Determination of the nighttime light imagery for urban city population using DMSP-OLS methods in Istanbul
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Determination of the nighttime light imagery for urban city population using DMSP-OLS methods in Istanbul Zeynep Ortakavak & Saye Nihan Çabuk & Mehmet Cetin & Muzeyyen Anil Senyel Kurkcuoglu & Alper Cabuk
Received: 9 October 2020 / Accepted: 10 November 2020 # Springer Nature Switzerland AG 2020
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
Z. Ortakavak Institute of Social Sciences, Anadolu University, Eskisehir, Turkey e-mail: [email protected] S. N. Çabuk Institute of Earth and Space Sciences, Eskisehir Technical University, Eskisehir, Turkey e-mail: [email protected] M. Cetin (*) Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey e-mail: [email protected] M. A. Senyel Kurkcuoglu Faculty of Architecture, Middle East Technical University, Ankara, Turkey e-mail: [email protected] A. Cabuk Faculty of Architecture and Design, Eskisehir Technical University, Eskisehir, Turkey e-mail: [email protected]
(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 besid
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