Equity Research on Parks in Fuzhou City Based on GIS and Space Syntax Model

sustainability

Article Accessibility and Street Network Characteristics of Urban Public Facility Spaces: Equity Research on Parks in Fuzhou City Based on GIS and Space Syntax Model

Bo-Xun Huang 1,2,*, Shang-Chia Chiou 1 and Wen-Ying Li 1,3,*

1 Graduate School of Design, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan; [email protected] 2 College of Arts College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou 350118, China 3 College of Architecture and Urban Planning, Fujian University of Technology, Fuzhou 350118, China * Correspondence: [email protected] (B.-X.H.); [email protected] (W.-Y.L.)

Received: 30 March 2020; Accepted: 27 April 2020; Published: 30 April 2020

Abstract: Urban green spaces are conducive to people’s physical, mental, and social health; however, in many cases, these beneﬁts are unevenly distributed in cities. This study explored the equity of urban green spaces in terms of accessibility and spatial morphology, speciﬁcally, (1) applied the geographic information system (GIS) accessibility index to the equity of parks in Fuzhou City; (2) discussed the accessibility of parks and the spatial morphological characteristics of streets from a space syntax analysis; (3) examined the correlation between the accessibility of parks in Fuzhou City and the spatial morphology of streets. The results provide a valuable reference for sustainable urban design and planning.

Keywords: spatial equity; accessibility; street network; urban parks; sustainability

1. Introduction Sustainable development has been studied from environmental, economic, and social aspects. Numerous researches have defined the connotation of the three aspects and explored the ways to reach a balance among the three aspects. Although many measures and evaluation methods have been proposed on environmental protection and economic development, it is rather difficult to define the scope of social equity. Moreover, the research related to social sustainability is not as diversified as the research on the other two aspects [1]. Cities are spaces where most people live, thus, the tangible and quantifiable spatial characteristics of cities contribute to defining the connotation of social sustainability [2]. Urban public green spaces refer to public and service facilities, which are planned by the state and the government to provide convenient services and to maintain good living quality. The spatial structure formed by location and scale in an urban environment is a topic widely discussed in the field of urban planning. Similar to wealth and resources, how public green spaces are fairly distributed to various groups affect the operation of the entire society. Therefore, the spatial planning and strategy for making public green spaces should be oriented towards realizing the social sustainability of cities. Urbanization has now become one of the global development agendas. The United Nations Sustainable Development Goals11 (SDG11) defined urbanization as the integration, protection, robustness, and sustainability of urban communities and human settlements. The United Nations expanded its SDG agencies to new urban management in 2016 [3], while the sustainability and

Sustainability 2020, 12, 3618; doi:10.3390/su12093618 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 3618 2 of 19 inclusiveness of cities depend on different standards, such as planning, fair spatial allocation, ecological services, urban management, green space quality, and socio-economic facilities. The social advantages provided by urban green spaces help to increase the personal satisfaction of urban residents [4–6]. It is widely recognized that human health is related to the measurement, quality, and equity of green spaces, and the environment requires that public places belonging to different social categories should be featured by sustainability and fair accessibility. Accessibility methods are available in planning documents and corporate verifiable records [7–11]. Accessibility is characterized by how the frame is used and transported, allowing the public to exercise or gain energy from the transport mode [12,13]. Street networks are regarded as the skeleton of cities, as they link the geographical units in the urban spaces. To a certain extent, the morphological structure of streets determines the connection breadth and strength of different functional areas in the urban environment, which affects the flow and operating efficiency of various resource elements and the urban spatial structure in cities [14–18]. There is a mutual promotional and restricting relationship between transportation networks and urban public facilities. Complex network methods have been deployed in many researches to evaluate the integrity and accessibility of street networks by analyzing their topological characteristics and spatial distribution [19–23]. Although some emerging researches have explored the relationship between street network characteristics and public facilities, most existing works focus on analyzing the accessibility of public facilities. Furthermore, few researches have addressed the street network of cities from a global perspective or studied whether the characteristics of urban morphological structures are related to the development of urban public facilities. In the past, the political and ethnical reasons for unfair facility distribution were discussed [24]; however, urban morphology was rarely addressed due to the differences in access or convenience to public facilities in different areas, as generated by street and texture planning. In this regard, spatial equity is an important standpoint in spatial planning to analyze the configuration and structure of public facilities, as well as assess the distribution equity of public resources in spatial or territorial terms [24]. Spatial equity emphasizes the relationship between equity and location, and suggests that public services and resources should be made equally available to groups in different spaces. Spatial inequality implies unfair spatial separation or spatial proximity between residents and public facilities, and such separation or proximity is caused by the spatial morphology connected or blocked by the buildings and roads in the urban environment. Therefore, in addition to the configuration of public green spaces, the road networks also affect the convenience of residents to access public services as a bridge between different activities. Therefore, the analysis of spatial equity should regard the road network structure as the foundation of an urban environment system, which provides a fair starting point for different groups in different areas to access public services. Previous researches on the spatial equity of public facilities adopted accessibility as an indicator [25,26], as accessibility provides a relatively complete grasp of the availability and spatial location of facilities. Even in different methods, accessibility takes into consideration land use, transportation, time, and personal factors to different degrees. Regarding the development of the indicators, first, the simple measurements of the distance to the facilities were used to represent accessibility, and then, the cumulative opportunity method was used to assess the number of facilities accessible to each person or district within a certain range. Recently, the potential model, the facility scale, and the characteristics that the facility benefits, which gradually decline over distance, have been simultaneously considered. However, all previous researches divided the research area into many spatial units, and then, calculated the accessibility of each unit. Therefore, this study analyzed whether residents have equal opportunity to access existing public facilities according to the texture of the urban road networks from the perspective of spatial equity. This study has two main contributions to the academic community. On one hand, from a methodological perspective, the proposed framework provides technical and methodological support for a better understanding of the relationship between urban facility construction and urban street Sustainability 2020, 12, 3618 3 of 19 structure. On the other hand, from an empirical perspective, the case study can guide decision-makers in sustainable urban development and urban space optimization.

2. Research Methodology

2.1. GIS A geographic information system (GIS) can be used to enhance facility planning and management of public facility spaces. One of the applications discussed in this paper is the measurement of accessibility and distribution equity provided by the park system, which helps to identify low-accessible areas and groups. The system also provides suggestions for the best locations to design new facilities, thus, maximizing accessibility and equity. Accessibility provides a relatively complete grasp of the availability and spatial location of facilities. Even in different methods, accessibility takes into account land use, transportation, time, and personal factors to different degrees; therefore, accessibility has been widely adopted by researchers of spatial equity. The results of accessibility calculations were further analyzed in geographic space to reveal the areas with better and worse accessibility, and how the distribution of facilities is unfair to certain groups. In reference to the theoretical basis, accessibility can be evaluated by three criteria: operability, interpretability, and dissemination, while the usefulness and limitations of different accessibility measurement methods can be judged according to the research topics. However, when using accessibility as an indicator, different methods should be considered to satisfy different situations and serve different purposes; in other words, there is no optimal accessibility indicator [27]. Instead, a most suitable measurement method should be identified to address the research purposes and criteria. In this study, the road network distance to the nearest facility was applied to measure accessibility, as it can clearly exhibit the aggregation error in accessibility measurement and has advantages in operability and interpretability. Moreover, its data requirements are not high, which makes it easier for researchers and planners to understand and assess the differences in accessibility in a more direct manner.

