In 2015, the UN Development Summit approved and adopted the 2030 Agenda for Sustainable Development, which sets out 17 Sustainable Development Goals (SDGs) covering three major areas, economic, social, and environmental, charting a course for the development of countries and international cooperation. The primary task of a comprehensive assessment of the practice of SDGs in a region is to localize the UN SDGs global indicator framework. At the first United Nations World Geospatial Information Congress in 2018, China released a quantitative assessment report “Deqing Practice Report on the Implementation of the 2030 Sustainable Development Agenda (2017)”. The report shows that most indicators of Deqing, Zhejiang, are close to the UN 2030 Sustainable Development Goals. Based on the SDG global indicator framework and geographic statistical information, the project team carried out a localized evaluation and analysis of the implementation of Deqing County, combining quantitative, qualitative, and localization data [1
], and offered the Deqing sample to the international community.
The 2030 Agenda for Sustainable Development aims to comprehensively address the three dimensions of development (social, economic, and environmental) from 2015 to 2030, and shift to a sustainable development path. Especially, SDG 11 promotes the construction of tolerant, secure, disaster-proof, and sustainable cities and human settlements, with a focus on improving living conditions, optimizing the habitable environment, and ensuring the safety of houses. In Deqing, the aspects of inclusivity, sustainability, and disaster resistance comprehensively reflect the county’s progress in the practice of creating a sustainable city and human settlements. SDG 11 contains 10 quantitative indicators, of which indicator 11.7 is the universal provision of safe, inclusive, accessible, and green public spaces to all people, especially women, children, the elderly, and people with disabilities. The aim of SDG 11 is to analyze the proportion of public spaces per capita to reflect livable environmental conditions. Combining SDG 11.7 with the actual situation of Deqing, and considering the availability and quantitative characteristics of the data, this paper focuses on urban built-up areas, analyzes the average proportion of public open spaces used by all, and provides a guarantee for the construction of urban public open spaces that can meet everyone’s needs.
Having enough public space enables cities and regions to function efficiently and fairly [3
]. Reduced public space has a negative impact on quality of life, social inclusion, infrastructure development, environmental sustainability and productivity. It has been recorded that good design and maintenance of streets and public spaces can reduce crime and violence, improve the quality of residents’ lives and the overall appearance of the city, and play a very important role in beautifying the image of the city [4
]. At the same time, the ratio of urban streets to public spaces is an important feature of urban space planning, and cities with sufficient streets and public spaces and greater connectivity are more livable and productive [5
]. Planning sufficient space for critical infrastructure sites such as water resources, sewers and waste collection, recreational spaces, green spaces and parks can help strengthen social cohesion and protect green, ecologically sustainable development. Making enough space to support formal and informal economic activities [6
], actively restoring and maintaining public spaces for various users, and providing services and opportunities for marginalized residents are all conducive to enhancing social cohesion and economic security.
Considering the impact of urban public open spaces on sustainable development, many scholars have conducted qualitative and quantitative analyses of urban public spaces at different levels and from multiple perspectives [7
]. Scholars have made extensive studies on modern urban planning and landscape design [8
], social perspectives [7
], spatial distribution and quality assessment [10
], etc. Qualitatively and quantitatively, different suggestions have been put forward for creating popular and ecological urban public open spaces, which enriches the construction concept of urban public spaces. Other scholars have conducted many studies on the equity of public space allocation [11
]. They evaluated and analyzed urban income, race, social economy, and other aspects, and advocated for the importance of environmental equity, which contributes to reducing the inequality of urban public spaces. However, few scholars have carried out spatial differentiation law analysis of all components of urban public open spaces, and most studies were confined to parts of public spaces, such as parks, squares, and green spaces, to carry out spatial distribution analysis and environmental assessment separately [3
]. The spatial distribution of public open spaces in terms of society, history, transportation, economy, and population was studied from multiple perspectives, but faced with the rapid development of urbanization today, solving the problem of public spaces in crowded areas has become a hot spot. There is an urgent need to improve residents’ quality of life, and all elements of public open spaces for the overall analysis to improve the safe and healthy life of residents are important.
According to the existing research results, the important index for evaluating urban public open spaces is the spatial pattern and formation mechanism. Generally speaking, evaluating the spatial pattern mainly involves quantitative analysis of the distribution form, agglomeration degree, and agglomeration mode of urban elements in order to describe the spatial clustering and stratum distribution characteristics of the space. The formation mechanism of urban public open spaces is related to the urban spatial structure system, public facilities factors, and distribution and intensity of human activities. Therefore, we want to use geographic information system (GIS) spatial autocorrelation analysis to describe the differences in urban public open spaces distribution, and use hot spot and overlay analysis to explore the driving factors that affect the formation of urban public open spaces.
