نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 دانشجوی دکترای شهرسازی، دانشکدة شهرسازی، دانشگاه تهران
2 دانشیار برنامه ریزی شهری و منطقه ای، دانشکدة هنر و معماری، دانشگاه تربیت مدرس
3 دکترای برنامه ریزی شهری و منطقه ای، دانشکدة معماری و شهرسازی، دانشگاه شهید بهشتی
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction
The positions of cities within an urban system have been studied on a variety of geographical scales ranging from the metropolitan and regional level to the national level. The two main approaches can be distinguished: attribute-based and interaction-based. First, focuses on the concentration of activities or functions in a node and characterize cities’ importance by using data on the internal attributes of nodes such as the population size, economic profiles, and the presence of transport and communication functions. Second, one could rank cities from an interaction perspective by using flow data and concentrates on the degree to which nodes interact with each other in the system of flows. Although the fundamentals of both approaches are well documented, the nature of the relationship between these two approaches has remained hitherto largely unexplored. Even though many studies employ either interaction- or node-attribute data to study the positions of cities in the urban system, relatively little is known about the relationships between these two different types of data. This study aims to examine the extent to which the positions of cities using the interaction- and node attribute data correlate with each other, and how possible (dis)similarities between the two can be explained.
Methodology
Although there are several types of flow that could be used for studying interaction, we have concentrated on flows of people travelling between distinct metropolitan areas for two reasons. First, face to- face relationships continue to be important for the development of urban systems. Second, it is the less frequent journeys undertaken over greater spatial distances rather than daily (commuting) journeys that are pertinent to the development of urban systems on the higher spatial scale. The analysis has been conducted separately for aerial and terrestrial journeys. In this study, we have employed data on long-distance mobility which has been collected in Comprehensive Transportation Studies of Iran in 2016 .As reported in this study, the origin- destination survey was carried out in 56 study areas which are according to political divisions. In this survey, a long-distance journey was defined as a journey to a destination more than 100 km away. With respect to node attributes, information on sociodemographic and economic was obtained from the Population and Housing Census data collected by Statistical Center of Iran (2016).
According to the enactment of Ministry of Roads and Urban Development, each city which has more than 500 thousand population is known as metropolitan; based on, in 2016 Iran has had 15 metropolitan, But in this study, the metropolitan areas were operationalized via the concept of functional urban regions (FURs) to represent the spatial units that are functionally interrelated in economic terms, because these can be compared with one another more easily. However, the delimitation of such areas is constrained by the availability of data in at least two respects. First, the functional interdependencies should ideally be derived from interaction data such as daily commuter flows. Second, because the flow data is only available for 56 defined study area of CTSI, the metropolitan areas necessarily is constrained to these areas.
Results and discussion
The results show that not only ranking of cities by interaction data differ for types of flow but also the relationships between interaction and node attributes differ for these types of flow. Tehran, Mashhad, Esfahan and Ahvaz there are in the highest orders based on aerial flow data while Tehran, Esfahan, Qom and Arak are the most important metropolitan areas based on aerial flow data terrestrial flow data. In addition, Tehran, Mashhad, Esfahan and Kerman has acquired the first to forth position respectively based on attribute-based data. This division indicates that nodes do not necessarily hold an important position on both aspects simultaneously. The difference between transportation modes in representation of rankings of cities by using interaction-based data _aerial and terrestrial_ has been approved: The choosing destinations in terrestrial transportation from each origin gravitate to nearer distances and the number of passengers is affected by distances more intensely which is known as “Distance Decay” factor. We also find that the differences between the two rankings can be explained to some extent by the fact that corporeal interaction is influenced by the “physical barriers” which means that the top ranking metropolitan areas are those located centrally in our study area, Iran such as Tehran, Esfahan and Qom.
Conclusion
In this study, we have considered to what extent the rankings of metropolitan areas using interaction and node-attribute data are correlated. Data on long-distance passenger mobility for aerial and terrestrial journeys and the attributes of the metropolitan areas have been used to generate the rankings of 15 metropolitan areas.The results shows that node attributes data tend to overestimate the importance of metropolitan areas that are not situated on central area of Iran like Kerman and Shiraz. These metropolitan areas function as central nodes in their regional economies and hold high positions on the economic attributes, but may have weaker relationships with other metropolitan areas. This contrast suggests that the physical barriers imposed by distance play a part in limiting the interaction between metropolitan areas as far as corporeal travel is concerned. Oppositely, the results shows that interaction data based on terrestrial flow tend to underestimate the importance of metropolitan areas that are not situated on central area of Iran like Zahedan and Orumia. This result may suggest that none of node attribute data or flow data are not sufficient to explain the positions of metropolitan areas on the two overall rankings, at least for the current data. Nevertheless, compared with terrestrial flows, aerial flows and node-attributes are more strongly correlated. Aerial-interaction and node-based data show a correlation factor of 0.85 suggesting that they are good proxies for one another. However, since different types of flow tend to have different characteristics, terrestrial-interaction and node-based data show a correlation factor of 0.36. It can be concluded that ranking of urban regions by means of node-based attributes can be better explained by aerial flow data than terrestrial flow data.
Keywords
Urban system, node-attribute approach, interaction approach, metropolitan areas of Iran, comparative analysis
کلیدواژه‌ها [English]
10. Afaghpoor, A., 2012, Examining and Analyzing of Spatial Structure and Organization in Iran Urban System Using Flows Analysis, Thesis Submitted for M.A in Urban and Regional Planning, Dadashpoor, H., Tarbiat Modares University.
