Urban Public Green Space Equity against the Context of High-Speed Urbanization in Wuhan, Central China

This study examines the relationship between equity of public green space and urban expansion/sprawl under high-speed urbanization. Equity of urban public green space indicates the degree to which urban public green spaces are distributed spatially in an equal way, with regard to the spatial variation of residents’ “need” for green space. In emerging economies such as China, especially in developing or underdeveloped cities such as Wuhan, central China, rapid urban growth challenges the capacity of the state to provide infrastructure and services for its urbanites equally. In order to research the relationship between industrial development and green space equity under the background of rapid urbanization, the use of quantitative methods to more accurately measure the degree of spatial inequality is essential. In this study, the accessibility of urban public green space in Wuhan is examined based on the two-step floating catchment area method (2SFCA) method at multilevel radius; the urban public green space accessibility of Wuhan in 2013 and 2016 are acquired, and the link between changes in accessibility of urban public green spaces and urban expansion in Wuhan is discussed. It is found that industrial development takes precedence over green space. With its vigorous development, industrial land attracts increasing population, resulting in the drastic decline of the service capacity of green spaces, which is not conducive to the long-term development of the city.


Background
Over the past few decades, China witnessed unprecedented urbanization, driven by multiple forces such as globalization, centralization, industrialization, and marketization. Between 1978 and 2013, the percentage of China's urbanization soared from 17.9% to 53.7%. Such rapid rate of urbanization gave rise not only to dramatic urban expansion, but also various challenges such as resource depletion, food insecurity, and land degradation [1,2]. Besides these negative impacts, China's rapid economic development and transition brought about escalated social contradictions in recent years. In particular, inequality across regions has caused increasing concerns among both decision-makers and the general public, because it indicates the unequal distribution of opportunities among regions, which may pose a threat to social stability or even national unity [3,4]. Making the issue more important, the Chinese people's awareness of the inequity has grown in recent years, in areas such as medical care, education, and other aspects of welfare, including urban public green space.
Urban public green space is one of the important elements of urban planning, and it is an integral component of a city's green infrastructure, playing an important part in urban ecological public since 2001; in 2002, investment was increased to build waterfront parks in Hankou, Wuchang, and Hanyang; the "Green Space Planning of Wuhan Central Urban Area (2000-2020)" proposes access to green space of residents within 500 m to their homes, a per capita green space area in the central urban area (within the inner city ring) of 8.03 m 2 /person, and a per capita green space area in the main urban area of 16.8 m 2 /person. In 2005, Wuhan became the 53rd national-level garden city. The "Green Space Planning of Wuhan Central Urban Area (2011-2020)" proposes access to green space for residents within 500 m to their homes, access to parks within 800 m, and view of lakes or rivers within 1000 m, by 2020. The addition of large areas of parks, green squares, and greenways greatly improved the green coverage, thus the overall environment and image of the city. The goals of the urban green space system plan have been constantly clarified and improved, and detailed and differentiated plans have been developed for each urban district.
The research area of this study is the urban development zone as delineated in the Wuhan City Master Plan (2010-2020), approved by the State Council ( Figure 1).

Research Scale and Data Source
In this study, traffic analysis zone (TAZ) [15] is used as the basic research unit which refers to zones divided in different sizes and shapes by the road network. In this study, the traffic condition in a TAZ is regarded as basically homogenous and thus can be regarded as a whole.
Data used in this study include:

Research Scale and Data Source
In this study, traffic analysis zone (TAZ) [15] is used as the basic research unit which refers to zones divided in different sizes and shapes by the road network. In this study, the traffic condition in a TAZ is regarded as basically homogenous and thus can be regarded as a whole.
Data used in this study include:

Data Processing
Data processing comprised three major steps, regarding urban public green space, identification of urban expansion area, and population data processing, as illustrated in Figure 2.

Data Processing
Data processing comprised three major steps, regarding urban public green space, identification of urban expansion area, and population data processing, as illustrated in Figure 2.

