Exploration on the spatial spillover effect of infrastructure network on urbanization: A case study in Wuhan urban agglomeration

Accepted Manuscript Title: Exploration on the spatial spillover effect of infrastructure network on urbanization: A case study in Wuhan urban agglomeration Authors: Chen Zeng, Yan Song, Dawei Cai, Peiying Hu, Huatai Cui, Jing Yang, Hongxia Zhang PII: DOI: Article Number:

S2210-6707(18)32528-9 https://doi.org/10.1016/j.scs.2019.101476 101476

Reference:

SCS 101476

To appear in: Received date: Revised date: Accepted date:

5 December 2018 12 February 2019 17 February 2019

Please cite this article as: Zeng C, Song Y, Cai D, Hu P, Cui H, Yang J, Zhang H, Exploration on the spatial spillover effect of infrastructure network on urbanization: A case study in Wuhan urban agglomeration, Sustainable Cities and Society (2019), https://doi.org/10.1016/j.scs.2019.101476 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Exploration on the spatial spillover effect of infrastructure network on urbanization: A case study in Wuhan urban agglomeration Chen Zeng1,2,3*, Yan Song4, Dawei Cai5, Peiying Hu6, Huatai Cui7, Jing Yang1, Hongxia Zhang8 1.

Department of Land Management, Huazhong Agricultural University, Wuhan, China, 430070

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Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101,

Beijing,

China The Center for Spatial Data Science, The University of Chicago, Chicago, U.S.A.

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Department of City and Regional Planning, University of North Carolina-Chapel hill, U.S.A

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College of Public Administration, Central China Normal University, Wuhan, China, 430079

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Department of Architecture and Construction Engineering, Dortmund university of technology, Dortmund, Germany, 44221

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School of Economics, Renmin University of China, 100872, Beijing, China

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Department of management，Hubei University of Education Wuhan, China, 430060

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Corresponding author: Chen Zeng ([email protected]) Yan Song ([email protected])

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Dawei Cai ([email protected]) Peiying Hu ([email protected])

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Huatai Cui ([email protected])

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Jing Yang ([email protected])

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Hongxia Zhang ([email protected])

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Highlight: —We devised road network-based and POI-based scenarios to explore the embedded spatial spillover influence of infrastructure network on urbanization. —The global integration values of the road axial lines and Points of Interest (POI) density were used to generate spatial weight matrices by the gravity model to formulate the two scenarios

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—The results revealed that local economic factors and the spatial influence of infrastructure network both contribute to the changes in urbanization with varying powers in different hypotheses and years.

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—Rational utilization of the embedded spatial spillover effect helps to formulate strategies for sustainable urbanization in improving resource use efficiency, achieving balanced development and promoting an integrated urban–rural development.

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Abstract: In the context of rapid urbanization, the rational spatial distribution of infrastructures, such as road network and public facilities is of great importance for the spatial optimization of infrastructure construction and sustainable regional development. In this study, we explored the spatial spillover influence of infrastructure network on urbanization in road network-based and POI-based hypotheses through spatial modeling in Wuhan urban agglomeration in 2005 and 2015. The global integration values of the road axial lines and Points of Interest (POI) density were used to generate spatial weight matrices by the gravity model to formulate the two hypotheses. The results revealed that local economic factors and the spatial influence of infrastructure both contribute to the changes in urbanization with varying powers in different hypotheses and years. In general, spatial spillover effects from neighbors in the form of road network and point-based facilities have weakened in recent years. However, the comparative magnitude has changed from road network dominated to similar functioning between road network and POI based facilities. It is concluded that the rational utilization of the embedded spatial spillover effect helps to formulate strategies for sustainable urbanization in improving resource use efficiency, achieving balanced development and promoting an integrated urban–rural development.

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Keywords: Spatial spillover effect; Infrastructure construction; Urbanization; Road network; POI

1. Introduction

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Urbanization and infrastructure construction are interrelated and have synergic effect on sustainable development worldwide (Haase et al., 2018; Li et al., 2018; Wang et al., 2018). Contemporary China is a developing country with high population density; more than half of its population resides in urban areas, resulting in urban land expansion and urban sprawl (You and Yang, 2017; Zhang and Wang, 2018). A number of megacities have been experiencing land-oriented urbanization, and infrastructure construction is a key driving factor in this process (Zeng et al., 2016; Xu and Yang , 2019). The rational spatial distribution of infrastructures, such as transportation network and public facilities, affects sustainable land use and demographics (Szeto, et al., 2015; Acheampong and Silva, 2015; Kasraian, et al., 2016; Wu, et al., 2017). In China’s New Urbanization Plan, a series of detailed targets on infrastructure construction have been raised as important components to realize social equity and eco-friendly urbanization (NNUP, 2014; Song et al., 2018; Ma et al., 2018). In this sense, the exploration on the influence of infrastructure on

urbanization is pragmatic and of great importance for the spatial optimization of infrastructure construction.

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In the past, when the influences of infrastructure on urbanization and related issues are examined, three mainstreams have been raised which are infrastructure and land use, socio-economic benefits of infrastructure construction, as well as the spatial spillover effect. First, infrastructure construction, including the construction of road and rail network, transit and other transportation facilities, has a close relationship with sustainable land use (Xu and Yang, 2019). It stimulates the expansion of urban built-up areas through the establishment of new stations (Kasraian, 2016) and further affects urban expansion and land and housing prices (Li et al., 2017). Transportation network is also found to affect spatial structure of urban landscape (Su et al., 2014) and the regional ecological environment (Mo et al., 2017). In China, urban land expansion has outpaced urban population growth in a number of regions with excessive land resource consumption (He et al., 2016; Wei et al., 2017). The rapidly expanding urban land areas in the context of massive transportation infrastructure construction improves the capability of accommodating more urban population, which potentially drives urbanization (Li et al., 2017). Secondly, the socio-economic benefits from urban transport infrastructure have been widely accepted as major tools in promoting the sustainable socioeconomic development and urbanization (Maparu and Mazumder, 2017; Sun and Cui, 2018). The bidirectional causal effects between transportation and economic growth have been examined with different magnitude of benefit between core cities and peripheral cities (Chen et al., 2016). It is also found that different types of roads and different landforms are inclined to produce benefits at different levels (Gerritse and Arribas-Bel, 2018;Yang et al., 2018). Furthermore, empirical studies have confirmed that transport infrastructure positively affects urbanization by facilitating economic agglomeration toward hub cities (Li, 2017; Ye et al., 2018). A bidirectional relationship between urbanization and transportation (Lin et al., 2018) and an inverted “U”-shaped relationship between urbanization and traffic density in local and neighboring cities (Han et al., 2018) have also been identified. Thirdly, spatial spillover effects are generally more pronounced in Western countries whereas the spatial linkages of infrastructure help to improve resource use efficiency and realize the sustainable regional development (Yang et al., 2018; Wang et al., 2018). In developed countries, Arbués et al. (2015) found that transport infrastructure positively affects the outputs of a region and its neighboring provinces by using the spatial Durbin model in Spain. In China, positive spillovers are high in economically similar provinces than that in other provinces, whereas high transportation networks often result in low or negative spillovers in underdeveloped provinces (Jiang et al., 2016). The influence of public surface transport infrastructure on regional outputs are also found to be mostly from spillover effects, and highways are proven to have a more overwhelming influence through local and spillover effects than railways, airports, and transits (Chen and Haynes, 2015). Furthermore, transport infrastructures have negative spatial impacts on urban environment (Xie et al., 2016) and landscape diversity (Su et al., 2014).

