Spatial Pattern and Hotspots of Urban Rail Public Transport to Public Access Using Geospatial Techniques in Selangor, Malaysia
Keywords:
spatial information, Geographic Information System (GIS), public transport, urban, access
Abstract
Currently, the trends in urban public transport have been changing over the years in developing countries for mobilization and accessibility development. Urban public transportation systems are the most popular in Selangor State, including big cities such as the Klang Valley Region. Objective measures of spatial pattern and hotspots have been used to understand how urban public transport development relate to open access. This method relies on specific spatial information and available web-based tool that shows the pattern primarily based on given vicinity and statistics connectivity. To date, several studies have finished tested in developed countries. In this study, we use Geographic Information Systems to analyse and consider hotspots identification precisely and efficaciously. Therefore, in this paper, we focus on two types of point sample evaluations – Gi* hot spot and point density analysis evaluation as statistical operations. Public rail transport was evaluated as a validation to describe the percentage of distribution of open access. The final result, GIS mapping capabilities to show that GIS's technology offers to the variation of urban public transport relate to public services, is to create maps and spatial interpretations.
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References
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Abdul Azeez, P., Mohammed Koya, K., Mathew, K. L., Temkar, G. S., & Khileri, R. A. 2016. GIS based mapping of spatio-temporal distribution pattern of ribbonfish Trichiurus lepturus (linnaeus, 1758) along Saurashtra coast, India. Indian Journal of Fisheries, 63(4), 10–14. https://doi.org/10.21077/ijf.2016.63.4.52912-02
Chew, B., Satpathy, A., & Wong, E. 2020. Geospatial analyses to determine academic success factors in California’s K-12 education. Annals of GIS, 26(2), 81–100. https://doi.org/10.1080/19475683.2020.1739141
Department of Agriculture (DOA). 2018. Landuse data, Selangor. Ministry of Agriculture Malaysia.
Department of Statistics (DOSM). 2018. Population census, Selangor. Department of Statistics Malaysia.
Ho, P. W., Ghadiri, S. M., & Rajagopal, P. 2017. Future Parking Demand at Rail Stations in Klang Valley. In MATEC Web of Conferences (Vol. 103). EDP Sciences. https://doi.org/10.1051/matecconf/201710309001
Jiang, Y., Fan, F., Zanoni, I. H., & Li, Y. 2013. Global diffusion pattern and hot spot analysis of vaccine-preventable diseases. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. 4, pp. 167–173). Copernicus GmbH. https://doi.org/10.5194/isprs-annals-IV-4-W2-167-2017
Khalil, N. 2017. Polytechnic: Carving a Niche in the Rail Industy, Ministry of Transport Malaysia.
Khosrow-Pour, D.B.A., M. 2019. Advanced Methodologies and Technologies in Engineering and Environmental Science. IGI Global. http://doi:10.4018/978-1-5225-7359-3.
Kulyk, V., & Sossa, R. 2018. Determining the tourist attractive regions by gis analysis using heatmaps. Geodesy and Cartography, 44(1), 22–27. https://doi.org/10.3846/gac.2018.882
Kuo, P., Lord, D., & Walden, T.D. 2011. Using geographical information systems to effectively organize police patrol routes by grouping hot spots of crash and crime data. Third International Conference on Road Safety and Simulation, September 14-16, 2011, Indianapolis, USA
Li, Y., & Liang, C. 2018. The Analysis of Spatial Pattern and Hotspots of Aviation Accident and Ranking the Potential Risk Airports Based on GIS Platform. Journal of Advanced Transportation, 2018. https://doi.org/10.1155/2018/4027498
Mafumbabete, C., Chivhenge, E., Museva, T., Zingi, G. K., & Ndongwe, M. R. 2019. Mapping the spatial variations in crime in rural Zimbabwe using geographic information systems. Cogent Social Sciences, 5(1). https://doi.org/10.1080/23311886.2019.1661606
Moyo, T., & Musakwa, W. 2019. Exploring the potential of crowd sourced data to map commuter points of interest: A case study of Johannesburg. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 1587–1592). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-2-W13-1587-2019
Olfatifar, M., Karami, M., Moghimbeigi, A., Motlagh, A., Rooshanaee, G., Partovipour, E., & Abdolahi, M. 2017. Spatial clustering of breast cancer: An epidemiological analysis of Iranian women. International Journal of Cancer Management, 10(1). https://doi.org/10.17795/ijcp-5402
Połom, M., Tarkowski, M., & Puzdrakiewicz, K. 2018. Urban Transformation in the Context of Rail Transport Development: The Case of a Newly Built Railway Line in Gdańsk (Poland). Journal of Advanced Transportation, 2018, 1–15. https://doi.org/10.1155/2018/1218041
S. Chainey. 2010. Advanced hotspot analysis: Spatial significance mapping using Gi*. UCL Jill Dando Institute of Crime Science, University College London, London.
