# Assessment of Urban Local High-Temperature Disaster Risk and the Spatially Heterogeneous Impacts of Blue-Green Space

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Overall Workflow

^{2}and AICc. Finally, the important UBGS landscape pattern characteristics that could be utilized to control the danger of an urban high-temperature disaster risk were identified, and the spatial factors were incorporated into the high-temperature disaster risk-mitigation approach. The main workflow of this article is shown in Figure 2.

#### 2.3. Data

#### 2.4. LCZ Classification

#### 2.5. High-Temperature-Disaster Risk Assessment

#### 2.5.1. D Factors

#### 2.5.2. S Factors

#### 2.5.3. V Factors

#### 2.5.4. T Methods

- (1)
- Calculation of the T index

_{j}is the comprehensive risk index of unit j, m is the number of indicators, X

_{ij}is the range normalization value of index j of unit i, and w

_{j}is the weight of index j. The sensitivity composite index S

_{j}and the vulnerability composite index V

_{j}were calculated by the same method.

- (2)
- Determination of the weight of each influencing factor

#### 2.6. Analysis of the Influence of the UBGS Landscape Pattern on T

_{i}represents the observation value of the dependent variable at the ith sample point; β

_{bwj}(u

_{i}, v

_{i}) is the regression coefficient of the jth independent variable at the ith sample point; bwj is the differential bandwidth used by the regression coefficient of the jth variable; (u

_{i}, v

_{i}) represents the spatial coordinates of the ith sample point; x

_{ij}is the observation value of the jth independent variable at the ith sample point; and ε

_{i}is the stochastic perturbation term.

^{2}, adjusted R

^{2}, and AICc were used as goodness-of-fit criteria to compare the performance of traditional OLS, classical GWR, and MGWR. Models that best fit the relationship between dependent and independent variables typically have higher adjusted R

^{2}, lower resists, and lower AICc [56,57].

## 3. Results

#### 3.1. LCZ Classification Map

_{B}(sparsely built mixed with scattered trees) and LCZ 9

_{E}(sparsely built mixed with bare rock or paved). LCZ 2 and LCZ 3 (compact buildings) made up a total of 4.36% of the construction category for the LCZ. The majority of Harbin’s buildings are intermediate and low-rise, and the distribution is generally open.

#### 3.2. Assessment of Disaster Risk

#### 3.2.1. D Factors

_{E}, and LCZ 9

_{F}, at 30%, 27%, and 21%, respectively. LCZ 3 and LCZ E accounted for 15% and 14% of their own ratios, indicating that in the relatively high-danger risk areas, compact LCZs, open LCZs, and LCZs with a larger proportion of hard ground and bare soil were the most common.

_{E}and LCZ 9

_{F}areas are in the construction phase and therefore have the potential to rise to high-danger risk areas. In contrast, most of the LCZ 9

_{A}, LCZ 9

_{B}, and LCZ 9

_{D}types were in low- or relatively low-risk classes, and the number of low-risk areas was LCZ 9

_{D}(20%) > LCZ 9

_{A}(19%) > LCZ 9B (2%). The proportion of vegetation cover in an LCZ unit may be an important factor in effectively resisting or reducing the risk of an LCZ disaster.

#### 3.2.2. S Factors

_{A}and LCZ 9

_{B}generally had a high proportion of vegetation cover, so the low-sensitivity risk amount accounted for a relatively high proportion, at 88% and 77%, respectively. The natural category LCZs were mainly low-sensitivity risk types, in which the ratio of high sensitivity (including high sensitivity and relatively high sensitivity) of LCZ A accounted for 14%, which may have been due to the high proportion of vegetation in the dense forest area in the study area but the large distance from water.

#### 3.2.3. V Factors

_{A}, reaching 47% and 42%. In the study area, the open LCZ had a higher population density or facility density. The vulnerability value of LCZ 3 was relatively low, which may have been due to the constraints of low-rise building environments in this type of LCZ, resulting in a limited number of people and facilities, and its vulnerability value was smaller than that of the open LCZ.

#### 3.2.4. T

^{−4}), low risk (2.24 × 10

^{−4}–5.43 × 10

^{−4}), relatively low risk (5.44 × 10

^{−4}–9.40 × 10

^{−4}), medium risk (9.40 × 10

^{−4}–1.443 × 10

^{−3}), relatively high risk (1.444 × 10

^{−3}–2.106 × 10

^{−3}), high risk (2.107 × 10

^{−3}–2.979 × 10

^{−3}), and extremely high risk (2.980 × 10

^{−3}–5.324 × 10

^{−3}), to obtain T.