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However, most of the studies exploring the influence of infrastructure on urbanization and related issues have seldom differentiate the function of infrastructure network in the form of line-based one and point-based one, or disaggregated infrastructure into systematic categories or groups in the context of rapid urbanization (Tan et al., 2018; Yang et al., 2018).. Generally, infrastructure often takes the form of lines such as road, rail and pipelines, whereas the service infrastructure also presents itself as points of interests (POIs) such as residence, tourist spots, and public facilities (Wu et al., 2016; Maparu and Mazumder, 2017). Extant research have attempted to classify transport infrastructure into railways, highways, transits, national roads, and provincial roads, among others, to explore the influence of infrastructure on urban development (Su et al., 2014; Chen and Haynes, 2015; Li et al., 2017). Nevertheless, infrastructure in the form of POIs, with respect to its impacts on urbanization, have rarely been investigated. Contemporary rapid infrastructure construction is diversified and is not restrained in road network. Facilities supporting telecommunications, water and electricity supply, commercial and scientific research service,

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education and governance also attracts population concentration and have close relationship with energy consumption, land use and sustainable regional development (Tan et al., 2018; Song et al., 2018). The balanced distribution of various public facilities also emerge to be symptoms of functional urbanization (Zhang, 2016). In this context, the exploration on the influence of infrastructure on urbanization is expected to take POIs into account, which can be accessible in the big data era (Zeng et al., 2018). Another key issue is the treatment of spatial spillover effects, which neglect the various forms of embedded spatial interactions in the widely applied spatial Durbin model. Previous studies often accommodate local and spillover effects of transportation on urbanization and related issues by incorporating the variable of transportation in neighboring units (Arbués et al., 2015; Xie et al., 2016; Han et al., 2018). However, in an increasingly networked environment, the functions of spatial interaction in infrastructure can be direct and indirect, as well as not limited spatial distances (Jiang et al., 2016; Gerritse and Arribas-Bel, 2018). Urbanization in adjacent neighbors is inclined to affect the local urbanization through the construction of housing and transportation (Li et al., 2017; Han et al., 2018). In this sense, infrastructure network is bound to function as a channel of spatial interaction over a single spillover effect. In an attempt to fill the gaps, we introduced road networkbased and POI-oriented hypotheses in the spatial weight matrix to explore the influences of infrastructure on urbanization and related issues. These topics are expounded in Sections 2. The results, discussion, and conclusion are presented in Sections 3, 4, and 5, respectively.

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2. Methodology and Materials

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2.1 Research area

Figure 1 Location of the Wuhan urban agglomeration

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The Wuhan urban agglomeration is located in Central China (29° 58′–31° 22′ N and 113° 41′–115° 05′ E) in the middle and lower reaches of the Yangtze River. It has a total area of 57.8 × 103 km2 and 38 million permanent residents in 2017. Its capital city is Wuhan. Moreover, it has eight prefectural cities: Huanggang, Xiaogan, Erzhou, Tianmen, Qianjiang, Xiantao, Hunagshi, and Xianning from north to south, with 48 counties. Its strategic position has endowed Wuhan with great opportunities, such as foreign investments, infrastructure construction, and socioeconomic development. In the past decade, Wuhan has been renowned for its unprecedented transportation construction in transits, highways, tunnels, and bridges, which have also propelled its urban expansion (Fang et al., 2012; Fan et al., 2017). In the 13th Five-Year Plan (2016–2020), the government is expected to invest approximately 245 billion yuan in rail transits to achieve a metro-network with 400 km length and 310 billion yuan in roads and bridges. Aside from the massive transport infrastructure construction in the core city (i.e., Wuhan), inter-city railways (with the objective of achieving 0.5 hr inter-city connection) and a ring expressway (with the objective of

achieving inter-city connection within 1 hr) have been completed or under active construction (Qian et al., 2015). Furthermore, the pilot project of the integrated transportation in Wuhan urban agglomeration has been approved by the National Bank and World Bank in 2014. Two sub-projects are also included, which are the construction of an intelligent transportation system in Wuhan and a transport infrastructure construction in Anlu, a county city in Xiaogan. The degree of transportation accessibility is highly correlated with historical context, administrative rank, and socio-economic development (Liu et al., 2015).

Driving factors and spatial influence of infrastructure network on urbanization

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2.2 Data description The spatial unit of our analysis is based on the county level, which consists of 48 administrative units in the Wuhan agglomeration. Primary data include land use data interpreted from LANDSAT remote sensing images with a spatial resolution of 30 m, socio-economic data, road network layer, and POI distribution data in Wuhan urban agglomeration in 2004-2005 and 2014-2015. Socio-economic data were extracted from the County-level Socioeconomic Statistical Yearbook in China (2006–2018), Statistical Yearbook in Hubei Province (2000–2018), Statistical Yearbook in Wuhan Urban Agglomeration (2014–2018), and Wuhan Statistical Yearbook (2000–2018). Road network data and POIs in 2005 and 2015 were retrieved from the Resource and Environment Data Cloud Platform (http://www.resdc.cn/). We have also captured POIs from the Location-based Service on the Baidu Map Open Platform (http://lbsyun.baidu.com/index.php?title=lbscloud).The original data types of the POIs include various categories, such as shopping malls, hospitals, residential areas, and restaurants. We uniformly extracted and grouped the POIs into categories of commercial centers, residential points, and factories to calculate their density and make a consistent dataset.

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Urbanization is closely related to demographic changes and is primarily driven by socio-economic development and infrastructure construction. It is measured in the traditional and universally applied manner, which is calculated as the ratio of the non-agricultural population to the total population. Both statistics can be directly retrieved from the Statistical Yearbook. Previous studies have examined spatial spillover effects in infrastructure construction. The present study further disaggregated infrastructure into point-based and line-based infrastructure to formulate relevant hypotheses (Figure 2). The first hypothesis assumes that the spatial spillover effect on urbanization is generated from line-based road network. We classified road network into expressways, roads at the national level, roads at the provincial highways, and roads at the county level. For each category, we calculated the integration values in spatial syntax and used the average values for all the categories to generate the ultimate global integration value for road network, which is further explained in Section 2.3.1. The second hypothesis assumes that the spatial spillover effect is caused by point-based facilities. However, POIs are hard to be comprehensively unified; the criteria are that the groups are supposed to cover most of the necessary sites, spots, and facilities. We collected and made a preliminary classification of POIs in Wuhan urban agglomeration, which is discussed in Section 2.3.2. The integration value of the road network and POI density were then used to formulate the spatial weight matrix for spatial modeling after being embedded in the gravity model.

Socio-economic development

Hypothesis 2: Spillover from point Point-based spatial spillover

POI facilities

Urbanization Infrastructure construction

Transportation network

Spatial spillover effect

Line-based spatial spillover

Hypothesis 1: Spillover from line

Figure 2 Driving factors of urbanization and the spatial spillover effect

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To justify our hypothesis, we applied spatial autoregressive model (SAM), spatial error model (SEM) and spatial Durbin model (SDM) to incorporate the spatial spillover effects. If we take the general spatial model form as E.Q. (1), the specifications of SAM, SEM and SDM are as E.Q.(2), E.Q. (3) and E.Q. (4). 𝑛 𝑈𝐵 = 𝛽0 + αW(𝐹𝑖𝑗 )𝑈𝐵′ + ∑𝑚 (1) 𝑖=1 𝛽𝑖 𝑥𝑖 + ∑𝑗=1 W(𝐹𝑖𝑗 ) 𝛽𝑗 𝑥𝑗 + 𝛾W(𝐹𝑖𝑗 )𝜀,

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(1) When 𝛽𝑗 = 0, E.Q.(1) turns out to be SAM with spatial spillover effect in dependent variable and error term. ′ 𝑈𝐵 = 𝛽0 + αW(𝐹𝑖𝑗 )𝑈𝐵′ 𝑈𝐵′ + ∑𝑚 𝑖=1 𝛽𝑖 𝑥𝑖 + 𝛾W(𝐹𝑖𝑗 )𝑈𝐵 𝜀,

(2)

(2) When α = 0 and 𝛽𝑗 = 0, E.Q.(1) turns out to be SEM with spatial spillover effect just in error term. ′ 𝑈𝐵 = 𝛽0 + ∑𝑚 𝑖=1 𝛽𝑖 𝑥𝑖 + 𝛾W(𝐹𝑖𝑗 )𝑈𝐵 𝜀,

(3)

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(3) When 𝛾 = 0, E.Q.(1) turns out to be SDM with spatial spillover effect in dependent variables and independent variables.