Said, S. N. B. M., Zahran, E.-S. M. M., & Shams, S. 2017. Forest Fire Risk Assessment Using Hotspot Analysis in GIS. The Open Civil Engineering Journal, 11(1), 786–801. https://doi.org/10.2174/1874149501711010786
Seman, B. B., & Masron, T. 2019. Hotspot analysis of hand foot and mouth disease (HFMD) using GIS in Kuching, Sarawak, Malaysia. Humanities and Social Sciences Reviews, 7(2), 36–44. https://doi.org/10.18510/hssr.2019.725
Shah, S. A., Neoh, H. M., Rahim, S. S. S. A., Azhar, Z. I., Hassan, M. R., Safian, N., & Jamal, R. 2014. Spatial analysis of colorectal cancer cases in Kuala Lumpur. Asian Pacific Journal of Cancer Prevention, 15(3), 1149–1154. https://doi.org/10.7314/APJCP.2014.15.3.1149
Sibanda, M., Dube, T., Bangamwabo, V. M., Mutanga, O., Shoko, C., & Gumindoga, W. 2016. Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley, Zimbabwe using Geographic Information Systems and remote sensing. Geocarto International, 31(9), 1006–1018. https://doi.org/10.1080/10106049.2015.1094529
Silverman, B. W. 1986. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall.
Uyan, M., Sert, E., Osmanli, N., & Eruc, R. 2017. Determination of Transportation Networks Base on The Optimal Public Transportation Policy Using Spatial And Network Analysis Methods: A Case Of The Konya, TURKEY. International Journal of Engineering and Geosciences, 2(1), 27–34. https://doi.org/10.26833/ijeg.286034
Wang, W., Chang, Z., Liu, X., Zhu, J., & Yu, W. 2017. Evaluation of Fire Ignition Probability Model by Using Spatial Autocorrelation Method: A Case Study in Yunnan Province. Atlantis Press. https://doi.org/10.2991/icammce-17.2017.35
Wubuli, A., Xue, F., Jiang, D., Yao, X., Upur, H., & Wushouer, Q. 2015. Socio-demographic predictors and distribution of pulmonary tuberculosis (TB) in Xinjiang, China: A spatial analysis. PLoS ONE, 10(12). https://doi.org/10.1371/journal.pone.0144010
Zheng, C., Fu, J., Li, Z., Lin, G., Jiang, D., & Zhou, X. N. 2018. Spatiotemporal variation and hot spot detection of visceral leishmaniasis disease in Kashi prefecture, China. International Journal of Environmental Research and Public Health, 15(12). https://doi.org/10.3390/ijerph15122784
Adofo, S. (2013). Challenges And Coping Strategies Of Student Nursing Mothers In Tertiary Institutions In The Greater Accra Region Of Ghana (M.Phil). University of Ghana.
Published
2021-01-06
How to Cite
Yahya, M. S. S., Mohd Safian, E. E. and Burhan, B. (2021) “Spatial Pattern and Hotspots of Urban Rail Public Transport to Public Access Using Geospatial Techniques in Selangor, Malaysia”, Malaysian Journal of Social Sciences and Humanities (MJSSH), 6(1), pp. 234 - 244. doi: 10.47405/mjssh.v6i1.634.
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