c) landing in Fujian; (d) typhoon demise

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Fig. 6. Smart traffic data brain in Wuhan: Assessment of emergency dredging

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Fig. 7. Smart traffic management brain in Wuhan: Smart emergency platform

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Table 1.
Research foci of geocomputation in social science

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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 (Li et al., 2014)
		Identification and evolution analysis of urban agglomeration and urban system (Yu et al., 2014)
		Analyses of the impact of urbanization on ecological environment (He et al., 2015)
	Mobile phone metadata	Socioeconomic status and socioeconomic characteristic of people were inferred (Blumenstock et al., 2015)
	Remote sensing image	Population consumption and asset changes were predicted (Jean et al., 2016)
	Remote sensing image and online rental information	Poverty measurement of urban internal space (Yuan et al., 2018)
	Street view image	The demographics and socioeconomic characteristics were estimated and voting trends in presidential elections were predicted (Fei-Fei L, 2017 )
	Street view image	Quantifying the street-visible greenery and estimating the economic benefits that the neighbor visible greenery would have on residential developments (Zhang and Dong, 2018)
	High-speed railway and airline networks	The influence of high-speed railway and air networks on urban system was analyzed (Yang et al., 2018)
Causal analysis of major social events	Nighttime light image	The impact of war was assessed (Witmer et al., 2011)
		Monitoring humanitarian crises (Li et al., 2011)
		The correlation between night light change and disaster loss in earthquake-stricken areas was analyzed (Zhang et al., 2018)
		Assessing the impact of three types of natural disasters: earthquakes, floods, and storms (Zhao et al., 2018)
Crowd activity in large cities	Mobile phone data	The taxi demand characteristics and potential land use patterns between two places were revealed (Kang et al., 2013)
		Human mobility was speculated to improve traffic planning and urban planning management (Zhu et al., 2015)
		The disparities in park access were explored (Xiao et al., 2019)
	Nighttime light image	The house vacancy rate was estimated. (Chen et al., 2015)
	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. (Bauer et al., 2013)
	Nighttime light image and taxi trajectories data	The nighttime light and taxi trajectory data were integrated to estimate population at micro levels. (Yu et al., 2019)
Research topics	Data type	Main results and conclusions
Crowd activity in large cities	Taxi trajectories data	The demand-supply of healthcare services was analyzed (Chen et al., 2018)
	Sharing bikes’ trajectories	Illegal parking behaviors were detected to ease traffic congestion (He et al., 2018)
	Transit smart card data	To discuss the influence of housing burden pressure on housing spatial distribution pattern (Li et al., 2018)
	Social media data	The development trend and spatial distribution law of emergency events are mined to provide decision-making basis for disaster emergency response (Wang et al., 2016)
	Street view image	Image detection methods are used to determine the presence of pedestrian and extract pedestrian count data (Li et al., 2015)
	Street view image	Examining associations between exposure to green and blue spaces as well as geriatric depression (Helbich et al., 2019)
	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 (Signorini et al., 2011)
		It reveals the users who made political comments onsocial networking sites were mostly urban males (Barbera and Rivero., 2014)
		The traveler’s family and workplace were estimated and the characteristics of human travel were depicted (Chang et al., 2017)

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Deren LI, Wei GUO, Xiaomeng CHANG, Xi LI. From earth observation to human observation: Geocomputation for social science[J]. Journal of Geographical Sciences, 2020, 30(2): 233

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Paper Information

Received: Dec. 16, 2018

Accepted: Apr. 15, 2019

Published Online: Sep. 29, 2020

The Author Email: GUO Wei (guowei98032@gmail.com)

DOI:10.1007/s11442-020-1725-8

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Table 1. Research foci of geocomputation in social science

Table 1. Research foci of geocomputation in social science

Table 1.
Research foci of geocomputation in social science

Table 1.
Research foci of geocomputation in social science