• الصفحة الرئيسية
  • An Analysis of the Spatial Structure of Cities from the Perspective of Spatial Balance in the Distribution of Functions and the Formation of Multi-Core Pattern (Case Study: Tabriz City)

شارک

عنوان URL للمقالة


رقم المقالة : IJAUD-2309-1703 (R1) زيارة : 403 الصفحة: 111 - 122

10.30495/ijaud.2023.75129.1703

نوع المخطوط: ابحاث

An Analysis of the Spatial Structure of Cities from the Perspective of Spatial Balance in the Distribution of Functions and the Formation of Multi-Core Pattern (Case Study: Tabriz City)

الموضوعات :

Ahmad Asadi 1 , Mohsen Ahadnejad Reveshty 2 , Hossein Tahmasebi Moghaddam 3

1 - Assistant Professor of Geography & Urban Planning, Bozorgmehr University of Qaenat, Qaenat, Iran.
2 - Associate Professor of Geography & Urban Planning, University of Zanjan, Zanjan, Iran
3 - Assistant Professor of Geography & Urban Planning, University of Zanjan, Zanjan, Iran

تاريخ الإرسال : 22 الخميس , صفر, 1445 تاريخ التأكيد : 25 الخميس , ربيع الثاني, 1445 تاريخ الإصدار : 03 الأحد , ذو الحجة, 1445

الکلمات المفتاحية: Tabriz city, Urban Functions, Single-core Pattern, Multi-Core Pattern, Urban Spatial Structure,

ملخص المقالة :

The multi-core spatial structure is one of the important and basic topics in urban planning studies, which is considered both as a framework for evaluating urban development and as a basic spatial development goal. Considering the importance of the multi-core spatial structure pattern, especially for large cities with high populations and diverse and complex activities, the present study aims to evaluate the spatial structure of Tabriz city from the perspective of multi-core pattern development based on the spatial distribution of functions and population across the city. The present study employs a Quantitative Correlational Research method. The documentary method is used for data collection, and various techniques such as mean center, standard deviational ellipse, kernel density estimation, location quotient (L.Q.), nearest neighbor index, and geographically weighted regression are applied for data analysis. The study examines six functions: religious, educational, commercial, healthcare, industrial, and administrative. The findings suggest that the spatial distribution of functional cores, except for industrial and commercial functions, exhibits a single-core spatial structure in the city center. Also, the results indicate that the correlation between functional cores and the population of Tabriz city, based on the spatial regression test, is moderate. Accordingly, an imbalance in the distribution of functional cores in Tabriz City is evident. Therefore, Tabriz is located in a single-core state, and due to the increase in the distribution of administrative and medical functions in the eastern and industries in the western, it can become a multi-core model.

المصادر:

