# Space Accessibility and Equity of Urban Green Space

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.1.1. Geographical Location

^{2}, and the household population is 3,095,000 (Figure 1).

#### 2.1.2. Natural Environment

#### 2.1.3. Vegetation Status

#### 2.2. Research Framework

#### 2.3. Data Sources

#### 2.3.1. Remote Sensing Images

#### 2.3.2. Road Network Data

#### 2.3.3. Demographic Variables

#### 2.4. Methodology and Research Process

#### 2.4.1. Space Syntax Integration

#### 2.4.2. Remote Sensing Image Classification

#### 2.4.3. Green Space Landscape Unit

#### 2.4.4. Landscape Pattern Analysis

_{i}is the difference between the ranks of the same pair (i = l, 2, 3…, n). If the absolute value of the correlation coefficient is greater than 0.90 and significant for p ≤ 0.01, there is a significant correlation between the indices. If the absolute value of the correlation coefficient is less than 0.90 but significant for p ≤ 0.01, this correlation coefficient can only indicate the trend of the change in the indices, i.e., the change in one index predicts the trend in the other index. If the absolute value of the correlation coefficient is less than 0.90 and p ≤ 0.01, then there is no meaningful correlation between the indices.

#### 2.4.5. Factor Analysis

#### 2.4.6. Green Space Accessibility Model Construction

- Green space accessibility calculation points

- 2.
- Green space accessibility calculation range

^{2}. The accessible area of Fuzhou City is 1.31 km

^{2}in Taijiang District, 1.28 km

^{2}in Gulou District, 1.09 km

^{2}in Cangshan District, 0.99 km

^{2}in Mawei District, and 0.89 km

^{2}in Jinan District. The size of the accessible area depends on the development of the transportation network, which can be used as an indicator of the accessibility of the regional transportation network.

- 3.
- Choice of accessibility factors

- 4.
- Weight analysis

## 3. Results

#### 3.1. Green Space Accessibility Metric Results

#### 3.2. Fairness of Green Space Based on Supply and Demand Index

#### 3.2.1. Green Space Supply Index in Fuzhou City

#### 3.2.2. Green Space Demand Index in Fuzhou City

- total population
- the proportion of the population over 65 years old.
- the proportion of the population aged 0~14 years old.
- the proportion of the foreign population.
- the proportion of females.

#### 3.2.3. Fairness Analysis of Green Space

## 4. Discussion

#### 4.1. Street Network Topology Pattern

#### 4.2. Remote Sensing Images and Landscape Pattern Analysis

#### 4.3. Model of Green Space Accessibility

#### 4.4. Study on the Equity of Green Space

## 5. Conclusions

#### 5.1. To Improve the Fairness and Accessibility of Green Spaces in Line with Urban Planning

#### 5.2. Updating and Improving Research Methods and Tools

#### 5.3. Adding Research Data from Different Time Periods

#### 5.4. Failure to Truly Understand the Public’s Preferences for Green Space Characteristics and Usage Habits

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Administrative District | Street | Total Population | Female Population | Population Aged 0 to 14 | Population Aged 65 and Over | Foreign Populations |
---|---|---|---|---|---|---|