Urban Public Green Space
Definition of urban public green space in this study is based on the five categories stipulated in the "urban green space classification standards" (CJJ/T 85-2002) by the Ministry of Construction in 2002, namely, comprehensive park, community park, specialized park, belt park, and roadside park. Among them, comprehensive park, community park, and roadside park are classified based on the scale, facility, target group, and service area of the park; specialized parks are classified based on their theme or features; while the belt parks are classified by their forms. Using vector data of urban green space of Wuhan in 2016, and comparing Google Earth historic images of Wuhan City in 2013 and 2016, the distribution map of green spaces of Wuhan City in 2013 was acquired, as illustrated in Figure 3.

Urban Public Green Space
Definition of urban public green space in this study is based on the five categories stipulated in the "urban green space classification standards" (CJJ/T 85-2002) by the Ministry of Construction in 2002, namely, comprehensive park, community park, specialized park, belt park, and roadside park. Among them, comprehensive park, community park, and roadside park are classified based on the scale, facility, target group, and service area of the park; specialized parks are classified based on their theme or features; while the belt parks are classified by their forms. Using vector data of urban green space of Wuhan in 2016, and comparing Google Earth historic images of Wuhan City in 2013 and 2016, the distribution map of green spaces of Wuhan City in 2013 was acquired, as illustrated in Figure 3.

Identification of Urban Expansion Area
Since the mid-20th century, developed western countries witnessed accelerated suburbanization. Urban built-up area spread out, resulting in excessive expansion of urban space and disordered sprawl. Scholars called this phenomenon "urban sprawl" [16,17]. Urban sprawl not only erodes open spaces such as farmland and forests around the city, but also negatively affects urban spatial structure, residential structure, urban transportation, ecological environment and visual quality [18]. Urban sprawl became a hot topic which caught attention in western academic disciplines such as urban planning, urban economics and environmental protection [19,20]. Researchers hence put forward concepts such as smart growth, compact city, and new urbanism, aiming to guide the healthy and orderly development of cities.
With the acceleration of economic globalization, many developing countries are also facing challenges brought by urban sprawl [21,22]. China is currently amidst rapid urbanization and the growth rate of its built-up area is faster than that of its population growth. As the trend of urban sprawl is still going strong in China, it is necessary to analyze and solve the problem of urban sprawl.
Among them, comprehensive park, community park, and roadside park are classified based on the scale, facility, target group, and service area of the park; specialized parks are classified based on their theme or features; while the belt parks are classified by their forms. Using vector data of urban green space of Wuhan in 2016, and comparing Google Earth historic images of Wuhan City in 2013 and 2016, the distribution map of green spaces of Wuhan City in 2013 was acquired, as illustrated in Figure 3.  To this end, one fundamental issue is to identify areas of urban expansion. In previous research, many scholars use the boundaries of urban built-up areas extracted from remote sensing from different time periods to identify and urban expansion. This study also uses this method to identify areas of urban expansion.
Currently, there is no uniform definition of the concept of built-up area, as demonstrated by the different understanding of built-up area by various scholars [23,24]. In general, built-up area refers to the area in the administrative area of a city, where buildings, public facilities, and roads are relatively concentrated. The built-up area described in this study is delineated by boundary information of urban built-up areas extracted from remote sensing imagery. The periphery of built-up area is usually rural, which is shown in remote sensing images as large areas of farmland or forest land, while the inside of the built-up area is usually aggregated buildings and public facilities.
Urban built-up area is identified with the built-up and bareness index (BBI) by using the reflectance from the NIR band to the SWIR1 band in the TM/ETM+ data. The normalized difference built-up index (NDBI) can usually be used to extract the urban built-up area; however, considering the developing area of urban space, the bare land areas will also be regarded as the urban built-up area. Thus, the built-up and bareness index is defined as follows: where R OLI2 , R OLI3 , and R OLI4 are the reflectance of the OLI data in band 2, 3, and 4, respectively.

