DOI QR코드

DOI QR Code

Comparative Evaluation of Catchment-wide Solar Radiation to Locate Silver-town

실버타운 입지를 위한 집수구역별 일사량 비교평가

  • Choi, Seon-Jeong (Department of Geography, Kyungpook National University) ;
  • Um, Jung-Sup (Department of Geography, Kyungpook National University)
  • Received : 2014.07.11
  • Accepted : 2014.10.17
  • Published : 2014.10.30

Abstract

It is usual to determine silver-town location by people's experienced knowledge or intuition considering many different type of thematic variables simultaneously. This paper is primarily intended to locate sunny silver-town according to catchment-wide solar radiation as single key variable. GIS based solar simulation realistically identified catchment-wide solar radiation in the study area using large scale spatial precision. More than 90% over the worst catchment were identified shadow surfaces while the optimal catchment was heavily covered by sunny radiation surfaces. It is confirmed that standard GIS technology can offers the viable method of measuring and comparing the catchment-wide solar radiation. Guidelines for a replicable methodology are presented to provide a strong theoretical basis for the standardization of factors involved in locating the sunny silver-town; delineation of catchment boundary, solar simulation, catchment-wide comparison etc. They could be used as an evidence to determine sunny catchment in comparison with other catchment, based solar simulation. It is anticipated that this research output could be used as a valuable reference to confirm the potential of introducing the new concept of "catchment specific solar radiation" to support more scientific and objective decision-making in the process of locating silver town.

Keywords

References

  1. Choi, Y, O., Study of standards for valuation of silver-town in Korea, Journal of Korean Association for Local Government Studies, Vol. 17, No. 3, 135-156,2005
  2. Han, H, K, Oh, D, S., A study on the planning guidelines of silver towns according to their locations, Journal of Architectural Institute of Korea, Vol. 18, No. 1, 95-10, 1998
  3. Son, J. W., Han, G, J. and Lee, T, K, A Study on the improvement directions and conditions of spacial composition according to location style of silver towns, Journal of Korea Institute of Healthcare Architecture, Vol. 7, No. 1, 7-14, 2001
  4. Shin, H, I., Jeon, H, S., Yang, O, J. and Cho G, S., A study on the land suitability analysis of silver town using neural network, Journal of Korean Society for Geospatial Information System, Vol. 8, No. 2, 117-127, 2000
  5. Kim, I, K. and Kim, O S., Effect of vitamin D supplementation on the physiological indices, muscle mass, and physical functions of aged women, The Journal of Korean Academic Society of Adult Nursing, Vol. 25, No. 2, 539-548, 2013 https://doi.org/10.7475/kjan.2013.25.5.539
  6. Park, M, K, Ground planning of atopic healing eco-town in Palgong mountain, Daegu-Gyeongbuk Development Institute, 2010
  7. Um, J, S., Evaluating explanatory power of solar intensity as determining factor of housing density in intermontane basin, Journal of Korean Association of Regional Geography Vol. 15, No. 6, 689-706, 2009
  8. Tabikl, S., Villegas, A, Zapata E. L. and Romero L. F., A fast GIS-tool to compute the maximum solar energy on very large terrains, Procedia Computer Science, 9, 364-372, 2012 https://doi.org/10.1016/j.procs.2012.04.039
  9. Freitas S., Catita, C. Redweik, P. and Brito C., Modelling solar potential in the urban environment: State-of-the-art review, Renewable and Sustainable Energy Reviews, 41, 915-931, 2010
  10. ESRI. ArcGIS Desktop: Release 10. Redlands, CA. Environmental Systems Research Institute. 2011
  11. Cromley, R. G., Classed versus unclassed Choropleth maps: a question of how many classes, Cartographica, 32, 15 - 28, 1995 https://doi.org/10.3138/J610-13NU-5537-0483
  12. Vandal N. and Hegman W. Micro-climate solar modeling over complex terrain: a validation study of ESRI solar analyst, technical Papers, ESRI User Conference Proceedings. 2007
  13. Wiginton LK., Nguyen HT. and Pearce J. M. Quantifying rooftop solar photovoltaic potentia for regional renewable energy policy, Computer Environment and Urban System, Vol. 34, No. 4, 345-57. 2010 https://doi.org/10.1016/j.compenvurbsys.2010.01.001
  14. Brito MC., Gomes N., Santos, T. and Tenedorio J. Photovoltaic potential in Lisbon suburb using LiDAR data, Solar Energy, Vol. 86, No. 1, 283-288. 2012 https://doi.org/10.1016/j.solener.2011.09.031
  15. Choi, Y., Rayl, J., Tammineedi, J. C., and Brownson, R.S., PV Analyst: Coupling ArcGIS with TRNSYS to assess distributed photovoltaic potential in urban areas, Vol. 85, No.11, pp.2924-2939. 2011 https://doi.org/10.1016/j.solener.2011.08.034
  16. Huang, S., Rich, R.L., Crabtree, C.S., and Fu, P., Modeling monthly near-surface air temperature from solar radiation and lapse rate:application over complex terrain in Yellowstone National Park, USA. Physical Geography 29, 158-178, 2008 https://doi.org/10.2747/0272-3646.29.2.158