Gulou | Gudong Street | 40,769 | 21,064 | 4577 | 5208 | 13,148 |

Gulou | Guxi Street | 67,420 | 34,483 | 7462 | 7568 | 25,096 |

Gulou | Wenquan Street | 67,911 | 33,959 | 6461 | 6544 | 24,603 |

Gulou | Dongjie Street | 32,438 | 17,033 | 4013 | 3891 | 8410 |

Gulou | Nanjie Street | 45,663 | 23,480 | 5102 | 5537 | 17,157 |

Gulou | Antai Street | 33,305 | 17,135 | 3812 | 4079 | 12,046 |

Gulou | Huada Street | 101,489 | 51,259 | 10,718 | 8834 | 24,416 |

Gulou | Shuibu Street | 45,746 | 23,463 | 5200 | 4930 | 20,402 |

Gulou | Wufeng Street | 109,429 | 55,378 | 13,571 | 8760 | 54,162 |

Gulou | Hongshan Town | 143,535 | 71,430 | 17,259 | 10,692 | 80,796 |

Taijiang | Yingzhou Street | 54,186 | 26,768 | 6115 | 4581 | 26,184 |

Taijiang | Houzhou Street | 44,047 | 21,443 | 3820 | 5654 | 13,232 |

Taijiang | yizhou Street | 40,407 | 20,728 | 4363 | 5223 | 16,839 |

Taijiang | Xingang Street | 49,609 | 23,930 | 4625 | 4298 | 19,177 |

Taijiang | Shanghai Street | 73,010 | 37,027 | 7285 | 8872 | 30,579 |

Taijiang | Cangxia Street | 43,654 | 22,250 | 4628 | 5817 | 15,720 |

Taijiang | Chating Street | 34,441 | 17,293 | 3527 | 4193 | 13,899 |

Taijiang | Yangzhong Street | 29,195 | 14,638 | 3031 | 3678 | 10,266 |

Taijiang | Aofeng Street | 43,802 | 21,258 | 6981 | 2754 | 20,100 |

Taijiang | Ninghua Street | 34,540 | 17,275 | 4144 | 3842 | 14,278 |

Cangshan | Cangqian Street | 23,663 | 12,179 | 3235 | 2935 | 6957 |

Cangshan | Dongshen Street | 13,195 | 6536 | 1636 | 1301 | 7230 |

Cangshan | Duihu Street | 35,785 | 18,934 | 2756 | 2476 | 20,647 |

Cangshan | Linjiang Street | 29,598 | 10,751 | 2457 | 2413 | 15,578 |

Cangshan | Sanchajie Street | 23,993 | 12,170 | 2850 | 2696 | 11,147 |

Cangshan | Shangdu Street | 43,622 | 22,292 | 5985 | 3284 | 23,736 |

Cangshan | Xiadu Street | 36,275 | 18,388 | 5085 | 3677 | 15,177 |

Cangshan | Jinshan Street | 80,791 | 39,762 | 15,436 | 3705 | 37,606 |

Cangshan | Cangshan Town | 30,247 | 14,496 | 3663 | 2007 | 17,017 |

Cangshan | Chengmen Town | 96,539 | 47,222 | 12,245 | 5893 | 31,972 |

Cangshan | Gaishan Town | 122,018 | 58,475 | 15,630 | 7042 | 57,989 |

Cangshan | Jianxin Town | 201,925 | 93,362 | 21,498 | 6348 | 147,161 |

Cangshan | Luozhou Town | 18,462 | 8186 | 1948 | 1162 | 8780 |

Cangshan | Hongxing Street | 6633 | 3217 | 663 | 508 | 1859 |

Mawei | Luoxing Street | 60,117 | 28,972 | 8442 | 2849 | 34,630 |

Mawei | Mawei Town | 63,299 | 29,695 | 6960 | 3000 | 41,325 |

Mawei | Tingjiang Town | 39,624 | 20,896 | 3560 | 3857 | 24,885 |

Mawei | Langqi Town | 68,889 | 34,770 | 7059 | 6025 | 28,592 |

Jinan | Chayuan Street | 90,045 | 45,011 | 9022 | 7947 | 52,350 |

Jinan | Wangzhuang Street | 46,980 | 24,122 | 6377 | 3479 | 26,241 |

Jinan | Xiangyuan Street | 48,717 | 23,909 | 6182 | 3277 | 30,622 |

Jinan | Gushan Town | 286,095 | 135,555 | 43,946 | 10,220 | 204,101 |