_{A}-LCZ 9

_{F}) and natural category LCZs (LCZ A-G) had almost no high risk (including relatively high risk, high risk, and very high risk), except LCZ 9

_{E}, where high risk accounted for 18% of the ratio.

#### 3.3. Regulatory Factors of T

^{2}of MGWR was higher than that of the classical GWR model, increasing from 0.829 for GWR to 0.870 for MGWR. The AICc value also decreased from 1600.141 for GWR to 1204.570 for MGWR. In general, MGWR had a better fitting degree, and the regression result was closer to the real level.

## 4. Discussions

#### 4.1. Quantitative Analysis of the UBGS Landscape Pattern on High-Temperature Disaster Risk

#### 4.2. The Necessity of Considering Space in the Mitigation of High-Temperature Disaster Risk

_{F}) and natural category LCZs (LCZ A-LCZ G). There is spatial heterogeneity in the risk of high temperature under different LCZs. Similar to previous research results, the risk of high-temperature disasters in building category LCZs was generally higher than that in natural category LCZs, but the risk of high-temperature disasters in building LCZs was not fully consistent with the traditional concept of high density > low density, high rise > lower rise [27]. LCZ 5 had a significantly higher risk of high-temperature disasters than other building LCZs, except LCZ 2 (which had a small sample size and could not be accurately judged). This indicated that there was strong spatial heterogeneity in the risk of high-temperature disasters under LCZ classification. However, this local heterogeneity has been almost completely ignored in climate-mitigation policies, and the one-size-fits-all design strategy causes spatial inequality. Therefore, LCZ-based local heat-temperature disaster risk information could be used as a reference for planners to develop more effective urban climate-mitigation strategies [62].

#### 4.3. Limitations and Prospects

## 5. Conclusions

- (1)
- The overall risk of local high-temperature disasters in Harbin City is low. The number of LCZs above a medium risk level is 189, accounting for 19.61% of all LCZs, and the high-value area (above a medium risk level) was distributed in the second ring road in a “one-line and one-group” way. The visualization of the high-temperature hazard risk map can accurately provide optimization targets for planners.
- (2)
- High-temperature disaster risk presented obvious spatial heterogeneity. The risk of high-temperature disaster of building category LCZs was generally higher than that of natural category LCZs. The highest risk of high-temperature disasters in building LCZs was LCZ 2, followed by LCZ 5. The overall risk of high-temperature disaster presented a compact/open category LCZs > Sparse LCZs > natural category LCZs format, which proved that UBGSs were an important means to regulate the risk of high-temperature disasters.
- (3)
- There was spatial heterogeneity in the influence of UBGSs on high-temperature disaster risk. All regional coefficients of AREA_MN had significant negative effects on high-temperature disaster risk. The coefficient estimates of NP, PD, SHAPE_MN were negative in most of the spaces, and a few were positive. The mean NP coefficients of LCZ 2 and LCZ 4 were positive. This shows that it was necessary to consider space in the mitigation of high-temperature disaster risk. These regulatory factors can provide targeted strategies for the mitigation of high-temperature disaster risk in the context of climate adaptation.
- (4)
- The regulation effect of UBGSs on the risk of high-temperature disaster was interfered with by the BH. When formulating mitigation strategies for different LCZ spaces with the same impact factors, architectural impacts should also be considered, but the specific quantitative analysis of their relationship needs to be further explored.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Parameters | Calculation Formula | Definition | Basic Data |
---|---|---|---|

BH | $BH=\frac{{\displaystyle {\sum}_{i=1}^{n}BSi\times BHi}}{{\displaystyle {\sum}_{i=1}^{n}BSi}}$ | BH is the average building height within the base unit. Where, n is the number of buildings in LCZ cell; BSi is the floor area of the building; BHi is the height of the building. | Building data |

BSF | $BSF=\frac{{\displaystyle {\sum}_{i=1}^{n}BSi}}{Ssite}$ | BSF refers to the proportion of land surface covered by buildings. Where n is the number of buildings in LCZ basic unit; BSi is the floor area of the building; Ssite is the total area of the base unit. | Building data |

## Appendix B

Parameters | Calculation Formula |
---|---|

NDBI | $NDBI=\frac{{R}_{SWIR}-{R}_{NIR}}{{R}_{SWIR}+{R}_{NIR}}$ Where, R _{SWIR} and R_{NIR} are the spectral reflectance of Band 5 and Band 6 of Landsat 8, respectively. |

FVC | $FVC={(NDVI-NDV{I}_{min})/(NDV{I}_{max}-NDV{I}_{min})}^{2}$ |

## Appendix C

Variable Class | Index | Calculation Mode |
---|---|---|

CLASS | Percent of Landscape (PLAND) | $PLAND={P}_{i}=\frac{{\displaystyle {\sum}_{j=1}^{n}{a}_{i,j}}}{{A}_{i}}\times 100$ |