Research Methods
Accessibility is a key concept used in human geography to evaluate the spatial distribution of public facilities. It is also adopted by the Chinese government as an important principle in public facility allocation. Accessibility is affected by many factors. Generally, the term accessibility we commonly use refers to spatial accessibility, which emphasizes the spatial nature of accessibility, ignoring nonspatial factors such as personal preference, race, and class [25,26].
As the evaluation of spatial accessibility can identify areas of scarcity of public services, it has been used as an effective way to measure the equity of the distribution of public service facilities [13,27,28] and has also drawn extensive attention from domestic and international researchers who have developed diverse research methods [29,30]. Among them, the two-step floating catchment area method (2SFCA) has been widely received and applied [31]. In subsequent research, various extensions to the methods emerged, forming a large family of 2SFCA models [32][33][34][35].
The basic idea of 2SFCA is shown in Figure 8. In the first search, a public facility j is used as the center point of supply, and the number k of demand points within search radius is identified to calculate the supply-demand ratio . In the second search, each point i is used as the center point of demands, and the number j of demand points within search radius is identified to calculate the supply-demand ratio . All Rj values are then summed to acquire the accessibility of point i.
where i stands for a demand point; j a supply point; represents the accessibility at the demand point i as calculated with the 2SFCA method; , is the distance between the demand point i and