Jinan | Xindian Town | 166,283 | 79,337 | 23,682 | 8524 | 116,719 |

Jinan | Yuefeng Town | 130,403 | 64,362 | 16,943 | 8893 | 84,000 |

Jinan | Huanxi Town | 11,965 | 5949 | 1297 | 1105 | 2716 |

Jinan | Shoushan hometown | 8091 | 3900 | 1080 | 1076 | 1122 |

Jinan | Rixi hometown | 3912 | 1797 | 498 | 541 | 580 |

Changle | Wuhang Street | 76,306 | 39,429 | 11,701 | 5556 | 38,392 |

Changle | Hangcheng Street | 71,320 | 34,714 | 9418 | 3385 | 47,713 |

Changle | Yingqian Street | 35,408 | 17,089 | 4829 | 3073 | 13,863 |

Changle | Zhanggang Street | 46,520 | 22,610 | 6446 | 3613 | 17,271 |

Changle | Shouzhan Town | 21,559 | 10,681 | 2805 | 2136 | 6883 |

Changle | Yutian Town | 32,841 | 15,927 | 5328 | 3107 | 5601 |

Changle | Songxia Town | 25,281 | 11,865 | 3439 | 1587 | 10,512 |

Changle | Jiangtian Town | 44,552 | 21,213 | 6659 | 3352 | 14,809 |

Changle | Gukui Town | 43,882 | 21,244 | 6347 | 3926 | 13,407 |

Changle | Wenwusha Town | 19,985 | 9314 | 2593 | 1334 | 4631 |

Changle | Heshang Town | 51,323 | 24,482 | 6285 | 4485 | 14,435 |

Changle | Hunan Town | 27,375 | 13,014 | 3125 | 1981 | 11,307 |

Changle | Jinfeng Town | 84,899 | 41,596 | 11,842 | 5713 | 39,156 |

Changle | Wenling Town | 27,680 | 13,655 | 3414 | 2617 | 9226 |

Changle | Meihua Town | 14,216 | 7214 | 1710 | 1502 | 3504 |

Changle | Tantou Town | 48,026 | 23,713 | 5153 | 4887 | 17,371 |

Changle | Luolian hometown | 6426 | 3152 | 953 | 780 | 1222 |

Changle | Houyu hometown | 5027 | 2677 | 435 | 631 | 2944 |

Landscape Pattern Index | Expression Formula | Ecological Significance |
---|---|---|

Landscape (PLAND) | $PLAND=\frac{{\sum}_{j}^{n}={1}^{{a}_{ij}}}{A}\left(100\right)$ | Knowing what proportion of the landscape is covered by patches gives an idea of the abundance of a certain landscape type. |

Largest Patch Index (LPI) | $LPI=\frac{ma{x}_{j}^{n}={1}^{{a}_{ij}}}{A}\left(100\right)$ | The maximum patch index at the type level describes the percentage of the largest patches in the landscape and is a simple measure of type dominance. |

Edge Density (ED) | $ED=\frac{{\sum}_{k}^{m}={1}^{{e}_{ik}}}{A}\left(10,000\right)$ | Edge diversity reveals the extent to which a landscape or type is divided by a boundary and is a direct reflection of the degree of landscape aggregation factors. |

Average Patch Area (AREA_MN) | $AREA\_MN=\frac{{\sum}_{i}^{n}={1}^{{a}_{ij}}}{{n}_{i}}\left(10,000\right)$ | AREA_MN represents an average condition that characterizes the fragmentation of the landscape type. |

Area-Weighted Mean Patch Area (AREA_AM) | $AREA\_AM={\sum}_{j=1}^{n}[{a}_{ij}(\frac{{a}_{ij}}{{\sum}_{j=1}^{n}{a}_{ij}})]$ | AREA_AM is a statistical form of patch area, which to some extent reflects the diversity of patch areas and the complexity of landscape patterns of landscape types. |

