DEVELOPMENT OF ZONAL TRAVEL COST MATRIX USING GOOGLE MAPS DATA
Article Sidebar
Published:
May 4, 2018
Main Article Content
Abstract
This research aims to develop a travel cost matrix of district level analysis zone by using distance and travel time from Google Maps as proxy variable of travel cost. The results indicated that a distance between districts from Google Maps include distance of roads in rural and urban network. Moreover, Google Maps use different speeds for calculating travel time on various road types. Therefore, Google Maps data are in accord with real travel condition and appropriate for travel cost matrix development.
Article Details
Section
บทความวิจัย
References
Candia, R.D. and Chiocchini, R. 2014. “Accessibility to node of interest: a practical application of the various forms of the impedance curves of two border regions.” Journal of the Geographical Institute “Jovan Cvijic” SASA, 64: 215–32.
Dijkstra, E.W. 1959. “A note on two problems in connection with graphs.” Numerische Mathematik, 1: 269–71.
Hwang, H.L. and Rollow, J. 2000. Data processing procedures and methodology for estimating trip distances for the 1995 American Travel Survey (ATS). Oak Ridge, TN: Oak Ridge National Laboratory; 2000 MAY. Report No.: ORNL/TM-2000/141.
Lovett, A., Haynes, R., Stunenberg, G., and Gale, S. 2002. “Car travel time and accessibility by bus to general practitioner services: a study using patient registers and GIS.” Social Science & Medicine, 55: 97-111.
Luo, W. and Wang, F. 2003. “Measures of spatial accessibility to health care in a GIS environment synthesis and a case study in the Chicago region.” Environment and Planning B: Planning and Design, 30: 865-84.
Naude, A., Jong, D.T. and Teeffelen, V.P. 1999. “Measuring accessibility with GIS tools: A case study of the wild coast of South Africa.” Transactions in GIS, 3: 381-95.
Shahid, R., Bertazzon, S., Knudtson, M.L. and Ghali, W.A. 2009. “Comparison of distance measures in spatial analytical modeling for health service planning.” BMC Health Services Research, 9: 1-14.
Wang, F. and Xu, Y. 2011. “Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications.” Annals of GIS, 17: 199-209.
Dijkstra, E.W. 1959. “A note on two problems in connection with graphs.” Numerische Mathematik, 1: 269–71.
Hwang, H.L. and Rollow, J. 2000. Data processing procedures and methodology for estimating trip distances for the 1995 American Travel Survey (ATS). Oak Ridge, TN: Oak Ridge National Laboratory; 2000 MAY. Report No.: ORNL/TM-2000/141.
Lovett, A., Haynes, R., Stunenberg, G., and Gale, S. 2002. “Car travel time and accessibility by bus to general practitioner services: a study using patient registers and GIS.” Social Science & Medicine, 55: 97-111.
Luo, W. and Wang, F. 2003. “Measures of spatial accessibility to health care in a GIS environment synthesis and a case study in the Chicago region.” Environment and Planning B: Planning and Design, 30: 865-84.
Naude, A., Jong, D.T. and Teeffelen, V.P. 1999. “Measuring accessibility with GIS tools: A case study of the wild coast of South Africa.” Transactions in GIS, 3: 381-95.
Shahid, R., Bertazzon, S., Knudtson, M.L. and Ghali, W.A. 2009. “Comparison of distance measures in spatial analytical modeling for health service planning.” BMC Health Services Research, 9: 1-14.
Wang, F. and Xu, Y. 2011. “Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications.” Annals of GIS, 17: 199-209.