Research Methods
Accessibility is a key concept used in human geography to evaluate the spatial distribution of public facilities. It is also adopted by the Chinese government as an important principle in public facility allocation. Accessibility is affected by many factors. Generally, the term accessibility we commonly use refers to spatial accessibility, which emphasizes the spatial nature of accessibility, ignoring nonspatial factors such as personal preference, race, and class [25,26].
As the evaluation of spatial accessibility can identify areas of scarcity of public services, it has been used as an effective way to measure the equity of the distribution of public service facilities [13,27,28] and has also drawn extensive attention from domestic and international researchers who have developed diverse research methods [29,30]. Among them, the two-step floating catchment area method (2SFCA) has been widely received and applied [31]. In subsequent research, various extensions to the methods emerged, forming a large family of 2SFCA models [32][33][34][35].
The basic idea of 2SFCA is shown in Figure 8. In the first search, a public facility j is used as the center point of supply, and the number k of demand points within search radius d 0 is identified to calculate the supply-demand ratio R j . In the second search, each point i is used as the center point of demands, and the number j of demand points within search radius d 0 is identified to calculate the supply-demand ratio R j . All Rj values are then summed to acquire the accessibility A F i of point i. where i stands for a demand point; j a supply point; A F i represents the accessibility at the demand point i as calculated with the 2SFCA method; d i, j is the distance between the demand point i and supply point j; R j is ratio of the facility size to the population of supply point j within the search radius d 0 ; S j represents the supply scale of supply point j; D k represents the demand scale of demand point k.
Sustainability 2020, 12, x FOR PEER REVIEW 9 of 21 supply point j; Rj is ratio of the facility size to the population of supply point j within the search radius ; represents the supply scale of supply point j; represents the demand scale of demand point k. Another commonly used method for evaluating accessibility is the gravity model, also known as the potential model. In fact, the 2SFCA and gravity model methods stem from the same theoretical framework, which consider the scale of both the supply and demand of facilities.
The two models take different approaches to the impact of the distance between supply and demand on accessibility. The gravity model method uses a continuous distance attenuation function, taking into account the decreasing service capability of facilities over distance; however, the method does not set a limit on the effective search radius of a facility. The 2SFCA method takes a dichotomy approach to distance attenuation, i.e., the accessibility is considered the same within the search radius threshold and completely unreachable outside the search radius.
In addition, there are several other commonly used methods for evaluating accessibility: the nearest distance method [37] considers only the distance factor, but not the scale of the supply or demand point; the Huff model [38] considers the facility scale and distance factor, but not the scale of demand; the kernel density method [25] stems from the same framework as the gravity model, which considers only the Euclidean distance, but not the impact of the actual traffic network or the scale of the demand point.
In summary, among the many evaluation methods of spatial accessibility, 2SFCA and the gravity model are the most widely used, and with the most comprehensive factors considered. Although the models share similar theoretical basis, 2SFCA is easier to understand and more operable due to its two-step floating catchment approach. Therefore, it has drawn much attention and gained great development. Another commonly used method for evaluating accessibility is the gravity model, also known as the potential model. In fact, the 2SFCA and gravity model methods stem from the same theoretical framework, which consider the scale of both the supply and demand of facilities.
The two models take different approaches to the impact of the distance between supply and demand on accessibility. The gravity model method uses a continuous distance attenuation function, taking into account the decreasing service capability of facilities over distance; however, the method does not set a limit on the effective search radius of a facility. The 2SFCA method takes a dichotomy approach to distance attenuation, i.e., the accessibility is considered the same within the search radius threshold and completely unreachable outside the search radius.
In addition, there are several other commonly used methods for evaluating accessibility: the nearest distance method [37] considers only the distance factor, but not the scale of the supply or demand point; the Huff model [38] considers the facility scale and distance factor, but not the scale of demand; the kernel density method [25] stems from the same framework as the gravity model, which considers only the Euclidean distance, but not the impact of the actual traffic network or the scale of the demand point.
In summary, among the many evaluation methods of spatial accessibility, 2SFCA and the gravity model are the most widely used, and with the most comprehensive factors considered. Although the models share similar theoretical basis, 2SFCA is easier to understand and more operable due to its two-step floating catchment approach. Therefore, it has drawn much attention and gained great development.
In this study, the multicatchment sizes 2SFCA, an improved form of 2SFCA, is adopted to calculate the accessibility of urban public green space. Based on the 2SFCA, the multilevel radius 2SFCA sets multilevel search radii in accordance with the scale of the facility from a supply perspective. This extended form is called multicatchment sizes 2SFCA (MC2SFCA).
The multicatchment sizes 2SFCA method analyzes the accessibility of park green space from two aspects: supply (area of green space) and demand (population distribution). At the TAZ scale, the center of mass can replace the population center of gravity, therefore the accessibility to the center of mass can be regarded as the average accessibility of the residents inside the TAZ. On the supply side, the urban public green spaces are classified in terms of different sizes and different service radii. This approach overcomes the weaknesses of traditional green space evaluation methods that ignore the spatial distribution, ecosystem service, and the social equity of resource provision of urban green spaces, and is more reasonable than the 2SFCA approach with a single radius and can more accurately reflect the actual travel of citizens.
Based on the information available in the study, the urban public green spaces are classified based on their sizes and are assigned with different service radii accordingly: municipal park, with an area 10-100 hm 2 , serves the entire city and the adjacent area at a radius of 3000 m; district park, with an area 5-10 hm 2 and a service radius of 1500 m; residential area park, with an area 2-5 hm 2 and a service radius of 750 m.
In accordance with the classification of park green spaces and using the corresponding service radius, the calculation method follows.
Using the park green space as the supply point j, the total number of demand points i within the service radius d 0 is searched, and the calculated supply-demand ratio R j represents the service capacity of the park green space: In Equation (5), R j is the service capacity of the park green space, S j is the area size of the park green space, d k,j is the distance between k and j, d 0 is the upper limit of travel distance, and D k is the total population within the search domain. In this first step, a search field is established using the park green space as the center and the supply-demand ratio of each park green space is calculated to study the usage of the park green space.
In the second step, for each demand point i, the park green spaces within the travel distance d 0 is searched and the total number j is calculated. The sum of all supply-demand ratios in the range is calculated as A i F , which represents the accessibility of each demand point i and can also be interpreted as the park green space resources actually available at the demand point.
In Equation (6), A F i is the spatial accessibility of the park green space resources for residents in the TAZ, R j is the area to population ratio of the park green space at location j in the search domain i, and d 0 is the upper limit of travel distance of residents.
This step establishes a search domain with the residential area in the TAZ as the center. Within the upper limit of the travel distance of residents, multiple park green spaces are covered, which provide green space for the residential area. By calculating the sum of the supply-demand ratio of the multiple parks, the spatial accessibility of the TAZ to park green space is obtained.