Standard Deviation of Patch Area (AREA_SD) | $AREA\_SD=\sqrt{\frac{{\sum}_{j=1}^{n}{[{a}_{ij}-\left(\frac{{\sum}_{j=1}^{n}{a}_{ij}}{{n}_{i}}\right)]}^{2}}{{n}_{i}}}$ | AREA_SD is a statistic of the area complexity of landscape type patches, reflecting the diversity and complexity of their landscape patches. |

Density of Patches(PD) | $PD=\frac{{N}_{i}}{{A}_{i}}$ | Landscape patches can reflect the degree of fragmentation of the landscape. |

Landscape Shape Index (LSI) | $LSI=\frac{0.25E}{\sqrt{A}}$ | When there is only one square patch in the landscape, LSI = 1; when the patch shape in the landscape is irregular or deviates from the square, the LSI value increases. |

Average Shape Index (SHAPE_MN) | $SHAPE\_MN=\frac{{{\displaystyle \sum}}_{i=1}^{m}{{\displaystyle \sum}}_{j=1}^{n}\left(\frac{0.25{p}_{ij}}{\sqrt{{a}_{ij}}}\right)}{{n}_{i}}$ | Reflecting the overall shape characteristics of the landscape pattern, SHAPE_MN = l when all patches in the landscape are square, and the value of SHAPE_MN increases when the shape of the patches deviates from square. |

Area-Weighted Mean Shape Index (SHAPE_AM) | $SHAPE\_AM={{\displaystyle \sum}}_{i=1}^{n}[(\frac{0.25{p}_{ij}}{\sqrt{{a}_{ij}}})(\frac{{a}_{ij}}{{{\displaystyle \sum}}_{i=1}^{n}{a}_{ij}})]$ | SHAPE_AM is one of the most important metrics for measuring the complexity of landscape spatial patterns and has implications for many ecological processes. |

Standard Deviation of Patch Shape Index (SHAPE_SD) | $SHAPE\_SD=\sqrt{\frac{{{\displaystyle \sum}}_{j=1}^{n}{[\frac{0.25{p}_{ij}}{\sqrt{{a}_{ij}}}-(\frac{{{\displaystyle \sum}}_{j=1}^{n}\left(\frac{0.25{p}_{ij}}{\sqrt{{a}_{ij}}}\right)}{{n}_{i}})]}^{2}}{{n}_{i}}}$ | SHAPE_SD is a statistic of landscape type patch shape complexity, reflecting the diversity and complexity of its landscape patches. |

Fractal Dimension (FRAC_AM) | $D=2\mathrm{log}\left(\frac{p}{4}\right)/\mathrm{log}\left(A\right)$ | The number of sub-dimensions is an important pointer to reflect the overall characteristics of the landscape pattern; the higher the number of sub-dimensions, the more complex the geometry of the landscape. |

Mean Euclidean Nearest Neighbor Index (ENN_MN) | $\mathrm{ENN}\_M{N}_{i}=\frac{{{\displaystyle \sum}}_{j=1}^{n}{h}_{ij}}{{n}_{i}}$ | ENN_MN measures the spatial pattern of the landscape. Generally speaking, a large ENN_MN value reflects that the patches of the same type are far apart and have a discrete distribution; on the contrary, it indicates that the patches of the same type are close to each other and have a clustered distribution. |

Isolation Index (SPLIT), | ${I}_{i}=\frac{\sqrt{{n}_{i}A}}{2{A}_{i}}$ | The separation index shows the relationship between separation and the number of patches, and the effect of the area of patches in the landscape. The greater the separation, the more dispersed the distribution of patches in the landscape. |

Aggregation Index(AI) | $AI=[{{\displaystyle \sum}}_{i=1}^{m}\left(\frac{{g}_{ii}}{max{g}_{ij}}\right){p}_{i}]\left(100\right)$ | At the patch type level, the agglomeration index is obtained from the connectivity matrix calculation and is used to measure the maximum number of possible connections for a given patch type. |