2.2. Interpretation of Spatial Inequality The Lorenz curve and the Gini coefficient were originally used to explore the equity of income or wealth distribution. Income is positively related to wealth status. If the total income of n individuals in the area is x, under absolutely fair wealth distribution, when the income of n individuals is plotted as a histogram, the income of each individual is x/n. When the values are accumulated to plot the Lorenz curve, the x-axis is the cumulative percentage of the population, and the y-axis is the cumulative percentage of the income. As each individual uniformly obtains equal 1/n of the total income, the Lorenz curve is a 45◦ oblique line between (0, 0) and (1, 1), which is also regarded as an absolutely fair oblique line. If the wealth distribution is unfair, an area A will be generated between the Lorenz curve and the o absolutely fair oblique line. The area below the curve is B and A + B is 0.5. The Gini coefficient is defined as A/(A + B). Under absolutely fair wealth distribution, A = 0 and the Gini Coefficient = 0. Under absolutely unfair wealth distribution, A = 0.5 and the Gini Coefficient = 1, where the Gini Coefficient = A/(A + B).

2.3. Space Syntax The argument raised by space syntax is that the pattern of movement in a city is likely to be shaped to a large extent by the topology of its route network alone, irrespective of all other factors (e.g., distribution of land uses) [28], therefore, the network itself, and the analysis of its shape, is the focus of space syntax analysis and is an area that remains minimally explored [29]. Space syntax provides an alternative method of measuring street connectivity. Originated in the ﬁeld of architecture and urban design, space syntax is generally used for characterizing and quantifying the spatial layout of buildings within urban spaces or enclosed spaces within streets based on topological methods [30,31]. Diﬀerent from intersection density, space syntax focuses on the topological distance within the network, Sustainability 2020, 12, 3618 4 of 19

which refers to the number of turns required to reach one location from another [32,33]. The calculation of space syntax has been explained in some researches [30,32,34,35]. In short, street integration is a Sustainabilitykey space 2020 syntax, 12, x measureFOR PEER REVIEW that shows the topological accessibility of one street segment to all4 of other 18 street segments within a deﬁned area (i.e., a certain distance from the center of the street). A more segmentshighly integrated within a defined street segment area (i.e., means a certain that itdistan takesce fewer from curvesthe center to reach of the the street). street A segment more highly from integratedother streets street in thesegment network. means Figure that1 ita takes shows fewer one streetcurves network, to reach andthe street Figure segment1b shows from the integrationother streets inlevel the network. (the red lineFigure represents 1a shows higher one street integration). network, and Figure 1b shows the integration level (the red line represents higher integration).

(a) (b)

FigureFigure 1. 1. StreetStreet network: network: (a (a) )street street network; network; ( (bb)) space space syntax syntax integration. integration.

PathPath selection selection is is also also crucial crucial for for pedestrians pedestrians [36]. [36]. The The literature literature on on pedestrian pedestrian route choice behaviorbehavior consistentlyconsistently reported reported that that travel travel time/distance time/distance is is the the key key determinant determinant of route choice; this meansmeans thatthat pedestrianspedestrians choose choose the the shortest shortest path/time path/time route route between between an origin an origin and anda destination. a destination. Even Evena more a recent more studyrecent in study Cambridge, in Cambridge, it was itdemonstrated was demonstrated that pede thatstrians pedestrians choose choose a route a route that thatis shorter is shorter than than the geographicthe geographic information information system system (GIS) (GIS) derived derived shorte shortestst path path route route by by taking taking cut cut throughs throughs and otherother pathspaths not not present present in in a atypical typical road road network network used used for for a a GIS-based analysis [[37–41].37–41]. Directional distancedistance (e.g.,(e.g., the the number number of of directional directional changes changes required required to to reach reach a a destination) destination) is a commonly usedused indicatorindicator toto represent represent street street configuration configuration in in the the space space syntax syntax literature literature [[30,42–44].30,42–44]. The segment analysisanalysis inin spacespace syntaxsyntax provides provides three three analysis analysis modes, modes, which which can can comprehensively comprehensively analyze analyze the topology, angleangle andand metricmetric of of the the street street network. network. The The difference difference between between these these analysis analysis modes modes lies in the definitiondefinition ofof thethe numbernumber of of "shortest “shortest paths". paths”. The The topological topological mode mode shor shortesttest path path is is the the path path with the fewest numbernumber ofof polylinepolyline breaks, breaks, or or the the path path with with the the fewest fewest number number of of other other segments. The angular mode shortestshortest pathpath isis the the path path with with the the smallest smallest turn turn angle angle between between tw twoo segments. The The metric mode is thatthat thethe shortestshortest pathpath is is the the shortest shortest distance distance between between two-line two-line segm segments.ents. In In this this study, study, the angular mode is selected,selected, whichwhich is isthe the most most commonly commonly used used mode mode in in line line segment segment analysis analysis [30]. [30].

2.4.Integrating2.4. Integrating GIS GIS and and Space Space Syntax Syntax DifferentDiﬀerent from from most most previous previous studies studies that that employed employed GIS GIS or space or space syntax, syntax, this this study study integrated integrated GIS andGIS space and spacesyntax syntax to yield to good yield data good support. data support. This appr Thisoach approach can be used can be in usedsimilar in or similar different or di studies,ﬀerent suchstudies, as for such the asdiscussion for the discussion of function of and function data. Fo andr example, data. For a example, study on aXian-lin study on campus Xian-lin of campusNanjing Normalof Nanjing University Normal integrated University GIS integrated and space GIS syntax and spaceto analyze syntax the to characteristics analyze the characteristics of the layout ofof thethe campuslayout ofspace the campusand buildings space and [45]. buildings In another [45]. study In another on Anhou study onTown’s Anhou new Town’s round new of round spatial of developmentspatial development morphology, morphology, an implementation an implementation method of method spatial ofmorphology spatial morphology planning that planning integrated that GISintegrated and space GIS andsyntax space was syntax used was for used quantitative for quantitative analysis analysis on onsyntactic syntactic variables variables of of integration, integrationintegration core, core, and and intelligibility intelligibility [46]. [46 To]. design To design a well-grounded a well-grounded system system of roadside of roadside rest areas rest (RRA) areas for(RRA) transit for travelers transit travelers and local and inhabitants local inhabitants in Latvia in and Latvia Lithuania, and Lithuania, the space the syntax space method syntax and method GIS- basedand GIS-basedanalysis were analysis used were to select used toplaces select for places the location for the location of RRA of on RRA the onLatvian–Lithuanian the Latvian–Lithuanian cross- bordercross-border roads [47]. roads To [47 understand]. To understand how pedestrian how pedestrian movement movement is generated is generated in relation in relation to the tourban the layouts and how to predict this movement in public spaces, GIS database, statistical methods, and space syntax were used and tested in the case of the municipality of Athens [48]. Space syntax quantitatively describes the spatial structure of cities from a cognitive perspective. GIS possesses excellent data analysis and efficient geographic modeling capabilities. A combination of space syntax and GIS could

