# Framework for Spatio-Temporal Distribution of Disasters and Influencing Factors: Exploratory Study of Tianjin, China

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## Abstract

**:**

## 1. Introduction

## 2. Framework Development

#### 2.1. Spatial and Temporal Distribution of Disasters

#### 2.1.1. Step 1: Time Series Analysis

_{1}, x

_{2}, …, x

_{n}) is the temporal distribution of disaster. A statistical parameter S

_{k}is defined as:

_{k}can be calculated by Equations (3) and (4), respectively.

_{k}is calculated as Equation (5)

_{k}= −UF

_{k}. The null hypothesis of no trend is rejected if |UF

_{k}| > 1.96 at 5% significance level. If the value of UF

_{k}is greater than 0, it indicates that the sequence has an upward trend; less than 0 indicates a downward trend. When it exceeds a critical straight line, it indicates a significant upward or downward trend. An intersection of two curves (UF

_{k}and UB

_{k}) located within confidence intervals indicates a significant mutation point.

#### 2.1.2. Step 2: Geographical Difference Analysis

#### 2.1.3. Step 3: Direction Distribution Analysis

_{i}and y

_{i}represent the spatial coordinates of the disaster location i, respectively; $\left\{\overline{X},\overline{Y}\right\}$ represents the weighted mean center of the spatial data set of disaster location; and n represents the total number of analytical units.

#### 2.1.4. Step 4: Spatial Autocorrelation Analysis

_{i}represents the disaster intensity value in spatial position i, x

_{j}represents the disaster intensity value in spatial position j, $\overline{x}$ represents the average value of disaster intensity in all spatial positions, and w

_{ij}is the spatial weight matrix. The value range of I is [−1, 1]. When I is less than 0, it means that the spatial data is negatively correlated. When greater than 0, it means positive correlation. If it is equal to zero, it is irrelevant.

_{Z}in the form of a scatter graph (Figure 3). The horizontal axis is Z, and the vertical axis is w

_{Z}. The Moran scatter diagram can be divided into four quadrants. The first quadrant represents the high-high correlation clustering pattern, indicating the distribution pattern in which high disaster intensity values are surrounded by high disaster intensity values. The second quadrant represents the spatial aggregation pattern of low-high correlation, which represents the distribution pattern of low disaster intensity surrounded by high disaster intensity. The third quadrant represents the low-low correlation spatial aggregation pattern, which represents the distribution pattern of low disaster intensity surrounded by low disaster intensity values. The fourth quadrant represents the high-low aggregation pattern, indicating the distribution pattern in which high disaster intensity values are surrounded by low disaster intensity values.

#### 2.2. Impact Analysis on Disasters from Factors

#### 2.2.1. Step 5: The Impact Analysis on Individual Disaster

_{h}is the number of analysis units of layer h; N is the number of analysis units in the whole research area; ${\sigma}_{h}^{2}$ is the variance of disaster intensity in layer h; and σ

^{2}is the global variance of disaster intensity in the study area. The range of q is [0, 1]. The larger the value of q, the stronger the explanatory power of the impact factor X on the spatial distribution of disaster intensity Y is, and vice versa.

_{1}and X

_{2}will increase or weaken the explanatory power of the spatial distribution of disaster intensity Y, or whether the effects of these factors on Y are independent of each other can be explained by the interaction detector. Its specific principle is shown in Figure 4.The interaction types are as follows: if q(X

_{1}∩X

_{2}) < min(q(X

_{1}),q(X

_{2})), the interaction results show nonlinear weakening; if min(q(X

_{1}),q(X

_{2})) < q(X

_{1}∩X

_{2}) < max(q(X

_{1}),q(X

_{2})), the interaction results show a single factor nonlinear weakening; if q(X

_{1}∩X

_{2}) > max(q(X

_{1}),q(X

_{2})), it shows a two-factor enhancement; if q(X

_{1}∩X

_{2}) = q(X

_{1}) + q(X

_{2}), the two independent variables are independent; and if q(X

_{1}∩X

_{2}) > q(X

_{1}) + q(X

_{2}), it shows nonlinear enhancement.