In recent years, many scholars have used GIS spatial analysis to carry out research on urban spatial pattern analysis. Shirowzhan [15
] proposed two classification algorithms that are based on spatial autocorrelation statistics, such as the Local Moran’s I and the Getis-Ord Gi*, which are computed over sample urban areas including complex terrain with diverse building characteristics, and used these algorithms to airborne lidar point clouds over the complex urban areas in order to generate highly accurate DEMs and classify the lidar points. Aghajani [16
] proposed a road accident analysis method, which operates through the use of the GIS spatial and temporal patterns in urban road accident prone locations; he also used the hot spot analysis with identification and data generation to help decision makers to take appropriate measures to decrease road accidents. Fan [17
] used the local spatial autocorrelation to characterize urban landscape fragmentation, and he compared two local spatial autocorrelation indices, the Getis statistic and the local Moran’s I, to evaluate the landscape pattern. Xia [18
] used the local indicator of spatial association to analyze the spatial relationships between urban land use intensity and urban vitality, and they found that there is a significant positive spatial autocorrelation between urban land use intensity and urban vitality according to global statistics. Shen [19
] used both global and local spatial autocorrelation analyses to demonstrate how urban sustainability was spatially distributed across neighborhoods and what patterns (random, dispersed, or clustered) could be statistically identified. Majumdar [20
] used the local Moran’s I to recognize the pattern of statistically significant LST increase by detecting clusters of localized hot spots. Some scholars also used the spatial autocorrelation analysis in the field of river network monitoring [21
], spatial variations analysis of NPP [22
], and agricultural spatial relationship analysis of drought propagation [23
]. According to these kinds of research results, we found that GIS spatial analysis methods can help us to identify the spatial pattern among kinds of urban objects, but most of their research only used the local indicators of spatial association (LISA) to judge the relationship between these events (such as road accident, land use classification, river network monitoring, and soon on) in urban environment, and lacked a quantitative description of the causes of these events. The aim of our research is not only to find the spatial pattern, but also to quantitatively analyze the formation of urban public open spaces.
Above all, public space is an indispensable component in the city. In view of all elements of public space being integral to the analysis, this paper is based on SDG 11.7, in combination with the practical situation of Deqing and existing data, adopting the method of geographic statistics and GIS spatial analysis of Deqing public open spaces for spatial differentiation pattern. Furthermore, the population data are analyzed to provide a quantitative assessment of and technical support for the sustainable development goal of building safe, inclusive, barrier-free, and green public open spaces in Deqing. It provides decision support for the management and planning of public open spaces.
This paper obtained data of public open spaces in the built-up area and surrounding streets of Deqing based on remote sensing interpretation. Based on the ideas of geographic statistics and spatial analysis, spatial statistical models such as spatial autocorrelation and correlation analysis were used to analyze the spatial distribution characteristics of public open spaces in the built-up area of Deqing in 2016 and its relationship with the population. The area LISA results show that high-value aggregation in public spaces is mainly distributed in the development zone in the west of the built-up area. The clustering of HL is mainly distributed in the center and edge of the built-up area. The edge area shows scattered distribution of low values of public space and has no spatial autocorrelation, but it still influenced the autocorrelation of public open spaces in the built-up area significantly. In addition, different attributes of public open spaces have shown different LISA results—the type has the most positive and significant global autocorrelation, but it also has the worst local aggregation, and the length of public open spaces has the similar local aggregation with the area.
Based on the result of correlation analysis, it is found that the spatial differentiation pattern of public open spaces is significantly related to population agglomeration. The further away from the built-up area, the less public space there is, and lower population agglomeration. The high-value cluster of type of public open spaces is related to the high-value cluster of population, and a low-value cluster of length is related to its cold spots. The results of geographically weighted regression show that different types of public open spaces have different relationships with the population, and the park has the most significant correlation with population agglomeration.
In addition, the assessment results of SDG 11.7.1 indicate that the per capita public open spaces area and green lands rate of Deqing’s built-up areas in 2016 have reached the goals of the UN 2030 Agenda for Sustainable Development. Therefore, it shows that an excellent livable environment and high level of sustainable development in Deqing’s built-up area, but the balance of urban and rural public open spaces needs to be further improved. This study provides a scientific basis for the optimization and construction of public open spaces in Deqing. It also provides experience and a demonstration for other regions in China to carry out quantitative assessment of SDGs, contribute Chinese wisdom to the global implementation of the sustainable development agenda, and propose Chinese solutions.
The results from this study could identify the spatial pattern of urban public open spaces by GIS spatial autocorrelation analysis, but it is worthwhile to state that this kind of spatial pattern difference is very susceptible to policy intervention, especially in cities in China. Although Chinese urban planners now attach great importance to the impact of urban public open spaces, considering the mismatch between urban spaces and socio-economic activities, the autocorrelation of urban public open spaces may change in the future. So, we need more data from different time periods to explore the formation rules
Additionally, the formation mechanism of urban public open spaces is complex and diverse, and it is reliable to analyze the relationship of urban public open spaces with population as the main factor, but if we want to thoroughly understand the formation mechanism of urban public open spaces, we need to analyze more factors, such as public facilities, roads and traffic, government investment, human behavior, and other socio-economic factors. In addition, Deqing is an example of a high degree of urban sustainable development worldwide, so the experimental results of this case are more ideal, but they may not necessarily applicable to other cities with low sustainable development. We need more cases to verify or obtain more general research conclusions. However, limited by data availability, selecting all the cities of different characteristics and development patterns is unachievable. In future studies, more factors may be analyzed based on the current analytical framework, and the proposed method may be applied to other cities to further examine whether the findings of the present study are suitable for various city types.