Heat Risk Assessment in Arid Zones Based on Local Climate Zones: A Case of Urumqi, China
Abstract
1. Introduction
- What is the distribution relationship between urban heat risk and LCZ types in arid zones?
- The heterogeneity of LCZ zoning in heat risk in arid zones.
- Strategy planning based on LCZ zoning in arid zones.
2. Materials and Methods
2.1. Study Area
2.2. Research Methodology
2.2.1. LCZ Mapping
2.2.2. Heat Risk System Selection
2.2.3. Data Sources and Standardization of Secondary Indicators
2.2.4. Improved CRITIC Assignment Methodology
- Calculation of contrast: The standard deviation is used in the CRITIC method as a mathematical representation of the degree of data dispersion within an indicator. According to the principle of statistics, the standard deviation, as an absolute measure of dispersion, is positively correlated with the fluctuation amplitude of the indicator data set: the standard deviation represents the contrast of the jth indicator, and an increase in the value of indicates that the heterogeneity of the distribution of the sample values of the jth indicator has increased significantly, and its information-carrying capacity and evaluation strength have also been increased. Based on the framework of information entropy theory, such indicators with high dispersion need to be given higher weights in the comprehensive evaluation process to accurately reflect their differentiated characteristics. The quantitative formula for indicator contrast can be expressed as follows:
- Calculation of conflictivity: this study defines conflictivity as a quantitative characterization of the degree of information redundancy between indicators. When two indicators show significant positive correlation, i.e., the value of conflictivity is small, it indicates that the information overlap between them is high, and their independence characteristics are subsequently decayed. This phenomenon will lead to highly correlated indicator groups forming information redundancy, weakening the marginal contribution of individual indicators in the comprehensive evaluation. Therefore, it is necessary to moderately reduce the weight proportion of such indicators in the weight allocation process to optimize the signal-to-noise ratio of the evaluation system. The conflicting formula is as follows:
- Calculation of information carrying capacity: synthesize contrast and conflict to calculate the amount of indicator information:Determine the weight according to the proportion of information; the greater the information carrying capacity, the greater the weight. Set the weight of the first indicator as :
2.2.5. Establishment of a Heat Risk Assessment System
3. Results
3.1. Description of the LCZ Map of Urumqi
3.2. Heat Risk Assessment Map of Urumqi
3.3. LCZ-Based Heat Risk Analysis
3.3.1. LCZ-Based Spatial Heterogeneity of Heat Risk
3.3.2. Spatial Heterogeneity of LCZ-Based Primary Indicators
4. Discussion
4.1. Heat Risk Heterogeneity in Arid Zones Under LCZ Framework
4.2. Neighborhood-Scale Coping Strategies for Heat Risk in Arid Zones
4.2.1. SHAP-Based Analysis of Indicator Contributions
4.2.2. Strategy Response
4.3. Limitations and Prospects
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
LCZ | Local Climate Zone |
HEVA | Hazard–Exposure–Vulnerability–Adaptability |
HEV | Hazard–Exposure–Vulnerability |
HVI | Heat Vulnerability Index |
SHAP | Shapley Additive Explanations |
RS | Remote Sensing |
GIS | Geographic Information System |
PCA | Principal Component Analysis |
EWM | Entropy Weight Method |
CRITIC | Criteria Importance Though Intercriteria Correlation |
LST | Land Surface Temperature |
AH | Average Height |
SUHI | Surface Urban Heat Island |
NDBI | Normalized Difference Built-up Index |
NDVI | Normalized Difference Vegetation Index |
POI | Point Of Information |
VIF | Variance Inflation Factor |
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Research Cities | Indicator System | Indicator Selection | Empowerment Methodology |
---|---|---|---|
Karachi, Pakistan [53] | HEV | Hazard: LST Exposure: population density Vulnerability: elderly population aged 60 years and over, children aged 10 years and under, illiterate population, building density, slum population, population with access to drinking water, bathrooms, electricity, children with a family size of 9 or more, poor population, normalized difference vegetation index (NDVI) | Principal Component Analysis (PCA) |