- Acheampong, R., & Agyemang, F. (2016). Quantifying the spatiotemporal patterns of settlement growth in a metropolitan region of Ghana. Geojournal, 84(4), 823–840. https://doi.org/10.1007/s10708-016-9719-x
- Acheampong, R.A. (2019). Spatial structure, intra-urban commuting patterns, and travel mode choice: Analyses of relationships in the Kumasi Metropolis, Ghana. Cities, 96, 1-14. https://doi.org/10.1016/j.cities.2019.102432
- Altschuler, A., Somkin, C., Adler, N. E. (2004). Local services and amenities, neighborhood social capital, and health. Social Science & Medicine, 59(6), 1219–1229. https://doi.org/10.1016/j.socscimed.2004.01.008
- Ashik, F.R., Alam Mim, S., & Neema, M.N. (2020). Towards vertical spatial equity of urban facilities: An integration of spatial and aspatial accessibility. Journal of Urban Management, (9), 77–92. https://doi.org/10.1016/j.jum.2019.11.004
- Buliung R.N. (2011). Wired people in wired places: Stories about machines and the geography of activity. Annals of the Association of American Geographers, 101, 1365–1381. https://doi.org/10.1080/00045608.2011.583568
- Burger, M.J., Goei, B., De van der Laan, L., & Huisman, F.J.M. (2011). Heterogeneous development of metropolitan spatial structure: Evidence from commuting patterns in English and Welsh city-regions, 1981–2001. Cities, 28(2), 160–170. https://doi.org/10.1016/j.cities.2010.11.006
- Champion, A.G. (2001). A changing demographic regime and evolving polycentric urban regions: Consequences for city population size, composition, and distribution. Urban Studies, 38(4), 657-677. https://doi.org/10.1080/00420980120035277
- Chapin. F.S., & Kaiser, E.J. (1984). Urban land use planning. University of Illinois Press Chicago. https://www.amazon.com/Urban-Land-Use-Planning-Fourth/dp/0252021010
- Chen, W., Liu, L., & Liang, Y. (2016). Based on POI data, retail center recognition and spatial aggregating feature analysis of retail formats in Guangzhou. Geography Research, 35 (4), 703-716. https://doi.org/10.11821/dlyj201604009
- Chen, W., Yenneti, K., Wei, Y., Yuan, F., Wu, J., & Gao, J. (2019). Polycentricity in the Yangtze River Delta urban agglomeration (YRDUA): More cohesion or more disparities? Sustainability, 11(11), 3106. https://doi.org/10.3390/su11113106
- Criekingen, M., Bachmann, M., Guisset, C., & Lennert, M. (2007). Towards polycentric cities. An investigation into the restructuring of intra-metropolitan spatial configurations in Europe. Belego, 2007(1), 31–51. https://doi.org/10.4000/belgeo.11629
- Ellingsen, W., & Leknes, E. (2012). The city region is a concept, object, and practice. Geografisk Tidsskrift-Danish Journal of Geography Journal of Geography, 66(4), 227–236. https://doi.org/10.1080/00291951.2012.708359
- Fernandez-Maldonado, A.M., Romein, A., Verkoren, O., & Pessoa, R.P.P. (2014). Polycentric structures in Latin American metropolitan regions: Identifying employment sub-centers. Regional Studies, 48(12), 1954–1971. https://doi.org/10.1080/00343404.2013.786827
- Finka, M. (2009). Sustainable territorial development and concepts of polycentricity in Slovak territorial development. Urban Research & Practice, 2(3), 332–343. https://doi.org/10.1080/17535060903319319
- Giuliano, G., Redfearn, C., Agarwal, A., Li, C., Zhuang, D. (2007). Employment concentrations in Los Angeles, 1980–2000. Environment & Planning A, 39 (12), 2935–2957. https://doi.org/10.1068/a393
- Glaeser, E.L. & Kahn, M.E. (2001). Decentralized Employment and the Transformation of the American City (February 2001). NBER Working Paper No. w8117, Available at SSRN: https://ssrn.com/abstract=259424
- Goess, S., de Jong, M., & Meijers, E. (2016). City branding in polycentric urban regions: Identification, profiling, and transformation in the Randstad and Rhine-ruhr. European Planning Studies, 24(11), 2036–2056. https://doi.org/10.1080/09654313.2016.1228832
- Guo, L., Zheng, C, & Huang, J. (2019). Commuting circle-based spatial structure optimization of megacities: a case study of Wuhan central city. Urban Planning, 10, 43-54.
- He, Q., Zeng, C., Xie, P., Tan, S., & Wu, J. (2019a). Comparison of urban growth patterns and changes between three urban agglomerations in China and three metropolises in the USA from 1995 to 2015. Sustainable Cities and Society, 50, 101649. https://doi.org/10.1016/j.scs.2019.101649
- He, Y., Zhou, G., Chengli, T., Fan, S., & Guo, X. (2019b). The spatial organization pattern of urban-rural integration in urban agglomerations in China: n agglomeration diffusion analysis of the population and firms. Habitat International, 87, 54–65. https://doi.org/10.1016/j.habitatint.2019.04.003
- Hoyler, M., Kloosterman, R. C., & Sokol, M. (2008). Polycentric puzzles - emerging megacity regions seen through the lens of advanced producer services. Regional Studies, 42(8), 1055–1064. https://doi.org/10.1080/00343400802389377
- Kauffmann, A. (2015). Is the 'central German metropolitan region' spatially integrated? An empirical assessment of commuting relations. Urban Studies, 53(9), 1–16. https://doi.org/10.1177/0042098015581124
- Krehl, A. (2015). Urban spatial structure: An interaction between employment and built-up volumes. Regional Studies, Regional Science, 2(1), 290-308. https://doi.org/10.1080/21681376.2015.1034293
- Krehl, A., & Siedentop, S. (2018). Towards a typology of urban centers and subcenters – evidence from German city regions. Urban Geography, 40(1), 58–82. https://doi.org/10.1080/02723638.2018.1500245
- Kuang, W., Liu, J., Shao, Q., & Sun, C. (2009). Spatio-temporal patterns and driving forces of urban expansion in Beijing Central City since 1932. Journal of Geo- Information Science, 11(4), 428–435. http://www.journalgis.cn/EN/Y2009/V11/I4/428
- Lan, F., Da, H., Wen, H., & Wang, y. (2019). Spatial structure evolution of urban agglomerations and its driving factors in Mainland China: From the monocentric to the polycentric dimension. Sustainability, 11(610), 610. https://doi.org/10.3390/su11030610
- Li, Y., & Monzur, T. (2018). The spatial structure of employment in the metropolitan region of Tokyo: A scale-view. Urban Geography, 39(2), 236–262. https://doi.org/10.1080/02723638.2017.1308182
- Liu, X., Gong, L., Gong, Y., & Liu, Y. (2015). Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43, 78-90. https://doi.org/10.1016/j.jtrangeo.2015.01.016
- Liu, Y., Fan, P., Yue, W., & Song, Y. (2018). Impacts of land finance on urban sprawl in China: the case of Chongqing. Land Use Policy, 72, 420-432. https://doi.org/10.1016/j.landusepol.2018.01.004
- Lizhu, D., Fung-Shuen, S. V., & Yanting, Z. (2013). Formation and delimitation of extended metropolitan regions in Central China: A case study in the changzhutan EMR. International Journal of Humanities and Social Science, 3(12), 265–277. https://www.researchgate.net/publication/308969739
- Lloyd, K. M. Auld, Ch. J. (2002). The role of leisure in determining quality of life: Issues of content and measurement. Social Indicators Research, 57(1), 43–71. https://doi.org/10.1023/A:1013879518210.
- Luo, Q., & Li, X. (2019). The spatial differentiation and influencing factors of urban centers in China based on VIIRS night light. Geography Research, 38(1), 157-168. http://www.dqxxkx.cn/EN/abstract/abstract42325.shtml
- Man, J. K., Zheng, S., & Ren, R. R. (2011). Housing policy and housing markets: trends, patterns, and affordability. In Y.J Man (ed.). China's housing reforms and outcomes. Cambridge: Lincoln Institute of Land Policy, 3–18. https://www.lincolninst.edu/sites/default/files/pubfiles/china-housing-reform-and-outcomes-chp.pdf.
- Master plan of Tabriz (2016). Consulting Engineers of Role of Environment, Ministry of Roads and Urban Development. General Department of Roads and Urban Development of East Azerbaijan Province, approved on 2016/11/14.
- Meijers, E. J., Burger, M. J. (2010). Spatial structure and productivity in U.S. metropolitan areas. Environment & Planning A, 42(6), 1383–1402. https://doi.org/10.1068/a42151
- Mennis, J. (2006). Mapping the Results of Geographically Weighted Regression. The Cartographic Journal, 43, 171-179. https://doi.org/10.1179/000870406X114658
- Nam, K., & Kim, B.H.S. (2017). The effect of spatial structure and dynamic externalities on local growth in the Seoul metropolitan area. Urban Policy and Research, 35(2), 165–179. https://doi.org/10.1080/08111146.2016.1159554
- Niu, X., Ding, L., & Song, X. (2014). Understanding the urban spatial structure of Shanghai's central city based on mobile phone data. Urban Planning Forum, 6, 71-77.
- Phelps, N. A. (2015). Sequel to suburbia: Glimpses of America's post-suburban future. Cambridge: MIT Press.
- Priemus, H. (2019). How housing, infrastructure, and water determined the spatial structure of the Randstad. European Planning Studies, 26(3), 546–570. https://doi.org/10.1080/09654313.2017.1402867
- Sat, N. (2018). Polycentricity in a developing world: a micro-regional analysis for morphological polycentricity in Turkey. GeoScape, 12(2), 64–77. https://doi.org/10.2478/geosc-2018-0007
- Sheng, J., Han, X., & Zhou, H. (2017). Spatially Varying Patterns of Afforestation/Reforestation and Socio-Economic Factors in China: A Geographically Weighted Regression Approach. Journal of Cleaner Production, 153, 362-371. https://doi.org/10.1016/j.jclepro.2016.06.055
- Shi, G., Jiang, N., & Yao, L. (2017). Study on the identification of urban center system based on GIS and POI-A case study of Shanghai. Modern Surveying and Mapping, 40(6), 27-30.
- Smetkowski, M. (2018). The role of exogenous and endogenous factors in the growth of regions in central and eastern Europe: The metropolitan/non-metropolitan divide in the pre-and post-crisis era. European Planning Studies, 26(2), 256–278. https://doi.org/10.1080/09654313.2017.1361585
- Statistical Center of Iran (1956-2016). General population and housing censuses.
- Sweet, M. N., Bullivant, B., & Kanaroglou, P. S. (2016). Are major Canadian city regions monocentric, polycentric, or dispersed? Urban Geography, 38(3), 445–471. https://doi.org/10.1080/02723638.2016.1200279
- Sýkora, L., Mulíˇcek, O., & Maier, K. (2009). City regions and polycentric territorial development: Concepts and practice. Journal Urban Research & Practice, 2(3), 233–239. https://doi.org/10.1080/17535060903319095
- Taubenböck, H., Wiesner, M., Felbier, A., Marconcini, M., Esch, S., & Dech, S. (2014). New dimensions of urban landscapes: The spatiotemporal evolution from a polynuclear area to a mega-region based on remote sensing data. Applied Geography, 47, 137–153. https://dx.doi.org/10.1016/j.apgeog.2013.12.002
- Trapero, E. S., Sanz, I. S., & Francés, J. M. D. U. (2015). Global metropolitan-regional scale in evolution: Metropolitan intermediary cities and metropolitan cities. European Planning Studies, 23(3), 568–596. https://doi.org/10.1080/09654313.2013.878691
- Veneri, P. (2010). Urban polycentricity and the costs of commuting: Evidence from Italian metropolitan regions. Growth and Change, 41(3), 403–429. https://doi.org/10.1111/j.1468-2257.2010.00531.x
- Wang, F., & Niu, F. (2019). Urban commercial spatial structure optimization in the metropolitan area of Beijing: A microscopic perspective. Sustainability, 11, 1103. https://doi.org/10.3390/su11041103
- Wei, C. H., Weidong, L., Wenqian, K. E., & Nyuying, W. (2018). Understanding spatial structures and organizational patterns of city networks in China: A highway passenger flow perspective. Journal of Geographical Sciences, 28(4), 477–494. https://doi.org/10.1007/s11442-018-1485-x
- Wei, L., Luo, Y., Wang, M., Cai, Y., Su, S., Li, B., & Ji, H. (2020). Multiscale identification of urban functional polycentricity for planning implications: An integrated approach using geo-big transport data and complex network modeling. Habitat International, 97, 102134. https://doi.org/10.1016/j.habitatint.2020.102134
- Zhang, L., Yue, W., & Liu, Y. (2017). Multidimensional analysis of the polycentric urban spatial structure: a case of Hangzhou. Economic Geography, 232(6), 69-77.

