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使用 DMSP-OLS 方法确定伊斯坦布尔的城市夜间灯光图像中的城市人口数量。

Determination of the nighttime light imagery for urban city population using DMSP-OLS methods in Istanbul.

机构信息

Institute of Social Sciences, Anadolu University, Eskisehir, Turkey.

Institute of Earth and Space Sciences, Eskisehir Technical University, Eskisehir, Turkey.

出版信息

Environ Monit Assess. 2020 Nov 26;192(12):790. doi: 10.1007/s10661-020-08735-y.

DOI:10.1007/s10661-020-08735-y
PMID:33242161
Abstract

Demography researchers and scientists have been effectively utilizing advanced technologies and methods such as geographical information systems, spatial statistics, georeferenced data, and satellite images for the last 25 years. Areal interpolation methods have also been adopted for the development of population density maps which are essential for a variety of social and environmental studies. Still, a good number of social scientists are skeptical about such technologies due to the complexity of methods and analyses. In this regard, a practical intelligent dasymetric mapping (IDM) tool that facilitates the implementation of the statistical analyses was used in this study to develop the population distribution map for the Istanbul metropolitan area via night light data provided by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and the census records of the study area. A population density map was also produced using the choropleth mapping method to enable to make a comparison of the traditional and intelligent population density mapping implementations. According to the dasymetric population density map, 38.5% of the study area fell into sparse density category while low, moderate, high, and very high population density class percentages were found to be 9.4%, 5.5%, 2.9%, and 0.1% respectively. On the other hand, the percentages of the same population density classes ranking from sparse to very high in the choropleth map were determined to be 90.7%, 7.3%, 1.7%, 0.3%, and 0%. In the change analysis made as a result of the classification, the changes between the city area and the population were revealed. During this period, the city area and population grew. Spatial change has also been interpreted by comparing it with population changes. There appears to be a remarkable increase in both surface area and population. It is observed that the increase is especially in the south and northwest of the city. With the population increase, the number of new residential areas has increased. It is thought that behind this growth, there are different reasons besides the effect of the increase in residential areas. When the environmental awareness of people has increased more than in the past centuries, new solutions should be produced in order to be more controlled, smart, and sustainable while planning the cities of the future. Considering that the development of technology and remote sensing techniques is progressing in parallel with this technology, this study in which GIS technologies integrated with satellite images are used, it is thought that it will contribute positively to the studies in this area in terms of regular development of urban areas, increasing the opportunity to make fast and correct decisions, and creating infrastructure for studies such as monitoring and prevention of illegal housing.

摘要

人口统计研究人员和科学家在过去 25 年中一直有效地利用地理信息系统、空间统计、地理参考数据和卫星图像等先进技术和方法。为了进行各种社会和环境研究,还采用了面域插值方法来开发人口密度图。然而,由于方法和分析的复杂性,仍然有相当数量的社会科学家对此类技术持怀疑态度。在这方面,本研究使用了一种实用的智能面域密度制图(IDM)工具,通过利用 Defense Meteorological Satellite Program 的 Operational Linescan System(DMSP-OLS)提供的夜间灯光数据和研究区域的人口普查记录,为伊斯坦布尔大都市区开发人口分布地图,来促进统计分析的实施。还使用了专题制图方法生成人口密度图,以便比较传统和智能人口密度制图的实施情况。根据面域人口密度图,研究区域的 38.5%属于稀疏密度类别,而低、中、高和极高人口密度类别百分比分别为 9.4%、5.5%、2.9%和 0.1%。另一方面,在专题地图中从稀疏到极高的相同人口密度类别的百分比分别确定为 90.7%、7.3%、1.7%、0.3%和 0%。在分类结果的变化分析中,揭示了城市区域和人口之间的变化。在此期间,城市区域和人口都在增长。通过与人口变化进行比较,也解释了空间变化。似乎无论是在表面积还是人口方面都有显著的增长。可以观察到,增长尤其发生在城市的南部和西北部。随着人口的增加,新的住宅区数量也有所增加。除了住宅区增加的影响之外,人们认为这种增长背后还有其他不同的原因。当人们的环境意识比过去几个世纪都有所增强时,应该为未来城市的规划制定更具控制性、智能性和可持续性的新解决方案。考虑到技术和遥感技术的发展与这项技术并行,本研究使用了集成卫星图像的 GIS 技术,因此,它将为城市区域的定期发展、快速和正确决策的机会增加以及为监测和预防非法住房等研究创造基础设施等方面的研究做出积极贡献。

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