Results
The accessibility results calculated using 2SFCA can be interpreted as the per capita park green area that is actually available to the residents. The common method of calculating per capita green space is to divide the total area of urban public green space by the total population in an administrative area or research area. This method ignores the actual distribution of the population, because in reality, residents do not go to urban public green spaces that are too far from their homes, even if they are located in their administrative area. Seemingly similar to the common approach of calculating per capita area of green space by dividing the total area by population, the 2SFCA method takes into account the impact of service radius, which is more in line with the actual situation-urban public green spaces located too far away are often of no service to residents. Therefore, it can be considered that the results of green space accessibility calculated by 2SFCA can be interpreted as the per capita park green area that people can actually use.

Results of Green Space Accessibility for 2013 and 2016
Accessibility in the research area is divided into eight levels, as shown in Figures 9 and 10, and the Changes in accessibility in 2016 over 2013 have been shown in Figure 11. calculating per capita area of green space by dividing the total area by population, the 2SFCA method takes into account the impact of service radius, which is more in line with the actual situation-urban public green spaces located too far away are often of no service to residents. Therefore, it can be considered that the results of green space accessibility calculated by 2SFCA can be interpreted as the per capita park green area that people can actually use.

Results of Green Space Accessibility for 2013 and 2016
Accessibility in the research area is divided into eight levels, as shown in Figures 9 and 10, and the Changes in accessibility in 2016 over 2013 have been shown in Figure 11.      For the year 2013, accessibility scores of park green spaces within the second ring of the city are low. Since park green space plays the role of disaster prevention and mitigation, this study refers to the "Planning Guideline on Earthquake and Emergency (Outdoor) Refuge in the Central Urban Area of Beijing," taking into account the carrying capacity of park green spaces and residents' comfort, and sets the lower limit of per capita land area at 2 m 2 /person. Evidently, part of the areas in the central city could not meet this standard. Then, judged by the more ideal values set in accordance with the "Green Space System Planning for the Central Urban Area of Wuhan (2011-2020)" and through rational distribution of the green space system, the per capita park green space in the central urban area (within the Inner Ring Road) reaches 8.03 m 2 /person, the per capita park green area in the main city area reaches 16.8 m 2 /person, all areas within the Second Ring Road are unable to meet the standard, and some areas between the Second Ring Road and Third Ring Road cannot meet this standard. As for the main urban area (excluding the Inner Ring Road area and the undeveloped areas with an accessibility of zero), some areas can meet the standards and others cannot.
The reason why all areas within the Inner Ring Road were unable to meet the standard lies in its dense population and the relative scarcity of green spaces. Furthermore, the area between the Second and Third Ring Road is a transitional area where the accessibility in areas between the Third Ring Road to the fringe is generally good, probably due to the small population and abundant and high-quality green spaces, while the zero accessibility areas in the urban fringe need to be better planned and built. The visualized results of the park green space accessibility for the year 2016 are similar to those for 2013. It can be seen that the overall development pattern of park green spaces in Wuhan from 2013 to 2016 is consistent.

