UNDERSTANDING THE URBAN POPULATION DENSITY AND GROWTH CONCENTRATION IN PALEMBANG INDONESIA

Muhammad Fajri Romdhoni

Abstract


ABSTRACT

 

This research is to understand the current condition of Density, population, and growth concentration in the city of Palembang. As one of the oldest cities in Indonesia, Palembang has been one of the twelve fastest growing cities in Indonesia, with more than one point six million people in 2018. According to the 2018 United Nations booklet (Nations, 2018) the Indonesian cities will grow with the annual rate of 2.4% to 6%, and undoubtedly this growth will impact the city’s urban fabric. This paper uses the Landsat images, and OpenStreetMap (OSM) data sets combined from the 2018 Population statistics in Palembang by overlaying the data to examine the statistical growth and urban density in the city of Palembang. The maps will also use the space syntax method and natural street analysis to determine the human behavior and traffic movement pattern and how the current urban layout should be examined. This street network's potential will serve as a narrative for further development to the city of Palembang.

 

Keywords: density, population growth, natural street, space syntax, Palembang.


Keywords


density; population growth; natural street; space syntax; Palembang.

Full Text:

PDF

References


Adiyanto, J., Nugroho, S., & Atyanta, A. (2018). ANALYSIS OF URBAN FORM AND INFRASTRUCTURE IN PALEMBANG. ATRIUM Jurnal Arsitektur, 4(1), 1–9.

Anderson, W. P., Kanaroglou, P. S., & Miller, E. J. (1996). Urban form, energy and the environment: A review of issues, evidence and policy. Urban Studies, 33(1), 7–35.

Bauer, M. E., Loffelholz, B. C., & Wilson, B. (2007). Estimating and mapping impervious surface area by regression analysis of Landsat imagery. In Remote sensing of impervious surfaces (pp. 31–48). CRC Press.

Bhatta, B. (2010). Analysis of urban growth and sprawl from remote sensing data. Springer Science & Business Media.

Glaeser, E. L., & Kahn, M. E. (2004). Sprawl and urban growth. In Handbook of regional and urban economics (Vol. 4, pp. 2481–2527). Elsevier.

Hillier, B. (2007). Space is the machine: A configurational theory of architecture. Space Syntax.

Hillier, B. H., & Hanson, J. (1984). J. 1984 The Social Logic of Space. Cambridge University.

Jabareen, Y. R. (2006). Sustainable urban forms: Their typologies, models, and concepts. Journal of Planning Education and Research, 26(1), 38–52.

Jiang, B., & Jia, T. (2011). Agent-based simulation of human movement shaped by the underlying street structure. International Journal of Geographical Information Science, 25(1), 51–64.

Jiang, B., Zhao, S., & Yin, J. (2008). Self-organized natural roads for predicting traffic flow: A sensitivity study. Journal of Statistical Mechanics: Theory and Experiment, 2008(07), P07008.

Joseph, M., Wang, L., & Wang, F. (2012). Using Landsat imagery and census data for urban population density modeling in Port-au-Prince, Haiti. GIScience & Remote Sensing, 49(2), 228–250.

Ma, D., Omer, I., Osaragi, T., Sandberg, M., & Jiang, B. (2019). Why topology matters in predicting human activities. Environment and Planning B: Urban Analytics and City Science, 46(7), 1297–1313.

Nations, U. (2018). The World’s Cities in 2018. https://www.un-ilibrary.org/content/publication/c93f4dc6-en

Norman, J., MacLean, H. L., & Kennedy, C. A. (2006). Comparing high and low residential density: Life-cycle analysis of energy use and greenhouse gas emissions. Journal of Urban Planning and Development, 132(1), 10–21.

Nugroho, S. (2012). Urban Morphology at Lowland Environment in Palembang. Department-DWCU, Yogyakarta.

Omer, I., & Jiang, B. (2015). Can cognitive inferences be made from aggregate traffic flow data? Computers, Environment and Urban Systems, 54, 219–229.

Subasinghe, S., Estoque, R., & Murayama, Y. (2016). Spatiotemporal analysis of urban growth using GIS and remote sensing: A case study of the Colombo metropolitan area, Sri Lanka. ISPRS International Journal of Geo-Information, 5(11), 197.

Wang, F. (2006). Quantitative methods and applications in GIS. CRC Press.

Wu, C., & Murray, A. T. (2003). Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84(4), 493–505.

Yang, X. (2006). Estimating landscape imperviousness index from satellite imagery. IEEE Geoscience and Remote Sensing Letters, 3(1), 6–9.

York University (Toronto, O. ) F. of E. S., & Lang, R. (1986). Residential density and energy conservation.




DOI: http://dx.doi.org/10.12962/j2355262x.v19i2.a7510

Refbacks

  • There are currently no refbacks.


 

Indexing and Abstracting :

         

 


Journal of Architecture & Environment is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

 

View JoAE Stats