#### 2.2.2. Step 6: The Impact Analysis on Multiple Disasters

_{ij}is the comprehensive impact index corresponding to the category j impact factors of the category i disaster. C

_{ij}is the comprehensive impact coefficient corresponding to the category j impact factors of the category i disaster. The value of the C

_{ij}is between [1, 2]. When the value of C

_{ij}is 1, it indicates that the influence degree of this impact factors on various disasters is balanced. The greater the value of C

_{ij}, the more uncoordinated the influence on various disasters. U

_{ij}represents the standardized weight value corresponding to the influence factors of category j of category i disaster.

## 3. Framework Application

#### 3.1. Study Area

#### 3.2. Input Data

#### 3.3. Results

#### 3.3.1. Spatial and Temporal Distribution of Disasters Results

#### Time Series Analysis of Disasters

_{k}of flood disasters were all greater than 0 from 2005 to 2015, indicating an upward trend during the decade. In 2012, the upward trend passed the significance test of 0.05 (U

_{0.05}= 1.96), showing a very significant upward trend in this year. After 2016, UF

_{k}began to be less than 0, showing a downward trend. The UF

_{k}and UB

_{k}curves intersect at two points within the confidence interval. The first intersect point is 2005, indicating that the number of flood disaster outbreaks began to mutate from 2005 and then showed a significant upward trend. The second intersection occurred between 2013 and 2014, and there was a second mutation here. The number of earthquake disasters has been on a downward trend since 2005, and the UF

_{k}in 2008 exceeded the critical lower bound, indicating that the decline was very obvious in that year. The trend of storm surge disasters is in a state of decline, and most years are below the critical value after 2013, reflecting a very significant decline trend during this period.

#### Geographical Difference Analysis of Disasters

#### Direction Distribution Analysis of Disasters

`.`The spatial distribution pattern of earthquakes and storm surges was “dispersed from southwest to northeast and concentrated from northwest to southeast”. The direction angle of storm surge disaster was 38.13°, which was larger than that of earthquake disaster. The direction of storm surge disaster was more obvious. The long axis of storm surge disaster direction distribution was obviously shorter than that of earthquake disaster direction distribution, indicating that the spatial aggregation of storm surge disaster was stronger than that of earthquake disaster.

