Peta Pola Sebaran Daya Beli Penduduk dan Pasar Retail Kota Banjarmasin
DOI:
https://doi.org/10.20527/jiep.v8i1.2595Keywords:
Purchasing power, spatial mapping, welfare composite index, retail markets, geographic information system, Banjarmasin CityAbstract
This study aims to map the estimated purchasing power of residents across 52 sub-districts in Banjarmasin City and to analyze the visual relationship between purchasing power and the distribution of retail markets. The data used include the 2017 Per Capita Monthly Consumption Expenditure from BPS and the 2018 PODES data, with the proportion of households in slum settlements and health subsidy recipients serving as proxies, which were processed into a Composite Welfare Index. The analysis reveals a significant disparity in purchasing power, ranging from IDR 1,030,976 to IDR 1,818,254 per capita. Sub-districts in the southern part of Banjarmasin generally exhibit the lowest purchasing power, while the northern and central areas are above the city average. Traditional markets are concentrated in the city center, whereas minimarkets are evenly distributed, primarily following areas with high purchasing power. These findings underscore the importance of spatial mapping-based policies to reduce economic disparities and optimize the distribution of retail services in urban areas.
References
Arif, M., & Gupta, K. (2020). Spatial development planning in peri-urban space of Burdwan City, West Bengal, India: statutory infrastructure as mediating factors. SN Applied Sciences, 2(11). https://doi.org/10.1007/s42452-020-03587-0
Badan Pusat Statistik (2018) ‘Potensi Desa 2018’, Badan Pusat Statistik. Jakarta: Badan Pusat Statistik.
Bigman, D. and Fofack, H. (2000) Geographical Targeting for Poverty Alleviation, The World Bank Economic Review. The World Bank. doi: 10.1596/0-8213-4625-3.
BPS Kota Banjarmasin (2018) Kecataman Banjarmasin Barat Dalam Angka 2018, BPS Kota Banjarmasin.
BPS Kota Banjarmasin (2018) Kecataman Banjarmasin Tengah Dalam Angka 2018, BPS Kota Banjarmasin.
BPS Kota Banjarmasin (2018) Kecataman Banjarmasin Timur Dalam Angka 2018, BPS Kota Banjarmasin.
BPS Kota Banjarmasin (2018) Kecataman Banjarmasin Selatan Dalam Angka 2018, BPS Kota Banjarmasin.
BPS Kota Banjarmasin (2018) Kecataman Banjarmasin Utara Dalam Angka 2018, BPS Kota Banjarmasin.
BPS Kota Banjarmasin (2018) Kota Banjarmasin Dalam Angka 2018, BPS Kota Banjarmasin.
Damborsky, M. (2007) ‘Location and Spatial Development’, in 2nd Central European Conference in Regional Science – CERS. Available at: http://www3.ekf.tuke.sk/cers/cers2007/PDF/Damborsky.pdf.
Fischer, M. M. (2015) ‘Spatial Analysis in Geography’, in International Encyclopedia of the Social & Behavioral Sciences: Second Edition. Elsevier Inc., pp. 94–99. doi: 10.1016/B978-0-08-097086-8.72054-X.
Fujita, M. (2010) ‘The evolution of spatial economics: From thünen to the new economic geography’, Japanese Economic Review, 61(1), pp. 1–32. doi: 10.1111/j.1468-5876.2009.00504.x.
Ganasegeran, K., Looi, I., Ch’Ng, A. S. H., Peariasamy, K. M., & Jamil, M. F. A. (2021). Influence of Population Density for COVID-19 Spread in Malaysia: An Ecological Study. International Journal of Environmental Research and Public Health, 18(18), 9866. https://doi.org/10.3390/ijerph18189866
Gibson, J. and McKenzie, D. (2007) ‘Using global positioning systems in household surveys for better economics and better policy’, World Bank Research Observer, 22(2), pp. 217–241. doi: 10.1093/wbro/lkm009.
