# Associations between Land-Use Patterns and Cardiovascular Disease Mortality in the Beijing—Tianjin–Hebei Megacity Region

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods and Data

#### 2.1. Methods

#### 2.1.1. Spatial Autocorrelation

_{i}and x

_{j}represent the ASMR values of study units i and j, respectively. $\stackrel{\u203e}{x}$ is the mean ASMR value of all units, and W

_{ij}is the spatial weight matrix. $m=\left({\sum}_{j=1,j\ne i}^{n}{x}_{j}^{2}\right)/(n-1)-{\stackrel{\u203e}{x}}^{2}$.

#### 2.1.2. Land-Use Patterns and Spatial Pattern Indices

#### 2.1.3. Estimating the Global Impact of Factors

_{μ}is the spatial neighbor weight matrix of the error term, λ

_{Err}refers to the regression coefficient of the spatial residual term, and ε is the independent and identically distributed error term.

#### 2.1.4. Estimating the Localized Effects of Factors

#### 2.2. Study Area

#### 2.3. Variables

#### 2.3.1. Research Data

- (1)
- Dependent variable

_{A}for study unit A was computed as follows.

- (2)
- Independent variables

^{2}, construction space covering about 18,900 km

^{2}, and ecological space that covers approximately 105,300 km

^{2}. According to China’s land-use classification standards, parks and green spaces that fall within urban space are classified as construction space. On the other hand, the ecological space in this study mainly refers to ecological land situated outside of urban spaces.

#### 2.3.2. Independent Variables Selection

## 3. Results

#### 3.1. Spatial Autocorrelation for ASMR

#### 3.2. Global Effects of Land-Use Pattern Characteristics

- Among the single-type distribution indices, AI
_{E}is significant at the p = 0.1 level with negative coefficients, indicating a negative correlation between ecological space clustering and ASMR, i.e., the more concentrated the ecological space, the stronger the CVD mortality inhibition. PD_{A}also showed a significant negative correlation with ASMR, which suggests that fragmented patches of agricultural space are positive for public health. - Among the bi-type interaction indices, LSI
_{AE}is significantly negative at the level of p = 0.1. This suggests that a more complex interaction between agricultural and ecological spaces is more effective at reducing CVD mortality. Conversely, a more regular pattern of these two types of land use in the combination zone will have a negative effect. The correlation between ED_{ET}and ASMR is more substantial, suggesting that a fragmented and staggered distribution between ecological and construction spaces will better control CVD mortality. Conversely, when these two types of land use are relatively close and compact, they provide greater health benefits. - Among the all-type land-use patterns, MESH has a significant positive association with ASMR, indicating that higher MESH values correspond to higher ASMR. MESH is defined as the fragmentation of various patch types within a given study unit. Greater fragmentation and decentralization lead to a more positive effect and containment of CVD mortality, whereas concentrating the three types of land use may increase CVD mortality.

#### 3.3. Local Effects of Land-Use Pattern Characteristics

_{E}has a negative correlation with ASMR in the central and southeastern BTH region. The strongest correlation was found in the southeastern region, indicating that decentralized ecological space can aid in suppressing CVD in these regions. In the western, northern, and eastern regions, AI

_{E}exhibited a positive correlation with ASMR, indicating that public health is more adversely affected in more decentralized ecological space. (2) The spatial clustering of LSI

_{AE}was relatively insignificant but showed significance in the central and northern regions of the study area. The indicator displayed a negative correlation with ASMR in the central portion of the study area. LSI

_{AE}’s definition of integrating agricultural and ecological spaces aids in decreasing ASMR. Conversely, a positive correlation was seen in the northwestern and northeastern regions of the study area, indicating that the mixing of the two types of land use will have a negative impact. (3) The correlation between ED

_{ET}and ASMR was negative in nearly all regions, indicating that ecological space and construction space are more fragmented than construction space alone. This fragmentation has a detrimental effect on public health and suggests that combining ecological and built-up space is not conducive to reducing CVD mortality. (4) A positive correlation between MESH and ASMR is evident in nearly all areas, signifying the detrimental effects of concentrating the three types of land use. (5) Moreover, the global model acknowledges the impact of PD

_{A}, yet significance is lacking for most regions at the local level.