Number of Patches (NP) | $NP=N$ | |

Patch density (PD) | $PD=\frac{{n}_{i}}{{A}_{i}}\times 10000$ | |

Largest Patch Index (LPI) | $LPI=\frac{{a}_{i,j}(\mathrm{max})}{{A}_{i}}\times 100$ | |

Edge Density (ED) | $ED=\frac{{E}_{ij}}{A}\left(10000\right)$ | |

Landscape Shape Index (LSI) | $LSI=0.25{E}_{i,j}/\sqrt{{a}_{i,j}}$ | |

Mean patch area (AREA_MN) | $AREA\_MN=\frac{{\displaystyle {\sum}_{j=1}^{n}{a}_{ij}}}{{N}_{i}}$ | |

Average shape index (SHAPE_MN) | $SHAPE\_MN=\frac{\frac{0.25{E}_{ij}}{\sqrt{A}}}{{N}_{i}}$ | |

Fractal (FRAC_AM) | $FRAC\_AM={\displaystyle \sum _{i=1}^{m}{\displaystyle \sum _{j=1}^{n}\left[\frac{2\mathrm{ln}\left(0.25{L}_{ij}\right)}{\mathrm{ln}\left({a}_{ij}\right)}\left(\frac{{a}_{ij}}{A}\right)\right]}}$ | |

CONNECT | $C=\frac{{\displaystyle {\sum}_{j\ne k}^{n}{c}_{j,k}}}{{n}_{i}\left({n}_{i}-1\right)/2}\times 100$ | |

COHESION | $COHESION=\left[1-\frac{{\displaystyle \sum _{j=1}^{m}{p}_{ij}}}{{\displaystyle \sum _{j=1}^{m}{p}_{ij}\sqrt{{a}_{ij}}}}\right]{\left[1-\frac{1}{\sqrt{A}}\right]}^{-1}\times 100$ | |

DIVISION | $DIVISION=\left[1-{\displaystyle \sum _{j=1}^{n}{\left(\frac{{a}_{ij}}{A}\right)}^{2}}\right]$ | |

Aggregation Index (AI_class) | $AI\_class=\left[\frac{{g}_{i,i}}{\mathrm{max}\to {g}_{i,i}}\right]$ | |

LAND | CONTAG | $CONTAG=\left[1+\frac{{\displaystyle \sum _{i=1}^{m}{\displaystyle \sum _{k=1}^{m}\left[\left({P}_{i}\right)\left(\frac{{g}_{ik}}{{\displaystyle \sum _{k=1}^{m}{g}_{ik}}}\right)\right]\left[\mathrm{ln}\left({P}_{i}\right)\left(\frac{{g}_{ik}}{{\displaystyle \sum _{k=1}^{m}{g}_{ik}}}\right)\right]}}}{2\mathrm{ln}\left(m\right)}\right]\left(100\right)$ |

Shannon’s Evenness Index (SHEI) | $SHEI=\frac{-{\displaystyle \sum _{i=1}^{m}\left({P}_{i}\times \mathrm{ln}{P}_{i}\right)}}{\mathrm{ln}m}$ | |

Aggregation Index (AI_land) | $AI\_land=\left[\frac{{g}_{ii}}{\mathrm{max}\to {g}_{ii}}\right]$ |

## Appendix D

**Figure A1.**The global autocorrelation Moran index of the combined risk of high temperature hazards in the study area.

## Appendix E

Independent Variable | t | p | Allowance | VIF |
---|---|---|---|---|

SHAPE_MN | −11.537 | 0.000 | 0.803 | 1.246 |

NP | −7.501 | 0.000 | 0.916 | 1.092 |

PD | 5.262 | 0.000 | 0.771 | 1.297 |

AREA_MN | −3.745 | 0.000 | 0.900 | 1.111 |

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**Figure 2.**An analytical framework for the influence of UBGSs on the spatial heterogeneity of high-temperature disaster risk under LCZs.