Urban Expansion
As seen in Figure 4 As seen in Table 1, the industrial land accounts for 23.03% of the total area of urban expansion. Furthermore, this ratio can be even higher if we set an impact range for industrial land, with the buildings within its influence considered as subsidiaries of some industries. Buildings, or buildings attached to the industry, will account for a larger proportion. When the influence range is set to 500 m, the area of the expanded area including industrial land is 52.65%, and when the range is set to 1000 m, the proportion is 53.79%. However, this is only one of the initial impacts we have set. In fact, many large industries will have a greater impact. However, it is undeniable that the expansion of Wuhan City is heavily influenced by industry. More than half of the city's expansion between 2013 and 2016 has been affected by industry. 2016 is extracted and its spatial distribution presented in Figure 12. The industrial land uses include industrial land types one to three. Comparing the expansion of the built-up area of Wuhan City and the distribution of industrial land areas in 2016, as shown in Figure 13, it is clear that the areas of urban expansion are the concentrated existing or planned industrial areas of Wuhan. Most of the expansion areas are affected by industrial land uses or have expanded outward around the industrial area.  As seen in Table 1, the industrial land accounts for 23.03% of the total area of urban expansion. Furthermore, this ratio can be even higher if we set an impact range for industrial land, with the buildings within its influence considered as subsidiaries of some industries. Buildings, or buildings attached to the industry, will account for a larger proportion. When the influence range is set to 500 m, the area of the expanded area including industrial land is 52.65%, and when the range is set to 1000 m, the proportion is 53.79%. However, this is only one of the initial impacts we have set. In fact, many large industries will have a greater impact. However, it is undeniable that the expansion of Wuhan City is heavily influenced by industry. More than half of the city's expansion between 2013 and 2016 has been affected by industry.     From Figure 15, it can be seen that the green space accessibility in most areas of Jianghan District and nearly half of Qiaokou District has improved within the Third Ring Road within the administrative region. This shows that the government attaches more importance to the planning and development of the region, and the construction of supporting facilities is more comprehensive.
In the Dongxihu District, there are also areas with an obvious increase in park green space accessibility. Different from the Jianghan District and Qiaokou District, which are located in the central urban area within the Third Ring Road, Dongxihu is an area that focuses on industrial development.
When areas with lowered score of accessibility are extracted, as shown in Figure 16, they form a continuous belt within the Caidian District, corresponding well with the industrial development and urban expansion areas in the region. This indicates that when planning and developing the area, the government did not promote the further development of the park green spaces in the area or only added a small amount. At the same time, some other districts with concentrated industrial development-Jiangxia District, Huangpi District, Qingshan District, and Xinzhou District-also saw a similar situation. In these districts, the ratio of areas with declined accessibility scores (including 0) is high. However, since these districts had relatively high base scores in green space accessibility, the decline in scores indicates that growing urban population in these districts puts more pressure on park green services. Therefore, the governments should attach more importance to the problem and plan accordingly. From Figure 15, it can be seen that the green space accessibility in most areas of Jianghan District and nearly half of Qiaokou District has improved within the Third Ring Road within the administrative region. This shows that the government attaches more importance to the planning and development of the region, and the construction of supporting facilities is more comprehensive.
In the Dongxihu District, there are also areas with an obvious increase in park green space accessibility. Different from the Jianghan District and Qiaokou District, which are located in the central urban area within the Third Ring Road, Dongxihu is an area that focuses on industrial development.
When areas with lowered score of accessibility are extracted, as shown in Figure 16, they form a continuous belt within the Caidian District, corresponding well with the industrial development and urban expansion areas in the region. This indicates that when planning and developing the area, the government did not promote the further development of the park green spaces in the area or only added a small amount. At the same time, some other districts with concentrated industrial development-Jiangxia District, Huangpi District, Qingshan District, and Xinzhou District-also saw a similar situation. In these districts, the ratio of areas with declined accessibility scores (including 0) is high. However, since these districts had relatively high base scores in green space accessibility, the decline in scores indicates that growing urban population in these districts puts more pressure on park green services. Therefore, the governments should attach more importance to the problem and plan accordingly.  However, the accessibility score of the Dongxihu District, which also has concentrated industrial development, saw increases in many areas, but still significantly lower than other districts with concentrated industrial development. Furthermore, it is positive that the number of park green spaces and population in the region are both growing. This reason may lie in the fact that development of this district has not met the expectations of the government.
The areas of Jiang'an District and Wuchang District located within the Third Ring Road are nonindustrial areas. Because these are well-established built-up areas with increasing population and low scores of accessibility, it is more difficult to increase the park green spaces in these districts. In addition, the scores of accessibility to green spaces are still declining in these two districts, with Jiang'an District being particularly serious, and its scores in areas along the Yangtze River declining severely.
As for Hannan District, it is located on the urban fringe with a small area located within the study area, and the majority of the district located in the suburbs of Wuhan outside the study area, so the data in the study do not fully reflect its condition. Therefore, it is not analyzed in the present study.
Overall, it is found that industrial development takes precedence over green space. With its vigorous development, industrial land attracts increasing population, resulting in the drastic decline of the service capacity of green spaces.