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𝑛 𝑈𝐵 = 𝛽0 + αW(𝐹𝑖𝑗 )𝑈𝐵′ + ∑𝑚 𝑖=1 𝛽𝑖 𝑥𝑖 + ∑𝑗=1 W(𝐹𝑖𝑗 ) 𝛽𝑗 𝑥𝑗 + 𝜀,

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Whereα and 𝛾 are the coefficients for the spatial lag term and spatial error terms;

(4)

𝛽 is the coefficient for the

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explanatory variables. 𝛽0 is the constant term; UB is urbanization rate and 𝑈𝐵′ is its neighboring values. X are

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the explanatory variables. The ultimate errors produced by spatial models are assumed to be independently distributed and conform to the normal distribution. W is an N×N spatial weight matrix, W(𝐹𝑖𝑗 ) denotes the spatial

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weight matrix generated from the gravity model by POI facility or road network, and μ is the error. Previous studies generally take the neighboring effect for certain explanatory variables as one of the independent variables to justify the spatial spillover effect. We incorporated the spatial spillover effect into the spatial weight matrix because we assume that the infrastructure network serves as the channels for the flow of capital, information, and resources. As an embedded spillover medium, it functions in a wide range of factors, which is not restrained in explanatory variables only. As a result, we incorporated the spatial spillover effect into the spatial weight matrix in SAM, SEM and SEM. To justify the application of spatial econometric model, we also made Moran’s I test and Lagrange Multiplier (LM) diagnosis. Moran’s index is a measure of spatial autocorrelation proposed by Moran and has been widely used in various studies to examine the existence of correlations in a signal among nearby locations in space (Moran, 1950; Bai et al., 2018). LM is a diagnostics for spatial dependence which was first derived in Anselin (1988). Together with Moran’s I, this statistics has also been embedded in spatial econometrics packages in various software, to identify spatial dependence and provide guidance for determining the existence of spatial autocorrelation in lags or errors (Anselin, 2001; Zhang and Wang, 2019). Furthermore, after considering the existing empirical studies and data accessibility, we have selected nine socioeconomic factors, namely, population density (PD; unit: km2), per capita GDP (PGDP; unit: RMB), proportion of the second sector to the total GDP (SGDP), proportion of the tertiary sector to the total GDP (TGDP), total industrial output per land (IOL; unit: RMB/m2), fixed asset investment per land (FAIL; unit: RMB/m2), total retail sales of consumer goods per capita (PTSC; unit: RMB), public revenue per capita (PPV; unit: RMB), and total agricultural

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output per capita (PAO; unit: RMB/m2), as potential driving factors. We then made correlation and regression analyses to choose the factors with the highest correlation and without multicollinearity. (1) Population density (PD; unit:/km2). It is calculated as the ratio between the total population and area. Urbanization, as defined by the ratio between urban to total populations, is closely related to demographic factors, such as migration and population quality (Xiong et al., 2018). However, these data are not publicly available and thus difficult to obtain. As such, PD can be a reasonable alternative. It has been used as the driving factor in the research of Démurger et al. (2002) on the role of geographic and political factors in regional development. Furthermore, this variable is typically employed as an explanatory one in modeling land urbanization and urban expansion in China (Lin et al., 2015; Zhou et al., 2018). (2) Per capita GDP (PGDP; unit: RMB). It is a widely used indicator to measure the socio-economic level. Regional differences in urbanization were discovered to be significantly correlated to per capita GDP (Lin et al., 2018). It has also been used as an indicator for the quality of social urbanization (Fang and Yu, 2016) and an index of urbanization quality (Zhao and Wang, 2018). (3) Proportion of the second sector to the total GDP (SGDP) and proportion of the tertiary sector to the total GDP (TGDP). These variables describe economic structure and urbanization, respectively. By themselves, they refer to the transformation of the primary industry from agriculture to non-agriculture industries, which are the second and tertiary sectors in China (Liang and Yang, 2019). These structural factors have been used as control variables to explain population and economic urbanization (Ye et al., 2018), applied as the indicators of the economic benefits of urban land utilization (Zhang and Wang, 2018), and served as the primary support and driving force of urbanization (Guan et al., 2018). (4) Total industrial output per land (IOL; unit: RMB/m2). Industrial output is a reflection of industrial development, which has long been regarded as the endogenous power of urbanization (Guan et al., 2018; Xiong et al., 2018). This variable has also been used as an indicator of the economic benefit of urban land utilization (Zhang and Wang, 2018). (5) Fixed asset investment per land (FAIL; unit: RMB/m2). It is a measure of capital spending, which directly influences the urbanization strategy of China (Guan et al., 2018). Ye et al. (2018) used it as a control variable to explain population and economic urbanization, and Zhang and Wang (2018) applied it as an indicator of the economic benefits of urban land utilization. (6) Total retail sales of consumer goods per capita (PTSC; unit: RMB). The total retail sales of consumer goods refers to the sales of physical commodity or income from catering services sold or provided by enterprises to individuals or social organizations. It is an important part of the tertiary industry in China, which has overtaken manufacturing in leading economic growth and urbanization (Gu et al., 2017). It has also been used to assess the urbanization strategy of China (Guan et al, 2018). (7) Public government revenue per capita (PPV; unit: RMB). The public government revenue primarily comprises sources in taxes, fees, surplus of public sector units, fines, and penalties. In the land economic-led urbanization, land revenue plays an important role (Gu at al., 2017), where the budgetary revenue of the local governments is used as an indicator of the economic benefits of urban land utilization (Zhang and Wang, 2018). (8) Total agricultural output per capita (PAO; unit: RMB/m2). The development of agriculture is an important driving force in urbanization because it provides sufficient food and raw materials, market, and surplus labor force (Xiong et al., 2018). In fact, at the incipient stage of reform and the market-led urbanization, it promoted agriculture productivity (Gu at al., 2017).

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𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑖𝑓 𝑠 = 1 D = ∑𝑛𝑠=1 s × 𝑁𝑠 = { 𝑙𝑜𝑐𝑎𝑙 𝑑𝑒𝑝𝑡ℎ 𝑖𝑓 𝑠 = 𝑘 , 𝑔𝑙𝑜𝑏𝑎𝑙 𝑑𝑒𝑝𝑡ℎ 𝑖𝑓 𝑠 = 𝑙 𝐷

D = 𝑛−1, 𝑅𝐴𝑖 =

̅ −1) 2(𝐷 𝑛−2

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2.3.1 Spatial syntax model of the road network Based on the graph theory, the spatial syntax model partitions an area with a set of axial lines and generates a series of metrics for the quantification of the intersected network. Morphologically, the intersected axial lines produce a series of indicators, such as control value, depth, connectivity, and local and global integration. They have been widely applied in the analysis of road and social networks and urban studies for the functional representation of spatial configuration in a network (Jiang and Claramunt, 2002; Liu et al., 2015; Zha et al., 2017). In our study, we used global integration value, an indicator of relational asymmetry, to measure the spatial distribution of the road network as it has better demonstration of the spatial characteristics of connectivity and accessibility than road density. In the spatial syntax model, the calculation of global integration is related to depth and is specified as follows:

,

(5)

(6) (7)

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where D is the depth, the depth in the spatial syntax model is an attribute of an axial line. If it is given the value of k in E.Q. (5), it considers k neighborhoods and which can be interpreted as the number of lines distant from k-steps

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to that axial line (Jiang and Claramunt, 2002; Zha et al., 2017 ). D is the average path length, RAi is the integration value, s is the shortest step from the node which is the intersection of the axial lines in the axial map., and Ns is the number of nodes with the shortest steps for a node. In the calculation of the depth value, a step is equivalent to the shortest distance and generally ranges from 1 to 3. In the first step, the depth is the connectivity value and is equals

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to the global depth in the third step. N is the number of the total axial lines, and the average depth value D is calculated as the depth value divided by n−1. The integration value is thus specified in Equation (7). In general, the integration level is relatively high when RAⅈ is higher than 1, and when the value of RAⅈ is between 0.4 and 0.6, the spatial distribution is relatively scattered. In our research, we have manually extracted the axial lines in ArcGIS for the end of each year to guarantee that they are located at the boundary of the study area and the extraction accuracy. We have calculated the global integration value for expressways, national ways, provincial highways, and prefectural highways in each county using Eqs. (5)–(7). Thereafter, we have averaged their values to obtain the ultimate global integration value for all roads in each county.