Sustainability 2020, 12, 3618 5 of 19

urban layouts and how to predict this movement in public spaces, GIS database, statistical methods, and space syntax were used and tested in the case of the municipality of Athens [48]. Space syntax quantitatively describes the spatial structure of cities from a cognitive perspective. GIS possesses Sustainability 2020, 12, x FOR PEER REVIEW 5 of 18 excellent data analysis and efficient geographic modeling capabilities. A combination of space syntax enhanceand GIS the could spatial enhance analysis the spatial ability analysis of GIS abilityand deepen of GIS the and quantitative deepen the quantitative research of researchspace syntax of space on urbansyntax space on urban structure. space structure. AA geographic geographic informationinformation system system (GIS) (GIS) can providecan provide a large numbera large ofnumber opportunities of opportunities for recreational for recreationalservice agencies service to agencies enhance to the enhance planning the and planning management and ma ofnagement their facilities. of their Infacilities. this study, In this one study, such oneapplication such application was demonstrated was demonstrated for measuring for themeasuring accessibility the accessibility and distribution and equitydistribution provided equity by providedthe park by system. the park As system. space syntaxAs space introduces syntax introd the conceptuces the of concept network of network scale, by scale, limiting by limiting the spatial the spatialdistance distance or radius or radius of network of network analysis, analysis, meaning meaning only considering only considering the topological the topological connection connection between betweenline segments line segments within a certainwithin range,a certain the range, potential the of potential street networks of street from networks both local from and both global local scales and globalwas measured. scales was measured. 3. Research Design 3. Research Design 3.1. Research Scope 3.1. Research Scope As the provincial capital of Fujian Province, Fuzhou City is the political, economic, and cultural As the provincial capital of Fujian Province, Fuzhou City is the political, economic, and cultural center of Fujian Province. It is also a seaside garden city that contains five districts and eight counties. center of Fujian Province. It is also a seaside garden city that contains five districts and eight counties. As of 2018, the city managed six districts, six counties, and one host county-level city with a total As of 2018, the city managed six districts, six counties, and one host county-level city with a total area area of 11,968 square kilometers. The research scope of this study includes four administrative of 11,968 square kilometers. The research scope of this study includes four administrative districts in districts in the center of Fuzhou City, Gulou District, Taijiang District, Jin’an District, and Cangshan the center of Fuzhou City, Gulou District, Taijiang District, Jin’an District, and Cangshan District District (Figure2a). These four districts are developed similarly and have complete roads and other (Figure 2a). These four districts are developed similarly and have complete roads and other infrastructures. Specifically, Jin’an District is far from the city center with bending terrain, inconvenient infrastructures. Specifically, Jin’an District is far from the city center with bending terrain, inconvenient traffic, and backward economic development. Therefore, the research area of the road network includes traffic, and backward economic development. Therefore, the research area of the road network includes the third ring roads of Fuzhou City (Figure2b). the third ring roads of Fuzhou City (Figure 2b).

(a) (b)

Figure Figure2. Research 2. Research scope: (a scope:) Gulou (a District,) Gulou Cangshan District, Cangshan District, Taijiang District, District, Taijiang Jin’an District, District Jin’an of DistrictFuzhou ofCity; (b) street network.Fuzhou City; (b) street network.

Sustainability 2020, 12, 3618 6 of 19

3.2. Research Data The basic data of the road network was obtained by OpenStreetMap and edited with ArcGIS to generate road segments. The coordinates of 3548 road segments were used as the starting points for calculating the respective requirements of public facilities. Through the API provided by Baidu Maps, Sustainabilitythe Baidu 2020 POI, 12, datax FOR were PEER retrieved REVIEW with parks and community committees as the keywords, and then6 of 18 imported into MS Excel. After data cleaning and coordinate correction, the latitudes and longitudes of importedparks andinto communities MS Excel. After in Fuzhou data cleaning City were and acquired. coordinate Within correction, the scope the of thelatitudes study, and the coordinates longitudes of parksof 120and parks communities and 412 communityin Fuzhou committeesCity were acquired were obtained. Within (Figure the scope3). of the study, the coordinates of 120 parks and 412 community committees were obtained (Figure 3).

(a) (b) (c)

FigureFigure 3. 3.ResearchResearch data: data: (a ()a )road road segments; segments; ( (bb)) parks; ((cc)) communitycommunity committees. committees.

3.3. 3.3.Research Research Framework Framework In orderIn order to calculate to calculate the theminimum minimum distance distance from from the thepark park demand demand point point to the to thepark park supply supply point, the point,Network the Analyst Network extension Analyst extension tool in ArcGIS tool in was ArcGIS used was for used analysis. for analysis. Based on Based the actual on the road actual network, road thisnetwork, study used this spatial study used analysis spatial tools analysis to solve tools complex to solve complexrouting routingproblems, problems, including including finding finding the best route,the generating best route, generating service areas, service creating areas, creating an OD an cost OD costmatrix matrix of ofstarting starting points points andand end end points, points, performing location-allocation of facilities, etc. To find the closest facility, the established road network performing location-allocation of facilities, etc. To find the closest facility, the established road network can be used as the actual action path, and the event points (demand points) and facilities (supply can be used as the actual action path, and the event points (demand points) and facilities (supply points) points) can be set up to calculate the space resistance from the starting point to the nearest facility, can be set up to calculate the space resistance from the starting point to the nearest facility, which refers which refers to the minimum road network distance in this study. When the information of event to the minimum road network distance in this study. When the information of event points and facilities points and facilities is read in network analysis, the results of different scenarios can be obtained under is read in network analysis, the results of different scenarios can be obtained under different settings. different settings. First, this study identified the difference between the community-based and the road First,segment-based this study identified accessibility the results.difference Second, betw theeen urban the community-based street morphology and was the investigated road segment-based from the accessibilityperspective results. of space Second, syntax, the and urban the global street integration morphology and localwas investigated integration were from calculated. the perspective Finally, of spacethe syntax, correlation and betweenthe global park integration accessibility and and local street integration spatial morphology were calculated. in Fuzhou Finally, City was the exploredcorrelation betweento provide park aaccessibility valuable reference and street for sustainable spatial morphology urban design in andFuzhou planning City (Figurewas explored4). to provide a valuable reference for sustainable urban design and planning (Figure 4).

Figure 4. Schematic for the research framework.

Sustainability 2020, 12, x FOR PEER REVIEW 6 of 18 imported into MS Excel. After data cleaning and coordinate correction, the latitudes and longitudes of parks and communities in Fuzhou City were acquired. Within the scope of the study, the coordinates of 120 parks and 412 community committees were obtained (Figure 3).

(a) (b) (c)

Figure 3. Research data: (a) road segments; (b) parks; (c) community committees.

3.3. Research Framework In order to calculate the minimum distance from the park demand point to the park supply point, the Network Analyst extension tool in ArcGIS was used for analysis. Based on the actual road network, this study used spatial analysis tools to solve complex routing problems, including finding the best route, generating service areas, creating an OD cost matrix of starting points and end points, performing location-allocation of facilities, etc. To find the closest facility, the established road network can be used as the actual action path, and the event points (demand points) and facilities (supply points) can be set up to calculate the space resistance from the starting point to the nearest facility, which refers to the minimum road network distance in this study. When the information of event points and facilities is read in network analysis, the results of different scenarios can be obtained under different settings. First, this study identified the difference between the community-based and the road segment-based accessibility results. Second, the urban street morphology was investigated from the perspective of space syntax, and the global integration and local integration were calculated. Finally, the correlation betweenSustainability park2020 accessibility, 12, 3618 and street spatial morphology in Fuzhou City was explored to provide7 of 19 a valuable reference for sustainable urban design and planning (Figure 4).