#### Spatial Autocorrelation Analysis of Disasters

#### 3.3.2. Impact Analysis on Disasters Results

#### Impact on Individual Disaster from Factors

- (1)
- Impact analysis on flood

- (2)
- Impact analysis on earthquake

- (3)
- Impact analysis on storm surge

#### The Comprehensive Impact on Multiple Disasters Results

## 4. Discussion

#### 4.1. Model Rationality and Superiority Explanation

#### 4.2. Framework Rationality and Superiority Explanation

#### 4.3. Limitations and Future Work

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Fernandez, P.; Mourato, S.; Moreira, M.; Pereira, L. A new approach for computing a flood vulnerability index using cluster analysis. Phys. Chem. Earth Parts A/B/C
**2016**, 94, 47–55. [Google Scholar] [CrossRef] - Yang, W.; Xu, K.; Lian, J.; Ma, C.; Bin, L. Integrated flood vulnerability assessment approach based on TOPSIS and Shannon entropy methods. Ecol. Indic.
**2018**, 89, 269–280. [Google Scholar] [CrossRef] - Zhang, R.; Chen, Y.; Zhang, X.; Ma, Q.; Ren, L. Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China. Int. J. Appl. Earth Obs.
**2022**, 108, 102717. [Google Scholar] [CrossRef] - Sajjad, M.; Chan, J.C.L.; Chopra, S.S. Rethinking disaster resilience in high-density cities: Towards an urban resilience knowledge system. Sustain. Cities Soc.
**2021**, 69, 102850. [Google Scholar] [CrossRef] - Xiong, X.; Lu, Y.; Li, Q. A Study of Urban Natural Disaster Vulnerability Assessment Based on PCA-TOPSIS Method, Singapore; Xu, J., Hajiyev, A., Nickel, S., Gen, M., Eds.; Springer: Singapore, 2017; pp. 49–60. [Google Scholar]
- Ghawana, T.; Pashova, L.; Zlatanova, S. Geospatial Data Utilisation in National Disaster Management Frameworks and the Priorities of Multilateral Disaster Management Frameworks: Case Studies of India and Bulgaria. ISPRS Int. J. Geo-Inf.
**2021**, 10, 610. [Google Scholar] [CrossRef] - Homeland Security. National Response Framework; Homeland Security: Washington, DC, USA, 2019; p. 57. [Google Scholar]
- Marx, S.; Barbeito, G.; Fleming, K.; Petrovic, B.; Pickl, S.; Thieken, A.; Zeidler, M. Synthesis Report on Disaster Risk Reduction and Climate Change Adaptation in Germany. In DKKV-Schriftenreihe, Enhancing Synergies for Disaster Prevention in the European Union; German Committee for Disaster Reduction Enhancing Synergies for Disaster Prevention in EurOpean Union: Bonn, Germany, 2017; p. 76. ISBN 978-3-00-058657-6. [Google Scholar]
- Molina, M.; Bayarri, S. A multinational SDI-based system to facilitate disaster risk management in the Andean Community. Comput. Geosci.
**2011**, 37, 1501–1510. [Google Scholar] [CrossRef] [Green Version] - Sparf, J.; Migliorini, M.E. Socioeconomic and Data Challenges Disaster Risk Reduction in Europe; United Nations Office for Disaster Risk Reduction: Geneva, Switzerland, 2019; Volume 16, p. 33. [Google Scholar]
- Burton, H.V.; Deierlein, G.; Lallemant, D.; Lin, T. Framework for Incorporating Probabilistic Building Performance in the Assessment of Community Seismic Resilience. J. Struct. Eng.
**2016**, 142, 1–11. [Google Scholar] [CrossRef] [Green Version] - Sediek, O.A.; El-Tawil, S.; McCormick, J. Integrating Household Decisions in Quantifying the Seismic Resilience of Communities. In Proceedings of the 17th World Conference on Earthquake Engineering (17WCEE), Sendai, Japan, 13–18 September 2020. [Google Scholar]
- Burton, H.V.; Deierlein, G.; Lallemant, D.; Singh, Y. Measuring the Impact of Enhanced Building Performance on the Seismic Resilience of a Residential Community. Earthq. Spectra.
**2017**, 33, 1347–1367. [Google Scholar] [CrossRef] - Sediek, O.A.; El-Tawil, S.; McCormick, J. Quantifying the Seismic Resilience of Communities: A Distributed Computing Framework. In Proceedings of the International Conference in Commemoration of 20th Anniversary of the 1999 Chi-Chi Earthquake, Taipei, Taiwan, 13 May 2019. [Google Scholar]
- Llasat, M.C. Flash flood evolution in North-Western Mediterranean. Atmos. Res.