COHESION Index (COHESION) | $COHESION=\left[1-\frac{{{\displaystyle \sum}}_{j=1}^{n}{p}_{ij}}{{{\displaystyle \sum}}_{j=1}^{n}{p}_{ij}\sqrt{{a}_{ij}}}\right]{[1-\frac{1}{\sqrt{A}}]}^{-1}()$ | The cohesiveness of the landscape is related to the distance between similar patches, the presence or absence of corridors, the frequency of intersection of different types of corridors, and the size of the network formed. |

PLAND | PD | LPI | ED | LSI | AREA_ MN | AREA_ AM | AREA_ SD | SHAPE_ MN | SHAPE_ AM | SHAPE_ SD | FRAC_ AM | COHESION | SPLIT | AI | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

PLAND | 1.000 | ||||||||||||||

PD | −0.569 ** | 1.000 | |||||||||||||

LPI | 0.980 ** | −0.664 ** | 1.000 | ||||||||||||

ED | −0.263 ** | 0.832 ** | −0.370 ** | 1.000 | |||||||||||

LSI | −0.541 ** | 0.933 ** | −0.636 ** | 0.920 ** | 1.000 | ||||||||||

AREA_ MN | 0.820 ** | −0.917 ** | 0.878 ** | −0.637 ** | −0.854 ** | 1.000 | |||||||||

AREA_AM | 0.967 ** | −0.708 ** | 0.996 ** | −0.418 ** | −0.677 ** | 0.901 ** | 1.000 | ||||||||

AREA_SD | 0.493 ** | −0.107 * | 0.491 ** | 0.053 | −0.133 ** | 0.276 ** | 0.484 ** | 1.000 | |||||||

SHAPE_MN | 0.401 ** | −0.588 ** | 0.429 ** | −0.200 ** | −0.333 ** | 0.605 ** | 0.443 ** | 0.114 * | 1.000 | ||||||

SHAPE_AM | 0.051 | 0.364 ** | 0.035 | 0.701 ** | 0.542 ** | −0.196 ** | 0.019 | 0.379 ** | 0.134 ** | 1.000 | |||||

SHAPE_SD | 0.075 | 0.311 ** | 0.044 | 0.599 ** | 0.473 ** | −0.173 ** | 0.027 | 0.592 ** | 0.182 ** | 0.847 ** | 1.000 | ||||

FRAC_AM | −0.144 ** | 0.518 ** | −0.172 ** | 0.786 ** | 0.691 ** | −0.390 ** | −0.190 ** | 0.247 ** | 0.037 | 0.970 ** | 0.824 ** | 1.000 | |||

COHESION | 0.942 ** | −0.771 ** | 0.980 ** | −0.460 ** | −0.713 ** | 0.937 ** | 0.989 ** | 0.443 ** | 0.506 ** | 0.011 | 0.024 | −0.196 ** | 1.000 | ||

SPLIT | −0.986 ** | 0.651 ** | −0.999 ** | 0.352 ** | 0.622 ** | −0.874 ** | −0.994 ** | −0.492 ** | −0.428 ** | −0.039 | −0.051 | 0.166 ** | −0.978 ** | 1.000 | |

AI | 0.756 ** | −0.896 ** | 0.0823 ** | −0.745 ** | −0.937 ** | 0.949 ** | 0.848 ** | 0.266 ** | 0.397 ** | −0.371 ** | −0.331 ** | −0.558 ** | 0.869 ** | −0.815 ** | 1.000 |

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**Figure 1.**Geographic location of Fuzhou City and study area: (

**a**) Fuzhou City location map; (

**b**) Fuzhou City study area.

**Figure 12.**Distribution of green space accessibility factors in urban areas of Fuzhou: (

**a**) green space accessibility area; (

**b**) green space shape complexity factor; (

**c**) green space aggregation factor; and (

**d**) road integration.

**Figure 15.**Spatial distribution of the single supply index of green space in urban areas of Fuzhou: (

**a**) green space accessibility area index; (

**b**) green space shape complexity index; (

**c**) green space aggregation index; and (

**d**) road integration index.

**Figure 16.**Index of green space demand in urban areas of Fuzhou: (

**a**) total population; (

**b**) female population; (

**c**) population aged 0 to 14; (

**d**) aged 65 and above; and (

**e**) foreign population.

Gulou District | Taijiang District | Cang Shan District | Jinan District | Mawei District | Changle District | Total | |
---|---|---|---|---|---|---|---|