## 4. Discussion

- (1)
- The moderate dispersion and organic combination of different types of land use enhances public health.

- (2)
- The impact of patch density characteristics depends on the unique properties of land use.

- (3)
- The spatial combination of patches has an impact on the role of each type of land use.

- (4)
- Varying levels of natural, social, and economic development lead to the spatial heterogeneity of impacts.

_{E}in the northwest of BTH has a positive impact, while in the southeast, it brings health benefits. This is because the northwestern section serves as the ecological reserve for the entire BTH and is predominantly forested land. However, the clustering of patches does not result in significant environmental quality enhancement. Instead, due to the excessively large area of ecological space and the scattered distribution of settlements, two problems were created: insufficient socialization from low population density, and difficulty supporting municipal facilities like heating. These issues are exacerbated by the region’s high average elevation and low temperature [15,45,46]. In contrast, densely populated towns and cities in the southeast could benefit from the provision of ecological patches clustered at scale, as they offer scarce ecological and recreational values that are pivotal to the region. Their positive benefits are evident.

_{AE}exhibits a negative correlation with CVD mortality in central BTH and a positive correlation in the peripheral region. The primary reason for this is the higher level of urbanization in the central area, where scenic and tranquil landscapes hold great allure and assume an essential role in the outdoor pursuits of the inhabitants. In peripheral areas where agricultural and ecological spaces are prevalent, most residents opt for artificial urban parks and public squares due to resource scarcity. However, the integration of these land uses has resulted in reduced agricultural production efficiency and poorer income and quality of life for rural populations, leading to negative impacts on their health.

_{ET}and CVD mortality in nearly all areas of BTH. However, the eastern coastal areas demonstrate the most pronounced effect, while the impact in northwestern areas is comparatively weaker. This discrepancy may be attributed to various ecological and industrial factors characteristic of this megacity region. The eastern area is marked by greater land salinization, comparatively fragile ecology [47], and concentrated coastal ports and petrochemical industries [48]. These factors lead to ecosystems that are particularly vulnerable to human activities, which are more likely to deplete the value of services when ecological and construction spaces are intertwined.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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Categories | Variables | Code | Calculation |
---|---|---|---|

Single-type distribution index | Mean of patch area | AREA_MN | ${\mathrm{A}\mathrm{R}\mathrm{E}\mathrm{A}}_{\mathrm{M}\mathrm{N}}=\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}\left(\mathrm{A}\mathrm{R}\mathrm{E}\mathrm{A}\left[{\mathrm{p}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{j}}\right]\right)$, where AREA[patch _{ij}] is the area of each patch in hectares. |

Patch density | PD | $\mathrm{P}\mathrm{D}=\frac{\mathrm{N}}{\mathrm{A}}\times 10000\times 100$, where N is the number of patches and A is the total landscape area in square meters. | |

Largest patch index | LPI | $\mathrm{L}\mathrm{P}\mathrm{I}=\frac{{\mathrm{m}\mathrm{a}\mathrm{x}}_{\mathrm{j}=1}^{\mathrm{n}}({\mathrm{a}}_{\mathrm{i}\mathrm{j}})}{\mathrm{A}}\times 100$, where max(a _{ij}) is the area of the patch in square meters and A is the total landscape area in square meters. | |

Mean shape index | SHAPE_MN | ${\mathrm{S}\mathrm{H}\mathrm{A}\mathrm{P}\mathrm{E}}_{\mathrm{M}\mathrm{N}}=\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}(\mathrm{S}\mathrm{H}\mathrm{A}\mathrm{P}\mathrm{E}[{\mathrm{p}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{j}}])$, where SHAPE[patch _{ij}] is the shape index of each patch. | |

Aggregation index | AI | $\mathrm{A}\mathrm{I}=\left[\frac{{\mathrm{g}}_{\mathrm{i}\mathrm{i}}}{\mathrm{m}\mathrm{a}\mathrm{x}-{\mathrm{g}}_{\mathrm{i}\mathrm{i}}}\right](100)$, where g _{ii} is the number of like adjacencies based on the single-count method and $\mathrm{m}\mathrm{a}\mathrm{x}-{\mathrm{g}}_{\mathrm{i}\mathrm{i}}$ is the class-wise maximum number of like adjacencies of class i. | |

