Relationship between urban morphology and land surface temperature-A case study of Nanjing City.

This study investigated the relationship between urban form and land surface temperature (LST) using the Multi-access Geographically Weighted Regression (MGWR) model. A case study on Nanjing City was conducted using building data, point-of-interest (POI) data, land use data, remote sensing data, and elevation data. The results show that the MGWR model can reveal the influence of altitude, urban green space, road, building height (BH), building density (BD) and POI on LST, with a superior fitting effect over the geographically weighted regression model. LST in Nanjing exhibits a significant spatial differentiation, and the distribution of LST hotspots is spatially consistent with the level of urban construction. In terms of the two-dimensional landscape pattern, LST decreases with altitude and increases with POI. In terms of the three-dimensional structure, building height has a positive correlation with LST. POI, urban roads, and urban buildings positively affect LST, while urban green space and altitude negatively affect LST. The results of this study were verified against existing findings. The LST of areas with high-rise and super high-rise buildings is lower than that of areas with mid-rise building, which can be attributed to the large number of shadow areas formed by high-rise and super high-rise buildings. A similar phenomenon was also observed between areas with medium- and high-density buildings. These findings provide a reference for urban architecture planning and can help to develop urban heat island adaptation strategies based on local conditions.

Introduction

With the continuous expansion and increase of the scale and number of cities in China, the boundary between urban and rural areas is becoming increasingly blurry. The urbanization rate of China’s permanent population reached 60.60% in 2019(http://data.stats.gov.cn).The continuous expansion of the scale of related industrial activities [1, 2] is driving economic growth, improving the employment environment, and increasing the income of residents [3-5]. However, this expansion has also led to negative effects on the quality of human settlements, social and economic development [6-9]. In particular, the urban heat island effect, especially urban heat waves associated with global warming, can increase the vulnerability of populations to various health issues, such as heatstroke, and even death [10]. Therefore, improving the urban thermal environment has become the focus of relevant scholars and institutions [11]. To measure the quality of the urban thermal environment and mitigate the negative effects, it is necessary to analyze the spatiotemporal pattern of the thermal environment and its influencing factors. For this purpose, various indicators have been developed. Among them, Land Surface Temperature (LST), a basic parameter in the fields of meteorology and ecological changes [12-14], has become an important indicator [15-17], facilitating detailed analyses [18, 19].

The relationship between urban landscape patterns, such as land use type and blue-green space [20], and surface temperature has received extensive attention worldwide. At present, researchers in the field of LST studies are focusing on the following aspects: (1) The impact of the two-dimensional urban landscape pattern on LST, analyzing the impact of different land use types on LST from the perspective of land use type and land use type transformation [1, 21]; (2) Statistical analysis of the mathematical relationship between surface factors and LST using indicators, such as normalized difference vegetation index, normalized difference moisture index, normalized difference built-up index, and building density (BD) [3, 22, 23]; (3) Correlation between the three-dimensional structure and LST based on indicators, such as building height (BH), floor area ratio (FAR), and sky view factor (SVF) [24-26]. It is worth noting that the process of urbanization involves a contradiction between population concentration and limited supply of construction land, which further leads to the rapid expansion of cities in the two-dimensional direction and the continuous increase of BH [27, 28]. However, most studies investigated the relationship between surface factors and LST through simple or single regression analysis, ignoring the spatial autocorrelation and spatial information between the two. In addition, research on the relationship between urban form and LST mostly focus on a single factor of urban form (two-dimensional or three-dimensional), especially the two-dimensional form. For mega cities famous for their ecology (Gardens) (with a permanent resident population of over 8.5 million), such as Nanjing, the lack of multi-factor analysis combined with the two-dimensional landscape pattern and three-dimensional structural form makes it difficult for existing studies to further explore the impact of urban spatial differences on urban thermal environment. In addition, the urban spatial planning system has undergone major changes, and the three-dimensional urban structure is undergoing significant changes [29, 30]. Therefore, the existing approach of analyzing the urban thermal environment needs to be clarified and the two-dimensional landscape and three-dimensional space of urban landscapes need to be optimized.