Inequity in Green Space Development
It can be seen from the results that not many districts in the Wuhan metropolitan area see increased accessibility scores of park green spaces. Combined with population data, it seems that districts with a higher accessibility score of park green space are generally less populated, while those with lower scores are more densely populated. Especially for areas within the central urban area, except for Jianghan District and Qiaokou District, the accessibility scores of park green space are all decreasing. The urban expansion area outside the Third Ring Road can basically be attributed to industrial development in Wuhan, which is accompanied by declined accessibility scores of park green space in respective districts.
Among various districts, Jianghan District, Qiaokou District, and Dongxihu District have attracted more government attention, as exemplified by urban renewal (ecological restoration) in Jianghan District and Qiaokou District, and strengthened ecological preservation efforts in parts of the Dongxihu District. The effect of these measures can be seen in the evident increase in accessibility scores of urban public green spaces in these districts.
As a district with relatively concentrated industrial development, the Dongxihu District has gained attention as can be seen in the strengthened ecological preservation efforts by the government. Through these efforts, the government intends to promote the development of the district, attract more people so as to ease the population pressure on other districts. However, the result did not meet the government's expectations. From the previous analysis, it is known that the ratio of population growth in Dongxihu District is not significant; even when compared with all other districts with concentrated industrial development, the ratio of population growth in Dongxihu District ranks only at the lower-middle level, indicating that although the government has tried to attract more people to the district in the past few years, the public did not seem to respond well to the government's endeavors, or they do not have a positive prospect on the development of the district. This surely deserved the attention of related departments.
At the same time, the practices of Wuhan City, which focuses on ecological development of certain areas, may result in inequality of urban public green spaces in Wuhan. This reflects the imbalanced development of Wuhan, which may negatively affect the decentralization of population in the urban area. This also calls for the attention of the relevant departments of the Wuhan Municipal Government.

