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Author Cabrera-Barona, P.F.; Bayón, M.; Durán, G.; Bonilla, A.; Mejía, V. url  doi
openurl 
  Title Generating and Mapping Amazonian Urban Regions Using a Geospatial Approach Type Journal Article
  Year 2020 Publication ISPRS International Journal of Geo-Information Abbreviated Journal Ijgi  
  Volume 9 Issue 7 Pages 453  
  Keywords Remote Sensing  
  Abstract (1) background: Urban representations of the Amazon are urgently needed in order tobetter understand the complexity of urban processes in this area of the World. So far, limited workthat represents Amazonian urban regions has been carried out. (2) methods: Our study area is theEcuadorian Amazon. We performed a K-means algorithm using six urban indicators: Urban fractaldimension, number of paved streets, urban radiant intensity (luminosity), and distances to theclosest new deforested areas, to oil pollution sources, and to mining pollution sources. We alsocarried out fieldwork to qualitatively validate our geospatial and statistical analyses. (3) results:We generated six Amazonian urban regions representing different urban configurations and processesof major cities, small cities, and emerging urban zones. The Amazonian urban regions generatedrepresent the urban systems of the Ecuadorian Amazon at a general scale, and correspond to theurban realities at a local scale. (4) conclusions: An Amazonian urban region is understood as a set ofurban zones that are dispersed and share common urban characteristics such a similar distance tooil pollution sources or similar urban radiant intensity. Our regionalization model represents thecomplexity of the Amazonian urban systems, and the applied methodology could be transferred toother Amazonian countries.  
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  ISSN 2220-9964 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3115  
Permanent link to this record
 

 
Author He, L.; Páez, A.; Jiao, J.; An, P.; Lu, C.; Mao, W.; Long, D. url  doi
openurl 
  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 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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  ISSN 2220-9964 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2997  
Permanent link to this record
 

 
Author Lin, J.; Shi, W. url  doi
openurl 
  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.  
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  ISSN 2220-9964 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2820  
Permanent link to this record
 

 
Author Wang,; Sutton,; Qi, url  doi
openurl 
  Title Global Mapping of GDP at 1 km2 Using VIIRS Nighttime Satellite Imagery Type Journal Article
  Year 2019 Publication ISPRS International Journal of Geo-Information Abbreviated Journal Ijgi  
  Volume 8 Issue 12 Pages 580  
  Keywords Remote Sensing  
  Abstract Frequent and rapid spatially explicit assessment of socioeconomic development is critical for achieving the Sustainable Development Goals (SDGs) at both national and global levels. Over the past decades, scientists have proposed many methods for estimating human activity on the Earth’s surface at various spatiotemporal scales using Defense Meteorological Satellite Program Operational Line System (DMSP-OLS) nighttime light (NTL) data. However, the DMSP-OLS NTL data and the associated processing methods have limited their reliability and applicability for systematic measuring and mapping of socioeconomic development. This study utilized Visible Infrared Imaging Radiometer Suite (VIIRS) NTL and the Isolation Forest machine learning algorithm for more intelligent data processing to capture human activities. We used machine learning and NTL data to map gross domestic product (GDP) at 1 km2. We then used these data products to derive inequality indexes (e.g., Gini coefficients) at nationally aggregate levels. This flexible approach processes the data in an unsupervised manner at various spatial scales. Our assessments show that this method produces accurate subnational GDP data products for mapping and monitoring human development uniformly across the globe.  
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  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 2787  
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Author Niu, W.; Xia, H.; Wang, R.; Pan, L.; Meng, Q.; Qin, Y.; Li, R.; Zhao, X.; Bian, X.; Zhao, W. url  doi
openurl 
  Title Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data Type Journal Article
  Year 2021 Publication ISPRS International Journal of Geo-Information Abbreviated Journal Ijgi  
  Volume 10 Issue 1 Pages 5  
  Keywords Remote Sensing  
  Abstract As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.  
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  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 3225  
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