This study aims to comprehensively explore the relationship between urban form (two-dimensional landscape pattern and three-dimensional structural form) and LST. For this purpose, the Multi-scale Geographically Weighted Regression (MGWR) model, a mono-window algorithm, was used to quantitatively analyze the spatial pattern of related surface factors in each local climate zone of Nanjing on LST. With this approach, this study addresses the lack of multi-factor analysis to a certain extent. The findings will provide support for regulating the urban thermal environment and improving the quality of urban human settlements.

Study area and data sources

Overview of the study area

Nanjing is located at 31°14’N-32°36’N, 118°22’E-119°14’E. It is a low altitude area with a humid subtropical climate (Cfa). According to meteorological records, On July 28, 2018, extreme high temperature weather occurred, which was 37.20°C. It has 11 districts with a total area of 6587 km2 and a built-up area of 817 km2. As of 2019, permanent residents account for a population of 8.5 million and an urbanization rate of 83.2%. The increasing concentration of population and amount of industrial activities are exacerbating the urban heat island effect (Fig 1).

Fig 1

Location of the study area.

The location of Nanjing in China and Jiangsu. examined in this paper, in Jiangsu Province (blue), in Nanjing City (red), and on the right side is the District of Nanjing city. This map is drawn by the authors. This map was created using ArcGIS ® software by Esri, ©National Platform for Common Geospatial Information Services, ©USGS, ©Resource and Environment Science and Data Center, ©Baidu, ©OpenStreetMap and contributors, Creative Commons Attribution 4.0(CC BY 4.0).

Data source and processing

In this study, building data, POI data, land use data, remote sensing data and elevation data were selected. The data sources and descriptions are shown in Table 1. BH and BD reflect the concentration of buildings in the vertical and horizontal directions, respectively. To a certain extent, the two have the greatest impact on the three-dimensional structure of the city, and the degree of LST has the most direct effect [16, 31, 32]. According to the 2019 Unified Standards for Civil Building Design, BH is divided into 3 types: low-rise civil buildings (≤27 m), high-rise civil buildings (27–100 m), and super high-rise buildings (>100 m) [31, 33]. Considering the findings of existing research and the status quo of the research area, with 20% and 40% as the boundary, BD was divided into three types: sparse, open and compact [34]. POI data were obtained through Baidu Map API, and the data were cleaned and selected for nuclear density analysis. LST data were obtained through inversion using ENVI5.3 software with Landsat 8 products, which were geometrically corrected [35]. Urban green space and aspect were obtained from digital elevation data using the ArcGIS 10.7 surface analysis function.

Table 1

Data source and description.

Data	Time	Data interpretation	Data sources
Building data	2019	Vector data	http://ditu.amap.com
POI	2019	Point data	http://ditu.amap.com
Land Use/Cover Change (LUCC)	2018	Raster data	http://www.resdc.cn/
		Landsat 8-OLI (Resolution 30 m)
Remote sensing	2019-08-12	Landsat 8-OLI (Resolution 30 m)	glovis.usgs.gov
DEM	2009	SRTM DEM (Resolution 30 m)	http://www.gscloud.cn/
Administrative boundary	2019	Vector data	https://www.tianditu.gov.cn/
			https://www.openstreetmap.org/

Methods and factor selection

Mono-window algorithm

LST is one of the important parameters in the study of surface energy balance. The commonly used methods of remote sensing inversion of LST mainly include the radiative transfer equation method, single window algorithm, single channel algorithm, and split window algorithm. Qin et al. [27] analyzed atmospheric water vapor content using the mono-window algorithm and found a significant negative correlation between atmospheric transmittance and the inversion error of LST [36]. Nanjing has many water bodies, and summer is mostly hot and humid. As the area features low atmospheric permeability and high accuracy of LST inversion, Landsat TM 6 band and Landsat 8 TIRS 10 were selected, and a mono-window algorithm was used to invert LST. The inversion formula can be expressed as follows: where T is surface inversion temperature (K); a and b are constants; T is luminance temperature (K); T is the average temperature of the atmosphere (K); and C and D are intermediate variables, which can be obtained from ε (surface specific emissivity) and τ (atmospheric transmittance in the thermal infrared band).

MGWR model

Compared with the classic geographically weighted regression (GWR) model, the kernel function and bandwidth selection of MGWR continue the selection criteria in the classic GWR, but the MGWR model adds spatially stable variables, and each regression coefficient β is based on local regression. Moreover, each bandwidth is different. The calculation formula is as follows: where bwj is the bandwidth of the regression coefficient of the j-th variable, (u, v) represents the coordinates of the i-th local point in geographic space, x is the influencing factor, and ε is the random error term.