نص كامل:


An Analysis of the Spatial Structure of Cities from the Perspective of Spatial Balance in the Distribution of Functions and the Formation of Multi-Core Pattern

(Case Study: Tabriz City)

 

Ahmad Asadi1*, Mohsen Ahadnejad Reveshty2, Hossein Tahmasebi Moghaddam3

 

1* Assistant Professor, Department of Geography and Urban Planning, Bozorgmehr University of Qaenat, Qaenat, Iran.

2 Associate Professor, Department of Geography and Urban Planning, Faculty of Humanities, University of Zanjan, Iran.

3 Department of Geography and Urban Planning, Faculty of Humanities, University of Zanjan, Iran.

 

ABSTRACT: The multi-core spatial structure is one of the important and basic topics in urban planning studies, which is considered both as a framework for evaluating urban development and as a basic spatial development goal. Considering the importance of the multi-core spatial structure pattern, especially for large cities with high populations and diverse and complex activities, the present study aims to evaluate the spatial structure of Tabriz city from the perspective of multi-core pattern development based on the spatial distribution of functions and population across the city. The present study employs a Quantitative Correlational Research method. The documentary method is used for data collection, and various techniques such as mean center, standard deviational ellipse, kernel density estimation, location quotient (L.Q.), nearest neighbor index, and geographically weighted regression are applied for data analysis. The study examines six functions: religious, educational, commercial, healthcare, industrial, and administrative. The findings suggest that the spatial distribution of functional cores, except for industrial and commercial functions, exhibits a single-core spatial structure in the city center. Also, the results indicate that the correlation between functional cores and the population of Tabriz city, based on the spatial regression test, is moderate. Accordingly, an imbalance in the distribution of functional cores in Tabriz City is evident. Therefore, Tabriz is located in a single-core state, and due to the increase in the distribution of administrative and medical functions in the eastern and industries in the western, it can become a multi-core model.

KEYWORDS: Urban Spatial Structure, Urban Functions, Single-Core Pattern, Multi-Core Pattern, Tabriz City.

 

­EMAIL: asadi@buqaen.ac.ir, https://orcid.org/0000-0002-3332-8825

 

INTRODUCTION

The spatial structure is one of the key concepts prevalent in urban planning and design and has undergone significant changes over the past century, attracting the attention of geographers, economists, and urban planners (Giuliano et al., 2007; Glaeser & Kahn, 2001; Meijers & Burger, 2010; Phelps, 2015). The spatial structure, with all its elements and structural components, interacts with the city and illustrates how these elements are situated and function within the city level. Also, the urban spatial structure embodies the established system and interrelations among physical components and land uses within an urban area, formed based on the reciprocal interactions of urban systems in time and space (Chapin & Kaiser, 1984). This relationship between elements and functions over time, in correlation with social population, economic indicators, and the prevailing management system, has led to the distribution of urban functions in the spatial matrix (urban form) in two forms: balanced and polarized. In this context, balance in the spatial structure of cities from the perspective of an equitable distribution of urban facilities and functions is considered an important indicator for improving quality of life and is a foundation of distributive justice policies (Altschuler et al., 2004; Lloyd & Auld, 2002). Therefore, establishing a balance in urban spatial structure and function distribution is essential for urban development processes. An evaluation of equilibrium and polarization in the distribution of urban functions could serve as a strategic perspective for future urban planning and design initiatives.

On the other hand, it can be stated that over the past century, the advent of knowledge and technologies such as transportation has triggered changes in the spatial structure of cities, prompting expansion from the city's center and primary development core towards the periphery, resulting in the formation of new functional centers (Buliung, 2011). Due to its multitude of centers, this novel form of urban structure and development, the multi-core model, can facilitate the formation of relatively uniform and homogeneous structures across the city and appropriately distribute activities; consequently, it is deemed the most appropriate structure for cities with a high population, significant functional density, and complex internal relationships. Given the significance of achieving balance in the distribution of urban functions within a city's spatial structure, as well as the transition towards a polycentric urban model, the primary goal of this study is to examine the distribution of functional cores within the city of Tabriz and assess the potential for its spatial structure to evolve into a polycentric model. Tabriz, the largest city in the northwestern region of Iran, has witnessed extensive changes in its physical and demographic structure from 1956 to 2016. The city's population has approximately sextupled in 60 years (from 289,996 to 1,733,033 people), and its physical expansion has increased almost sixteen-fold (from 1,170 hectares to 19,000 hectares (Statistical Center of Iran, 1956-2016).

Along with this increase in size and population, the city's physical fabric has also undergone extensive changes, including the emergence of various towers and skyscrapers in different parts of the city and the settlement of more than 450,000 individuals in informal settlements, especially in the northern part of the city (Master plan of Tabriz, 2016). Additionally, with the population increase, issues such as increased traffic and air pollution, particularly in the city center, and excessive pressure on certain parts of the city have become evident. Therefore, examining the city's spatial structure from a balanced perspective in the distribution of various functions is essential. This can provide solutions for appropriately distributing functions and populations and rethinking the city's spatial structure, setting the stage for achieving sustainable urban development and realizing a polycentric development model. Furthermore, in cities within developing nations like Tabriz, considering the dominance of a technocracy approach and mounting spatial inequalities that exist across various dimensions, particularly in function distribution, through the identification of spatial structure and the presentation of plans and strategies, it becomes feasible to reduce spatial imbalances and realize spatial justice in the distribution of functions and the establishment of a favorable spatial structure pattern, such as multi-core, and a suitable distribution of functions and population. For this purpose, answering the following questions is the basis of the current research:

-       What is the spatial distribution of different functions in Tabriz city?

-       What is the spatial structure of Tabriz city from the perspective of development patterns (single-core and multi-core)?