Perth, Australia [54] | HEV | Hazard: LST Exposure: population density Vulnerability: age structure, ethnicity, socio-economic disadvantage, employment, living space, need for assistance, income, availability of internet connection and mobility | Equal Weighting System |
Birmingham, UK [7] | HEV | Hazard: high resolution urban heat island maps Exposure: detailed commercial data per household Vulnerability: filtered exposure layer | Equal Weighting System |
Changzhou, China [16] | HEV | Hazard: LST, frequency of high temperatures Exposure: population density, frequency of high temperatures Vulnerability: MNDWI, NDVI, night light data, population density over 65 years old | Entropy Weight Method (EWM) |
Yangtze River Delta City Cluster, China [55] | HEV | Hazard: surface temperature Exposure: combination of enhanced vegetation index, nighttime lighting Vulnerability: elderly population aged ≥65 years, elderly population aged 60 years or older living alone, illiteracy or semi-illiteracy rate among population aged 15 years or older, total number of beds in health facilities, number of air conditioners per 100 households, GDP per capita | PCA/EWM |
Hong Kong, China [40] | HEV | Hazard: hours of heat, hours of heat at night Exposure: population density Vulnerability: population aged 65 and over, uneducated population, households living alone, low-income households, renter population, population living in non-residential buildings | PCA |
Santiago, Chile [15] | HVI | Exposure: LST Sensitivity: population over 60 years old, population with young children under 5 years old, population with disabilities, family structure, level of education, unemployed population Adaptability: population with access to communication technology, population with access to water supply, housing materials, medical facilities, road network density, NDVI | PCA |
Beijing-Tianjin-Hebei, China [56] | HVI | Hazard: daily surface temperature Exposure: population density Sensitivity: elderly population, child population, female population, nursing homes, kindergartens and primary schools Adaptability: water cover, vegetation cover, night lighting, medical buildings | EWM |
Turin, Italy [10] | HEVA | Hazard: urban heat island distribution Exposure: the over-65 population Vulnerability: women over the age of 65, population over 85 years old, low education rate, social isolation, housing overcrowding, ethnic minorities coming from poor countries with high migration pressure, presence of ischemic heart disease, presence of cerebral vasculopathies, presence of heart failure, presence of diabetes, residential buildings in poor conditions, building density, population density, distance from watercourses Adaptability: percentage of green area, average number of floors, possibility of conversion to green roofs, proximity to social welfare facilities, proximity to cool places | Equal Weighting System |
Chengdu-Chongqing City Cluster, China [57] | HEVA | Hazard: frequency of heat waves, maximum body temperature of heat waves, maximum duration of heat waves, total duration of heat waves Exposure: habitat index Vulnerability: time to hospital Adaptability: river network density, gross regional product, number of beds in healthcare facilities, general public budget expenditure, residents’ savings balance | Criteria Importance Though Intercriteria Correlation (CRITIC) |
Dalian, China [45] | HEVA | Hazard: radiant heat stress intensity Exposure: population density, point of information (POI) density, road network density Vulnerability: child population, elderly population, women’s population, twitter heat Adaptability: year of construction, hot shelter sites, medical sites, GDP | CRITIC |
Primary Indicators | Secondary Indicators | Meaning of Indicators | Direction | Spatialization Methodology | Data Sources |
---|---|---|---|---|---|
Hazard | LST | Characterizing the intensity of surface thermal radiation | + | remote sensing inversion | LANDSAT8 remote sensing imagery |
Exposure | Population Density | Characterizing anthropogenic heat exposure | + | Arcgis Kernel Density Tool | https://lbsyun.baidu.com. accessed on 1 August 2024. |
POI Density | Characterizing the clustering effect of urban functions | + | Arcgis Kernel Density Tool | BIGEMAP | |
Road Network Density | Characterizing the level of transport facilities | + | Arcgis Kernel Density Tool | BIGEMAP | |
Vulnerability | Child Population | Characterizing the distribution of sensitive populations | + | Arcgis Kernel Density Tool | BIGEMAP |
