Utilizing Nighttime Photos to Locate Attraction Zones at the Metropolitan Scale: An Analysis of Istanbul
DOI:
https://doi.org/10.15320/ICONARP.2022.221Keywords:
Night-time image, GIS (geographic information systems) and RS (remote sensing) integration, city attraction zones, urban scale, IstanbulAbstract
Up-to-date information about different forms of land-use (residential areas, industrial areas, central business districts, recreational areas, etc.) is essential for city planning processes to obtain better urban and regional planning decisions. Traditional methods (e.g., field surveying or WEB/GPS based data collection) used to gather up-to-date information can often contain some errors and can also be time-consuming and expensive, especially for large metropolitan urban areas. With the integration of Remote Sensing (RS) and Geographic Information Systems (GIS) related technologies, the difficulty of providing up-to-date information about different types of land-use can be greatly reduced. On the other hand, in terms of urban and regional planning, the level of utilization of these technologies is still considered to be insufficient. In this respect, authors wanted to draw attention to another possible usage of night-time data in urban and regional planning discipline for the purpose of determination of the location, size, and hierarchy of the attraction zones in urban scale which are mostly composed of central business districts (CBD), commercial zones, touristic corridors and/or concentration areas etc., as these regions are more illuminated areas compared to other zones of a city. Thus, a methodology based on GIS and RS integration and spatial and statistical analysis capabilities of GIS is presented in this study to determine the boundary and size of the attraction zones and their hierarchical levels by using the night-time imageries. To show how effective the suggested model is, the proposed methodology has been used in the city of Istanbul. The assessment of the location, size, and hierarchy of the attraction zones could give an essential decision support for the decision makers, especially those working in the urban planning discipline, as the attraction zones of cities need to be developed in a more specific and detailed manner. Thus, the model's outputs' reliability and potential applications in the field of urban planning are also examined.
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References
Dobson, J.E., Bright, E.A, Coleman, P.R., Durfee, R.C., and Worley, B.A. (2000). Landscan: A Global Population Database for Estimating Populations at Risk, Photogrammetric Engineering and Remote Sensing, Vol. 66, No.7, pp. 849-857.
Doll, C.N.H., Muller, J-P., Morley, J.G. (2006). Mapping regional economic activity from night-time light satellite imagery, Ecological Economics, vol.57, pp. 75-92.
Doll, C.N.H. (2010). Proceedings of the Asia-Pacific Advanced Network, Population detection profiles of DMSP-OLS night-time imagery by regions of the world, v. 30, p. 190-206.
Elvidge, C.D., Cinzano, P., Pettit, D.R., Arvesen, J., Sutton, P., Small, C., Nemanı, R., Longcore, T., Rich, C., Safran, J., Weeks, J., and Ebener, S. (2007). The Nightsat mission concept, International Journal of Remote Sensing, Vol. 28, No.12, pp. 2645-2670.
Elvidge, C. D., Baugh, K., Zhizhin, M., Chi Hsu, F. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights, Proceedings of the Asia-Pacific Advanced Network v. 35, p. 62-69. http://dx.doi.org/10.7125/APAN.35.7 ISSN 2227-3026.
Henderson, M., Yeh, E.T., Gong, P., Elvidge, C., Baugh, K. (2003). Validation of urban boundaries derived from global night-time satellite imagery, International Journal of Remote Sensing, vol. 24, No.3, pp.595-609.
IMP. (2009). 1/100.000 ölçekli İstanbul Çevre Düzeni Planı ve Plan Raporu, Bimtaş, IMP- İstanbul Metropolitan Planlama ve Kentsel Tasarım Merkezi, Tepebaşı, Istanbul
Ju Y., Dronova I. , Ma Q., Zhang X. (2017). Analysis of urbanization dynamics in mainland China using pixel-based night-time light trajectories from 1992 to 2013, International Journal Of Remote Sensing, Vol. 38, No. 6047–6072.
Levin, N., Johansen, K., Hacker, J. M., Phinn, S. (2014). A new source for high spatial resolution night time images — The EROS-B commercial satellite, Remote Sensing of Environment, Vol. 149, page.1–12.
Levin N., Phinn, S. (2016). Illuminating the capabilities of Landsat 8 for mapping night lights, Remote Sensing of Environment, vol.182, pp. 27–38.
Levin N., Zhang, Q. (2017). A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas, Remote Sensing of Environment, Vol. 190, pp. 366–382.
Li, D., Zhao, X., Li, X. (2016). Remote sensing of human beings – a perspective from nighttime light, vol.19, pp.69-79.
Li X., Zhou, Y. (2017). Urban mapping using DMSP/OLS stable night-time light: a review, International Journal of Remote Sensing, VOL. 38, pp. 6030–6046.
Liu, Q., Sutton, P.C., Elvidge, C.D. (2011). Relationship between nighttime imagery and population density for Hong Kong, Proceedings of the Asia-Pacific Advanced Network, v.31, pp.79-90.
Ma, T., Zhou, C., Pei, T., Haynie, S., Fan, J. (2012). Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities, Remote Sensing of Environment, vol. 124, pp.99-107.
Ma, T., Zhou, Y., Zhou, C., Haynie, S., Pei, T., Xu, T. (2014). Night-time light derived estimation of spatio-temporal characteristics of urbanization dynamics using DMSP/OLS satellite data, Remote Sensing of Environment, vol. 158, pp.453-464.
Press W.H., Teukolsky S.A., Flannery B.P. (2002). Numerical Recipes in C, The Art of Scientific Computing Second Edition, Cambridge University Press Cambridge New York Port Chester Melbourne Sydney pp. 644-645, (ISBN 0-521-43108-5).
Shao, Z., Liu, C. (2014). The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics: A Case Study in the Yangtze River Delta, Remote Sensing, vol.6, pp. 9359-9378.
Yu, B., Shi, K., Hu, Y., Huang, C., Chen, Z., Wu, J. (2015). Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, pp.1217-1228.
Zhang Q, Seto K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data, Remote Sensing of Environment, vol. 115, pp. 2320-2329.
Web1. (2022, November 10). (date of last controlled).
https://ngdc.noaa.gov/eog/sensors/ols.html
Web 2. (2022, November 10). (date of last controlled). https://eol.jsc.nasa.gov/SearchPhotos/photo.pl?mission=ISS006&roll=E&frame=42606
Web 3. (2022, November 10). (date of last controlled). https://eol.jsc.nasa.gov/SearchPhotos/photo.pl?mission=ISS032&roll=E&frame=23534
Web 4. (2022, November 10). (date of last controlled). https://asterweb.jpl.nasa.gov/gdem.asp
Web 5. (2022, November 10). (date of last controlled). https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-kernel-density-works.htm
Web 6. (2022, November 10). (date of last controlled). http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm
Web 7. (2022, November 10). (date of last controlled). https://download.geofabrik.de/
Web 8. (2022, November 10). (date of last controlled). https://www.goktugbeser.com/istanbul-merkezi-is-alanlarinin-gelisimi
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