 

Literature Review

The spatial structure of cities is influenced by internal factors and driving and developing forces that create significant changes in the spatial structure, especially in metropolises, by establishing economic, communicative, and physical infrastructures (Smetkowski, 2018; Sweet et al., 2016; Trapero et al., 2015). These forces and stimuli include economic factors, such as income and economic productivity, public investment, and accessibility, as well as physical factors and infrastructure development, such as communication factors, physical growth, and economic and social reconstruction (Fernandez-Maldonado et al., 2014; Lizhu et al., 2013; Wei et al., 2018). Additionally, public investment in the development of service infrastructures and large urban projects, which have a direct impact on the location and establishment of activities, is another key stimulant in shaping and changing the spatial structure (Chen et al., 2019; Criekingen et al., 2007; Li & Monzur, 2018; Priemus, 2019). In this regard, (Criekingen et al. 2007) state that large-scale urban projects undertaken through public investments directly shape and solidify the polycentric spatial structure. On the other hand, the process of globalization and engagement in the global network and economic competition (Goess et al., 2016; Sat, 2018; Taubenböck et al., 2014), as well as foreign investments (Finka, 2009; Smetkowski, 2018), have led to the concentration of economic activities in specific regions based on local conditions and contexts (Hoyler et al., 2008), significantly changing the spatial structure of metropolises. For instance, (Lan et al., 2019) have identified foreign investment as a significant factor in shaping a polycentric pattern in 20 large city regions in China. Therefore, economic agglomeration is one of the primary elements leading to the concentration and formation of new cores (Acheampong & Agyemang, 2016; He et al., 2019a; Nam & Kim, 2017). It signifies access to employment opportunities and direct connections with specialized producers, drastically altering the spatial structure. In other words, the intensification of centrifugal forces fosters an increased interest in economic entities outside the main urban center. Simultaneously, a decrease in transportation costs also increases the tendency to reside in the suburbs and operate outside the principal center. In the 1980s in China, the industry's orientation towards the city outskirts increased due to land reforms, a rise in land prices in the main center, and regulations to remove industries from cities. This change led to the formation of subsidiary industrial centers outside the main center and the emergence of suburbanization and spatial dispersion (Man et al., 2011). In this regard, several studies underscore the role of transportation facilities and proximity to the city's central core as significant influences on the transformation of the spatial structure (Krehl & Siedentop, 2018; Wang & Niu, 2019). Hence, commuting, defined as the daily transition between places of work and residence, is deemed a crucial determinant of access factors (Burger et al., 2011; Ellingsen & Leknes, 2012; Kauffmann, 2015; Veneri, 2010). This factor significantly alters the spatial structure and is linked to the prevailing social, cultural, economic, and political factors influencing it. Based on the presented information, cities' spatial structure has tended towards polycentric development, in which economic agglomeration and transport expansion have had a notable impact. Also, the prevalence of the polycentric pattern in large cities indicates that the single-core city is an outdated or inefficient model. The most appropriate structure for cities with high populations, high activity density, and complex internal relations is a polycentric model, which, due to its multiple centers, can shape relatively uniform and homogeneous structures at the city level and appropriately distribute activities.

On the other hand, polycentric patterns show a high degree of flexibility in relation to urban developments and can provide many opportunities for a city's future growth and development (Ashik et al., 2020). The examination of the formation of polycentric cities globally indicates that these types of cities have either been shaped as a result of premeditated planning (like Shanghai) or self-organization (like Jakarta) (Champion, 2001). In some cases, urban policies in different aspects have played a significant role in shaping cities like Guangzhou and Shenzhen. In general, the process of forming polycentric cities can be represented in Figure 1.

 

 

17 

Fig. 1: Transformation of urban spatial structure from single-core to multi-core based on population-functional cores

Source: (Champion, 2001; Wei et al., 2020).

 

Figure 1, which combines the theories of Champion (2001) and Wei et al. (2020), shows that population cores expand in the city's periphery due to centrifugal forces. Initially, functional cores (service-commerce) exist in a monocentric form; smaller centers develop around the primary center. Eventually, centers are similar to the main ones in different parts of the city. Furthermore, these functional cores respond to the needs of their local populations and cater to the needs of people from other areas.

In summary, it can be said that developing a polycentric city is a spatial and geographical process in which multiple urban centers attract and expand various functions (Kuang et al., 2009). One of the benefits of multi-core urban planning is the ability to overcome a range of issues typically concentrated around a specific core, such as traffic congestion, environmental pollution, and the formation of urban heat islands (He et al., 2019b). In addition, this type of urban spatial structure provides numerous choices for residents, companies, and other resources within a networked urban system, marking a departure from the limited and enclosed mono-centric city systems of the past (Sýkora et al., 2009). Nowadays, we see an increasing political endorsement for planning strategies promoting polycentric (multi-core) urban development. Furthermore, considering the importance and necessity of realizing polycentric (multi-core) cities, many researchers have conducted extensive studies on the spatial structures of multi-core urban from different perspectives. For example, they have explored urban spatial forms, analyzed urban spatial structures, central systems, and the factors influencing them, utilizing data on land use, building characteristics, and other relevant factors (Chen et al., 2016; Liu et al., 2015; Luo & Li, 2019; Wei et al., 2020; Zhang et al., 2017). Other researchers have adopted a population scale perspective, conducting studies to unearth the relationships between workspace, residential space, employment centers, and urban public activity centers based on resident population, employment, and recreation data (Liu et al., 2018; Niu et al., 2014; Shi et al., 2017). Simultaneously, some researchers have looked at transport and commuting to identify the forms and functions of polycentric cities, scrutinizing data related to transportation facility density, road network density, and smart card records (Guo et al., 2019; Zhang et al., 2017).

Upon reviewing various perspectives and research, it becomes clear that the spatial structure of a city consists of two dimensions: morphological and functional. The morphological dimension is represented by population distribution, employment, land usage, transportation networks, and built structure volume (Krehl, 2015). The functional dimension, conversely, is indicated by the flow of goods, services, and interactions between individuals and between people and infrastructure (Wei et al., 2020). Furthermore, from a morphological perspective, a multi-core structure refers to a spatial organization where multiple adjacent centers exist within a single urban system. From a functional perspective, the multi-core structure illustrates the connections between different centers, such as daily commuting flows and the capacity of commercial networks (Acheampong, 2019). Finally, it can be said that the multi-core structure with high flexibility and balanced distribution of functions and population is considered an important urban spatial structure model (opposite to the single-core structure). Based on this, the theoretical model of the research can be drawn as described in Figure 2.

img0 

Fig. 2: Theoretical model of research

 

 

MATERIALS AND METHOD

The methodology employed in the current study is a quantitative correlational research approach. Information gathering was conducted through documentary methods, such as accessing municipal archives and obtaining statistical data related to the existing land-use patterns and digital maps. In this study, an evaluation of the spatial structure of the city of Tabriz is carried out, with a specific focus on six functional cores: religious, educational, commercial, healthcare, industrial, and administrative. The research data analysis involves assessing these functional cores' spatial distribution and density by utilizing methods such as mean center calculation, standard deviational ellipse, kernel density estimation, and the Location Quotient (L.Q.) method. The Nearest Neighbor Index (NNI) examines spatial relationships among functions. Moreover, the spatial equilibrium is scrutinized, particularly the relationship between functional cores and population distribution, by applying Geographically Weighted Regression. Also, the software used in this research is ArcGIS10.8.

 

Kernel density analysis method

This method is among spatial analyses in ArcGIS software and is used under density estimation for linear and point features. This analysis is one of the best methods for identifying high-density points, which can be used to understand the concentration and density of a phenomenon.