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2.3.2 Classification and calculation of POIs POIs have great potential in indicating the POIs of public facilities and have been widely applied in urban studies. To unify the types of POIs for comparison in 2005 and 2015, we classified POIs into groups of commercial sites, public facilities, tourist spots, and administrative facilities based on their functions. The points in each category and sub-category are specified as follows. Commercial sites: This category includes the sub-categories of commercial and service network, and financial services. Specifically, the concerned points refer to hotels, restaurants, shopping malls, supermarkets, theaters, communication offices, recreation and entertainment sites, and banks. Public facilities: This category includes the sub-categories of research and education, medical services, and transportation sites. Specifically, the concerned points refer to schools, universities, hospitals, clinics, railway stations, gas stations, parking lots, and toll stations. Tourist spots: The concerned points for this category include parks, scenic spots, and museums.

Administrative facilities: This category includes government and security buildings. After the collection of POIs in each category and sub-categories, we then calculated the density of POIs in each county (E.Q. (8)).

D𝑖 =

𝑁𝑐 +𝑁𝑆 +𝑁𝑇 +𝑁𝐴 𝑆𝑖

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where D𝑖 is the POI density, S𝑖 is the area in the ith county, and NC, NS, NT, and NA are the number of POIs in the categories of commercial sites, public facilities, tourist spots, and administrative facilities.

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2.3.3 Generation of embedded spatial influence through the gravity model

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After the calculation of the integration value of the road network and POI density in each county in Wuhan urban agglomeration, we then measured the embedded spatial influence using the gravity model in Equation (7) to generate the spatial weight matrix for spatial modeling. Gravity model is designed to mimic gravitational interaction as described by the law of universal gravitation of Newton, and has been applied in various fields such as trade (Mátyás, 1997), migration (Simini et al., 2012) and transportation (Zhong et al., 2018). It helps to calculate the volume of flow or interaction of specific attributes between two or more location. The estimated gravity has the positive relationship with the attributes and negative relationship with the spatial distances, and provides reasonable indicator of the magnitude of the spatial interaction (Isard, 2017). In our study, we applied Gravity model to generate the spatial weight matrices based on the integration value of the road network and POI density. The gravity model was selected because the construction of transportation and public facility have accumulated learning and scale effects. That is, two spatial units that have high accessibility in road network or high level of POI density are inclined to learn from each other and give rise to a great spatial interaction. Our rationale is that the spatial spillover effect of transportation infrastructure on regional economy or productivity indicates that the construction of transportation infrastructure not only exerts a local impact (Arbués et al., 2015; Knaap and Oosterhaven, 2017), but also influences the neighboring spatial units. In the meantime, the close interaction between land use and transportation indicates similar land use patterns when transportation infrastructures are of similar scales in different spatial units (Liu et al., 2016). Spatial sustainable land use is a regional issue, which necessitates spatial interaction with regard to urban expansion, farmland, and ecological land protection (Kneese, 2016). Consequently, infrastructure construction has been increasingly regarded as a regional issue, where the synergic effects from neighboring spatial units have been highlighted.

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After calculating the integration value of the road network and POI density in each county in the Wuhan urban agglomeration, we have then measured the embedded spatial influence using the gravity model in Eq. (9) to generate the spatial weight matrix for spatial modeling. The specification of the gravity model is as follows:

Fⅈj =

Gi(f(RA or D))⋅Gj(f(RA or D)) d2

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Where Fij is the gravity force generated from road network, Gi and Gj are the values in the ith and jth observation unit, 𝑓(𝑅𝐴 𝑜𝑟 𝐷) indicates that the value of G can either be the integration value of road network or the POI density, and d is the spatial Euclidean distance. Finally, we have performed the row-standardization for the spatial weight matrices to guarantee the sum of each row as 1 for spatial modeling. After these three steps, the global integrated values have been transformed into the line-based road network.

3. Results

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3.1 Spatial distribution of POIs Figure 3 illustrates the spatial distribution of infrastructures in the categories of commercial sites, public facilities, scenic spots, and administrative buildings, and Table 1 presents the corresponding density for the four types of infrastructure and related secondary classifications in 2005 and 2015. The tremendous increment of infrastructure points is apparent in the past decade and the dense clustering in the city center is distinct. Moreover, the growth rate of commercial sites is the greatest, and they rapidly increase from the city center to the outer rings in Wuhan urban agglomeration. The expansion of the commercial and service networks contribute a large share to the growth. Public facilities used to be the points with the most number and was relatively scattered in 2005, but its growth rate lags behind commercial sites and administrative buildings, which is attributed mostly to the growth in medical services. Administrative buildings and scenic spots are two types of POIs with comparatively less number, with the former demonstrating a rapid and scattered growth.

(a) 2004-2005

(b) 2014-2015

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Figure 3 POI distribution in (a) 2004-2005 and (b) 2014-2015 in Wuhan urban agglomeration

Table 1 POI density in the primary and secondary classifications

Primary

Density

2004-2005

2014-2015

Growth rate

SYWF

0.002

2.1789

1088

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Classification

A

GGWF

0.026

0.5216

Density

Secondary

19

JQJG

0.0027

0.0215

7

XZBG

0.0051

0.2458

47

Classification

2004-2005

2014-2015

Growth rate

SYWD

0.0015

2.0122

1340

JRFW

0.0005

0.1667

332

KYJY

0.0081

0.2101

25

YLFW

0.0014

0.2195

156

JTWD

0.0166

0.0921

5

Notes: SYWF (commercial sites), GGWF (public facilities), JQJG (scenic spots), XZBG (administrative buildings), SYWD (commercial and service networks), JRWF (financial services), KYJY (research and education), YLFW (medical services), and JTWD (transportation sites)

3.2 Spatial aggregation level of road network The global integration values for expressways, national highways, provincial highways and prefectural highways in

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2005 and 2015 are presented in Table 2. Expressways and national highways had the highest integration value in 2005 and 2015, respectively, whereas prefectural highways continue to have the lowest integration value. This finding implies that roads with high levels generally have more integrated axial lines and national highways show the greatest growth in integration values. Figure 4 illustrates the spatial distribution of the road axis with different integration values. Four lines had an integration value of more than 4 in 2005, and the number has increased to 6 with increased distance in 2015, indicating the increment of the integration level.

Table 2 Change in the global integration value in different road types in the Wuhan urban agglomeration area in 2005 and 2015

Indicator

Road type Expressways

1.56

1.88

1.53

1.89

1.30

1.66

1.14

1.46

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A

N

Provincial highways Prefectural highways

2015

U

National highways Global integration value

2005

Figure 4 Spatial distribution of the global integration value of axial road networks in 2005 and 2015

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The spatial distribution of the average values of global integration value in each county is illustrated in Figure 5. High integrated road networks appeared in the urban districts of Wuhan or around the core urban area and counties with the most scattered road network in the periphery of the Wuhan urban agglomeration in 2005. After a decade, the integration value has increased to a higher level and the counties in the western and northern areas around the core urban district of Wuhan all demonstrate high integration values. The most asymmetric distribution of road network appeared in Tongshan, a prefectural county in the southeastern part, in 2015.

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Figure 5 Spatial distribution and variation of the Global integration value of axial road network at county level in 2005 and 2015 .

