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He, L.; Páez, A.; Jiao, J.; An, P.; Lu, C.; Mao, W.; Long, D. |
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Title  |
Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data |
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Journal Article |
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2020 |
Publication |
ISPRS International Journal of Geo-Information |
Abbreviated Journal |
Ijgi |
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9 |
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6 |
Pages |
342 |
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Keywords |
Remote Sensing; Public Safety |
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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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2220-9964 |
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GFZ @ kyba @ |
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2997 |
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Aceituno, J.; Sánchez, S.F.; Aceituno, F.J.; GaladÃ-EnrÃquez, D.; Negro, J.J.; Soriguer, R.C.; Gomez, G.S. |
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Title |
An All-Sky Transmission Monitor: ASTMON |
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Journal Article |
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2011 |
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Publications of the Astronomical Society of the Pacific |
Abbreviated Journal |
Publications of the Astronomical Society of the Pacific |
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123 |
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907 |
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1076-1086 |
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Keywords |
monitoring; light at night; light pollution; skyglow |
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We present here the All-Sky Transmission Monitor (ASTMON), designed to perform a continuous monitoring of the surface brightness of the complete night sky in several bands. The data acquired are used to derive, in addition, a subsequent map of the multiband atmospheric extinction at any location in the sky and a map of the cloud coverage. The instrument has been manufactured to withstand extreme weather conditions and to remain operative. Designed to be fully robotic, it is ideal to be installed outdoors as a permanent monitoring station. The preliminary results based on two of the currently operative units (at Doñana National Park, Huelva, and at the Calar Alto Observatory, AlmerÃa, Spain) are presented here. The parameters derived using ASTMON are in good agreement with those previously reported, which illustrates the validity of the design and the accuracy of the manufacturing. The information provided by this instrument will be presented in forthcoming articles, once we have accumulated a statistically significant amount of data. |
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0004-6280 |
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IDA @ john @ |
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195 |
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Bowden, J. |
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An Analysis of Factors Affecting Catches of Insects in Light-Traps |
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1982 |
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Bulletin of Entomological Research |
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Bull. Entomol. Res. |
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72 |
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4 |
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535-556 |
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Ecology; Animals |
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Analysis of published data on catches of insects in light-traps with a variety of light sources and of different designs showed that all conformed to the previously proposed model describing the functioning of a light-trap: catch = constant × where W = trap illumination and I = background illumination. Light-trap catches in differing cloud conditions and in open and woodland situations also varied as predicted by the model. A table of correction factors for different amounts of cloud cover is provided. The results are discussed in relation to use of light-traps and interpretation of light-trap data. |
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0007-4853 |
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GFZ @ kyba @ |
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2589 |
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Duan, X.; Hu, Q.; Zhao, P.; Wang, S.; Ai, M. |
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Title |
An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery |
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Journal Article |
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Year |
2020 |
Publication |
Remote Sensing |
Abbreviated Journal |
Remote Sensing |
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12 |
Issue |
6 |
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1029 |
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Remote Sensing |
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Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities. |
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2072-4292 |
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GFZ @ kyba @ |
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2870 |
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Author |
Lu, L.; Weng, Q.; Xie, Y.; Guo, H.; Li, Q. |
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Title |
An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery |
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Journal Article |
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Year |
2019 |
Publication |
Energy |
Abbreviated Journal |
Energy |
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189 |
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116351 |
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Keywords |
Remote Sensing; Energy; electric power consumption; Night lights |
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Industrialization and urbanization have led to a remarkable increase of electric power consumption (EPC) during the past decades. To assess the changing patterns of EPC at the global scale, this study utilized nighttime lights in conjunction with population and built-up datasets to map EPC at 1 km resolution. Firstly, the inter-calibrated nighttime light data were enhanced using the V4.0 Gridded Population Density data and the Global Human Settlement Layer. Secondly, linear models were calibrated to relate EPC to the enhanced nighttime light data; these models were then employed to estimate per-pixel EPC in 2000 and 2013. Finally, the spatiotemporal patterns of EPC between the periods were analyzed at the country, continental, and global scales. The evaluation of the EPC estimation shows a reasonable accuracy at the provincial scale with R2 of 0.8429. Over 30% of the human settlements in Asia, Europe, and North America showed apparent EPC growth. At the national scale, moderate and high EPC growth was observed in 45% of the built-up areas in East Asia. The spatial clustering patterns revealed that EPC decreased in Russia and the Western Europe. This study provides fresh insight into the spatial pattern and variations of global electric power consumption. |
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Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, PR China; qweng(at)indstate.edu |
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Elsevier |
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English |
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English |
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0360-5442 |
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GFZ @ kyba @ |
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2701 |
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