 

Location Quotient (LQ)

The L.Q. index or spatial share index is a model that illustrates the spatial distribution of urban uses and the degree of their specialization and polarization. In this model, the relative concentration of a function in a specific area or region is presented compared to the whole area, region, or entire country. The formula for the Location Quotient model is (Equation 1):

Equation 1:

img0 

In this equation:

LQ = Location Quotient.

Mi = Land use I in a particular region.

M = Total land use in that same region.

Ri = Land use I in the city.

R = Total land use in the same city.

 

Nearest Neighbor Index (NNI)

The Nearest Neighbor Index provides a straightforward and efficient approach to assess the concentration of points within a specific geographical area. This test allows for the examination of point distribution clustering. When the Nearest Neighbor Index yields a value of one, it indicates a random distribution of the analyzed data. A value less than one suggests a clustered distribution, while a value greater than one signifies a uniform and random data distribution. Furthermore, a Z index value below one and negative indicates a clustered distribution.

 

Geographically Weighted Regression (GWR)

GWR (Geographically Weighted Regression) is a localized spatial statistical method. It is employed when the units of measurement for the variable under consideration vary from one location to another, facilitating the analysis of spatial heterogeneity (Mennis, 2006). The mentioned regression is an extension of the linear regression model that analyzes the spatial and locational features of the study area (Sheng et al., 2017). GWR produces spatial data that indicates spatial variations in the relationships between variables. The resultant maps from these data play a pivotal role in identifying and interpreting spatial heterogeneities (Mennis, 2006).

 

 

Study Area

In terms of geographical context, Iran is the world's eighteenth-largest country. It is situated in the northern and eastern hemispheres, nestled in the western part of the Iranian plateau within the Asian continent, and is categorized among the countries of the Middle East. Iran spans an area of 1,745,150 square kilometers and shares its northern boundaries with Armenia, Azerbaijan, Turkmenistan (all part of the former Soviet Union), and the Caspian Sea. To the east, it neighbors Afghanistan and Pakistan; to the West, Turkey, and Iraq; and the south, it adjoins the Persian Gulf and the Gulf of Oman.

Tabriz, the capital of the East Azerbaijan Province, stands as one of Iran's principal cities. As the largest city in the country's northwestern region, Tabriz is recognized as the hub for administrative, communicative, commercial, political, industrial, cultural, and military affairs. Following Tehran, Mashhad, Isfahan, Karaj, and Shiraz, Tabriz is Iran's sixth most populous city (Figure 3).

C:\Users\Shahrokh\Desktop\New folder\1.png 

Fig. 3: The geographical positioning of Tabriz in Northwestern Iran and its ten-region division.

 

RESULTS AND DISCUSSION

Spatial Distribution of Functional Cores in the City of Tabriz

To examine the spatial distribution and area of the current status of the functional cores under study, statistical information from the municipality and the land use map of Tabriz City were utilized. The evaluation of the area of various functions in Tabriz suggests that the majority of religious and commercial functions are in Regions 8 and 4, the majority of administrative functions are in Regions 2 and 1, the majority of educational functions are in Regions 3 and 4, the majority of medical functions are in Regions 2 and 3, and the majority of industrial functions are in Regions 6 and 7. Table 1 examines the area of the functions under investigation in the city of Tabriz, broken down by region.

 

Table 1: Assessment of Urban Functions Area in the Decentralized Regions of Tabriz City (Square Meter)

Region

Religious

Educational

Commercial

Healthcare

Administrative

Industrial

Population

Area (Hectares)

1

30851

254518

450785

93308

434054

41310

249484

1541

2

22327

273199

288763

297807

527448

157466

212551

2080

3

39347

473443

448545

198175

729047

39748

210542

2785

4

57304

468903

603354

75722

181421

536587

94087

2540

5

11414

317794

318007

23016

36822

2003936

303988

3153

6

16780

299278

281495

36713

196526

5779666

303587

7218

7

17787

156671

518920

9755

99058

3236611

301257

2892

8

96174

93132

661929

11943

148061

16451

44855

388

9

1608

6620

65308

461

9247

90994

48855

803

10

42692

203528

263652

110127

72767

57393

257741

1051

Total

336285

2547078

3900757

861026

2434449

12230163

2026947

24451

 

Table 2 also shows the spatial analysis of functional cores and their distribution density in Tabriz city.

 

Table 2: Spatial Analysis of Functional Cores and Their Distribution Density in the City of Tabriz

Functional Cores

Geometric Center

Average Centers

Standard Deviation Ellipse

Kernel Density Estimation

Religious

Centroid of the urban function in Region 8

The pattern of functionality expansion from the northwest to the southeast

Distribution of the central core in regions 8 and 4 and the sub-cores around the central core, especially in the central and northern regions of the city

Educational

Centroid of the urban function in Regions 8 and 3

The pattern of functionality expansion from the northwest to the southeast

Distribution of the central core in Regions 8, 3, and 10 and the formation of a second core in Region 1

Commercial

Centroid of the urban function in Region 8

The pattern of functionality expansion from West to east, with a greater tendency to the north

Formation of strong cores in the northern part of the city and regions 1, 4, and 10

Healthcare

Centroid of the urban function in Regions 8 and 3

The pattern of functionality expansion from the northwest to the southeast

Strong core in the city center and regions 8 and 3

Administrative

Centroid of the urban function in Region 8

The pattern of functionality expansion from West to east

Distribution of the central core in Region 8 and subordinate centers in Regions 1, 2, 3, and 4

Industrial

Centroid of the urban function in Region 8

The pattern of functionality expansion from the northwest to the southeast

Polycentric distribution in the West of the city (northwest and southwest)

 

The analysis of the spatial distribution of urban functions reveals that most of these functions' average centers and centroids are concentrated within the city center, particularly in regions 8 and 3. Except for industrial and commercial activities, the spatial distribution of functions follows a single-core spatial structure centered around the city center (Figure 4). Additionally, the standard deviation ellipse further indicates the concentrated distribution of functions in the city center, extending towards Tabriz's eastern and somewhat northern parts (Figure 5). Considering the development limitations in the northern region, functions are anticipated to expand more toward the eastern areas. As a result, it can be concluded that Tabriz exhibits a clear emphasis on its city center, while the eastern part maintains a relatively balanced state in certain functions, indicating a partial manifestation of bipolarity in the city's spatial structure.

 

صنایع 

Fig. 4: The pattern of functional core distribution across the city of Tabriz

 

Untitled 

Fig. 5: The center of gravity and standard deviation ellipse of function distribution across the city of Tabriz

 

An alternative method for investigating the spatial distribution of functional cores utilizes the location quotient or L.Q. This has been calculated for each function, using the area of the urban functions under study within the ten regions of Tabriz concerning the areas of the regions and the city as a whole. The location quotient model posits that if the L.Q. value in a location is less than 0.99, this demonstrates that the location possesses a minimal share of that activity. If the index is between 1 and 1.99, this denotes a relative concentration of the activity in that location. Finally, an L.Q. index exceeding 2 signifies a high activity concentration within the specific location.