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3.3 Local driving factors of urbanization The results of the correlation analysis reveal that most of the socio-economic factors have significant correlation coefficients with urbanization and differences were found in the potential driving factors in 2005 and 2015 (Table 3). The industrial sector proportion and fixed asset investment positively correlate with urbanization in 2005. However, both of them seem to be uncorrelated in 2015. The total industrial output was uncorrelated with urbanization in 2005 and 2015. The influence of several socio-economic factors on urbanization declined, except for TGDP. Furthermore, the tertiary sector appeared to be more powerful in 2015 than in 2005, and PAO was the only factor that has a negative influence on urbanization. Table 3 Correlation coefficients for the potential driving factors and urbanization PGDP

SGDP

TGDP

IOL

FAIL

PTSC

PPV

PAO

2005

0.800***

0.798***

0.487***

0.381***

−0.046

0.414***

0.614***

0.576***

−0.646***

2015

0.661***

0.572***

−0.138

0.565***

0.052

0.128

0.570***

0.340**

−0.576***

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PD

A

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Finally we choose PGDP, TGDP and PAO as the explanatory variables to model urbanization and the reasons primarily lie in two aspects. The first one is that PGDP, TGDP, and PAO were of high absolute correlation coefficients in 2005 and 2015. Although the correlation coefficient of TGDP is less than that in 2005, its contribution increased significantly in 2015. The second reason is avoiding the multicollinearity problem. For these reasons, we have omitted PD from the model although it has the highest correlation coefficient with urbanization. PD is also highly correlated with several factors in the list. As a result, given the consideration of unified comparison and multicollinearity problem in 2005 and 2015, we ultimately chose these three factors—PGDP, TGDP, and PAO as the explanatory variables. Consequently, we have ultimately selected PGDP, TGDP, and PAO as explanatory variables given the unified comparison and multicollinearity problem in 2005 and 2015. The scatter plots and linear regression line with R2 among the three variables and urbanization in 2005 and 2015 are shown in Figure 6. PGDP and TGDP show a positive relationship with urbanization, whereas PAO appears to be negatively correlated with urbanization. The simple linear regression between PGDP and urbanization in 2005 has the highest R2. Although R2 for the regression between TGDP and urbanization is the lowest, it is the only variable that shows an increasing trend from 2005 to 2015. PGDP (RMB)

TGDP

60000

1

50000

0.8

40000

0.6 R² = 0.1454

30000 R² = 0.6366

20000

0.4 0.2

10000 Urbanization

0

Urbanization

0 0.2

0.4

0.6

0.8

1

1.2

0

0.2

(a) Urbanization and PGDP in 2005 PAO (RMB/m2)

500000

0.4

0.6

120000

PGDP (RMB)

100000

400000

80000

300000

40000 20000

Urbanization

0

0.2

0.4

0.6

0.8

1

0.3

1.2

A

Urbanization

0 0.9

ED

0.7

1.1

PAO (RMB/m2)

R² = 0.3318

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R² = 0.3195

0.2

0.9

N

160 140 120 100 80 60 40 20 0

0.8

0.4

0.7

(d) Urbanization and PGDP in 2015

TGDP

0.5

0.5

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(c) Urbanization and PAO in 2005

0.6

1.2

Urbanization

0

0

0.3

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R² = 0.4171

100000

1

R² = 0.327

60000 200000

1

0.8

(b) Urbanization and TGDP in 2005

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0

1.1

(e) Urbanization and TGDP in 2015

Urbanization

0.3

0.5

0.7

0.9

1.1

(f) Urbanization and PAO in 2015

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Figure 6 Scatter plots of urbanization and local driving factors

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3.4 Spatial spillover effects on urbanization in two hypotheses The results of the diagnoses for spatial dependence and spatial regression models in the form of SAM, SEM and SDM in 2005 and 2015 are shown in Tables 4 and Table 5 respectively. Results assert that the values of Moran’s I were all statistically significant in both hypotheses in 2005 and in 2015 (p < 0.01). Generally, positive spatial autocorrelation was more apparent in 2005 than in 2015, while the values of Moran’s I appeared similar. With respect to the result of the LM test, a slight spatial correlation was apparent in both hypotheses in 2005, and in the line-based hypothesis in 2015, except in the point-based hypothesis in 2015. Therefore, we have applied SAM, SEM, and SDM to explore the spatial correlation and driving forces of urbanization in both hypotheses and in both years. Generally, the coefficients are similar in both hypotheses regardless of local driving factors or spatial spillover effects whereas there is still differences on the magnitude of the spatial spillover effect between the line-based hypothesis and the POI_based hypothesis. In 2005, the spatial lag coefficients were both significant in both hypotheses with the values being 0.7620 and 0.6660 respectively in SAM. In SEM, the coefficient in spatial error term was only significant in the point-based hypothesis whereas W_PGDP and WTGDP are both significant in SDM in these two hypotheses. The direct influence of the neighbors existed and functioned through the road network and

point-based infrastructure facilities based on the results from SAM. However, this spatial spillover effect is more apparent through the spatial distribution of the road network than point-based infrastructure facilities. In the pointoriented hypothesis, the spatial correlation is inclined to exist in error terms. The local socio-economic influences are also more powerful in explaining urbanization than those in the line-based hypothesis. Table 4 Spatial regression results in 2005

Line-based Hypothesis

Point-oriented Hypothesis Moran's I: 0.0646***; LM error: 4.70**

Variable

2005SAM

2005SEM

2005SDM

2005SAM

2005SEM

2005SDM

Constant

-0.2333**

0.2741***

1.1418***

-0.1985**

-0.0366

1.1434***

PGDP

1.4261×10−5***

4.6634×10−6***

1.6585×10−5***

1.4730×10−5***

1.771×10−5***

1.6588×10−5***

TGDP

0.4697***

0.5541***

0.2308

0.4913***

0.7197***

0.2308

AO

−6.2588×10-7***

−1.7382×10-7***

−4.6425×10-7***

−6.315×10−7***

−5.532×10-7***

−4.6434×10-7***

W_UB

0.7620***

0.1170

0.6660***

W_TGDP

-3.4113***

W_PAO

−2.183×10-6 0.3510

R2 Adjusted

R2

4.0556×10−5** -3.4110*** −2.1887×10-6

0.7030***

0.7732

0.6509

0.8661

0.7578

0.6271

0.8465

0.7796

0.8001

0.8661

0.7645

0.7865

0.8465

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Error

0.1040

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W_PGDP

4.0523×10−5**

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Moran's I: 0.0646***; LM error: 4.03**

N

Note: No. of observation: 48 counties; dependent variable: urbanization rate; ***, **, * refer to the significance level at 1%, 5% and

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10%, respectively.

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In 2015, the spatial lag coefficients were both significant in spatially lagged autoregressive term W_UB in SAM and in the spatially lagged independent variables W_TGDP and W_AO in SDM in both hypotheses. The spatial influences have attenuated with the direct spatial spillover effect slightly more prominent in the point-oriented hypothesis. The indirect spatial spillover effect have become negative and spatial correlations in errors have not been identified in both hypotheses. Table 5 Spatial regression results in 2015

PT

Line-based

Point-oriented Moran's I: 0.0211***; LM error: 2.01

Variable

2015SAM

2015SDM

2015SAM

Constant

-0.0034

0.2766***

0.9114*

-0.0051

0.2764***

0.9043*

PGDP

3.8608×10−6***

4.6574×10−6***

2.9303×10−6**

3.8559×10−6***

4.6564×10−6***

2.9264×10−6**

TGDP

0.4717***

0.5542***

0.3323**

0.4713***

0.5543***

0.3323**

PAO

-1.7173×10-7***

−1.7404×10-7***

−1.6965×10-7***

-1.7166×10−7***

-1.7395×10-7***

-1.6949×10-7***

W_UB

0.5250*

0.1170

0.5280*

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Moran's I: 0.0211***; LM error: 6.93*** 2015SEM

2015SEM

2015SDM

0.1280 8.5545×10−6

W_TGDP

-1.2087***

-1.21***

W_PAO

-1.2351×10-6*

-1.2298×10-6*

A

W_PGDP

8.5862×10−6

0.3560

Error

0.3600

R2

0.6458

0.6506

0.6911

0.6457

0.6506

0.6911

Adjusted R2

0.6217

0.6268

0.6459

0.6216

0.6268

0.6459

Note: No. of Observation: 48 counties; Dependent variable: Urbanization rate; ***, **, * refer to the significance level at 1%, 5% and 10%, respectively.