FigureFigure 4. 4. SchematicSchematic for for the the research research framework. framework.

4. Accessibility Analysis

1. Import the road network data (.shp ﬁle) of communities and road segments collected through Baidu Maps POI into ArcMap, and create a new road network dataset. 2. Enter the starting points of the road network analysis (412 data units of communities, 3548 spatial data units of road segments). 3. Enter the end points of the road network analysis (120 data units of parks). 4. Perform “the closest facilities” in Network Analysis of ArcGIS to obtain the minimum road network distance between the starting points and the end points as the accessibility.

Figure5 shows the preliminary results of the spatial distribution of the community-based and road segment-based accessibility results. Hereunder, the road segment-based and the community-based accessibility results were analyzed by descriptive statistics. There are 412 spatial units calculated by the community-based accessibility results, and 3548 spatial units accessible by the road segment-based accessibility results accessibility. Figure6 shows that the frequency distribution of the road segment-based and the community-based accessibility results are similar, both of which are right-skewed and exhibit obvious central tendency. Through statistical calculation (Table1), the community-based skewness is 2.38, indicating that the mean is greater than the median, and that the accessibility of most spatial units is better than the overall mean. The road segment-based accessibility results skewness is 1.76, which is smaller than the community-based skewness, but has a similar right-skewed pattern. The community-based kurtosis index is 8.10, it shows a sharp peak, and its concentration trend is more signiﬁcant than the result of 3.955 based on the midpoint of road segment-based accessibility. Sustainability 2020, 12, x FOR PEER REVIEW 7 of 18

4. Accessibility Analysis

4.1. Accessibility Measurement With the community-based and road segment-based accessibility calculations, through the road Network Analyst tool provided by ArcGIS, the information of the starting points (demand points) and end points (supply points) were imported, and the measurement method of the road network dataset and spatial impedance (actual road network distance) was set [49]. The analysis steps are as follows: 1. Import the road network data (.shp file) of communities and road segments collected through Baidu Maps POI into ArcMap, and create a new road network dataset. 2. Enter the starting points of the road network analysis (412 data units of communities, 3548 spatial data units of road segments). 3. Enter the end points of the road network analysis (120 data units of parks). 4. Perform “the closest facilities” in Network Analysis of ArcGIS to obtain the minimum road network distance between the starting points and the end points as the accessibility. SustainabilityFigure 52020 shows, 12, 3618 the preliminary results of the spatial distribution of the community-based and8 ofroad 19 segment-based accessibility results.

(a) (b)

FigureFigure 5. Spatial 5. Spatialunits and units accessibility and accessibility results: results:(a) community-based (a) community-based accessibility accessibility results; (b results;) road segment-based (b) road Sustainabilityaccessibilitysegment-based 2020 results., 12, x FOR accessibility PEER REVIEW results. 8 of 18 Hereunder, the road segment-based and the community-based accessibility results were analyzed by descriptive statistics. There areMean= 412 1218.71 spatial units calculated by the community-basedMean= 897.27 accessibility Standard Deviation=1151.59 Standard Deviation=735.85 results, and 3548 spatial units accessibleN=412 by the road segment-based accessibilityN=3548 results accessibility. Figure 6 shows that the frequency distribution of the road segment-based and the community-based accessibility results are similar, both of which are right-skewed and exhibit obvious central tendency. Through statistical calculation (Table 1), the community-based skewness is 2.38, indicating that the mean is greater than the median, and that the accessibility of most spatial units is better than the overall mean. The road segment-based accessibility results skewness is 1.76, which is smaller than the community-based skewness, but has a similar right-skewed pattern. The community-based kurtosis index is 8.10, it shows a sharp peak, and its concentration trend is more significant than the result of 3.955 based on the midpoint of road segment-based accessibility.

(a) (b)

Figure 6. Frequency distribution:(a) community-based accessibility results of frequency distribution; Figure 6. Frequency distribution:(a) community-based accessibility results of frequency distribution; (b) road (b) road segment-based accessibility results of frequency distribution. segment-based accessibility results of frequency distribution. Table 1. In terms of community-basedCommunity-based accessibilityaccessibility, and to reac roadh the segment-based nearest park accessibility facility requires results walking of descriptive statistics. 1218.71 m on average; however, in terms of the road segment-based accessibility, to reach the nearest park facility requires walking 897.27 m onStandard average. The dispersion of the community-based N Mean Median Variance Skewness Kurtosis accessibility results (1151.59) is also higher thanDeviation that of the road segment-based accessibility results Standard Standard (735.85). Overall, theStatistic road Statisticsegment-based Statistic accessibility Statistic Statisticresults Statisticyield a more consistentStatistic and better Error Error outcome. Whether the difference in accessibility is truly reflected should be judged by exhibiting the Community 412 1218.70 868.46 1151.59 1326164.06 2.38 0.120 8.108 0.240 districtAccessibility divisions in the space. Road Segment 3548 897.27 679.99 735.84 541472.32 1.76 0.041 3.955 0.082 Accessibility Table 1. Community-based accessibility and road segment-based accessibility results of descriptive statistics.

Standard In terms ofN community-based Mean Median accessibility, to reachVariance the nearestSkewness park facility requiresKurtosis walking Deviation 1218.71 m on average; however, in terms of the road segment-based accessibility, to reach the nearest Standard Standard Statistic Statistic Statistic Statistic Statistic Statistic Statistic park facility requires walking 897.27 m on average. The dispersion of the community-basederror accessibilityerror resultsCommunity (1151.59) is also higher than that of the road segment-based accessibility results (735.85). Overall, 412 1218.70 868.46 1151.59 1326164.06 2.38 .120 8.108 .240 theAccessibility road segment-based accessibility results yield a more consistent and better outcome. Whether Road Segment 3548 897.27 679.99 735.84 541472.32 1.76 .041 3.955 .082 Accessibility

4.2. Global Spatial Autocorrelation In order to investigate spatial clustering, including the scattered or random distribution patterns of the two results, the Spatial Statistics tool in ArcGIS was used to calculate Moran’s I of global spatial autocorrelation: ∑∑ ̅̅ I = ∑∑ ∑̅ Based on point data, the spatial weight matrix was established with the square of the reciprocal of the distance 1/d2ij. Only the closer points are mutually affected by each other. The results are shown in Table 2. The P values are lower than 0.01, which is significant. Specifically, Moran’s I of the community accessibility is 1.4175, and Moran’s I of the road segment accessibility is 0.6172. The random distribution is less likely to be applicable; instead, a more obvious clustering pattern is demonstrated in the range of Fuzhou City. The results show the spatial positively-correlated mode, in which accessibility is directly proportional to the spatial cluster. In addition, community accessibility is more highly clustered than road segment accessibility.

Sustainability 2020, 12, 3618 9 of 19 the diﬀerence in accessibility is truly reﬂected should be judged by exhibiting the district divisions in the space.