**2014**, 149, 230–243. [Google Scholar] [CrossRef] [Green Version] - Saharia, M.; Kirstetter, P.; Vergara, H.; Gourley, J.J.; Hong, Y. Characterization of floods in the United States. J. Hydrol.
**2017**, 548, 524–535. [Google Scholar] [CrossRef] - Kourgialas, N.N.; Karatzas, G.P. A national scale flood hazard mapping methodology: The case of Greece—Protection and adaptation policy approaches. Sci. Total Environ.
**2017**, 601–602, 441–452. [Google Scholar] [CrossRef] [PubMed] - Li, C.; Chai, Y.; Yang, L.; Li, H. Spatio-temporal distribution of flood disasters and analysis of influencing factors in Africa. Nat. Hazards
**2016**, 82, 721–731. [Google Scholar] [CrossRef] - Masozera, M.; Bailey, M.; Kerchner, C. Distribution of impacts of natural disasters across income groups: A case study of New Orleans. Ecol. Econ.
**2007**, 63, 299–306. [Google Scholar] [CrossRef] - Shi, X.; Liu, S.; Yang, S.; Liu, Q.; Tan, J.; Guo, Z. Spatial–temporal distribution of storm surge damage in the coastal areas of China. Nat. Hazards
**2015**, 79, 237–247. [Google Scholar] [CrossRef] - Julia, A.J.; Irena, F.C.; Kendra, L.H.; Robert, J.W.; Mary, B.A.; Melinda, H.B.; Emery, B.; Warren, A.B.; John, L.C.; Alan, C.; et al. Ecosystem Processes and Human Influences Regulate Streamflow Response to Climate Change at Long-Term Ecological Research Sites. Bioscience
**2012**, 62, 390–404. [Google Scholar] - Pande, R.K. Flash flood disasters in Uttarakhand. Disaster Prev. Manag.
**2010**, 19, 565–570. [Google Scholar] [CrossRef] - Zielinski, T. Catastrophic flood effects in alpine/foothill fluvial system (a case study from the Sudetes Mts, SW Poland). Geomorphology
**2003**, 54, 293–306. [Google Scholar] [CrossRef] [Green Version] - Vinet, F. Geographical analysis of damage due to flash floods in southern France: The cases of 12–13 November 1999 and 8–9 September 2002. Appl. Geogr.
**2008**, 28, 323–336. [Google Scholar] [CrossRef] - Zhuang, J.; Peng, J.; Zhu, X.; Huang, W. Scenario-Based Risk Assessment of Earthquake Disaster Using Slope Displacement, PGA, and Population Density in the Guyuan Region, China. Int. J. Geo-Inf.
**2019**, 8, 85. [Google Scholar] [CrossRef] [Green Version] - You, Z.; Jiang, Q. The Study on Land Use Disaster Prevention Plan Based on Storm Surge Risk Regionalization in Jiangsu Coastal. Adv. Mater. Res.
**2013**, 33, 836–840. [Google Scholar] [CrossRef] - Chen, F.; Yu, P.; Wu, X.; Zhu, Y. Refined risk assessment of storm surge disaster in coastal plain: A case study of pingyang county. J. Trop. Meteorol.
**2019**, 25, 304–311. [Google Scholar] - Zhao, G.; Tian, P.; Mu, X.; Jiao, J.; Wang, F.; Gao, P. Quantifying the impact of climate variability and human activities on streamflow in the middle reaches of the Yellow River basin, China. J. Hydrol.
**2014**, 519, 387–398. [Google Scholar] [CrossRef] - Dong, L.; Liang, H. Spatial analysis on China’s regional air pollutants and CO2 emissions: Emission pattern and regional disparity. Atmos. Environ.
**2014**, 92, 280–291. [Google Scholar] [CrossRef] - Peng, J.; Chen, S.; Lü, H.; Liu, Y.; Wu, J. Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011. Remote Sens. Environ.
**2016**, 174, 109–121. [Google Scholar] [CrossRef] - Liu, Y.; Liu, J.; Zhou, Y. Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. J. Rural Stud.
**2017**, 52, 66–75. [Google Scholar] [CrossRef] - Wang, J.; Zhang, T.; Fu, B. A measure of spatial stratified heterogeneity. Ecol. Indic.
**2016**, 67, 250–256. [Google Scholar] [CrossRef] - Lazear, E.P. The Peter Principle: A Theory of Decline. J. Polit. Econ.
**2004**, 112, S141–S163. [Google Scholar] [CrossRef] [Green Version] - Yang, W.; Xu, K.; Lian, J.; Bin, L.; Ma, C. Multiple flood vulnerability assessment approach based on fuzzy comprehensive evaluation method and coordinated development degree model. J. Environ. Manage.
**2018**, 213, 440–450. [Google Scholar] [CrossRef] - Diakakis, M.; Deligiannakis, G.; Pallikarakis, A.; Skordoulis, M. Factors controlling the spatial distribution of flash flooding in the complex environment of a metropolitan urban area. The case of Athens 2013 flash flood event. Int. J. Disast. Risk. Reduct.
**2016**, 18, 171–180. [Google Scholar] [CrossRef] - Chatterjee, K.; Choudhury, D. Variations in shear wave velocity and soil site class in Kolkata city using regression and sensitivity analysis. Nat. Hazards
**2013**, 69, 2057–2082. [Google Scholar] [CrossRef] - Wang, S.; Mu, L.; Qi, M.; Yu, Z.; Yao, Z.; Zhao, E. Quantitative risk assessment of storm surge using GIS techniques and open data: A case study of Daya Bay Zone, China. J. Environ. Manage.
**2021**, 289, 112514. [Google Scholar] [CrossRef] [PubMed]