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With respect to the traditional explanatory variables, both PGDP and TGDP have shown positive influences whereas negative contribution of AO is diagnosed. The influences of PGDP is more powerful in SAM and less strong in SDM in 2015 than that in 2005 whereas the influences of PAO has continued to decrease in both hypotheses. The contribution of TGDP remains stable except that the coefficients have changed from being insignificant to positively significant in SDM. From 2005 to 2015, there are three primary changes. The first one is that the direct spatial spillover effect which is embodied in the coefficient of W_UB has declined and the high level of contribution has changed from line-based hypothesis to point-oriented hypothesis. The second one is that although the local influences of agricultural development have declined, the spatial spillover effects of W_PAO have become negatively significant in both hypotheses. Thirdly, the local economic development level PGDP has made greater contribution in 2015 than that in 2005 whereas its spatial spillover effect has turned out to be insignificant in both hypotheses.

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The highest adjusted R2 appears in 2005 and the lowest one is in 2015 in the line-based hypothesis. The results indicate that the local economic development and the direct and indirect spatial spillover effect in the neighborhood both attributes to the promotion of urbanization. The direct influence of the neighbors exist and functions through both the road network and the point-based infrastructure facilities. However, this spatial spillover effect is more apparent through the spatial distribution of road network than that through point-based infrastructure facilities in 2005 whereas both of them are functioning with similar magnitude in 2015. The local socio-economic influences are also more powerful in explaining urbanization in the point-based hypothesis than that in the line-based one in 2005 whereas the situation changed to the contrary in 2015. The finding also verifies that the urbanization of the Wuhan agglomeration is primarily driven by economic development, especially growth in the tertiary sector, while this process has paid the price in agricultural development. However, these negative relationships that have relaxed though the negative spatial influence from agriculture were strengthened from 2005 to 2015. We have combined the data in 2005 and 2015 to perform a preliminary spatiotemporal modeling. Table 6 exhibits the results. In the long term, the contribution of the local economic factors and direct spatial influence are highly prominent in the point-based hypothesis. In the line-based hypothesis, we have corroborated that, although the local economic factor positively contributes, corresponding indirect spatial spillover effects become negative. On the contrary, the direct spatial spillover effect W_UB turns out to be positively significant. In the point-based hypothesis, the local economic driving forces are higher than those in the line-based one, as well as the positive spatial spillover effect of W_UB. However, the indirect spatial influence has shown attenuation. Table 6 Spatial regression results in 2015

Line-based hypothesis

Point-based hypothesis

Constant_Coef

-0.5545**

-6.79***

PGDP_Coef

3.70×10−6***

4.50×10−6***

TGDP_Coef

0.3314**

0.4476***

AO_Coef

−2.00×10−7***

−2.00×10−7**

W_UB_Coef

3.0888***

8.24***

W_PGDP_Coef

-1.13×10−5***

/

W_TGDP_Coef

-1.40**

-1.18***

W_AO_Coef

/

/

R2

0.7656

0.7388

Adjusted R2

0.7498

0.7243

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Variable

Note: No. of Observation: 48 counties; Dependent variable: Urbanization rate

4. Discussion We set up a road network-based and POI-oriented hypotheses to investigate the spillover effect of infrastructure construction on urbanization through the embedded spatial interaction matrix by using Wuhan urban agglomeration as the case study area. The results confirm that urbanization is driven by economic development and spillover effects with varying magnitude.

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The primary contributions of our study lies in the classification of infrastructure into POIs and road networks and the investigation on the spatial spillover effect on urbanization through the spatial weight matrix. Abundant research have explored the causal relationship or the uni- and/or bidirectional relationship between transportation and economic development or other urban development issues, which provides the theoretical and empirical bases for the hypothesis of the influence of infrastructure on urbanization. However, China has long witnessed a “pseudo” urbanization, during which the functionality of the infrastructure has become an important part to characterize a “healthy” urbanization. In this study, we adopted the universally accepted definition of urbanization which is the proportion of urban population to the total population. The traditional transportation network alone cannot easily manifest the comprehensive features of infrastructure construction to the urbanization process. The multi-nuclei urban pattern has gained an increasing popularity compared with the traditional sector or concentric spatial pattern, which necessitates the considerations of infrastructures, such as commercial, residential, and administrative buildings, and various public facilities when the exploration on the influence of infrastructure is performed. As a result, we extracted POIs in a big data environment and uniformed the categories as commercial sites, public facilities, tourist spots, and administrative facilities for comparison in 2005 and 2015. These are important pointbased infrastructures, which potentially shape the urban spatial pattern and prompt demographic migration to urban areas. Road network was also considered, and the types of highways, national roads, provincial roads, and county roads are incorporated to calculate the integration level, which is the indicator for the spatial aggregation of the road network. Meanwhile, the other innovation is the setting up of POI-based and road network-oriented hypotheses in spatial regression modeling. The calculated integration index and POI density were used to generate the spatial weight matrix through the gravity model. Then, they were embedded into the SAM, SEM and SDM to gauge the spatial spillover effect and explore the driving factors. The spatial econometric models have superiority in the treatment of spatial spillover influence as it incorporates the spatial interaction in the spatial weight matrix other than making another explanatory variable in the regression model. This approach has paradoxically assumed that infrastructure network generates a spatial interaction and serves as the crucial channels, which further stresses the spatial influence of infrastructures. The preliminary also confirmed the existence of spatial lags using the generated spatial weight matrices for further modeling. In this sense, the hypothesis of the presence of the embedded spatial influence is theoretical and pragmatic.

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The contributions of economic development and spatial influence also vary in different spatial models and years. In general, economic factors, such as PGDP, TGDP, and PAO, and spatial influence have better performance in explaining urbanization in 2005 than that in 2015. Although the Moran’s I test has confirmed the existence of spatial autocorrelation, the direct spatial lag coefficient has attenuated in both hypotheses in SAM and the indirect spatial spillover effects have also declined and turned out to be negative from 2005 to 2015. This finding indicates that urbanization is a spatio-temporal process, and the spatial spillover effects from neighbors in the form of road network and point-based facilities have weakened in recent years. However, the comparative magnitude has changed from road network dominated to similar functioning between road network and POI based facilities. The attenuation

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of the spatial spillover effect in the line based hypothesis and the reinforcement in the POI hypothesis, as well as the change in indirect spatial spillover effects can be explained in three aspects. The first one is that public facilities have developed dramatically, which increasingly promotes the urbanization process and serves the formulation of significant spatial spillover effects. Sufficient access to public facilities is an important target for new urbanization, especially at the county level (Yi et al., 2015). From 2005 to 2015, point-based facilities have increased dramatically in the Wuhan agglomeration, and commercial sites and public facilities are among those with the most increment. Accordingly, these points are capable of being a channel to expand socio-economic development and facilitates the rural migration and urbanization processes (Guan et al., 2018). The second one is the substitution of the contribution of the line-based road network from road to rail. Previously, the coverage of highways, county and township roads has affected road accessibility for production flow, information flow, and non-agricultural market entrance of rural production factors, which affect urbanization development at the county level (Yang, 2016). With increased urbanization rapidly improving the road system, its significant spatial influences have thus reasonably declined, whereas the rail system has arisen to improve inter-urban immigration (Baum-Snow, 2017; Wang, 2018). Third, urbanization has gradually transformed from economy-driven to sustainable and integrated regional development, and rural development has increasingly become a key issue (Soja, 2016). Urban areas pertain to the clusters of various industrial and modern technologies with high economic levels and large proportions of the tertiary sectors, which potentially induce spatial aggregation in the urban population (You and Yang, 2017). However, as we gradually enter into an era that underlines people orientation and sustainable urbanization, the contribution of traditionally high-yielding economic development to urbanization has declined (Chen et al., 2016). On the contrary, direct and indirect spatial spillover effects have been prominent, and the local and neighboring agricultural outputs functioned in 2015 in the POI-based hypothesis. This finding is a manifestation of contemporary Chinese new urbanization as well, which has transformed from economy-driven to sustainable regional development mode (Long et al., 2011; Lang et al, 2016).

