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Author Zhou, H.; Liu, L.; Lan, M.; Yang, B.; Wang, Z.
Title Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA Type Journal Article
Year 2019 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 11 Issue 17 Pages 1958
Keywords Remote Sensing; Public Safety; Crime
Abstract 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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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial (down) 2828
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Author Li, J.; Xu, Y.; Cui, W.; Ji, M.; Su, B.; Wu, Y.; Wang, J.
Title Investigation of Nighttime Light Pollution in Nanjing, China by Mapping Illuminance from Field Observations and Luojia 1-01 Imagery Type Journal Article
Year 2020 Publication Sustainability Abbreviated Journal Sustainability
Volume 12 Issue 2 Pages 681
Keywords Remote Sensing
Abstract In recent years, the number of artificial light sources has tremendously increased with the development of lighting technology and the economy. Nighttime light pollution has been an increasing environmental problem, resulting in negative impacts on human health and the ecological environment. Detailed knowledge of light pollution is important for the planning and management of urban lighting. In this study, light pollution in Nanjing, China was monitored and analyzed using field observations and a 130-m resolution Luojia 1-01 nighttime light imagery. Combined with in situ observations and satellite imagery, a variety of empirical models were established for estimating ambient illuminance at night. Cross-validation was employed to assess the performance of these models, indicating that the third-degree polynomials model had the best performance (MAE = 5.06 lx, R2 = 0.81). The developed third-degree polynomial model was then applied to the Luojia 1-01 image to map the nighttime illuminance in Nanjing. The nighttime illuminance depicted the spatial pattern of the light environment over Nanjing and also indicated some heavily light-polluted areas. Some lit areas were residential areas, whose high brightness had negative effects on residents and need particular attention. This study provides a quantitative and objective reference for the light pollution management in Nanjing, and also a reference for light pollution survey in other regions.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2071-1050 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial (down) 2823
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Author Ehrlich, D.; Schiavina, M.; Pesaresi, M.; Kemper, T.
Title Detecting spatial pattern of inequalities from remote sensing – Towards mapping of deprived communities and poverty Type Journal Article
Year 2018 Publication EUR 29465 EN Abbreviated Journal
Volume Issue Pages JRC113941
Keywords Remote Sensing
Abstract Spatial inequalities across the globe are not easy to detect and satellite data have shown to be of use in this task. Earth Observation (EO) data combined with other information sources can provide complementary information to those derived from traditional methods. This research shows patterns of inequalities emerging by combining global night lights measured from Earth Observation, population density and built-up in 2015. The focus of the paper is to describe the spatial patterns that emerge by combing the three variables. This work focuses on processing EO data to derive information products, and in combining built-up- and population density with night-time lights emission. The built-up surface was derived entirely from remote sensing archives using artificial intelligence and pattern recognition techniques. The built-up was combined with population census data to derive population density. Also the night-time lights emission data were available from EO satellite sensors. The three layers are subsequently combined as three colour compositions based on the three primary colours (i.e. red, green and blue) to display the “spatial human settlement pattern” maps. These GHSL nightlights provide insights in inequalities across the globe. Many patterns seem to be associated with countries income. Typically, high income countries are very well lit at night, low income countries are poorly lit at night. All larger cities of the world are lit at night, those in low-income countries are often less well lit than cites in high-income countries. There are also important differences in nightlights emission in conflict areas, or along borders of countries. This report provides a selected number of patterns that are described at the regional, national and local scale. However, in depth analysis would be required to assess more precisely that relation between wealth access to energy and countries GDP, for example. This work also addresses regional inequality in GHSL nightlights in Slovakia. The country was selected to address the deprivation of the Roma minority community. The work aims to relate the information from the GHSL nightlights with that collected from field survey and census information conducted at the national level. Socio-economic data available at subnational level was correlated with nightlight. The analysis shows that despite the potential of GHSL nightlights in identifying deprived areas, the measurement scale of satellite derived nightlights at 375 x 375 m to 750 x 750 m pixel size is too coarse to capture the inequalities of deprived communities that occur at finer scale. In addition, in the European context, the gradient of inequality is not strong enough to produce strong evidence. Although there is a specific pattern of GHSL nightlights in settlements with high Roma presence, this cannot be used to identify such areas among the others. This work is part of the exploratory data analysis conducted within the GHSL team. The exploratory analysis will be followed by more quantitative assessments that will be available in future work.
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Corporate Author Thesis
Publisher European Union Place of Publication Luxembourg Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-92-79-97528-8 Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial (down) 2821
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Author Lin, J.; Shi, W.
Title Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light Type Journal Article
Year 2020 Publication ISPRS International Journal of Geo-Information Abbreviated Journal Ijgi
Volume 9 Issue 1 Pages 32
Keywords Remote Sensing
Abstract The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2220-9964 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial (down) 2820
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Author Arderne, C.; Zorn, C.; Nicolas, C.; Koks, E.E.
Title Predictive mapping of the global power system using open data Type Journal Article
Year 2020 Publication Scientific Data Abbreviated Journal Sci Data
Volume 7 Issue 1 Pages 19
Keywords Remote Sensing
Abstract Limited data on global power infrastructure makes it difficult to respond to challenges in electricity access and climate change. Although high-voltage data on transmission networks are often available, medium- and low-voltage data are often non-existent or unavailable. This presents a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. Using state-of-the-art algorithms in geospatial data analysis, we create a first composite map of the global power system with an open license. We find that 97% of the global population lives within 10 km of a MV line, but with large variations between regions and income levels. We show an accuracy of 75% across our validation set of 14 countries, and we demonstrate the value of these data at both a national and regional level. The results from this study pave the way for improved efforts in electricity modelling and planning and are an important step in tackling the Sustainable Development Goals.
Address Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2052-4463 ISBN Medium
Area Expedition Conference
Notes PMID:31941897 Approved no
Call Number GFZ @ kyba @ Serial (down) 2816
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