Elderly Population | Characterizing the distribution of sensitive populations | + | Arcgis Kernel Density Tool | BIGEMAP | |
Year of Construction | Characterizing building thermal regulation capacity | - | IDW interpolation | https://wulumuqi.anjuke.com. accessed on 5 August 2024. | |
Adaptability | Housing Value | Characterizing the ability to regulate living conditions | - | IDW interpolation | https://wulumuqi.anjuke.com. accessed on 5 August 2024. |
High-temperature Shelter | Characterizing public thermal adaptability resource allocation | - | Arcgis Kernel Density Tool | BIGEMAP | |
Medical Facilities | Characterizing health security capacity | - | Arcgis Kernel Density Tool | BIGEMAP | |
GDP | Characterizing the potential for heat adaptability inputs | - | IDW interpolation | https://www.resdc.cn. accessed on 5 August 2024. |
Primary Indicators | Primary Indicators Weights | Secondary Indicators | Secondary Indicators Weights |
---|---|---|---|
Hazard | 0.367 | LST | 1.000 |
Exposure | 0.195 | Population Density | 0.317 |
POI Density | 0.419 | ||
Road Network Density | 0.264 | ||
Vulnerability | 0.207 | Child Population | 0.253 |
Elderly Population | 0.240 | ||
Year of Construction | 0.507 | ||
Adaptability | 0.231 | Housing Value | 0.332 |
High-temperature Shelter | 0.206 | ||
Medical Facilities | 0.202 | ||
GDP | 0.260 |
Research Cities | Köppen–Geiger Climate Classification | Risk Indicators | Highest Heat Risk Built-Up Type LCZ | Lowest Heat Risk Built-Up Type LCZ |
---|---|---|---|---|
Urumqi, China (this study) | BSk | HEVA | LCZ 2 | LCZ 9 |
Karachi, Pakistan [53] | BWh | HEV | LCZ 3 | LCZ 8 |
Kabul, Afghanistan [63] | BSk | LST SUHI | LCZ 8 LCZ 8 | LCZ 2 LCZ 35 |
Phoenix, AZ, USA [20] | BWh | LST | LCZ 8 | LCZ 4 |
Las Vegas, NV, USA [20] | BWh | LST | LCZ 8 | LCZ 4 |
50 cities in the world [64] | / | SUHI | LCZ 2 | LCZ 9 |
Cardiff, UK [8] | Cfb | LST SUHI | LCZ 3 LCZ 8 | LCZ 9 LCZ 9 |
Changzhou, China [16] | Cfa | HEV | LCZ 1 | LCZ 9 |
Harbin, China [65] | Dwb | HEV | LCZ 2 | LCZ 9A |
Research Cities | Köppen–Geiger Climate Classification | Risk Indicators | Highest Heat Risk Land-Cover Type LCZs | Lowest Heat Risk Land-Cover Type LCZs |
---|---|---|---|---|
Urumqi, China (this study) | BSk | HEVA | LCZ E | LCZ G |
Karachi, Pakistan [53] | BWh | HEV | LCZ E | LCZ G |
Kabul, Afghanistan [63] | BSk | LST SUHI | LCZ F LCZ F | LCZ B LCZ B |
Phoenix, AZ, USA [20] | BWh | LST | LCZ E | LCZ G |
Las Vegas, NV, USA [20] | BWh | LST | LCZ F | LCZ G |
50 cities in the world [64] | / | SUHI | LCZ F | LCZ D |
Cardiff, UK [8] | Cfb | LST SUHI | LCZ F LCZ F | LCZ A LCZ A |
Changzhou, China [16] | Cfa | HEV | LCZ E | LCZ G |
Harbin, China [65] | Dwb | HEV | LCZ E | LCZ G |
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | ||
---|---|---|---|---|---|---|---|
B | Std. Error | Beta | Tolerance | VIF | |||
(Constant) | 34,531,757.206 | 151,893.600 | 227.342 | 0.000 | |||
LST | 15,783.415 | 72.598 | 0.154 | 217.409 | 0.000 | 0.994 | 1.006 |
Population Density | 2794.473 | 89.165 | 0.038 | 31.341 | 0.000 | 0.335 | 2.983 |
Year of Construction | −17,188.481 | 75.457 | −0.187 | −227.793 | 0.000 | 0.739 | 1.353 |
Housing Value | 39.129 | 0.218 | 0.149 | 179.252 | 0.000 | 0.727 | 1.375 |
High Temperature Shelter | −270.473 | 17.490 | −0.026 | −15.465 | 0.000 | 0.171 | 5.840 |
Medical Facilities | 8590.258 | 343.350 | 0.047 | 25.019 | 0.000 | 0.140 | 7.138 |
GDP | −0.184 | 0.023 | −0.007 | −8.099 | 0.000 | 0.665 | 1.503 |
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Lan, H.; Zhang, H.; Gao, J.; Bai, J.; Wang, H.; Lu, C.; Geng, H. Heat Risk Assessment in Arid Zones Based on Local Climate Zones: A Case of Urumqi, China. Buildings 2025, 15, 1672. https://doi.org/10.3390/buildings15101672
Lan H, Zhang H, Gao J, Bai J, Wang H, Lu C, Geng H. Heat Risk Assessment in Arid Zones Based on Local Climate Zones: A Case of Urumqi, China. Buildings. 2025; 15(10):1672. https://doi.org/10.3390/buildings15101672
Chicago/Turabian StyleLan, Hongxuan, Hongchi Zhang, Jialu Gao, Jin Bai, Hanxuan Wang, Cheng Lu, and Haoxuan Geng. 2025. "Heat Risk Assessment in Arid Zones Based on Local Climate Zones: A Case of Urumqi, China" Buildings 15, no. 10: 1672. https://doi.org/10.3390/buildings15101672
APA StyleLan, H., Zhang, H., Gao, J., Bai, J., Wang, H., Lu, C., & Geng, H. (2025). Heat Risk Assessment in Arid Zones Based on Local Climate Zones: A Case of Urumqi, China. Buildings, 15(10), 1672. https://doi.org/10.3390/buildings15101672