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Although we have performed an initiative study on the exploration of the influence from infrastructure on urbanization, some limitations still need to be considered for further investigation. First, the road network and POIs need refined quantification. Integration values in road network and POI density have been intuitively applied to formulate the spatial weight matrix, whereas the refined classification of road network and POI have not yet been taken into account due to data availability and uniformity. For example, the elaborate desegregation of transportation network into highways, railways, roads at different levels, or POIs into commercial, industrial, residential, and administrative points are desired to be implemented for spatial modeling to produce more pragmatic policy implications. Second, the driving factors are primarily concerned with economic development, whereas urbanization is a complicated process where demographic migration and institutions are also crucial driving factors. These factors have not been included due to data accessibility and multicollinearity problem. Moreover, the generation of the spatial weight matrix is based on the simple gravity model with a hypothesis that high values are inclined to produce a great force. The embedded spatial influence generated from infrastructure, such as transportation or public facilities, can even be complex with the changing mechanisms of forces, which necessitate further exploration in the future.

5. Conclusion and Implications In this study, we explored the influence of infrastructure network on urbanization in road network-based and POIbased hypotheses through spatial modeling in Wuhan urban agglomeration in 2005 and 2015. The global integration

values of the road axial lines and POI density were used to generate spatial weight matrices by using the gravity model to formulate the two hypotheses. The results revealed that local economic factors and the spatial influence of infrastructure both contribute to the changes in urbanization with varying powers in different hypotheses and years.

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The contributions of our study also lie in helping to formulate strategies for sustainable urbanization which is embodied in three aspects. First, utilizing the embedded spatial spillover effect helps in alleviating land use pressure and in improving resource use efficiency, thereby promoting sustainable urbanization. In the past decade, several cities have been tremendously urbanized through transport and facility construction. The inevitable sprawling of transportation networks and various facilities has encroached large amounts of farmland and ecological land, which are detrimental for sustainable development. Although the role of infrastructure network as the spatial channel that promotes regional urbanization has been justified in many studies, the embedded spatial spillover effect continues to hold considerable potential to be exploited in transportation or urbanization planning to transform the phenomenon from infrastructure-dominated to infrastructure-promoted urbanization. The rational spatial arrangement helps in reducing the use of land areas in transportation and in improving transportation land efficiency. Second, the spatial linkages of infrastructures and facilities can be strengthened to achieve balanced development and thus realize sustainable regional development. Results revealed differences in the spatial integration levels of the various types of transport lines, such as railways and expressways, and density of POIs in various categories. Although insignificant results (P<0.1) have been produced when we attempted to distinguish the spatial spillover influences of these disaggregated lines and points on urbanization, the spatial clustering or scattering of the lines and points is deemed to influence demographic flow, as well as local and regional socio-economic developments. To stimulate spatial interactions, government officials are expected to attach importance to the spatial distribution of facilities, which are not confined to transportation infrastructure, to stimulate spatial interactions. The progressive and orderly layout of various infrastructures at the regional level is of vital importance for sustainable regional development. Third, the reorientation of the role in economic and rural developments helps in promoting an integrated urban–rural development and in realizing sustainable urbanization strategies. The results corroborated that economic factors are widely accepted impetus to urbanization, while the contributions appear to be strengthened. In the meantime, the negative influence of rural outputs on urbanization should be given attention. This negative relationship indicates that the amicable relationship between urbanization and rural development has not been fully realized. In the context of rapid urbanization and rural revitalization, infrastructure construction in rural areas should also be strengthened to improve rural economic development and living standards for an integrated urban–rural development. The sustainable urbanization strategy emphasized the mutual benefits in urban and rural areas. The infrastructure network is expected to be established comprehensively with a sufficient focus on rural development to achieve a sustainable vibrant urban–rural relationship.

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Acknowledgement This research is supported by the Natural Science Foundation of China (41771563, 41501179), and Hubei Chenguang Talented Youth Development Foundation. The author is also grateful to the colleagues from Chinese Cities Research Center in the Department of City and Regional Planning, University of North Carolina-Chapel Hill for their valuable advice.

References: Acheampong, R. A., Silva, E. A. (2015). Land use–transport interaction modeling: A review of the literature and future research directions. Journal of Transport and Land use, 8(3), 11-38.

Anselin, L. (1988). Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geographical analysis, 20(1), 1-17. Anselin, Luc. "Spatial econometrics." A companion to theoretical econometrics 310330 (2001). Arbués, P., Banos, J. F., Mayor, M. (2015). The spatial productivity of transportation infrastructure. Transportation Research Part A: Policy and Practice, 75, 166-177. Bai, Y., Deng, X., Jiang, S., Zhang, Q., Wang, Z. (2018). Exploring the relationship between urbanization and urban ecoefficiency: Evidence from prefecture-level cities in China. Journal of cleaner production, 195, 1487-1496. Baum-Snow, N., Brandt, L., Henderson, J. V., Turner, M. A., Zhang, Q. (2017). Roads, railroads, and decentralization of Chinese cities. Review of Economics and Statistics, 99(3), 435-448.

IP T

Chen, Y., Chang, K. T., Han, F., Karacsonyi, D., Qian, Q. (2016). Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China. Habitat International, 51, 59-69.

Chen, Z., Haynes, K. E. (2015). Public surface transportation and regional output: A spatial panel approach. Papers in

SC R

Regional Science, 94(4), 727-751.

Démurger, S., Sachs, J. D., Woo, W. T., Bao, S., Chang, G., Mellinger, A. (2002). Geography, economic policy, and regional development in China. Asian Economic Papers, 1(1), 146-197.

Gerritse, M., Arribas-Bel, D. (2018). Concrete agglomeration benefits: do roads improve urban connections or just attract more people?. Regional Studies, 52(8), 1134-1149.

N

U

Gu, C., Hu, L., Cook, I. G. (2017). China’s urbanization in 1949–2015: Processes and driving forces. Chinese Geographical Science, 27(6), 847-859. Guan, X., Wei, H., Lu, S., Su, H. (2018). Mismatch distribution of population and industry in China: Pattern, problems and driving factors. Applied Geography, 97, 61-74.

A

Haase, D., Güneralp, B., Dahiya, B., Bai, X., Elmqvist, T. (2018). Global Urbanization. The Urban Planet: Knowledge

M

Towards Sustainable Cities, 19.

Han, F., Xie, R., Lai, M. (2018). Traffic density, congestion externalities, and urbanization in China. Spatial Economic Analysis, 1-22. Geography, 37(3), 436-457.

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He, C., Zhou, Y., Huang, Z. (2016). Fiscal decentralization, political centralization, and land urbanization in China. Urban Isard, W. (2017). Gravity and spatial interaction models. In Methods of interregional and regional analysis (pp. 243-

PT

280). Routledge.

Jiang, B. and Claramunt, C., 2002. Integration of space syntax into GIS: new perspectives for urban morphology. Transactions in GIS, 6(3), pp.295-309.

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Jiang, X., Zhang, L., Xiong, C., Wang, R. (2016). Transportation and regional economic development: analysis of spatial spillovers in China provincial regions. Networks and Spatial Economics, 16(3), 769-790.