Selection of impact factors

According to the principle of surface heat radiation and thermodynamic characteristics, LST is affected by thermal channels and near-surface gas LST. In low-altitude areas such as Nanjing, changes in surface radiation caused by topography, impervious surfaces, human Activity significantly affect the regional surface radiation. Referring to previous studies [1, 37–40], combined with the actual situation in the study area, and considering issues such as data availability, this study selected the factors listed in Tables 2 and 3.

Table 2

Descriptions of major explanatory variables.

Variable name	Unit	Description
Intercept	°C	Model intercept term, inversion of location factors.
Altitude	m	Altitude of Nanjing.
Urban Green Space	m2	Urban green space and park distribution in Nanjing.
Road	Classification	Distribution of roads of grade three and above.
Building Height (BH)	Classification	1 is a low-rise building, 2 is a high-rise building, and 3 is a super high-rise building.
Building Density (BD)	Classification	1 is sparse, 2 is open building, 3 is compact building
POI	-	Spatial distribution of industrial activities.

Table 3

Statistics of LST in each district.

District	Minimum Temperature	Maximum Temperature	Cold spot	Proportion	Hot spot	Proportion
Xuanwu	22.978	31.570	413***	0.074	2172***	0.391
Yuhuatai	22.924	34.608	98***	0.030	331***	0.103
Qinhuai	24.506	31.744	24*	0.006	Not significant	Not significant
Gulou	22.856	30.141	39***	0.008	Not significant	Not significant
Jianye	22.887	33.677	169***	0.047	Not significant	Not significant
Qixia	19.923	37.121	61***	0.030	838**	0.410
Pukou	22.845	35.846	481***	0.106	2771**	0.609
Luhe	20.666	33.437	927***	0.147	3452	0.550
Lishui	20.204	33.871	694***	0.131	3323**	0.659
Jiangning	21.724	36.274	761***	0.163	2056***	0.441
Gaochun	21.104	35.072	417***	0.119	1681***	0.48

***indicates a confidence level of 0.99

** indicates a confidence level of 0.95

* indicates a confidence level of 0.90.

Results and analysis

Spatial pattern characteristics of LST

Using Landsat 8 images, the overall distribution of LST was inversed according to the single window algorithm (Fig 2). The highest LST of Nanjing was 37.121°C, the lowest was 19.923°C, and the average was 28.525°C. According to an analysis of hot spots, excluding outliers and the spatial distribution of LST As shown in Fig 3.

Fig 2

Spatial distribution of LST in Nanjing.

The Land surface temperature (LST)of Nanjing city and its districts, (a) Jianye; (b) Gulou; (c) Luhe; (d) Qixia; (e) Pukou; (f) Qinhuai; (g) Jiangning; (h) Xuanwu; (i) Yuhuatai; (j)Lishui;(k)Gaochun. This map is drawn by the authors. This map was created using ArcGIS ® software by Esri, ©National Platform for Common Geospatial Information Services, ©OpenStreetMap and contributors, Creative Commons Attribution 4.0(CC BY 4.0).

Fig 3

Hot spot analysis of LST.

Hot spot analysis of Land surface temperature (LST). (a) Jianye; (b) Gulou; (c) Luhe; (d) Qixia; (e) Pukou; (f) Qinhuai; (g) Jiangning; (h) Xuanwu; (i) Yuhuatai; (j) Lishui; (k) Gaochun. This map is drawn by the authors. This map was created using ArcGIS ® software by Esri,©National Platform for Common Geospatial Information Services, ©OpenStreetMap and contributors, Creative Commons Attribution 4.0(CC BY 4.0).

Overall, hotspots in Nanjing are concentrated mainly in Xuanwu District, Qixia District, Pukou District, Lishui District, Jiangning District, and Gaochun District. The highest value of LST was 37.12°C in Qixia District, over an area located near the Lingang Industrial Concentration Zone in Qixia District and Nanjing Economic and Technological Development Zone. The area along the river between the two zones is largely covered by industrial land and residential land, with little vegetation. At the same time, the lowest value of LST in Nanjing was also observed in Qixia District, specifically in the Qixia Mountain. The area lies 268 m above sea level. The land use type is woodland, and the vegetation coverage is relatively high, with less interference from human activities. The distribution characteristics of LST hotspots in other areas are related to the cluster distribution of urban land, rural residential areas and other construction land, and the scattered distribution of waters and forests and grasslands, and are strongly affected by human activities.