**Figure 7.**Mann–Kendall test for (

**a**) flood disaster, (

**b**) earthquake disaster, (

**c**) storm surge disaster.

**Figure 11.**Direction distribution map for (

**a**) flood disaster, (

**b**) earthquake disaster, (

**c**) storm surge disaster, (

**d**) multi-disaster.

**Figure 12.**Moran scatter plot of (

**a**) flood disaster, (

**b**) earthquake disaster, and (

**c**) storm surge disaster.

**Figure 14.**(

**a**) Geo-spatial distribution of flood disaster Moran scatter map, (

**b**) geo-spatial distribution of earthquake disasters Moran scatter map, (

**c**) geo-spatial distribution of storm surge disaster Moran scatter map.

Category | Factor | Data Type | Data Sources |
---|---|---|---|

Disaster data | Historical flood disasters | Numerical value | China Meteorological Disaster Yearbook, Tianjin Oceanic Bureau, Tianjin Seismological Bureau official website (http://www.tjdzj.gov.cn, accessed on 15 March 2022) |

Historical earthquake disasters | |||

Historical storm surge disasters | |||

Influence factors | Rainfall | Raster data | Center for Resources and Environmental Science and Data, Chinese Academy of Sciences (http://www.resdc.cn, accessed on 15 March 2022) |

Elevation | |||

Slope | |||

Soil type | |||

Land use type (LU) | |||

Vegetation coverage (VC) | |||

Drainage density (DD) | |||

Population (POP) | |||

Gross Domestic Product (GDP) | |||

Secondary geological disasters (SGD) | Atlas | Regional atlas of geological hazard susceptibility in China | |

Elderly population | Numerical value | Tianjin Statistical Yearbook | |

Thousand hospital beds (THB) |

Disaster Types | Moran’s Index | Z-Score | p-Value |
---|---|---|---|

Flood | 0.063509 | 2.056821 | 0.039703 |

Earthquake | 0.266178 | 5.211636 | 0.000014 |

Storm surge | −0.001058 | 1.045604 | 0.295744 |

Type | Rainfall | Elevation | Slope | Soil | LU | VC | DD | POP | GDP |
---|---|---|---|---|---|---|---|---|---|

Rainfall | 0.732 | ||||||||

Elevation | 0.748 | 0.167 | |||||||

Slope | 0.766 | 0.169 | 0.158 | ||||||

Soil | 0.802 | 0.465 | 0.471 | 0.451 | |||||

LU | 0.881 | 0.330 | 0.339 | 0.612 | 0.233 | ||||

VC | 0.871 | 0.314 | 0.327 | 0.571 | 0.392 | 0.214 | |||

DD | 0.803 | 0.224 | 0.234 | 0.531 | 0.412 | 0.360 | 0.081 | ||

POP | 0.930 | 0.441 | 0.462 | 0.633 | 0.554 | 0.531 | 0.610 | 0.425 | |

GDP | 0.926 | 0.340 | 0.350 | 0.611 | 0.549 | 0.505 | 0.450 | 0.789 | 0.317 |

Type | POP | EPOP | THB | GDP | SGD | Elevation | Slope | Soil | LU |
---|---|---|---|---|---|---|---|---|---|