Kasraian, D., Maat, K., Stead, D., van Wee, B. (2016). Long-term impacts of transport infrastructure networks on land-use change: an international review of empirical studies. Transport Reviews, 36(6), 772-792. Knaap, T., Oosterhaven, J. (2017). Spatial economic impacts of transport infrastructure investments. In Transport Projects,

A

Programmes and Policies (pp. 87-105). Routledge. Kneese, A. V. (2016). Transportation and urban land. Routledge. Lang, W., Chen, T., & Li, X. (2016). A new style of urbanization in China: Transformation of urban rural communities. Habitat International, 55, 1-9. Li, C., Song, Y., Chen, Y. (2017). Infrastructure Development and Urbanization in China. In China’s Urbanization and Socioeconomic Impact (pp. 91-107). Springer, Singapore. Li, G., Zhang, X., Mirzaei, P. A., Zhang, J., Zhao, Z. (2018). Urban heat island effect of a typical valley city in China: Responds to the global warming and rapid urbanization. Sustainable Cities and Society, 38, 736-745.

Lin, W., Wu, M., Zhang, Y., Zeng, R., Zheng, X., Shao, L., Zhao LY, Li SX, and Tang, Y. (2018). Regional differences of urbanization in China and its driving factors. Science China Earth Sciences, 1-14.

Lin, X., Wang, Y., Wang, S., Wang, D. (2015). Spatial differences and driving forces of land urbanization in China. Journal of Geographical Sciences, 25(5), 545-558. Long, H., Zou, J., Pykett, J., Li, Y. (2011). Analysis of rural transformation development in China since the turn of the new millennium. Applied Geography, 31(3), 1094-1105. Liu, X., Kang, C., Gong, L., Liu, Y. (2016). Incorporating spatial interaction patterns in classifying and understanding urban land use. International Journal of Geographical Information Science, 30(2), 334-350.

IP T

Liang, W., Yang, M. (2019). Urbanization, economic growth and environmental pollution: Evidence from China. Sustainable Computing: Informatics and Systems, 21, 1-9. Liu C L, Yu RL, Duan D Z, The evolution of spatial accessibility of urban-rural road network based on the space syntax in Wuhan metropolitan area, Scientia Geographica Sinica, 2015, 35(6), 698-707. (in Chinese)

SC R

Ma, L., Cheng, W., Qi, J. (2018). Coordinated evaluation and development model of oasis urbanization from the perspective of new urbanization: A case study in Shandan County of Heixi Corridor, China. Sustainable Cities and Society, 39, 78-92.

Maparu, T. S., Mazumder, T. N. (2017). Transport infrastructure, economic development and urbanization in India (1990– 2011): Is there any causal relationship?. Transportation Research Part A: Policy and Practice, 100, 319-336.

U

Mátyás, L. (1997). Proper econometric specification of the gravity model. World Economy, 20(3), 363-368. Mo, W., Wang, Y., Zhang, Y., Zhuang, D. (2017). Impacts of road network expansion on landscape ecological risk in a

N

megacity, China: A case study of Beijing. Science of the Total Environment, 574, 1000-1011. Moran, P. A. P. (1950). "Notes on Continuous Stochastic Phenomena". Biometrika. 37 (1): 17–23

New

Urbanization

Planning

(NNUP),

council,

2014,

http://www.gov.cn/zhengce/2014-

M

03/16/content_2640075.htm

State

A

National

Simini, F., González, M. C., Maritan, A., Barabási, A. L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 96.

ED

Soja, E. (2017). 13 Accentuate the regional1. In The Post-Urban World: Emergent Transformation of Cities and Regions in the Innovative Global Economy, 197.

Song, Q., Zhou, N., Liu, T., Siehr, S. A., Qi, Y. (2018). Investigation of a “coupling model” of coordination between low-

PT

carbon development and urbanization in China. Energy policy, 121, 346-354. Su, S., Xiao, R., Li, D. (2014). Impacts of transportation routes on landscape diversity: a comparison of different route types and their combined effects. Environmental management, 53(3), 636-647.

CC E

Sun, Y., Cui, Y. (2018). Evaluating the coordinated development of economic, social and environmental benefits of urban public transportation infrastructure: Case study of four Chinese autonomous municipalities. Transport Policy, 66, 116126.

Szeto, W. Y., Jiang, Y., Wang, D. Z. W., Sumalee, A. (2015). A sustainable road network design problem with land use transportation interaction over time. Networks and Spatial Economics, 15(3), 791-822.

A

Tan, R., Liu, K., Lin, B. (2018). Transportation infrastructure development and China’s energy intensive industries-A road development perspective. Energy, 149, 587-596. Wang, L., Xue, X., Zhao, Z., Wang, Z. (2018). The impacts of transportation infrastructure on sustainable development: Emerging trends and challenges. International journal of environmental research and public health, 15(6), 1172. Wei, Y. D., Li, H., & Yue, W. (2017). Urban land expansion and regional inequality in transitional China. Landscape and Urban Planning, 163, 17-31.

Wu, C., Wei, Y. D., Huang, X., Chen, B. (2017). Economic transition, spatial development and urban land use efficiency in the Yangtze River Delta, China. Habitat International, 63, 67-78.

Wu, Y., Li, X., Lin, G. C. (2016). Reproducing the city of the spectacle: Mega-events, local debts, and infrastructure-led urbanization in China. Cities, 53, 51-60. Xie, R., Fang, J., Liu, C. (2016). Impact and spatial spillover effect of transport infrastructure on urban environment. Energy Procedia, 104, 227-232. Xu WT, Yang, LC. (2019). Evaluating the urban land use plan with transit accessibility. Sustainable cities and society, 45, 474-485.

Xiong, N., Huang, Z., Zhao, S., Shen, L. (2018). Driving Factors for Promoting Urbanization of Small Towns in Southwest China. In Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate (pp. 651-663). Springer, Singapore. integration of infrastructure networks. Sustainable Cities and Society, 42, 407-422.

IP T

Yang, Y., Ng, S. T., Xu, F. J., Skitmore, M. (2018). Towards sustainable and resilient high density cities through better Yang R (2016). Spatial coupling cooperative analysis of road transport superiority and urbanization at county level in

SC R

China. Progress in Geography, 35(7), 806-815.

Ye, C., Sun, C., Chen, L. (2018). New evidence for the impact of financial agglomeration on urbanization from a spatial econometrics analysis. Journal of Cleaner Production, 200, 65-73. You, H., Yang, X. (2017). Urban expansion in 30 megacities of China: Categorizing the driving force profiles to inform the urbanization policy. Land Use Policy, 68, 531-551.

U

Zhang, X. (2016). Sustainable urbanization: a bi-dimensional matrix model. Journal of Cleaner Production, 134, 425433.

N

Yi, Z., Jun, Z., Jianzhao, Z. (2015). Public Service Facilities Planning For Counties. Planners, 3, 005. Wuhan. Sustainable cities and society, 40, 296-306.

A

Zeng, C., Song, Y., He, Q., Shen, F. (2018). Spatially explicit assessment on urban vitality: Case studies in Chicago and

agglomeration. Habitat International, 55, 46-57.

M

Zeng, C., Zhang, A., Xu, S. (2016). Urbanization and administrative restructuring: A case study on the Wuhan urban Zha CL, Peng MJ, Liu YF, Song LY. Spatio-temporal evolution and correlation analysis of road network accessibility and

ED

economy contact in Wuhan Metropolitan area, Economic Geography, 2017, 12, 74-81. (in Chinese) Zhang, W., Wang, M. Y. (2018). Spatial-temporal characteristics and determinants of land urbanization quality in China: Evidence from 285 prefecture-level cities. Sustainable Cities and Society, 38, 70-79.

PT

Zhao, J., Wang, M. (2018). A novel assessment of urbanization quality and its applications. Physica A: Statistical Mechanics and its Applications, 508, 141-154. Zhong, H., Wang, J., Yip, T. L., Gu, Y. (2018). An innovative gravity-based approach to assess vulnerability of a

CC E

Hazmat road transportation network: A case study of Guangzhou, China. Transportation Research Part D: Transport and Environment, 62, 659-671.

A

Zhou, T., Jiang, G., Zhang, R., Zheng, Q., Ma, W., Zhao, Q., Li, Y. (2018). Addressing the rural in situ urbanization (RISU) in the Beijing–Tianjin–Hebei region: Spatio-temporal pattern and driving mechanism. Cities, 75, 59-71.