 

Table 3: Calculation of the L.Q. index of urban functions in the ten regions of the city of Tabriz

Region

Religious

Educational

Commercial

Medical

Administrative

Industrial

1

1/46

1/59

1/84

1/80

2/84

0/05

2

0/78

1/26

0/87

4/09

2/56

0/15

3

0/99

1/63

1/01

2/03

2/64

0/03

4

1/65

1/77

1/49

0/85

0/72

0/42

5

0/26

0/97

0/63

0/21

0/12

1/27

6

0/17

0/40

0/24

0/14

0/27

1/6

7

0/45

0/52

1/12

0/09

0/34

2/24

8

18/02

2/31

10/73

0/88

3/85

0/08

9

0/15

0/08

0/511

0/02

0/12

0/23

10

2/96

1/86

1/58

2/99

0/70

0/11

 

The obtained results (Table 3) indicate that among the six main functions examined, two functions - medical and administrative - in regions 2 and 3, four functions - religious, educational, commercial, and administrative - in region 8, two functions - religious and medical - in region 10, administrative function in region 1, and industrial function in region 7 had a high location quotient over 2. In this regard, it can be stated that Tabriz has four functional cores, namely regions 2, 3, 8, and 10, and due to the formation of specialization in these areas, the potential to form a multi-core city in Tabriz is possible. It can also be noted that Region 8 of Tabriz has a high concentration of religious and commercial functions. Region 7 is also considered the sole industrial hub of the city.

Overall, it can be stated that the city of Tabriz is currently observable as bipolar, with the central region (Region 8 and, to some extent, 10) and the eastern part of the city (Regions 2 and 3) allocating most of the functions to themselves. However, considering that Regions 1 and 7 also had a location quotient above 2 in two functions, and with the trend of function expansion in the north of the city, with appropriate planning and a balanced distribution of functions in other areas (south and West), the prerequisites for developing a multi-core structure can be established in the city of Tabriz. Conversely, areas 5, 6, 7, and 9 have exhibited the lowest concentration of functions, highlighting the imperative of appropriate urban planning to facilitate the distribution of functions within these regions.

 

The Spatial Distribution Pattern of Urban Functions and the Spatial Balance of Function Distribution with the Population

This section begins with evaluating spatial relationships in the urban function distribution using the Nearest Neighbor Index (NNI) statistical test, followed by applying the weighted regression method to examine the spatial balance in the function and population distribution. The results from the NNI test, applied to the study data, reveal that the test value and the Z index for functional cores are both less than one. This finding indicates a clustered distribution pattern of functional cores throughout Tabriz (Table 4).

 

Table 4: Distribution of Functional Cores in the City of Tabriz Based on the Nearest Neighbor Average Test

Functional Cores

Z-Score

N.N. Ratio Index

Significance Level

Distribution Pattern

Religious

-85.18

0.46

0

Clustered

Educational

-101.85

0.51

0

Clustered

Commercial

-67.09

0.46

0

Clustered

Medical

-84.73

0.53

0

Clustered

Administrative

-48.61

0.63

0

Clustered

Industrial

-93.15

0.35

0

Clustered

 

Based on spatial regression analysis, an evaluation of spatial correlations between the functional cores and the population of Tabriz city reveals a moderate correlation between these two variables (Table 5). This finding underscores an imbalance in the distribution of urban functions across Tabriz city. In addition, within the functional cores, the most balanced distribution in urban areas—considering the existing population—is associated with commercial and educational functions. In contrast, the least balanced distribution is observed in a medical function. The Akaike information criterion further suggests a small sum of squares error concerning functional cores and population, evidenced by a variance in the error that is less than 0.05.

 

Table 5: Geographically Weighted Regression of Functional Cores and Population Relationship

Functional Cores/Population

Determination Coefficient

Adjusted Determination Coefficient

Akaike Information Criterion (AICc)

Error Variance

Religious/Population

0.49

0.47

53.72

0.017

Educational/Population

0.65

0.62

8.19

0.024

Commercial/Population

0.73

0.69

-14.24

0.009

Medical/Population

0.20

0.17

35.78

0.039

Administrative/Population

0.25

0.23

44.38

0.027

Industrial/Population

0.33

0.31

37.79

0.021

 

Moreover, based on the output layer of the geographically weighted regression and depicted in Figure 6, the final results indicate that the most desirable relationship between functional cores and population corresponds to regions 2 and 8.

 

C:\Users\pc.asadi\Downloads\00.jpg 

Fig. 6: Relationship Between Functional Cores and population based on Geographically Weighted Regression

 

CONCLUSION

Spatial structure manifests influential forces acting over time in space, illustrating the order and relationships among physical elements and uses in urban areas. Gradual and step-by-step changes in the spatial structure take place to adapt to needs, fostering spatial balance in the distribution of spatial functions concerning communication axes, population, and urban form. However, changes in spatial structure, driven by influential factors such as society, economy, and existing political and managerial systems, do not always move towards spatial equilibrium. These changes sometimes lead to spatial inequality and polarization in the city's spatial structure. One of the theoretical approaches to achieving spatial balance involves the realization of multi-core centers within the spatial structure of cities. In this context, the present study aims to evaluate the spatial structure of Tabriz City from the perspective of the distribution of functional cores, its alignment with population distribution, and the realization of a multi-core spatial structure. The findings of this study suggest that the distribution of various functions within the city of Tabriz occurs in a clustered pattern.

In most cases, the city's functional cores are configured in a single-core spatial structure centered around the city's core and areas 8, 2, and 3. The expansion of functional cores has also predominantly been from West to east in Tabriz and, in some instances, northward. Furthermore, the results indicate that the spatial relationship between the urban functional cores and the population of the metropolis of Tabriz has been moderate, at around 59 percent. This illuminates the imbalance in the distribution of urban functions within Tabriz based on its population.

From a broad perspective, considering the distribution of functional cores, the results identify the following two patterns in line with the spatial structure of the metropolis of Tabriz:

 

a) Primary Center (Predominant Model)

Due to its commercial function, the city's central sector is the city's primary center where most of the population movement is observed. Increased mobility trends in various hours of the day are directed toward this area. Aside from the number of people employed in this region, which attracts a significant crowd to the main center daily, the existence of diverse functions, particularly commercial ones like Tabriz Bazaar, draws a large influx of citizens to the city center for various purchases, as well as some administrative and healthcare services. Moreover, this area's urban role and function are amplified by suburban, inter-regional, national, and even international functions, all of which contribute to the population's mobility toward the main city center for shopping, commerce, administrative services, and healthcare services.

 

b) Subsidiary Centers

In recent years, the subsidiary centers in Tabriz have expanded and evolved in diverse functions. For instance, the dispersal of administrative services from the central district, the establishment of well-equipped healthcare centers such as the Valiasr International Hospital in the eastern part of the city, and the construction of commercial complexes across various regions have all induced human movements at different urban scales. The presence of specific functions, such as industries in the West, has also facilitated population movements in these areas. However, unlike the city center, which is consistently and continuously used over time and fulfills its role and functions, secondary centers have a sectional function. Therefore, it can be stated that subsidiary centers, while recognizing citizens' preferences and benefiting from composite functions, should facilitate the formation of a polycentric city pattern.

Based on the research results, it can be concluded that the dominant spatial structure in the city of Tabriz, from the perspective of various functional aspects, has predominantly been single-core and clustered, with a few exceptions. On the other hand, existing approaches in urban space have resulted in the polarization of the city across various dimensions. This can be seen in the dualism present in the social and physical structure of the city (informal and formal residences), as well as between the eastern and western regions of the city. The Western areas, in particular, have been disadvantaged regarding access to various functions. Therefore, to achieve spatial balance in the distribution of functional cores relative to population, it is essential that strategic planning is incorporated into urban development schemes.

