Spatial assessment of urban heatwave vulnerability of coupling adaptability based on BPNN model: a case study of Xiamen City, China

Figures & data

Figure 1. Location of Xiamen City.

Table 1. Data types, accuracy, and source.

Data types	Resolution	Time	Data source
Population data	100m	2020	World Pop (https://www.worldpop.org/)
Statistical data	–	2020	Statistical yearbook of Xiamen City
DEM	30m	–	Geospatial data cloud (http://www.gscloud.cn/. )
MOD13Q1	250m	2020	Geospatial data cloud (http://www.gscloud.cn/)
Landsat8 OLI	30m	2015 ∼ 2020	Geospatial data cloud (http://www.gscloud.cn/)
GDP	1000m	2015	Resource and Environment Science and Data Center (http://www.resdc.cn/)
Vector data	Administrative boundary, POI data (park, health, educational, and sport)

Figure 2. Conceptualization of UHV.

Table 2. Evaluation index system of UHV.

Target layer	Criterion layer	Indicator layer	Preprocessing method	Attributes
Urban heatwave vulnerability	Exposure	Surface temperature	Surface temperature inversion	+
		Population destiny^①	Random forests model	+
	Susceptibility	Elevation	–	–
		Vegetation coverage	Dimidiate pixel model	–
		Drainage density	Density analysis	–
		Land use type	Reclassification	Qualitative
		Building density	–	+
	Adaptability	Under 5 years old	Statistical analysis	+
		Over 65 years old		+
		Educational qualifications^②		–
		GDP density		–
		Fairness of summer facilities	Equity analysis of park, health, educational and sport	–

Note: ① Selected multisource remote sensing data as variable factors, census data as correction parameters, used random forest model to count the contribution of each variable to population density, and obtained a population distribution map of 100 m × 100m through district density mapping in 2020. ② The adaptability of urban residents was reflected by age structure and education qualifications, which was realized by counting the proportion of corresponding indicators at the street scale.

Table 3. Types, number, weight, and service radius of summer facilities.

Facilities types	Park			Educational and sport			Health
	Neighborhood	District	Municipality	Neighborhood	District	Municipality	Neighborhood	District	Municipality
Weight	0.045	0.090	0.135	0.019	0.097	0.253	0.057	0.114	0.189
Number	68	67	118	146	40	10	18	15	4
Radius(m)	500	1000	1500	500	2500	6500	1500	3000	5000

Figure 3. Compound process of UHV based on BPNN model.

Figure 4. Spatial distribution of 12 indicators of UHV.

Figure 5. R value of training results.

Figure 6. Spatial distribution of UHV.

Table 4. District statistics of UHV (km², %).

UHV grade	Siming		Huli		Haicang		Jimei		Xiang′an		Tong′an		Xiamen
	Area	Ratio	Area	Ratio	Area	Ratio	Area	Ratio	Area	Ratio	Area	Ratio	Area	Ratio
Low	0.2	0.25	0	0	22.1	11.70	46.7	17.19	14.5	3.42	233.6	34.31	317.1	18.45
Slight	12.9	16.23	0	0	22.0	11.64	48.4	17.81	72.0	16.98	166.4	24.44	321.7	18.72
Moderate	15.1	18.99	6.7	9.13	32.3	17.10	48.3	17.77	61.9	14.60	130.6	19.18	294.9	17.16
Strong	19.2	24.15	13.1	17.85	67.9	35.95	78.6	28.93	200.9	47.38	114.5	16.81	494.2	28.76
Extreme	32.1	40.38	53.6	73.02	44.6	23.61	49.7	18.29	74.7	17.62	35.8	5.26	290.5	16.91
Total	79.5	100	73.4	100	188.9	100	271.7	100	424.0	100	680.9	100	1718.4	100

Figure 7. Hot spot analysis of UHV.

Figure 8. Coordinating relations of UHV.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, Yang HF.