4.2. Global Spatial Autocorrelation In order to investigate spatial clustering, including the scattered or random distribution patterns of the two results, the Spatial Statistics tool in ArcGIS was used to calculate Moran’s I of global spatial autocorrelation: Pn Pn n i=1 j=1 Wij(Xi x) xj x I = − − Pn Pn Pn 2 i=1 j=1 wij (x x) n=1 i − Based on point data, the spatial weight matrix was established with the square of the reciprocal of the distance 1/d2ij. Only the closer points are mutually affected by each other. The results are shown in Table2. The P values are lower than 0.01, which is significant. Specifically, Moran’s I of the community accessibility is 1.4175, and Moran’s I of the road segment accessibility is 0.6172. The random distribution is less likely to be applicable; instead, a more obvious clustering pattern is demonstrated in the range of Fuzhou City. The results show the spatial positively-correlated mode, in which accessibility is directly proportional to the spatial cluster. In addition, community accessibility is more highly clustered than road segment accessibility.

Table 2. Moran’s I index spatial autocorrelation analysis.

Community Accessibility Road Segment Accessibility Moran’s I index 1.4175 0.6172 Standard Deviation 0.008642 0.000071 Z Score 15.2751 73.4414 P value 0.000000 0.000000

Then, this study further examined the category of the accessibility cluster with the high/low clustering analysis tools (high/low clustering (Getis-Ord General G)). The General G index is also an inferential statistical method that uses limited data to estimate global characteristics. When the returned P value is small and statistically significant, the null hypothesis can be rejected. If the Z score is positive, the observed General G index tends to be higher than the expected General G index, which indicates that high attributes are clustered in the study area; if the Z score is negative, the observed General G index tends to be lower than the expected General G index, which indicates that low attributes are clustered in the study area. The analysis results in Table3 show that the Z score and P value of the community accessibility are 10.85 and 0.00, respectively, while the Z score and P value of the road segment accessibility are 2.30 and 0.02, respectively, which are both significant. The results reveal a high accessibility cluster, which means that the points with a short distance to parks are more concentrated. Specifically, community accessibility has higher clusters than road segment accessibility, and the communities near the park are more clustered.

Table 3. High/Low Clustering (Getis-Ord General G).

Community Accessibility Road Segment Accessibility General G 0.002884 0.000104 Standard Deviation 0.000000 0.000000 Z Score 10.856255 2.303481 P value 0.000000 0.021252 Sustainability 2020, 12, 3618 10 of 19

4.3. Local Spatial Autocorrelation While global spatial autocorrelation can be used to analyze the global spatial pattern of one characteristic attribute, this spatial pattern is not identical throughout the study area, which means that the space is heterogeneous. To describe how this characteristic attribute is distributed in space, the local differences of spatial autocorrelation in the study area should be discussed first. Therefore, in order to further understand the spatial distribution of accessibility, cluster and outlier analysis (Ansenlin Local Moran’s I) was used to perform local spatial autocorrelation analysis, and the results can be used to identify local differences in park accessibility from the perspective of spatial distribution. The high-high area means that the periphery of the highly-accessible spatial units also has low accessibility (high values represent low accessibility); conversely, the low-low area means that the periphery of the low-accessible spatial units also has high accessibility (low values represent high accessibility). The high-low and low-high areas are transitional areas; while a not significant area means that the local spatial autocorrelation is not significant, as shown in Figure7. Sustainability 2020, 12, x FOR PEER REVIEW 10 of 18

(a) (b)

FigureFigure 7. 7. ClusterCluster and and outlier outlier analysis: analysis: (a ()a )community community accessibility accessibility; ; ( (bb)) road road segment segment accessibility. accessibility.

TheThe analysis analysis results results show show that that the the overall overall trend trend of of accessibility accessibility is issimilar. similar. The The low-low low-low area area is concentratedis concentrated in the in center, the center, which which includes includes the roads the roads close closeto parks, to parks, and most and of most the ofhigh-high the high-high area is distributedarea is distributed in the south in the of souththe city of thedistrict, city district,while only while a small only apart small is partin the is east in the and east north, and north,which includeswhichincludes the roads the far roads from farparks from and parks the areas and the with areas low with accessibility. low accessibility. Although Although the overall the trends overall of thetrends two are of the similar, two are the similar, road segment the road accessibility segment accessibility is more accurate is more and accurate the lo andcation the is location more accurate; is more therefore,accurate; it therefore, is capable it of is capturing capable of more capturing accurate more accessibility accurate accessibilitystatus. This study status. used This hot study spot used analysis hot (Getis-Ordspot analysis Gi*) (Getis-Ord to demonstrate Gi*) to a demonstratemore prominen a moret clustering prominent effect, clustering as shown eﬀ ect,in Figure as shown 8. in Figure8.

4.4. Spatial Inequality Index The Lorenz curve and Gini coefficient were originally used to explore the equity of income or wealth distribution; income is positively related to wealth status. Hereunder, the concepts of the Lorenz curve and the Gini coefficient were introduced to the accessibility results to show spatial inequality. The Lorenz curve, as plotted from the accumulated community-based and road segment-based accessibility results, is shown in Figure9. The curvature of the community accessibility curve is higher than that of the road segment accessibility curve. The calculated spatial Gini coefficient (inequality index) of community accessibility is 0.7, and the calculated spatial inequality index of road segment accessibility is 0.29384, which implies that calculating accessibility by street segments can output a more fair result, while spatial units based on community accessibility produce less fair space.

(a) (b)

Figure 8. Hot spot analysis (Getis-Ord Gi*) analysis: (a) community accessibility; (b) road segment accessibility.

4.4. Spatial Inequality Index The Lorenz curve and Gini coefficient were originally used to explore the equity of income or wealth distribution; income is positively related to wealth status. Hereunder, the concepts of the Lorenz curve and the Gini coefficient were introduced to the accessibility results to show spatial inequality. The Lorenz curve, as plotted from the accumulated community-based and road segment-based accessibility results, is shown in Figure 9. The curvature of the community accessibility curve is higher than that of the road segment accessibility curve. The calculated spatial Gini coefficient (inequality index) of community accessibility is 0.7, and the calculated spatial inequality index of road segment accessibility is 0.29384, which implies that calculating accessibility by street segments can output a more fair result, while spatial units based on community accessibility produce less fair space.

Sustainability 2020, 12, x FOR PEER REVIEW 10 of 18

(a) (b)

Figure 7. Cluster and outlier analysis: (a) community accessibility ; (b) road segment accessibility. The analysis results show that the overall trend of accessibility is similar. The low-low area is concentrated in the center, which includes the roads close to parks, and most of the high-high area is distributed in the south of the city district, while only a small part is in the east and north, which includes the roads far from parks and the areas with low accessibility. Although the overall trends of the two are similar, the road segment accessibility is more accurate and the location is more accurate; Sustainability 2020, 12, 3618 11 of 19 therefore, it is capable of capturing more accurate accessibility status. This study used hot spot analysis (Getis-Ord Gi*) to demonstrate a more prominent clustering effect, as shown in Figure 8.

(a) (b)

Figure Figure8. Hot spot 8. analysisHot spot (Getis-Ord analysis Gi*) (Getis-Ord analysis: Gi*) (a) community analysis: (accessibility;a) community (b) road accessibility; segment accessibility. (b) road Sustainabilitysegment 2020, 12 accessibility., x FOR PEER REVIEW 11 of 18 4.4. Spatial Inequality Index 1.00 The Lorenz curve and Gini coefficient were originally used to explore the equity of income or wealth distribution; income is0.90 positively related to wealth status. Hereunder, the concepts of the Lorenz curve and the Gini coefficient0.80 were introduced to the accessibility results to show spatial inequality.