Distribution of impact factors

After cleaning, calibrating, deleting anomalies, and other processing of building data, a total of 164,581 building areas were obtained (Fig 4A). Low-rise, high-rise, and super high-rise buildings accounted for 64.20%, 32.84%, and 2.96%, respectively. Low-, medium-, and high-density buildings accounted for 57.89%, 26.60%, and 15.50%, respectively. The proportion of building area in each district is as follows: Qinhuai District (29.23%) > Xuanwu District (15.17%) > Yuhuatai District (11.16%) > Jianye District (9.91%) > Jiangning District (9.91%) > Pukou District (9.65%) > Qixia District (8.75%) > Luhe District (4.02%) > Lishui District (1.751%) > Gaochun District (0.420%) > Gulou District (0.03%).

Fig 4

Spatial distribution of impact factors.

Spatial distribution of impact factors. (a) Building date, open midrise (blue), Compare highrise (green), Spare lowrise (red),data source (http://ditu.amap.com); https://www.openstreetmap.org; (b) Kernel density of POI, data source (http://ditu.amap.com); (c) Line density of Road, data source (http://ditu.amap.com); (d) Urban Green Space(green), data source (http://www.resdc.cn/); (e) DEM, data source (http://www.gscloud.cn/). This map is drawn by the authors. This map was created using ArcGIS ® software by Esri,©National Platform for Common Geospatial Information Services, ©Resource and Environment Science and Data Center, ©Baidu, ©OpenStreetMap and contributors, Creative Commons Attribution 4.0(CC BY 4.0).

Regarding land occupied by buildings in terms of BH (Table 4), Gaochun District has fewer building areas (blocks) at 692 blocks, mainly comprising low-rise buildings with a small number of high-rise buildings. Gulou District has a very high proportion of super high-rise buildings, with a very low proportion of low-rise buildings and a moderate proportion of high-rise civil buildings. Low-rise buildings are mainly distributed in Jiangning District, Yuhuatai District, Qinhuai District, Pukou District, and Xuanwu District. Lishui District and Qixia District mainly comprise low-rise and high-rise buildings, with a relatively low proportion of high-rise buildings. Jianye District is dominated by low-rise buildings, with high-rise buildings accounting for 38.24%, and the proportion of super high-rise buildings is relatively low. The main building types in Luhe District are low-rise and high-rise buildings, with a low distribution of super high-rise buildings.

Table 4

Statistics of buildings in each district.

District	Building area (piece)	Proportion of various building heights (BH)			Proportion of building density (BD)
		Low-rise building	High-rise buildings	Super high-rise buildings	Spare buildings	Open buildings	Compact buildings
Gaochun	692	96.53%	3.47%	0.00%	96.24%	3.03%	0.72%
Lishui	2881	50.57%	46.27%	3.16%	47.66%	33.32%	19.02
Jiangning	16312	62.31%	35.53%	2.16%	49.66%	35.78%	14.57%
Yuhuatai	18362	65.60%	31.19%	3.21%	60.11%	22.06%	17.84%
Jianye	16317	55.28%	38.24%	6.48%	50.59%	24.03%	25.39%
Qinhuai	48104	67.48%	30.23%	2.29%	62.15%	22.19%	15.66%
Gulou	55	7.27%	21.82%	70.91%	5.46%	5.45%	89.09%
Xuanwu	24962	68.41%	30.30%	1.29%	61.57%	26.22%	12.21%
Qixia	14398	47.88%	46.07%	6.06%	41.14%	40.66%	18.20%
Pukou	15884	76.11%	21.08%	2.81%	72.07%	18.30%	9.64%
Luhe	6614	38.25%	52.60%	9.15%	30.72%	44.80%	24.48%