Gielen, E., Riutort-Mayol, G., Palencia Jiménez, J. S., & Miralles I Garcia, J. L. (2019). Cost assessment of urban sprawl on municipal services using hierarchical regression. Environment and Planning B: Urban Analytics and City Science, 48(2), 280–297. https://doi.org/10.1177/2399808319869345
Gorter, C. and Nijkamp, P. (2015) ‘Location Theory’, Elseview Ltd. Elsevier Inc., pp. 287–292. doi: 10.1016/B978-0-08-097086-8.72029-0.
Hernández, T. and Bennison, D. (2000) ‘The art and science of retail location decisions’, International Journal of Retail & Distribution Management, 28(8), pp. 357–367. doi: 10.1108/09590550010337391.
Imansyah, M. H. et al. (2019) Transformasi Pasar Tradisional Kota Banjarmasin di Era Disrupsi. Banjarmasin, Bappeda Litbang Kota Banjarmasin.
Larsson, J. P. and Öner, Ö. (2014) ‘Location and co-location in retail: A probabilistic approach using geo-coded data for metropolitan retail markets’, Annals of Regional Science. Springer Verlag, 52(2), pp. 385–408. doi: 10.1007/s00168-014-0591-7.
Martino, N., Girling, C., & Lu, Y. (2021). Urban form and livability: socioeconomic and built environment indicators. Buildings and Cities, 2(1), 220–243. https://doi.org/10.5334/bc.82
Naess, P. (2010). Residential location, travel, and energy use in the Hangzhou Metropolitan Area. Journal of Transport and Land Use, 3(3). https://doi.org/10.5198/jtlu.v3i3.98
Öner, O. (2014) Retail Location, Jönköping International Business School, Jönköping University. Jönköping International Business School.
Segal, D. B. (1999) ‘Retail Trade Area Analysis: Concepts and New Approaches’, The Journal of Database Marketing, 6(3), pp. 267–277.
Shi, C., Ou, Z., Meng, B., Li, X., & Yuan, Y. (2023). Examining Changes in Consumer Spatial Structure and Sustainable Development Issues in Beijing before and after the Outbreak of COVID-19. Sustainability, 15(23), 16451. https://doi.org/10.3390/su152316451
Shih, W.-Y., Ahmad, S., Chen, Y.-C., Lin, T.-P., & Mabon, L. (2020). Spatial relationship between land development pattern and intra-urban thermal variations in Taipei. Sustainable Cities and Society, 62(2018), 102415. https://doi.org/10.1016/j.scs.2020.102415
Stevens, F. R., Gaughan, A. E., Nieves, J. J., King, A., Sorichetta, A., Linard, C., & Tatem, A. J. (2019). Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South. International Journal of Digital Earth, 13(1), 78–100. https://doi.org/10.1080/17538947.2019.1633424
Tripathi, S. (2013). Do Large Agglomerations Lead to Economic Growth? Evidence from Urban India. Review of Urban & Regional Development Studies, 25(3), 176–200. https://doi.org/10.1111/rurd.12014
Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. Applied Geography, 157, 102973. https://doi.org/10.1016/j.apgeog.2023.102973
Wang, Q., & Yang, X. (2018). Urbanization impact on residential energy consumption in China: the roles of income, urbanization level, and urban density. Environmental Science and Pollution Research, 26(4), 3542–3555. https://doi.org/10.1007/s11356-018-3863-4
Wang, Y., Feng, Y., Han, K., Zheng, Z., & Dai, P. (2025). Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors. Buildings, 15(2), 195. https://doi.org/10.3390/buildings15020195
Worrall, L. (1994) ‘The role of GIS-based spatial analysis in strategic management in local government’, Computers, Environment and Urban Systems, 18(5), pp. 323–332. doi: 10.1016/0198-9715(94)90014-0.
Worrall, L. and Bond, D. (1997) ‘Geographical Information Systems, Spatial Analysis and Public Policy: the British Experience’, International Statistical Review, 65(3), pp. 365–379.
Zhang, J., Yu, Z., Wang, X., & Li, Y. (2023). Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception. Land, 12(7), 1322. https://doi.org/10.3390/land12071322