11. Chalabi, M., 1995, Network Analysis in Sociology. Vol. 3, Issue 5, PP. 9-48.
12. Dadashpoor, H. and Rostami, F., 2017, Measuring spatial proportionality between service availability, accessibility and mobility: Empirical evidence using spatial equity approach in Iran. Journal of Transport Geography. Vol. 65, PP. 44-55.
13. Dadashpoor, H. and Afaghpoor, A., 2016, The New Epistemic and Theoretical Rationality Governing the Spatial Organization of Urban Systems. Interdisciplinary Studies in the Humanities, Vol. 8, No. 2, PP. 1-28.
14. Dadashpoor, H.; Afaghpoor, A. and Mamdoohi, A.R., 2014, Analysis of Spatial Organization in Urban Network Based on Air Flows of People: Empirical Evidence for Iran. Human Geography Research Quarterly. Vol. 46, Issue 1, PP. 125-150.
15. Ministry of Roads and Urban Development. 2016, Comprehensive Transportation Studies of Iran (CTSI), Tehran, Iran.
16. Azimi, N., 2003, The Methodology of Urban Networks in Regional Plans, Urban Planning and Architecture Research Center of Iran, Tehran, Iran.
17. Statistics Center of Iran, 2016, Statistical Survey Report, Tehran, Iran.
18. Batten, D. F., 1995, Network cities: Creative urban agglomerations for the 21st century. Urban Studies, Vol. 32, No. 2, PP. 313-327.
19. Berry, Brian J. L., 1964, Cities as systems: Within systems of cities. Chicago: University of Chicago.
20. Camagni, R. P. and Salone, C., 1993, Network urban structures in Northern Italy: elements for a theoretical framework. Urban Studies, Vol. 30, No. 6, PP. 1053-1064.
21. Christaller, W., 1933, Central places in southern Germany (Carlisle W. Baskin, Trans.). Englewood Cliffs, NJ: Prentice-Hall. (In German).
22. Dadashpoor, H.; Afaghpoor, A. and Allen, 2017, A methodology to assess the spatial configuration of urban systems in Iran from an interaction perspective. GeoJournal, Vol. 82, PP. 109-129.
23. Derudder, B. and Taylor, P. J., 2005, The Cliquishness of World Cities. Global Networks, Vol. 5, No. 1, PP. 71-91.
24. Frandberg, L. and Vilhelmson, B., 2003, Personal mobility: A corporeal dimension of transnationalisation. The case of long-distance travel from Sweden. Environment and planning, Vol. 35, No. 10, PP. 1751-1768.
25. Hall, P. and Hay, D., 1980, Growth Centers in the European urban system. London: Heinemann.
26. Janelle, D. G., 1969, Spatial reorganization: a model and concept. Annals of the Association of American Geographers, Vol. 59, No. 2, PP. 348-364.
27. Limtanakool, N.; Dijst, M. and Schwanen, T., 2007a, A theoretical framework and methodology for characterizing urban systems based on flows of people: empirical evidence for France and Germany. Urban Studies, Vol. 11, No. 1, PP. 2123-2145.
28. Limtanakool, N.; Schwanen, T. and Dijst, M., 2007b, Ranking functional urban regions: A comparison of interaction and node attribute data. Cities, Vol. 24, No. 1, PP. 26-42.
29. Meyer, D R., 1986, The world system of cities: relations between international financial metropolises and South American cities. Social Forces, Vol. 64, No. 3, PP. 553-581.
30. Mitchelson, R. L. and Wheeler, J. O., 1994, The flow of information in a global economy: the role of the American urban system in 1990. Annals of the Association of American Geographers, Vol. 84, No. 1, PP. 87-107.
31. Neal, Z. P., 2010, Refining the air traffic approach: An analysis of the US city network. Urban Studies, Vol. 47, No. 10, PP. 2195-2215.
32. Parr, J. B., 2004, The polycentric urban region: A closer inspection. Regional Studies, Vol. 38, No. 3, PP. 31-240.
33. Robinson, J., 2005, Urban geography: world cities, or a world of cities. Progress in Human Geography. Vol. 29, No. 6, PP. 757-765.
34. Short, J. R., 2004, Black holes and loose connections in a global urban network. The Professional Geographer, Vol. 56, No. 2, PP. 295-302.
35. Simmons, J. W., 1978, The organization of the urban system. In Systems of Cities: Reading on Structure, Growth, and Policy, (eds.) L S Bourne and J W Simmons. PP. 61-69. Oxford University Press, New York.
36. Smith, D. A. and Timberlake, M., 2001, World city networks and hierarchies, 1977–1997: an empirical analysis of global air travel links. American Behavioral Scientist, Vol. 44, No. 10, PP. 1656-1678.
37. Taylor, P. J., 2004, World Network: A Global Urban Analysis. Routledge, London.
38. Taylor, P. J.; Derudder, B. and Witlox, F., 2006, comparing airline passenger destinations with global service connectivities: a worldwide empirical study of 214 cities. GaWC Research Bulletin, p196. Available from: http://www.lboro.ac.uk/gawc/ rb/rb196.html.
39. Urry, J., 2003, Social networks, travel and talk. British Journal ofSociology, Vol. 54, No. 2, PP. 155-175.
40. Van Der Knaap, B. and Wall, R., 2002., Linking Scale and Urban Network Development. The European Metropolis 1920-2000. Berlin: European Science Foundation.
41. Wang, Y.; Gu, Y.; Dou, M. and Qiao, M., 2018, Using Spatial Semantics and Interactions to Identify Urban Functional Regions. International Journal of Geo-Information, Vol. 7, No. 4.
)