Bi-type interaction index | Contagion | CONTAG | $\mathrm{C}\mathrm{O}\mathrm{N}\mathrm{T}\mathrm{A}\mathrm{G}=1+\frac{{\sum}_{\mathrm{q}=1}^{{\mathrm{n}}_{\mathrm{a}}}{\mathrm{p}}_{\mathrm{q}}\mathrm{ln}\left({\mathrm{p}}_{\mathrm{q}}\right)}{2\mathrm{ln}\left(\mathrm{t}\right)}$, where p _{q} the adjacency table for all classes divided by the sum of that table and t the number of classes in the landscape. |

Landscape shape index | LSI | $\mathrm{L}\mathrm{S}\mathrm{I}=\frac{\mathrm{E}}{{\mathrm{E}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$, where E is the total edge length in cell surfaces and E _{min} is the minimum total edge length in cell surfaces. | |

Edge Density | ED | $\mathrm{E}\mathrm{D}=\frac{\mathrm{E}}{\mathrm{A}}\times 10000$, where E is the total landscape edge in meters and A is the total landscape area in square meters. | |

All-type index | Landscape shape index | LSI | $\mathrm{L}\mathrm{S}\mathrm{I}=\frac{\mathrm{E}}{{\mathrm{E}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$, where E is the total edge length in cell surfaces and E _{min} is the minimum total edge length in cell surfaces. |

Patch density | PD | $\mathrm{P}\mathrm{D}=\frac{\mathrm{N}}{\mathrm{A}}\times 10000\times 100$, where N is the number of patches and A is the total landscape area in square meters. | |

Landscape division index | DIVISION | $\mathrm{D}\mathrm{I}\mathrm{V}\mathrm{I}\mathrm{S}\mathrm{I}\mathrm{O}\mathrm{N}=1-{\sum}_{\mathrm{i}=1}^{\mathrm{m}}{\sum}_{\mathrm{j}=1}^{\mathrm{n}}{\left(\frac{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}}{\mathrm{A}}\right)}^{2}$, where a _{ij} is the area in square meters and A is the total landscape area in square meters. | |

Splitting index | SPLIT | $\mathrm{S}\mathrm{P}\mathrm{L}\mathrm{I}\mathrm{T}=\frac{{\mathrm{A}}^{2}}{{\sum}_{\mathrm{i}=1}^{\mathrm{m}}{\sum}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}^{2}}$, where a _{ij} is the patch area in square meters and A is the total landscape area. | |

Effective mesh size | MESH | $\mathrm{M}\mathrm{E}\mathrm{S}\mathrm{H}=\frac{{\sum}_{\mathrm{i}=1}^{\mathrm{m}}{\sum}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}^{2}}{\mathrm{A}}\times \frac{1}{10000}$, where a _{ij} is the patch area in square meters and A is the total landscape area in square meters. | |

Largest patch index | LPI | $\mathrm{L}\mathrm{P}\mathrm{I}=\frac{\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{a}}_{\mathrm{i}\mathrm{j}}\right)}{\mathrm{A}}\times 100$, where max(a _{ij}) is the area of the patch in square meters and A is the total landscape area in square meters. | |

Edge density | ED | $\mathrm{E}\mathrm{D}=\frac{\mathrm{E}}{\mathrm{A}}\times 10000$, where E is the total landscape edge in meters and A is the total landscape area in square meters. |

Types | Variables | Code | Mean | Std. | Min. | Max. |
---|---|---|---|---|---|---|

Dependent variable | ASMR | CVD | −0.045 | 1.012 | −2.302 | 2.038 |

Independent variables | Aggregation index for ecological space | AI_{E} | −0.018 | 1.005 | −3.051 | 1.393 |

Aggregation index for construction space | AI_{T} | 0.037 | 1.022 | −1.735 | 3.647 | |

Patch density for agricultural space | PD_{A} | 0.054 | 1.019 | −0.847 | 3.625 | |

Contagion for agricultural and ecological space | CONTAG_{AE} | −0.018 | 1.001 | −1.783 | 1.199 | |

Landscape shape index for agricultural and ecological space | LSI_{AE} | 0.063 | 1.004 | −1.443 | 3.976 | |