The Lorenz curve, as plotted0.70 from the accumulated community-based and road segment-based accessibility results, is shown in Figure 9. The curvature of the community accessibility curve is higher 0.60 than that of the road segment accessibility curve. The calculated spatial Gini coefficient (inequality index) of community accessibility0.50 is 0.7, and the calculated spatial inequality index of road segment

accessibility is 0.29384, which 0.40implies that calculating accessibility by street segments can output a more fair result, while spatial units based on community accessibility produce less fair space. 0.30

0.20

0.10

0.00 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Absolute mean line Lorenz Curve of road center points Accessibility Lorenz Curve of Community points accessibility

FigureFigure 9.9. LorenzLorenz curve.curve. TheThe spatial spatial distribution distribution of ofparks parks seeks seeks to toenable enable the the most most residents residents to to reach reach a anearby nearby park park at at a a relativelyrelatively small small distance, distance, and and only only a small a small number number of of residents residents have have to to travel travel long long distances distances to to access access services.services. In Inthe the accessibility accessibility calculation, calculation, the the distance distance of ofoutliers outliers and and extremes extremes to to the the nearest nearest facility facility differsdiffers greatly greatly from from other other spatial spatial units, units, which which includes includes the small the small number number of residents of residents who need who to need travel to longtravel distances long distancesto reach a park to reach facility. a park Among facility. the above Among park the accessibility above park calculation accessibility and calculation analysis from and twoanalysis different from perspectives, two different the perspectives,calculated road the segment calculated accessibility road segment is better accessibility in terms isof better data, inand terms the dataof data,is more and concentrated. the data is more According concentrated. to the Accordingoverall spatial to the autocorrelation overall spatial autocorrelationanalysis, road segment analysis, accessibilityroad segment is more accessibility evenly distri is morebuted, evenly and distributed,the park resources and the enjoyed park resources by residents enjoyed are by also residents relatively are even.also The relatively Lorenz even. curve The and Lorenz Gini coefficient curve and Giniindicate coe ffithatcient road indicate segment that accessib road segmentility is accessibilitycloser to the is absolutelycloser to fair the line. absolutely fair line.

5. Space Syntax: Spatial Structural Analysis of Streets and Parks

5.1. Overview and Problems of Spatial Distribution of Parks Based on the relationship between the travel modes and frequencies of the residents, as well as mean road segment accessibility, the service radiuses of 400 and 900 m with optimal accessibility were set up. Buffer zones were applied in 120 parks in four administrative districts of Fuzhou City; most of the communities in Gulou District and Taijiang District are basically within 400 m of a certain park. A small part of Cangshan District and Jin’an District can access a certain park within 400 m. If the mean road segment accessibility is 900m, most communities in the northeast of Jin’an District and the southeast of Cangshan District cannot access any park within 400 m (Figure 10), which shows that, with rapid urban development, the population has expanded, and the downtown has been well developed. As most of the park resources are concentrated in the downtown, most residential areas far from the downtown still lack access to parks.

Sustainability 2020, 12, 3618 12 of 19

5. Space Syntax: Spatial Structural Analysis of Streets and Parks

5.1. Overview and Problems of Spatial Distribution of Parks Based on the relationship between the travel modes and frequencies of the residents, as well as mean road segment accessibility, the service radiuses of 400 and 900 m with optimal accessibility were set up. Buﬀer zones were applied in 120 parks in four administrative districts of Fuzhou City; most of the communities in Gulou District and Taijiang District are basically within 400 m of a certain park. A small part of Cangshan District and Jin’an District can access a certain park within 400 m. If the mean road segment accessibility is 900m, most communities in the northeast of Jin’an District and the southeast of Cangshan District cannot access any park within 400 m (Figure 10), which shows that, with rapid urban development, the population has expanded, and the downtown has been well developed. As most of the park resources are concentrated in the downtown, most residential areas far Sustainabilityfrom the downtown2020, 12, x FOR still PEER lack REVIEW access to parks. 12 of 18

(a) (b)

Figure Figure10. Analysis 10. Analysis of buffer of buzonesﬀer zonesin Fuzhou in Fuzhou City: ( City:a) a park (a) a service park service radius radius of 300 of m; 300 (b m;) a (parkb) a park service service radius of 900 m. radius of 900 m.

5.2. Intrinsic Connection between Spatial Structure of Streets and Space Syntax 5.2. Intrinsic Connection between Spatial Structure of Streets and Space Syntax TheThe basis basis of of the the space space syntax syntax theory theory lies lies in in its its potential potential social dimension inin thethe spatialspatial structurestructure ofof citiescities and and buildings, buildings, which which interacts interacts with with the the spatial spatial na natureture of social activitiesactivities [[30].30]. TheThe streetstreet networksnetworks inin space space syntax syntax are are mainly mainly represented represented by by a a set set of of lin linearear elements referred toto asas axialaxial graphs,graphs, whilewhile thethe simultaneitysimultaneity between between different different elements elements is is measured measured by by the the spatial spatial structure structure [30]. [30]. AccordingAccording to to space space syntax, syntax, local local sp spacesaces are are different different segments segments in the city, whichwhich areare connectedconnected byby a anatural natural flow flow of of people. people. The The flow flow of of people people dominate dominatess the the layout of urban spaces toto aa largelarge extentextent andand dependsdepends on on the the perception perception of of the the composition composition of urba urbann spaces [[50].50]. The characteristics ofof spatialspatial layoutlayout andand the the harmony harmony between between different different spaces spaces are are prec preciselyisely produced byby thethe interactioninteraction betweenbetween thethe overalloverall space space and and the the flow flow of of people people.. This This symbiotic symbiotic relationship relationship between local andand globalglobal spacesspacesis isthe the connectionconnection between between different different spatial spatial levels levels of of the the city; city; however, however, this invisible logiclogic isis oftenoften neglectedneglectedin in existingexisting planning planning researches. researches. TheThe space space syntax syntax theory, theory, as as proposed proposed by by Hillier Hillier et al., regards a citycity asas aa complexcomplex andand dynamicdynamic spatialspatial configuration, configuration, and and translates translates the the spatial spatial structure structure in in reality reality into a quantifiable quantifiable syntactic model.model. InIn the the translation translation process, process, the the spat spatialial perception perception is is translated translated into into the the organizational organizational conceptualization, thus,thus, completing completing the the thinking thinking jump jump of of inferring inferring the the invisible invisible from the visible.visible. TheThe corecore ideaidea ofof spacespace syntaxsyntax is is that that the the basic basic connection connection of of urban urban spatial spatial or organizationganization is the flowflow ofof peoplepeople [[50].50]. OnOn oneone hand,hand, urbanurban structures structures affect affect the the natural natural flow flow of of people people and and traffic; traffic; on thethe other hand,hand, thethe spatialspatial perceptionperception andand action action of of people people largely largely determine determine the the layout of urban spaces [[51].51]. SyntacticSyntactic analysisanalysis cancan predictpredict the possible flow of people in each street by calculating the accessibility of the street network, thus, determining the relative convenience of the space. As space syntax is consistent with the concept of accessibility, it was applied in this paper to review the current layout of cultural facilities and select the highly-accessible areas. Then, combined with the park accessibility analysis by GIS, comprehensive layout recommendations and sustainable development trends were proposed. Syntax summarizes the spatial system as an axial model, which corresponds to the linear flow of people in the city. Since public facilities have hierarchical attributes corresponding to the scope of services and functions, the natural flow of people thus generates hierarchical movement and communication networks at different scales according to different traffic types. To correspond to the accessibility analysis of park facilities in Fuzhou City, the segment model, as derived from the axial model, was adopted in this paper to expand the accessibility measurement from topological distance (number of turns between two axis) to angular distance (accumulated turning angle of the axis). In addition, the accessibility potential of streets on different urban scales can be measured by different network analysis radiuses; for example, a syntax calculation with a 400 m radius can reflect the

