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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 (down) 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.
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 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2828
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Author He, L.; Páez, A.; Jiao, J.; An, P.; Lu, C.; Mao, W.; Long, D.
Title Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data Type Journal Article
Year 2020 Publication ISPRS International Journal of Geo-Information Abbreviated Journal Ijgi
Volume 9 Issue 6 Pages 342
Keywords (down) Remote Sensing; Public Safety
Abstract In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts.
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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 2220-9964 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2997
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Author Franklin, M.; Chau, K.; Cushing, L.J.; Johnston, J.
Title Characterizing flaring from unconventional oil and gas operations in south Texas using satellite observations Type Journal Article
Year 2019 Publication Environmental Science & Technology Abbreviated Journal Environ Sci Technol
Volume 53 Issue 4 Pages 2220-2228
Keywords (down) Remote Sensing; petroleum; Texas; United States; VIIRS-DNB; Eagle Ford Shale; flaring; oil and gas
Abstract Over the past decade, increases in high-volume hydraulic fracturing for oil and gas extraction in the United States have raised concerns with residents living near wells. Flaring, or the combustion of petroleum products into the open atmosphere, is a common practice associated with oil and gas exploration and production, and has been under-examined as a potential source of exposure. We leveraged data from the Visible Infrared Imaging Spectroradiometer (VIIRS) Nightfire satellite product to characterize the extent of flaring in the Eagle Ford Shale region of south Texas, one of the most productive in the nation. Spatiotemporal hierarchical clustering identified flaring sources, and a regression-based approach combining VIIRS information with reported estimates of vented and flared gas from the Railroad Commission of Texas enabled estimation of flared gas volume at each flare. We identified 43,887 distinct oil and gas flares in the study region from 2012-2016, with a peak in activity in 2014 and an estimated 4.5 billion cubic meters of total gas volume flared over the study period. A comparison with well permit data indicated the majority of flares were associated with oil-producing (82%) and horizontally-drilled (92%) wells. Of the 49 counties in the region, 5 accounted for 71% of the total flaring. Our results suggest flaring may be a significant environmental exposure in parts of this region.
Address Department of Preventive Medicine, University of Southern California, Los Angeles California 90032, United States; meredith.franklin(at)usc.edu
Corporate Author Thesis
Publisher ACS Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0013-936X ISBN Medium
Area Expedition Conference
Notes PMID:30657671 Approved no
Call Number GFZ @ kyba @ Serial 2175
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Author Mann, M.; Melaas, E.; Malik, A.
Title Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India Type Journal Article
Year 2016 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 8 Issue 9 Pages 711
Keywords (down) Remote Sensing; NPP-VIIRS; VIIRS-DNB; India; South Asia
Abstract Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity reliability for western India at a finer spatial scale. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite together with newly-available data from networked household voltage meters. Our results point to the possibilities of this approach as well as areas for refinement. With currently available training data, we find a limited ability to detect individual outages identified by household-level measurements of electricity voltage. This is likely due to the relatively small number of individual outages observed in our preliminary data. However, we find that the approach can estimate electricity reliability rates for individual locations fairly well, with the predicted versus actual regression yielding an R2 > 0.5. We also find that, despite the after midnight overpass time of the SNPP satellite, the reliability estimates derived are representative of daytime reliability.
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 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ kyba @ Serial 1515
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Author Song, J.; Tong, X.; Wang, L.; Zhao, C.; Prishchepov, A.V.
Title Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach Type Journal Article
Year 2019 Publication Landscape and Urban Planning Abbreviated Journal Landscape and Urban Planning
Volume 190 Issue Pages 103580
Keywords (down) Remote Sensing; nighttime light; numerical methods
Abstract Spatial distribution information on population density is essential for understanding urban dynamics. In recent decades, remote sensing techniques have often been applied to assess population density, particularly night-time light data (NTL). However, such attempts have resulted in mapped population density at coarse/medium resolution, which often limits the applicability of such data for fine-scale territorial planning. The improved quality and availability of multi-source remote sensing imagery and location-based service data (LBS) (from mobile networks or social media) offers new potential for providing more accurate population information at the micro-scale level. In this paper, we developed a fine-scale population distribution mapping approach by combining the functional zones (FZ) mapped with high-resolution satellite images, NTL data, and LBS data. Considering the possible variations in the relationship between population distribution and nightlight brightness in functional zones, we tested and found spatial heterogeneity of the relationship between NTL and the population density of LBS samples. Geographically weighted regression (GWR) was thus implemented to test potential improvements to the mapping accuracy. The performance of the following four models was evaluated: only ordinary least squares regression (OLS), only GWR, OLS with functional zones (OLS&FZ) and GWR with functional zones (GWR&FZ). The results showed that NTL-based GWR&FZ was the most accurate and robust approach, with an accuracy of 0.71, while the mapped population density was at a unit of 30 m spatial resolution. The detailed population density maps developed in our approach can contribute to fine-scale urban planning, healthcare and emergency responses in many parts of the world.
Address Department of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, Denmark; songjinchao08(at)163.com
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0169-2046 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2516
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