Edge density for agricultural and construction space | ED_{AT} | 0.020 | 1.015 | −2.100 | 2.657 | |

Edge density for ecological and construction space | ED_{ET} | 0.044 | 1.021 | −0.621 | 4.829 | |

Effective mesh size for 3 types of land use | MESH | 0.023 | 1.047 | −0.875 | 7.051 | |

Splitting index for 3 types of land use | SPLIT | 0.038 | 1.013 | −0.804 | 6.006 | |

Control variables | Gross domestic product per capita | GDP | 0.028 | 1.051 | −0.319 | 9.894 |

Percentage of output from polluting industries | POLLUTE | −0.011 | 1.050 | −1.568 | 6.780 | |

Percentage of low-educated population (below high school) | LOW_EDU | −0.052 | 1.018 | −3.383 | 1.012 | |

Percentage of low-income population (below 2500 yuan/month) | LOW_INCOME | −0.061 | 1.026 | −2.410 | 0.983 | |

Hospital beds per capita | BED | −0.017 | 1.028 | −1.943 | 4.540 | |

Accessibility for healthcare services | ACCESS | 0.020 | 1.046 | −0.650 | 6.678 |

_{E}exclusively measures the ecological space category, whereas LSI

_{AE}compares the relative links between agricultural and ecological spaces.

Types | Categories | Variables | OLS Model | SEM Model | ||
---|---|---|---|---|---|---|

Coefficient | p-Value | Coefficient | p-Value | |||

Landscape pattern variables | Univariate land-use pattern | AI_{E} | −0.105 | 0.397 | −0.179 † | 0.088 |

AI_{U} | 0.059 | 0.705 | −0.118 | 0.428 | ||

PD_{A} | −0.433 ** | 0.008 | −0.261 † | 0.076 | ||

Bivariate interactive land-use pattern | CONTAG_{AE} | −0.253 | 0.180 | −0.151 | 0.371 | |

LSI_{AE} | −0.269 † | 0.072 | −0.256 † | 0.072 | ||

ED_{AT} | 0.163 | 0.243 | 0.085 | 0.529 | ||

ED_{ET} | 0.111 | 0.414 | 0.248 † | 0.069 | ||

Multivariate land-use pattern | MESH | 0.180 | 0.172 | 0.296 * | 0.025 | |

SPLIT | 0.249 | 0.152 | 0.123 | 0.387 | ||

Control variables | Economic and social factors | GDP | 0.154 | 0.153 | 0.129 | 0.148 |

POLLUTE | 0.146 | 0.187 | 0.107 | 0.299 | ||

LOW_EDU | 0.684 ** | 0.001 | 0.578 ** | 0.004 | ||

LOW_INCOME | −0.133 | 0.342 | −0.170 | 0.286 | ||

Healthcare services | BED | −0.067 | 0.572 | −0.040 | 0.705 | |

ACCESS | 0.110 | 0.291 | 0.217 * | 0.012 | ||

Statistical diagnosis | R-squared: 0.400 Adjusted R-squared: 0.302 Log likelihood: −126.472 AIC: 284.944 Moran’s I (error): 2.139 *** Lagrange Multiplier (error): 4.7389 ** Robust LM (error): 5.703 ** | R-squared: 0.457 Log likelihood: −124.182 AIC: 280.364 |

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**MDPI and ACS Style**

Kan, C.; Ma, Q.; Liu, A.; Yuan, Z.
Associations between Land-Use Patterns and Cardiovascular Disease Mortality in the Beijing—Tianjin–Hebei Megacity Region. *Land* **2023**, *12*, 2176.
https://doi.org/10.3390/land12122176

**AMA Style**

Kan C, Ma Q, Liu A, Yuan Z.
Associations between Land-Use Patterns and Cardiovascular Disease Mortality in the Beijing—Tianjin–Hebei Megacity Region. *Land*. 2023; 12(12):2176.
https://doi.org/10.3390/land12122176

**Chicago/Turabian Style**

Kan, Changcheng, Qiwei Ma, Anqi Liu, and Zhaoyu Yuan.
2023. "Associations between Land-Use Patterns and Cardiovascular Disease Mortality in the Beijing—Tianjin–Hebei Megacity Region" *Land* 12, no. 12: 2176.
https://doi.org/10.3390/land12122176