The research proposes the following strategies to establish a multi-core spatial structure in the city of Tabriz:

ü       Distribute administrative services evenly across all sectors, reducing the centralization of these services.

ü       Regulate the impact of land brokers and real estate speculators to mitigate the spatial imbalances they cause in the value of residential and commercial lands.

ü       Locating and establishing functional cores considering the population increase in the western and northern parts of the city.

ü       Foster connectivity and coordination between essential city elements, such as different activities and population distributions across various regions.

ü       Developing the functions of subsidiary cores to alleviate pressure on central areas.

 

AUTHOR CONTRIBUTIONS

A. Asadi performed the literature review, interpreted the data, and prepared the manuscript text and edition. M. Ahadnejad Reveshty performed the analysis and interpreted the data. H. Tahmasebi Moghaddam performed the literature review, compiled the data, and manuscript preparation.

 

ACKNOWLEDGEMENT

The authors would like to acknowledge the financial support of Bozorgmehr University of Qaenat for this research under contract number 39251.

 

 

CONFLICT OF INTEREST

The authors declare no potential conflict of interest regarding the publication of this work. In addition, the ethical issues, including plagiarism, informed consent, misconduct, data fabrication and falsification, double publication and, or submission, and redundancy, have been completely witnessed by the authors.

 

REFERENCES

Acheampong, R., & Agyemang, F. (2016). Quantifying the spatiotemporal patterns of settlement growth in a metropolitan region of Ghana. Geojournal, 84(4), 823–840. https://doi.org/10.1007/s10708-016-9719-x

Acheampong, R.A. (2019). Spatial structure, intra-urban commuting patterns, and travel mode choice: Analyses of relationships in the Kumasi Metropolis, Ghana. Cities, 96, 1-14. https://doi.org/10.1016/j.cities.2019.102432

Altschuler, A., Somkin, C., Adler, N. E. (2004). Local services and amenities, neighborhood social capital, and health. Social Science & Medicine, 59(6), 1219–1229. https://doi.org/10.1016/j.socscimed.2004.01.008

Ashik, F.R., Alam Mim, S., & Neema, M.N. (2020). Towards vertical spatial equity of urban facilities: An integration of spatial and aspatial accessibility. Journal of Urban Management, (9), 77–92. https://doi.org/10.1016/j.jum.2019.11.004

Buliung R.N. (2011). Wired people in wired places: Stories about machines and the geography of activity. Annals of the Association of American Geographers, 101, 1365–1381. https://doi.org/10.1080/00045608.2011.583568

Burger, M.J., Goei, B., De van der Laan, L., & Huisman, F.J.M. (2011). Heterogeneous development of metropolitan spatial structure: Evidence from commuting patterns in English and Welsh city-regions, 1981–2001. Cities, 28(2), 160–170. https://doi.org/10.1016/j.cities.2010.11.006

Champion, A.G. (2001). A changing demographic regime and evolving polycentric urban regions: Consequences for city population size, composition, and distribution. Urban Studies, 38(4), 657-677. https://doi.org/10.1080/00420980120035277

Chapin. F.S., & Kaiser, E.J. (1984). Urban land use planning. University of Illinois Press Chicago. https://www.amazon.com/Urban-Land-Use-Planning-Fourth/dp/0252021010

Chen, W., Liu, L., & Liang, Y. (2016). Based on POI data, retail center recognition and spatial aggregating feature analysis of retail formats in Guangzhou. Geography Research, 35 (4), 703-716. https://doi.org/10.11821/dlyj201604009

Chen, W., Yenneti, K., Wei, Y., Yuan, F., Wu, J., & Gao, J. (2019). Polycentricity in the Yangtze River Delta urban agglomeration (YRDUA): More cohesion or more disparities? Sustainability, 11(11), 3106. https://doi.org/10.3390/su11113106

Criekingen, M., Bachmann, M., Guisset, C., & Lennert, M. (2007). Towards polycentric cities. An investigation into the restructuring of intra-metropolitan spatial configurations in Europe. Belego, 2007(1), 31–51. https://doi.org/10.4000/belgeo.11629

Ellingsen, W., & Leknes, E. (2012). The city region is a concept, object, and practice. Geografisk Tidsskrift-Danish Journal of Geography Journal of Geography, 66(4), 227–236. https://doi.org/10.1080/00291951.2012.708359

Fernandez-Maldonado, A.M., Romein, A., Verkoren, O., & Pessoa, R.P.P. (2014). Polycentric structures in Latin American metropolitan regions: Identifying employment sub-centers. Regional Studies, 48(12), 1954–1971. https://doi.org/10.1080/00343404.2013.786827

Finka, M. (2009). Sustainable territorial development and concepts of polycentricity in Slovak territorial development. Urban Research & Practice, 2(3), 332–343. https://doi.org/10.1080/17535060903319319

Giuliano, G., Redfearn, C., Agarwal, A., Li, C., Zhuang, D. (2007). Employment concentrations in Los Angeles, 1980–2000. Environment & Planning A, 39 (12), 2935–2957. https://doi.org/10.1068/a393

Glaeser, E.L. & Kahn, M.E. (2001). Decentralized Employment and the Transformation of the American City (February 2001). NBER Working Paper No. w8117, Available at SSRN: https://ssrn.com/abstract=259424

Goess, S., de Jong, M., & Meijers, E. (2016). City branding in polycentric urban regions: Identification, profiling, and transformation in the Randstad and Rhine-ruhr. European Planning Studies, 24(11), 2036–2056. https://doi.org/10.1080/09654313.2016.1228832

Guo, L., Zheng, C, & Huang, J. (2019). Commuting circle-based spatial structure optimization of megacities: a case study of Wuhan central city. Urban Planning, 10, 43-54.

He, Q., Zeng, C., Xie, P., Tan, S., & Wu, J. (2019a). Comparison of urban growth patterns and changes between three urban agglomerations in China and three metropolises in the USA from 1995 to 2015. Sustainable Cities and Society, 50, 101649. https://doi.org/10.1016/j.scs.2019.101649

He, Y., Zhou, G., Chengli, T., Fan, S., & Guo, X. (2019b). The spatial organization pattern of urban-rural integration in urban agglomerations in China: n agglomeration diffusion analysis of the population and firms. Habitat International, 87, 54–65. https://doi.org/10.1016/j.habitatint.2019.04.003

Hoyler, M., Kloosterman, R. C., & Sokol, M. (2008). Polycentric puzzles - emerging megacity regions seen through the lens of advanced producer services. Regional Studies, 42(8), 1055–1064. https://doi.org/10.1080/00343400802389377

Kauffmann, A. (2015). Is the 'central German metropolitan region' spatially integrated? An empirical assessment of commuting relations. Urban Studies, 53(9), 1–16. https://doi.org/10.1177/0042098015581124

Krehl, A. (2015). Urban spatial structure: An interaction between employment and built-up volumes. Regional Studies, Regional Science, 2(1), 290-308. https://doi.org/10.1080/21681376.2015.1034293

Krehl, A., & Siedentop, S. (2018). Towards a typology of urban centers and subcenters – evidence from German city regions. Urban Geography, 40(1), 58–82. https://doi.org/10.1080/02723638.2018.1500245