Regarding the proportion of land occupied by buildings in terms of BD (Table 4), Gaochun District is dominated by low-density buildings, with a very low proportion of medium- and high-density buildings. The proportion of buildings is relatively low in Gulou District, with high-density buildings accounting for 89.09%, and low- and medium-density buildings accounting for 10.91%. Pukou District and Xuanwu District are dominated by low-density buildings, with no medium-density buildings; Yuhuatai District and Qinhuai District mainly include low-density buildings, with some moderate and high-density buildings; Lishui District and Jiangning District mainly include low-density buildings and medium-density buildings. Jianye District has a relatively high proportion of low-density buildings, and a moderate proportion of medium- and high-density buildings. Qixia District is dominated by low- and medium-density buildings. Moreover, the proportions of the two are similar, and the distribution of high-density buildings is relatively small. Luhe District has a relatively high proportion of medium-density buildings, and moderate proportions of low- and high-density buildings.

POI represents the distribution of urban physical facilities. After data crawling and cleaning, a total of 238423 POI were obtained, with a bandwidth of 0.6 km. The core density of POI in Nanjing was 2.18–996.91/km2, showing a spatial distribution of "low around and high in the center" (Fig 4B). The distribution of POI and nuclear density in each region are shown in Table 5. The overall number of POI in Jiangning District, Pukou District, and Luhe District was higher than that in other districts, and the overall number of POI was the lowest in Gaochun district. With an extremely low value, the regional nuclear density was the lowest in Yuhuatai District, whereas it was the highest in Qinhuai District. The spatial clustering distribution of POI in each region exhibited different trends. Qixia District and Luhe District corresponded to the distribution of "upper low high", with percentages of points between the lowest value and standard deviation of 61.16% and 53.49%. Jiangning District and Yuhuatai District showed a distribution trend of "low in the lower part and high in the upper part", with percentages of points between the lowest value and standard deviation of 61.66% and 52.73%, respectively. Pukou District and Jianye District showed a distribution trend of "left low and right high", with percentages of 56.915% and 56.64%, respectively, for points between the minimum value and standard deviation. Xuanwu District and Qinling District showed percentages of 56.915% and 56.64%, respectively. Huaihe District exhibited a distribution trend of "right low, left high" with percentages of 51.86% and 43.01%. Gaochun District and Lishui District showed distribution trends of "low edge, high middle", with percentages of 52.66% and 51.64%. The overall distribution of POI nuclear density in Gulou District was uniform.

Table 5

POI statistics of each district.

District	POI (piece)	Proportion (%)	Density (pcs/km2)	Standard deviation
Gaochun	10517	4.411	2.852–727.334	65.092
Lishui	14788	6.202	2.938–749.145	62.990
Jiangning	59429	24.926	3.046–776.841	94.372
Yuhuatai	16329	6.849	2.183–556.779	112.759
Jianye	10592	4.443	2.677–682.616	149.710
Qinhuai	15374	6.448	3.909–996.906	193.122
Gulou	18938	7.943	3.540–902.626	183.731
Xuanwu	14226	5.967	3.085–786.590	145.226
Qixia	23998	10.065	2.377–606.095	91.722
Pukou	28579	11.987	2.841–724.402	92.142
Luhe	25653	10.759	2.332–594.628	72.375
Total	238423	100

After checking the topology of road network data, they were calibrated based on Google Earth Pro2019. Referring to existing research and the actual situation of the research area, highways, urban traffic arterial roads, and urban branch roads were reserved (Fig 4C), setting a 1 km × 1 km grid, and the road network density (road network density = total road length km/grid area km2) was calculated. The results are shown in Table 6, for which abnormal values were removed. The higher the road network density, the stronger the regional road accessibility, promoting travel among urban residents. The higher the connectivity, the more convenient the regional traffic; conversely, the lower the density of the road network, the less the distribution of arterial roads and highways in the area, which greatly reduces road accessibility and decreases the convenience of residents to travel. Combining Figs 1 and 2, the road network in Nanjing shows uneven development. Areas with high levels of road network density are mainly distributed in the central area, including the districts of Jianye, Gulou, and Yuhuatai, which may be clustered with buildings. It is related to the dense distribution of entertainment venues such as cultural centers. Areas with low levels of road network density are mainly distributed in the south, northeast, and northwest regions, such as the districts of Gaochun, Qixia, Luhe, and Pukou, which may have large areas of water and forests. The distribution of grassland and farmland is related to the scattered distribution of rural settlements.

Table 6

Road statistics of each district.