Industrial Development and Expansion
Wuhan is the birthplace of China's modern industry and has always been an important industrial town in modern times. Since the founding of People's Republic of China in 1949 to the mid-1980s, Wuhan ranked at the top in the country in terms of industrial output. After the reform and opening-up, with shifting focus in national development strategy and industrial development reform, Wuhan gradually lost its status as a national central city.
With the proposal of the strategy of the Rise of Central China, Wuhan seized the opportunity to recapture its industrial glory. In the "Twelfth Five-Year Plan," Wuhan proposed the "Doubling of Industrial Development" program, which sets the objective of doubling the values of the city's major economic indicators and the scale of its pillar industries in the next five years. The pressures arising from these doubling objectives, as can be seen from the industrial distribution map of Wuhan, need governing by the administrative districts around the center area of Wuhan.
In 2011, Wuhan City officially launched the "Doubling of Industrial Development" program which achieved remarkable results in just one year of implementation, and the total industrial output value above the designated size reached 739 billion yuan, an increase of 21%. In 2013, the "Doubling of Industrial Development" program accelerated in an comprehensive way, forming four major industrial zones: Great Optics Valley District, China Auto-City, Linkong Economic Zone, and Lingang Industrial Zone.
These industrial-driven developments are not only reflected in the economy, but also urban expansion. The development of modern metropolitan areas shows a pattern of peripheral diffusion that is caused by various factors, including population density, environmental capacity, and employment saturation in the central area of the city. In healthy urbanization, the city's outward expansion should be orderly, and the development of the city should prioritize smart growth [39,40].
The urban expansion/sprawl driven by the "Doubling of Industrial Development" program is an industry-led expansion/sprawl that occurs mainly in the suburban areas. In this race for industrial development, the several suburban districts try to gain the support of the main urban area of Wuhan. Therefore, they placed their industrial zones between their own urban area and the central urban area of Wuhan so as to maximize the connection with the central city. Correspondingly, other types of land use are placed at the side away from Wuhan. This phenomenon can be clearly observed in the map of urban expansion and analysis results in previous sections, which shows that the urban expansion of Wuhan City is basically affected and consistent with the industrial distribution of Wuhan. From Figure 13, it can be seen that expansion areas around industrial development are large, except for those close to more mature and old industrial areas (the chemical industry area, Qingshan District and Hanyang industrial area) and the Hannan district, which is excluded from the analysis. This land replacement model is produced under the pressure of the "Doubling of Industrial Development" program, which is contradictory to the principle that satellite towns should balance land uses. One of the consequences is that the planned green spaces between the suburban areas and the central urban area (the "six green wedges" proposed in the in 2008 Wuhan City Master Plan) are gradually being eroded, and its manifestation is the declined accessibility scores for large urban public green space in industrial areas. In addition, industrial areas are expanding rapidly, making it hard for the planning and development of urban public green spaces to keep up. This "expansion" model of the main urban area makes the city's "smart growth" strategy meaningless, and will also lead to the negligence of the ecological value of park green spaces by planning and construction practitioners.
More in-depth studies are required to see whether this will affect the future development of Wuhan City or bring new problems to the urban development of Wuhan.

Contributions and Limitations
There are three contributions of this study. First, the building of a comprehensive framework, combined with the multilevel radius 2SFCA method is used to measure the temporal-spatial differences in the supply and demand of park services. The 2SFCA methods and improved approaches are often used to measure the accessibility of public facilities in terms of the ratio of supply and demand. Therefore, differences between different regions can be compared to measure change in accessibility and equity. Secondly, remote sensing images are used to identify the urban expansion area and related urban data, which helps to analyze the city and understand its development, and to explore the impact of rapid urban expansion on the people, the provision of public services in the city, and to discuss the reasons behind urban expansion. Finally, using the results of the analysis, impacts of Wuhan's planning and development on its ecology and green spaces are discussed.
Although improved methods for calculations are introduced, the present study still has some limitations. First is the lack of more detailed road network data. If more precise road network data were available, accessibility could then be measured by road network distance instead of Euclidean distance; even entrances of residential areas and urban public green spaces can be used as the origin and destination of the road network distance analysis, which could make the accessibility scores more accurate and realistic. At the same time, the impact of ways of travel on service radius could be studied to fully understand accessibility; after all, the residents' behavior may have a significant impact on accessibility. In addition, as analysis on microscale happens, evaluation of changes in availability and equity could be improved across various types of demands, which may be key directions for future studies.

Conclusions
In this study, accessibility of urban public green spaces in Wuhan City and its changes over two different years are evaluated using the multilevel radius 2SFCA method; through the study, the distribution and accessibility of urban public green spaces in Wuhan City in the year 2013 and 2016 are presented, and it is found that Wuhan tends to focus on the ecological development of certain areas, which causes inequity of urban public green spaces between different districts in Wuhan, and also reflects the imbalanced development of Wuhan. Secondly, by looking at the urban expansion of Wuhan, it is found that the city's expansion/sprawl is mainly driven by industrial development. This kind of industrial-led expansion/sprawl may lead to the negligence of the ecological value of park green spaces by planning and construction practitioners. Both problems require the attention and intervention of relevant government departments.