Sustainability 2020, 12, 3618 13 of 19 the possible flow of people in each street by calculating the accessibility of the street network, thus, determining the relative convenience of the space. As space syntax is consistent with the concept of accessibility, it was applied in this paper to review the current layout of cultural facilities and select the highly-accessible areas. Then, combined with the park accessibility analysis by GIS, comprehensive layout recommendations and sustainable development trends were proposed. Syntax summarizes the spatial system as an axial model, which corresponds to the linear flow of people in the city. Since public facilities have hierarchical attributes corresponding to the scope of services and functions, the natural flow of people thus generates hierarchical movement and communication networks at different scales according to different traffic types. To correspond to the accessibility analysis of park facilities in Fuzhou City, the segment model, as derived from the axial model, was adopted in this paper to expand the accessibility measurement from topological distance (number of turns between two axis) to angular distance (accumulated turning angle of the axis). In addition, the accessibility potential of streets on different urban scales can be measured by different network analysis radiuses; for example, a syntax calculation with a 400 m radius can reflect Sustainability 2020, 12, x FOR PEER REVIEW 13 of 18 the accessibility of the space within a 5 min walk. As the mean road segment accessibility is 900, budgeta syntax with budget a radius with of a radius900 m can of 900 be mused can to be calculate used to calculateall the accessible all the accessible objects in objects the space, in the as space,shown inas Figure shown 11. in Figure 11.

(a) (b) (c)

FigureFigure 11. 11. StreetStreet integration: integration: ( (aa)) global global integration analysis; ((bb)) 400400 mm integrationintegration analysis; analysis; ( c()c) 900 900 m m integrationintegration analysis. analysis.

ThisThis study study extracted extracted the the integration integration of a street wherewhere aa parkpark isis locatedlocated from from global global integration, integration, 400m400m integration, integration, and and 900m 900m integration as the accessibilityaccessibility attributesattributes of of the the park. park.Specifically, Specifically, the the streets streets withwith an an integration integration ranking ranking in the top 20%20% formform thethe foregroundforeground network,network, asas defined defined by by Professor Professor Hillier,Hillier, which which constitute the main skeleton ofof thethe urbanurban spaces.spaces. TheThe streetsstreets with with an an integration integration ranking ranking inin the the top top 10% 10% form form the integration core thatthat referrefer toto thethe spacespace withwith the the best best accessibility. accessibility. The The streets streets withwith an an integration integration ranking in the bottombottom 80%80% formform thethe backgroundbackgroundnetwork network where where residents residents travel travel lessless efficiently. efficiently. The The quantitative results in Figure 1212 showshow that,that, inin thethe global global integration integration calculation, calculation, 28%28% of of the the parks parks are located in the foregroundforeground network,network, 8%8% ofof whichwhich areare included included in in the the integration integration core.core. In In the the 400 400 m m integration integration calculation, calculation, 17% 17% of of the the parks parks are are located located in inthe the foreground foreground network, network, 7% of7% which of which are included are included in the in theintegration integration core. core. In the In the900 900m integration m integration calculation, calculation, 23% 23% of ofthe the parks parks are locatedare located in the in theforeground foreground network, network, 6% 6% of ofwhich which ar aree included included in in the the integration core.core. BothBoth locallocal and and globalglobal integration integration calculations calculations reveal particularly unevenuneven parkpark distribution.distribution. Most Most parks parks are are distributed distributed inin low-accessible low-accessible areas, areas, while while only only a a few few parks parks are distributed in the mainmain skeletonskeleton oror integratedintegrated core.core.

Park distribution integration 100% 90% 80% 70% 72% 60% 83% 77% 50% 40% 30% 20% 20% 17% 10% 10% 8% 0% 7% 6% Integration Integration 400 Integration 900

Main skeleton Integration core Low accessibility

Figure 12. Park distribution integration. This study subdivided the integration of the streets where the parks are located in the foreground network. According to the analysis of the three integration calculations, Gulou District has the most highly-accessible parks, followed by Taijiang District, Cangshan District, and Jin’an District, respectively. The top 20% of the streets based on road segment accessibility are similar to the results of Sustainability 2020, 12, x FOR PEER REVIEW 13 of 18 accessibility of the space within a 5 min walk. As the mean road segment accessibility is 900, a syntax budget with a radius of 900 m can be used to calculate all the accessible objects in the space, as shown in Figure 11.

(a) (b) (c)

Figure 11. Street integration: (a) global integration analysis; (b) 400 m integration analysis; (c) 900 m integration analysis. This study extracted the integration of a street where a park is located from global integration, 400m integration, and 900m integration as the accessibility attributes of the park. Specifically, the streets with an integration ranking in the top 20% form the foreground network, as defined by Professor Hillier, which constitute the main skeleton of the urban spaces. The streets with an integration ranking in the top 10% form the integration core that refer to the space with the best accessibility. The streets with an integration ranking in the bottom 80% form the background network where residents travel less efficiently. The quantitative results in Figure 12 show that, in the global integration calculation, 28% of the parks are located in the foreground network, 8% of which are included in the integration core. In the 400 m integration calculation, 17% of the parks are located in the foreground network, 7% of which are included in the integration core. In the 900 m integration calculation, 23% of the parks are located in the foreground network, 6% of which are included in the integration core. Both local and globalSustainability integration2020, 12, 3618calculations reveal particularly uneven park distribution. Most parks are distributed14 of 19 in low-accessible areas, while only a few parks are distributed in the main skeleton or integrated core.

Park distribution integration 100% 90% 80% 70% 72% 60% 83% 77% 50% 40% 30% 20% 20% 17% 10% 10% 8% 0% 7% 6% Integration Integration 400 Integration 900

Main skeleton Integration core Low accessibility

Figure 12. Park distribution integration. Figure 12. Park distribution integration. This study subdivided the integration of the streets where the parks are located in the foreground This study subdivided the integration of the streets where the parks are located in the foreground network. According to the analysis of the three integration calculations, Gulou District has the network. According to the analysis of the three integration calculations, Gulou District has the most most highly-accessible parks, followed by Taijiang District, Cangshan District, and Jin’an District, highly-accessible parks, followed by Taijiang District, Cangshan District, and Jin’an District, respectively. The top 20% of the streets based on road segment accessibility are similar to the results of the integration analysis. Gulou District has the most highly-accessible roads, which means there are more points close to parks (Table4).

Table 4. Percentages of streets where the park is located with an integration ranking in the top 20%.

