Night-time lights: A global, long term look at links to socio-economic trends

Proville, Jeremy; Zavala-Araiza, Daniel; Wagner, Gernot

doi:10.1371/journal.pone.0174610

Cited by 86 publications

(46 citation statements)

References 42 publications

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“…The elasticity between luminosity and electricity consumption is between 0.9 and 1.1, as reported by Proville et al (2017) and the original work of Elvidge et al (1997). This suggests that a 1% drop in luminosity corresponds to a similar 1% drop in electricity consumption.…”

Section: Figsupporting

confidence: 69%

More than an urban legend: the short- and long-run effects of unplanned fertility shocks

Fetzer

Pardo

Shanghavi³

2018

J Popul Econ

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This paper examines the short-and long-run effects of a quasi-exogenous variation in fertility behavior due to a yearlong period of power rationing in Colombia in 1992. We show that power shortages caused a mini baby boom and that the increase in fertility was unplanned and persistent: the time in between births was reduced and overall lifetime fertility increased. We also present evidence suggesting that women who had a baby due to the outage found themselves in worse socioeconomic conditions 12 years later.

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Section: Figsupporting

confidence: 69%

More than an urban legend: the short- and long-run effects of unplanned fertility shocks

Fetzer

Pardo

Shanghavi³

2018

J Popul Econ

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“…Nightlight images give an opportunity to compare changes in night light intensity, which relates to electricity availability, for before and after a disaster. It has been shown that a strong correlation exists between night light intensity and GDP [112,113]. Furthermore, it has also been suggested as a potential proxy for economic resilience assessment [114].…”

Section: Macro Regional and Urban Economicsmentioning

confidence: 99%

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods.While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery.2 of 30 threats, to reduce own overall vulnerability, and to allow the community to recover from adverse impacts when they occur. Decades of disaster research offer extensive findings in this respect [4][5][6][7]. Remote sensing (RS)-as an effective and rapid tool for monitoring large areas-is essential for the acquisition of geospatial data, which in turn constitutes the basis for risk assessment and management. RS is widely used for various aspects of the DRM, ranging from vulnerability [8] to rapid damage assessments [9], for diverse areas ranging from coastal ecosystems [10] to complex urban settings [11], and for disasters as diverse as landslides [12,13] or cyclones [14].Numerous methods have been developed to extract information from RS data to identify, characterize, or quantify different phases of the disaster risk cycle: response, recovery, prevention/mitigation, and preparedness [15]. However, early studies predominantly considered the physical side of the assessments for both ...

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“…The nightlight satellite data can measure several aspects of the city. First, nightlight satellite data can measure socioeconomic developments, such as gross domestic product [25][26][27][28], carbon emission [25,[29][30][31], electricity consumption [32][33][34][35], house vacancy [36], and population study [37][38][39]. Second, nightlight satellite data can measure urban areas in cities.…”

Section: Edges Measured By Nightlight Satellite Datamentioning

confidence: 99%

“…Relationship between areas with composite edges, representing a combined set of all edges related to social and physical features, and crime remains untouched in the literature. Nightlight satellite data, reflection of combined effect of socioeconomic developments [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] and urban constructions [40][41][42][43][44][45][46][47][48][49][50][51], could be a suitable source to measure such composite edges. This study aims to explore the possible impact of composite edges measured by nightlight gradients on street robbery and burglary.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

et al. 2019

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Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

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Night-time lights: A global, long term look at links to socio-economic trends

Cited by 86 publications

References 42 publications

More than an urban legend: the short- and long-run effects of unplanned fertility shocks

More than an urban legend: the short- and long-run effects of unplanned fertility shocks

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

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