Kuang, W., Liu, J., Shao, Q., & Sun, C. (2009). Spatio-temporal patterns and driving forces of urban expansion in Beijing Central City since 1932. Journal of Geo- Information Science, 11(4), 428–435. http://www.journalgis.cn/EN/Y2009/V11/I4/428

Lan, F., Da, H., Wen, H., & Wang, y. (2019). Spatial structure evolution of urban agglomerations and its driving factors in Mainland China: From the monocentric to the polycentric dimension. Sustainability, 11(610), 610. https://doi.org/10.3390/su11030610

Li, Y., & Monzur, T. (2018). The spatial structure of employment in the metropolitan region of Tokyo: A scale-view. Urban Geography, 39(2), 236–262. https://doi.org/10.1080/02723638.2017.1308182

Liu, X., Gong, L., Gong, Y., & Liu, Y. (2015). Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43, 78-90. https://doi.org/10.1016/j.jtrangeo.2015.01.016

Liu, Y., Fan, P., Yue, W., & Song, Y. (2018). Impacts of land finance on urban sprawl in China: the case of Chongqing. Land Use Policy, 72, 420-432. https://doi.org/10.1016/j.landusepol.2018.01.004

Lizhu, D., Fung-Shuen, S. V., & Yanting, Z. (2013). Formation and delimitation of extended metropolitan regions in Central China: A case study in the changzhutan EMR. International Journal of Humanities and Social Science, 3(12), 265–277. https://www.researchgate.net/publication/308969739

Lloyd, K. M. Auld, Ch. J. (2002). The role of leisure in determining quality of life: Issues of content and measurement. Social Indicators Research, 57(1), 43–71. https://doi.org/10.1023/A:1013879518210.

Luo, Q., & Li, X. (2019). The spatial differentiation and influencing factors of urban centers in China based on VIIRS night light. Geography Research, 38(1), 157-168. http://www.dqxxkx.cn/EN/abstract/abstract42325.shtml

Man, J. K., Zheng, S., & Ren, R. R. (2011). Housing policy and housing markets: trends, patterns, and affordability. In Y.J Man (ed.). China's housing reforms and outcomes. Cambridge: Lincoln Institute of Land Policy, 3–18. https://www.lincolninst.edu/sites/default/files/pubfiles/china-housing-reform-and-outcomes-chp.pdf.

Master plan of Tabriz (2016). Consulting Engineers of Role of Environment, Ministry of Roads and Urban Development. General Department of Roads and Urban Development of East Azerbaijan Province, approved on 2016/11/14.

Meijers, E. J., Burger, M. J. (2010). Spatial structure and productivity in U.S. metropolitan areas. Environment & Planning A, 42(6), 1383–1402. https://doi.org/10.1068/a42151

Mennis, J. (2006). Mapping the Results of Geographically Weighted Regression. The Cartographic Journal, 43, 171-179. https://doi.org/10.1179/000870406X114658

Nam, K., & Kim, B.H.S. (2017). The effect of spatial structure and dynamic externalities on local growth in the Seoul metropolitan area. Urban Policy and Research, 35(2), 165–179. https://doi.org/10.1080/08111146.2016.1159554

Niu, X., Ding, L., & Song, X. (2014). Understanding the urban spatial structure of Shanghai's central city based on mobile phone data. Urban Planning Forum, 6, 71-77.

Phelps, N. A. (2015). Sequel to suburbia: Glimpses of America's post-suburban future. Cambridge: MIT Press.

Priemus, H. (2019). How housing, infrastructure, and water determined the spatial structure of the Randstad. European Planning Studies, 26(3), 546–570. https://doi.org/10.1080/09654313.2017.1402867

Sat, N. (2018). Polycentricity in a developing world: a micro-regional analysis for morphological polycentricity in Turkey. GeoScape, 12(2), 64–77. https://doi.org/10.2478/geosc-2018-0007

Sheng, J., Han, X., & Zhou, H. (2017). Spatially Varying Patterns of Afforestation/Reforestation and Socio-Economic Factors in China: A Geographically Weighted Regression Approach. Journal of Cleaner Production, 153, 362-371. https://doi.org/10.1016/j.jclepro.2016.06.055

Shi, G., Jiang, N., & Yao, L. (2017). Study on the identification of urban center system based on GIS and POI-A case study of Shanghai. Modern Surveying and Mapping, 40(6), 27-30.

Smetkowski, M. (2018). The role of exogenous and endogenous factors in the growth of regions in central and eastern Europe: The metropolitan/non-metropolitan divide in the pre-and post-crisis era. European Planning Studies, 26(2), 256–278. https://doi.org/10.1080/09654313.2017.1361585

Statistical Center of Iran (1956-2016). General population and housing censuses.

Sweet, M. N., Bullivant, B., & Kanaroglou, P. S. (2016). Are major Canadian city regions monocentric, polycentric, or dispersed? Urban Geography, 38(3), 445–471. https://doi.org/10.1080/02723638.2016.1200279

Sýkora, L., Mulíˇcek, O., & Maier, K. (2009). City regions and polycentric territorial development: Concepts and practice. Journal Urban Research & Practice, 2(3), 233–239. https://doi.org/10.1080/17535060903319095

Taubenböck, H., Wiesner, M., Felbier, A., Marconcini, M., Esch, S., & Dech, S. (2014). New dimensions of urban landscapes: The spatiotemporal evolution from a polynuclear area to a mega-region based on remote sensing data. Applied Geography, 47, 137–153. https://dx.doi.org/10.1016/j.apgeog.2013.12.002

Trapero, E. S., Sanz, I. S., & Francés, J. M. D. U. (2015). Global metropolitan-regional scale in evolution: Metropolitan intermediary cities and metropolitan cities. European Planning Studies, 23(3), 568–596. https://doi.org/10.1080/09654313.2013.878691

Veneri, P. (2010). Urban polycentricity and the costs of commuting: Evidence from Italian metropolitan regions. Growth and Change, 41(3), 403–429. https://doi.org/10.1111/j.1468-2257.2010.00531.x

Wang, F., & Niu, F. (2019). Urban commercial spatial structure optimization in the metropolitan area of Beijing: A microscopic perspective. Sustainability, 11, 1103. https://doi.org/10.3390/su11041103

Wei, C. H., Weidong, L., Wenqian, K. E., & Nyuying, W. (2018). Understanding spatial structures and organizational patterns of city networks in China: A highway passenger flow perspective. Journal of Geographical Sciences, 28(4), 477–494. https://doi.org/10.1007/s11442-018-1485-x

Wei, L., Luo, Y., Wang, M., Cai, Y., Su, S., Li, B., & Ji, H. (2020). Multiscale identification of urban functional polycentricity for planning implications: An integrated approach using geo-big transport data and complex network modeling. Habitat International, 97, 102134. https://doi.org/10.1016/j.habitatint.2020.102134

Zhang, L., Yue, W., & Liu, Y. (2017). Multidimensional analysis of the polycentric urban spatial structure: a case of Hangzhou. Economic Geography, 232(6), 69-77. 

 

 

 

 

 


سند

Sanad is a platform for managing Azad University publications

الروابط

المراكز ذات الصلة

دعامة

الصفحات الرسمية

حقوق هذا الموقع الإلكتروني مملوكة لنظام SANAD Press Management. © 1442-1446

الصفحة الرئيسية| دخول| معلومات عنا| اتصل بنا|
[English] [فارسی] [en] [fa]