Average value | 620.04 | 641.48 | 619.93 | 415.51 | 531.69 | 482.83 | 514.28 |

Standard deviation | 58.71 | 30.15 | 41.01 | 203.79 | 138.83 | 64.75 | 150.87 |

Number of segments | 944 | 541 | 2017 | 2983 | 977 | 2947 | 10,409 |

Type Code | Type Name | Description of Land Type |
---|---|---|

1 | vegetation | Urban woodland, park green space, protective green space, subsidiary green space, road green belts, residential area greenery, and green belts around the city. |

2 | construction land | Includes commercial building land, transportation land, residential land, industrial land, unvegetated bare land, industrial and mining land, special land, etc. |

3 | cultivated land | Includes land occupied by crop cultivation, including paddy fields, dry land, etc. |

4 | water | Including rivers, lakes, reservoirs, coastal waters, etc. (excluding paddy fields and mudflats). |

Landscape Pattern Index | N | Min Value | Max Value | Mean Value | Standard Deviation |
---|---|---|---|---|---|

PLAND | 485 | 1.2920 | 99.9322 | 54.159960 | 30.7942202 |

PD | 485 | 0.2475 | 36.4759 | 6.299242 | 7.2109512 |

LPI | 485 | 0.4233 | 99.9322 | 48.134889 | 34.9398279 |

ED | 485 | 0.8292 | 170.0588 | 61.415113 | 38.1500532 |

LSI | 485 | 1.0376 | 16.1528 | 5.739334 | 3.6041949 |

AREA_MN | 485 | 0.3033 | 401.4000 | 72.245199 | 112.2738604 |

AREA_AM | 485 | 0.8055 | 401.4000 | 182.873622 | 144.6140559 |

AREA_SD | 485 | 0.0000 | 196.1550 | 50.391546 | 60.5577562 |

SHAPE_MN | 485 | 1.0376 | 3.5625 | 1.533616 | 0.3809547 |

SHAPE_AM | 485 | 1.0376 | 9.4691 | 3.128739 | 1.2533318 |

SHAPE_SD | 485 | 0.0000 | 2.2131 | 0.629029 | 0.4028415 |

FRAC_AM | 485 | 1.0049 | 1.3056 | 1.152776 | 0.0531038 |

COHESION | 485 | 59.6835 | 99.9995 | 95.890443 | 5.9247636 |

SPLIT | 485 | 1.0014 | 14,072.0119 | 190.473403 | 1012.3020546 |

AI | 485 | 45.9649 | 99.9426 | 86.301793 | 13.0960904 |

Landscape Pattern Index | Main Components | |
---|---|---|

1 | 2 | |

Eigenvalue | 2.435 | 1.856 |

Variance contribution ratio (%) | 40.576 | 30.932 |

Cumulative variance contribution ratio (%) | 40.576 | 71.508 |

ED | 0.866 | −0.322 |

AREA_SD | 0.072 | 0.811 |

SHAPE_MN | −0.213 | 0.526 |

SHAPE_SD | 0.870 | 0.359 |

FRAC_AM | 0.934 | 0.089 |

COHESION | −0.076 | 0.825 |

Landscape Pattern Index | Main Components | |
---|---|---|

1 | 2 | |

Eigenvalue | 2.411 | 1.699 |

Variance contribution ratio (%) | 48.223 | 33.978 |

Cumulative variance contribution ratio (%) | 48.223 | 82.201 |

ED | 0.834 | −0.417 |

AREA_SD | 0.134 | 0.878 |

SHAPE_SD | 0.894 | 0.304 |

FRAC_AM | 0.948 | −0.048 |

COHESION | 0.004 | 0.812 |

Landscape Pattern Index | Main Components | |