**Figure 6.**(

**a**) The proportion of D numbers across different LCZ types; (

**b**) The proportion of S numbers across different LCZ types; (

**c**) The proportion of V numbers across different LCZ types.; (

**d**) The proportion of T numbers across different LCZ types.

**Figure 7.**(

**a**) Estimation of spatial distribution of NP coefficients; (

**b**) Estimation of spatial distribution of PD coefficients; (

**c**) Estimation of spatial distribution of AREA_MN coefficients; (

**d**) Estimation of spatial distribution of SHAPE_MN coefficients.

**Figure 8.**Statistical distribution of four typical landscape pattern index coefficients under different LCZ types; (

**a**) NP; (

**b**) PD; (

**c**) AREA_MN; (

**d**) SHAPE_MN.

Remote Sensing Data | |||
---|---|---|---|

Time | Resolution | Data Sources | |

Sentinel-2A | 23 August 2022 | 10 m | European Space Agency |

Landsat 8 | 15 September 2019 | 30 m | USGS |

Data Category | Data Sources | Data Content |
---|---|---|

Building (Vector data) | Baidu Map | Building structure outline, height, the number of floors (stories) |

Road network (Vector data) | Baidu Map | Urban road |

POI (Vector data) | Baidu API | Name of point of interest, latitude and longitude |

Demographic (Raster data) | World Pop | Population distribution, age, sex |

Building Category LCZs | Definition | Natural Category LCZs | Definition | |
---|---|---|---|---|

LCZ 1 | Compact high-rise | LCZ A | Dense trees | |

(BSF > 40%, BH ≥ 30 m) | ||||

LCZ 2 | Compact midrise | LCZ B | Scattered trees | |

(BSF ≥ 40%, 12 m ≤ BH < 30 m) | ||||

LCZ 3 | Compact low-rise | LCZ D | Low plants | |

(BSF > 40%, BH < 12 m) | ||||

LCZ 4 | Open high-rise | LCZ E | Bare rock or paved | |

(20% ≤ BSF < 40%, BH ≥ 30 m) | ||||

LCZ 5 | Open midrise | LCZ F | Bare soil or sand | |

(20% ≤ BSF < 40%, 12 m ≤ BH < 30 m) | ||||

LCZ 6 | Open low-rise | LCZ G | Water | |

(20% ≤ BSF < 40%, BH < 12 m) | ||||

LCZ 9 (Sparsely built) | LCZ 9_{A} | Sparsely built mixed with Dense trees (10% ≤ BSF < 20%) | —— | —— |

LCZ 9_{B} | Sparsely built mixed with scattered trees (10% ≤ BSF < 20%) | —— | —— | |

LCZ 9_{D} | Sparsely built mixed with low plants (10% ≤ BSF < 20%) | —— | —— | |

LCZ 9_{E} | Sparsely built mixed with bare rock or paved (10% ≤ BSF < 20%) | —— | —— | |

LCZ 9_{F} | Sparsely built mixed with bare soil or sand (10% ≤ BSF < 20%) | —— | —— |

Target Layer | Weighted Value | Criterion Layer | Weighted Value | Secondary Index | Weighted Value | Three-Level Index | Weighted Value |
---|---|---|---|---|---|---|---|

High-Temperature-Disaster Risk Assessment | 1 | Disaster-Causing Danger | 0.4139 | Surface temperature | 0.285 | ||

Development intensity | 0.1289 | Building density | 0.0945 | ||||

Normalized building index | 0.0344 | ||||||

Disaster-Generating Sensitivity | 0.1161 | Water proximity | 0.0727 | ||||

Fractional Vegetation Cover | 0.0434 | ||||||

Disaster-Bearing Vulnerability | 0.4701 | population density | 0.24 | population density under 15 years old | 0.1199 | ||

population density over 60 years old | 0.1201 | ||||||

facility density | 0.2301 | living facility density | 0.1397 | ||||

production facility density | 0.0904 |

Index | OLS | GWR | MGWR |
---|---|---|---|

R^{2} | 0.231 | 0.829 | 0.87 |

Adj R^{2} | 0.228 | 0.779 | 0.841 |

AICc | 2494.536 | 1600.141 | 1204.57 |

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## Share and Cite

**MDPI and ACS Style**

Zhang, X.; Ye, R.; Fu, X.
Assessment of Urban Local High-Temperature Disaster Risk and the Spatially Heterogeneous Impacts of Blue-Green Space. *Atmosphere* **2023**, *14*, 1652.
https://doi.org/10.3390/atmos14111652

**AMA Style**

Zhang X, Ye R, Fu X.
Assessment of Urban Local High-Temperature Disaster Risk and the Spatially Heterogeneous Impacts of Blue-Green Space. *Atmosphere*. 2023; 14(11):1652.
https://doi.org/10.3390/atmos14111652

**Chicago/Turabian Style**

Zhang, Xinyu, Ruihan Ye, and Xingyuan Fu.
2023. "Assessment of Urban Local High-Temperature Disaster Risk and the Spatially Heterogeneous Impacts of Blue-Green Space" *Atmosphere* 14, no. 11: 1652.
https://doi.org/10.3390/atmos14111652