POP | 0.652 | ||||||||

EPOP | 0.791 | 0.172 | |||||||

THB | 0.789 | 0.757 | 0.503 | ||||||

GDP | 0.772 | 0.821 | 0.842 | 0.624 | |||||

SGD | 0.679 | 0.320 | 0.524 | 0.742 | 0.185 | ||||

Elevation | 0.667 | 0.221 | 0.531 | 0.653 | 0.191 | 0.063 | |||

Slope | 0.657 | 0.205 | 0.537 | 0.628 | 0.189 | 0.069 | 0.004 | ||

Soil | 0.719 | 0.431 | 0.628 | 0.715 | 0.295 | 0.197 | 0.203 | 0.193 | |

LU | 0.716 | 0.542 | 0.654 | 0.701 | 0.474 | 0.422 | 0.417 | 0.510 | 0.371 |

Type | Meteorology | AT | GL | Elevation | Slope | Soil | LU | POP | GDP |
---|---|---|---|---|---|---|---|---|---|

Meteorology | 1 | 2 | 3 | 3 | 4 | 6 | 5 | 2 | 2 |

AT | 1/2 | 1 | 2 | 2 | 3 | 5 | 4 | 1 | 1 |

GL | 1/3 | 1/2 | 1 | 1 | 2 | 4 | 3 | 1/2 | 1/2 |

Elevation | 1/3 | 1/2 | 1 | 1 | 2 | 4 | 3 | 1/2 | 1/2 |

Slope | 1/4 | 1/3 | 1/2 | 1/2 | 1 | 3 | 2 | 1/3 | 1/3 |

Soil | 1/6 | 1/5 | 1/4 | 1/4 | 1/3 | 1 | 1/2 | 1/5 | 1/5 |

LU | 1/5 | 1/4 | 1/3 | 1/3 | 1/2 | 2 | 1 | 1/4 | 1/4 |

POP | 1/2 | 1 | 2 | 2 | 3 | 5 | 4 | 1 | 1 |

GDP | 1/2 | 1 | 2 | 2 | 3 | 5 | 4 | 1 | 1 |

Type | Flood | Earthquake | Storm Surge | C_{ij} | H_{ij} |
---|---|---|---|---|---|

Elevation | 0.057 | 0.023 | 0.090 | 1.706 | 0.289 |

Slope | 0.162 | 0.001 | 0.057 | 1.988 | 0.438 |

Soil | 0.084 | 0.070 | 0.027 | 1.656 | 0.298 |

LU | 0.077 | 0.134 | 0.038 | 1.682 | 0.419 |

POP | 0.153 | 0.236 | 0.151 | 1.401 | 0.756 |

GDP | 0.114 | 0.225 | 0.151 | 1.482 | 0.727 |

Model | Type | Elevation | Slope | Soil | LU | POP | GDP |
---|---|---|---|---|---|---|---|

TRSM | Score | 0.169 | 0.221 | 0.18 | 0.249 | 0.539 | 0.49 |

Rank | 6 | 4 | 5 | 3 | 1 | 2 | |

CIEM | Score | 0.289 | 0.438 | 0.298 | 0.419 | 0.756 | 0.727 |

Rank | 6 | 3 | 5 | 4 | 1 | 2 |

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## Share and Cite

**MDPI and ACS Style**

Yang, W.; Hu, D.; Jiang, X.; Dun, X.; Hou, B.; Zheng, C.; Chen, C.; Zhuang, R.
Framework for Spatio-Temporal Distribution of Disasters and Influencing Factors: Exploratory Study of Tianjin, China. *Sustainability* **2022**, *14*, 10488.
https://doi.org/10.3390/su141710488

**AMA Style**

Yang W, Hu D, Jiang X, Dun X, Hou B, Zheng C, Chen C, Zhuang R.
Framework for Spatio-Temporal Distribution of Disasters and Influencing Factors: Exploratory Study of Tianjin, China. *Sustainability*. 2022; 14(17):10488.
https://doi.org/10.3390/su141710488

**Chicago/Turabian Style**

Yang, Weichao, De Hu, Xuelian Jiang, Xuebo Dun, Bingtao Hou, Chuanxing Zheng, Caixia Chen, and Rong Zhuang.
2022. "Framework for Spatio-Temporal Distribution of Disasters and Influencing Factors: Exploratory Study of Tianjin, China" *Sustainability* 14, no. 17: 10488.
https://doi.org/10.3390/su141710488