---|---|---|

1 | 2 | |

ED | 0.362 | −0.221 |

AREA_SD | 0.019 | 0.519 |

SHAPE_SD | 0.357 | 0.205 |

FRAC_AM | 0.394 | −0.001 |

COHESION | −0.032 | 0.477 |

Accessibility Area of Green Space Factor | Complexity of Green Space Factor | Aggregation of Green Space Factor | Integration |
---|---|---|---|

0.38 | 0.18 | 0.18 | 0.26 |

Administrative District | Gulou District | Taijiang District | Cangshan District | Jinan District | Mawei District | Changle District | |
---|---|---|---|---|---|---|---|

Statistical Values | |||||||

Average value | 189.49 | 194.95 | 170.06 | 112.53 | 140.38 | 120.47 | |

Standard deviation | 29.25 | 11.34 | 54.45 | 66.55 | 75.81 | 61.66 | |

Grid number | 48 | 32 | 171 | 617 | 319 | 806 |

Level | Number of Streets (Blocks) | Average Value | Standard Deviation | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

CA | F1 | F2 | In | CA | F1 | F2 | In | CA | F1 | F2 | In | |

lower | 13 | 5 | 14 | 2 | 0.098 | 0.109 | 0.083 | 0.152 | 0.058 | 0.075 | 0.039 | 0.152 |

low | 17 | 21 | 13 | 9 | 0.274 | 0.332 | 0.193 | 0.587 | 0.038 | 0.591 | 0.026 | 0.034 |

moderate | 14 | 13 | 15 | 13 | 0.398 | 0.543 | 0.372 | 0.717 | 0.041 | 0.056 | 0.052 | 0.042 |

high | 11 | 10 | 13 | 21 | 0.598 | 0.745 | 0.668 | 0.908 | 0.058 | 0.046 | 0.069 | 0.026 |

higher | 10 | 16 | 10 | 20 | 0.843 | 0.912 | 0.899 | 0.973 | 0.083 | 0.049 | 0.056 | 0.018 |

Level | Total Population | Female Population | Population Aged 0 to 14 | Population Aged 65 and Above | Foreign Population |
---|---|---|---|---|---|

Lower | 14 | 7 | 7 | 5 | 9 |

Low | 31 | 19 | 21 | 23 | 17 |

Moderate | 14 | 13 | 18 | 11 | 20 |

High | 4 | 11 | 14 | 21 | 11 |

Higher | 2 | 15 | 5 | 5 | 8 |

Supply and Demand Level | Number of Streets (Blocks) | Number of Streets (%) | Street Area (%) |
---|---|---|---|

Extreme supply deficit | 7 | 10.77 | 9.57 |

Supply deficit | 12 | 18.46 | 15.65 |

Supply-Demand Balance | 17 | 26.15 | 18.66 |

Supply Surplus | 20 | 30.76 | 24.75 |

Extreme Supply Surplus | 9 | 13.85 | 31.37 |

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## Share and Cite

**MDPI and ACS Style**

Huang, B.-X.; Li, W.-Y.; Ma, W.-J.; Xiao, H. Space Accessibility and Equity of Urban Green Space. *Land* **2023**, *12*, 766.
https://doi.org/10.3390/land12040766

**AMA Style**

Huang B-X, Li W-Y, Ma W-J, Xiao H. Space Accessibility and Equity of Urban Green Space. *Land*. 2023; 12(4):766.
https://doi.org/10.3390/land12040766

**Chicago/Turabian Style**

Huang, Bo-Xun, Wen-Ying Li, Wen-Juan Ma, and Hua Xiao. 2023. "Space Accessibility and Equity of Urban Green Space" *Land* 12, no. 4: 766.
https://doi.org/10.3390/land12040766