Integration Integration 900 Integration 400 Road Segment Accessibility 10% 20% 10% 20% 10% 20% 10% 20% Gulou District 4 10 5 13 5 7 120 329 Taijiang District 4 6 3 5 3 3 72 148 Cangshan District 0 4 0 2 0 4 55 130 Jin’an District 2 4 0 0 1 2 49 102

6. Conclusions and Discussion

6.1. Conclusions From a methodological perspective as GIS has more comprehensive spatial data management and geographic analysis capabilities, the metric distance analysis of GIS and the topological analysis of space syntax were integrated in this study to construct the spatial geographic information of cultural facilities, Depthmap information of the street network in Fuzhou City, and a database. First, GIS was used to establish network data and analyze the accessibility indicators. The space syntax was then adopted to superimpose the urban street network analysis model with park points. Next, spatial analysis was performed on the current park layout. The conclusions of this study are proposed in the following sections (Figure 13). Sustainability 2020, 12, x FOR PEER REVIEW 14 of 18 respectively. The top 20% of the streets based on road segment accessibility are similar to the results of the integration analysis. Gulou District has the most highly-accessible roads, which means there are more points close to parks (Table 4).

Table 4. Percentages of streets where the park is located with an integration ranking in the top 20%.

Integration Integration 900 Integration 400 Road segment accessibility 10% 20% 10% 20% 10% 20% 10% 20% Gulou District 4 10 5 13 5 7 120 329 Taijiang District 4 6 3 5 3 3 72 148 Cangshan District 0 4 0 2 0 4 55 130 Jin’an District 2 4 0 0 1 2 49 102

6. Conclusion and Discussion

6.1. Conclusion From a methodological perspective as GIS has more comprehensive spatial data management and geographic analysis capabilities, the metric distance analysis of GIS and the topological analysis of space syntax were integrated in this study to construct the spatial geographic information of cultural facilities, Depthmap information of the street network in Fuzhou City, and a database. First, GIS was used to establish network data and analyze the accessibility indicators. The space syntax was then adopted to superimpose the urban street network analysis model with park points. Next, spatial analysisSustainability was2020 performed, 12, 3618 on the current park layout. The conclusions of this study are proposed15 in of the 19 following sections (Figure 13).

(a) (b)

Figure Figure13. Accessibility 13. Accessibility and integration and integration analysis: analysis: (a) hot spot (a) hotanalysis spot analysisof road segment of road segmentaccessibility; accessibility; (b) global road integration.(b) global road integration.

(1)(1) InIn the the study study of of park park equity, equity, in in order order to to yield yield better better accessibility calculation results fromfrom thethe GIS,GIS, accessibilityaccessibility was was calculated calculated based based on on both both ur urbanban communities andand roadroad segments.segments. SinceSince thethe community-basedcommunity-based accessibility accessibility calculation calculation method method assumes assumes that that all thethe communitycommunity individualsindividuals havehave the the same same accessibility accessibility as asthe the community community cent center,er, it ignores it ignores the the spatial spatial differences differences within within the spatialthe spatial unit, unit,and andthus, thus, cannot cannot detect detect subtle subtle differences differences in in the the community. community. Therefore, aa moremore accurateaccurate road road segment segment was was adopted adopted as asthe the spatial spatial unit. unit. Later, Later, spatial spatial autocorrelation autocorrelation analysis, analysis, the Lorenzthe Lorenz curve, curve, and Gini and coefficient Gini coeffi analysiscient analysis revealed revealed relatively relatively unfair park unfair accessibility park accessibility in Fuzhou in City,Fuzhou as shown City, asin Figure shown 12. in FigureThe hot 12 spot. The analysis hot spot of the analysis road ofsegment the road accessibility segment accessibilityshowed that highshowed accessibility that high is accessibilityconcentrated is in concentrated the cold spot in areas, the cold which spot indicates areas, which that most indicates of the that residents most of the residents in Gulou District and Taijiang District can access park resources at only a small distance. Most of the residents in Jin’an District and Cangshan District have poor accessibility to parks, especially the residents in the east of Jin’an District and the south of Cangshan District. (2) In this paper, the integration of space syntax was used to describe the spatial characteristics of parks and streets. The integration reflects how physically close a space is to all other spaces, which refers to its potential as a destination; a more integrated road has higher accessibility. From the analysis of the global integration results, most of the parks are located on streets with low integration. High integration is more consistent with the GIS accessibility analysis result. The parks located on highly-integrated streets are collectively distributed in the center of the map, as well as on most streets in Gulou District and Taijiang District. The accessibility of most streets in the north of Jin’an District, and the north and south of Cangshan District is low, which is an important reason for unfair resources. (3) The results of this study show that when the streets where the parks are located are more integrated, the accessibility to the parks is also higher; the two also prove the unfair distribution of green space resources in parks of Fuzhou City. From the perspective of space syntax integration, the rankings of global integration, 400 m integration, and 900 m integration are Gulou District, Taijiang District, Cangshan District, and Jin’an District. For the GIS network analysis, the top 20% of streets in the road segment accessibility rankings is in the same order. Gulou District has the most highly-integrated streets, followed by Taijiang District, Cangshan District, and Jin’an District, respectively. Sustainability 2020, 12, 3618 16 of 19

6.2. Discussion (1) This paper provides a new perspective for optimizing the spatial layout of urban public facilities in terms of equity and accessibility. The research method integrated GIS and the space syntax theory, and is universally applicable to other cities and regions. In the future, with urban renewal and cultural development, public cultural facilities will play a more important role in enhancing the humanistic value of a city. Fuzhou City is rich in park resources and has a good structural order of street space; however it is unevenly distributed, as most of the resources are concentrated in the old downtown. Future development can refer to the 400 m and 900 m integration measures, as proposed in this study, and adopt a multi-center clustering development structure, which can focus on the construction of areas with low urban spatial accessibility to maximize the utilization of park facilities. In addition, the layout of parks and public facilities should consider both homogeneity and efficiency optimization, while the key spaces with structural advantages should be constructed to maximize efficiency and equity. (2) The spatial inequality in this study emphasizes the difference in proximity and convenience of different areas to public services due to street network planning and public facility allocation. Therefore, the road segments were regarded as spatial units, and their distance to the facilities through the actual road network was calculated. However, as the density of road segments does not equal the density of the actual population, it is possible that one road segment carrying 500 residents and another road segment carrying 5000 individuals are considered equally important. In addition, the facility size, the service level, and the use of surrounding land lead to different urban park benefits. Future studies may consider the above factors according to their research motivations and purposes, and reflect them in the research design. (3) The accessibility of road networks plays a vital role in affecting sustainable urban development, while the spatial equity of public facility layout, such as parks, is directly related to the quality of the living environment of residents on different streets. In particular, accessibility has a profound influence on the use frequency of public facilities, such as parks, by low-income groups, the elderly, children, and the disabled in the urban areas. At present, the rationality of the spatial distribution pattern of parks is examined by determining the service radius of parks according to the park levels in China, which refers to achieving full coverage of park services through planning. The main indicators include the number of parks, park area per resident, and spatial layout homogeneity. Quantitative research of resident demands and spatial equity of park layout is still insufficient. However, many researches abroad have shown that, on many streets with high social demands, despite high park accessibility, the residents are reluctant to use the parks [52], which is due to the park quality, such as comfort, safety, quality, and artistic value. As this problem involves the discrepancy between resident demands and behaviors, further questionnaire surveys and analysis are needed to accurately grasp the resident demands and behaviors, which is also a top priority for the author to explore in the future.

Author Contributions: B.-X.H. contributed to the conceptual design of the study, data collection, drafting the article, and final approval. W.-Y.L. contributed to the conceptual design of the study, and data collection. S.-C.C. contributed to the conceptual design of the study, supervision of the progress, and final approval. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.

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