District	Road network density (km/km2)	Standard deviation (km/km2)
Gaochun	0.011–15.645	2.933
Lishui	0.009–15.516	3.436
Jiangning	0.004–15.573	3.853
Yuhuatai	0.037–18.111	5.377
Jianye	0.068–16.816	6.174
Qinhuai	1.109–14.263	4.910
Gulou	0.143–15.805	5.647
Xuanwu	0.110–14.534	5.163
Qixia	0.009–12.112	4.431
Pukou	0.004–14.918	3.199
Luhe	0.002–12.878	3.371

In terms of altitude and urban green space, Nanjing is in a low altitude area, dominated by lacustrine plains and valley bottoms, with a small amount (39.2% of the total area) of undulating mountains and hills, plains, depressions, rivers, and lakes. The Ningzhen Mountains and Jiangbei Laoshan straddles the central part of the city, and the south is a geomorphologically complex area composed of topographical units, such as low mountains, valley plains, and rivers. As mountainous areas, the highest altitudes of Xuanwu, Pukou, and Jiangning districts are higher than those of other regions (Fig 4E). The density of urban green space (total area of urban gardens and green space/total area of urban land) is shown in Fig 4D. The urban green space in Nanjing shows a widely variable spatial distribution, mainly concentrated in Xuanwu Lake, Zijin Mountain, and ancient city walls in the city center. Overall, in addition to urban green spaces near the "Central Park", they are concentrated in the Riverside Scenic Belt, Qinhuai River Scenic Belt, and Pukou Central Park.

Relationship between LST and impact factors

To quantitatively analyze the spatial distribution characteristics of LST and its factors in Nanjing, altitude, urban green space, slope, aspect, BD, BH, and POI were analyzed. A correlation test of the variables was conducted, and the results showed that the Moran’s between slope and aspect is less than 0.2, with a small spatial correlation. As this index failed the Moran’s test, it was not included in the global variable. The remaining indexes all showed values greater than 0.7, with some at the 1% level, showing significant spatial positive correlation. Accordingly, they were included in the local variables for calculation.

Table 7 shows that the goodness of fit (R2) of MGWR is slightly higher than that of the precision GWR model, and the value of the corrected Akaike information criterion (AICc) is also lower than that of the classic GWR model. Therefore, MGWR can be assessed to have higher performance than the classic GWR. Comparing the residual sum of squares, the value of MGWR was also smaller than that of GWR. Moreover, MGWR requires fewer parameters to obtain a regression result closer to the true value.

Table 7

Model index of GWR and MGWR.

Model	MGWR	GWR
R2	0.928	0.883
AICC	1303	1186
Adj. R2	0.902	0.875
Residual sum of squares	1323	1977
Sig.	0.000*	0.000*

* Indicates that the test passed at the 5% significance level.

The spatial distribution of each influencing factor is shown in Fig 4. The MGWR analysis results show that the spatial distribution of each influencing factor and LST have significant similarities and differences. LST decreased with increasing altitude, urban green space, and BH, whereas it increased with increasing values of BD and POI. The goodness of fit between POI and LST was the highest, reaching 0.96, followed by that between BD and LST (0.95) and between BH and LST (0.94). The values of goodness of fit between altitude, urban green space and LST were good (0.73, 0.61, respectively), and that between aspect and LST was average (0.37). These results further show that the natural environment is the basic factor affecting the spatial distribution of LST, and changes in the surface environment caused by human activities have a particularly significant impact on urban LST. Industrial facilities and business districts, such as Xinjiekou business district in Qinhuai District, Taipingmen business district in Xuanwu District, and Shuiximen business district in Jianye District, are concentrated in Xuanwu District, Qinhuai District, Gulou District, Jianye District, and other downtown areas, which also account for the main low-rise buildings and super-high buildings in Nanjing. At the same time, a large number of buildings are concentrated in the central area, which further increases the flow of people. The concentrated POI distribution has a significant positive correlation with the spatial distribution of LST.

The statistical description of each coefficient of MGWR is shown in Table 5. The Intercept represents the positive influence of location factors on LST. The value of Intercept was between -0.49 and 1.46, the average value was 0.485, and the standard deviation was 0.975, indicating that under the same natural conditions, the influencing factors would change the LST of Nanjing by -0.49–1.46°C, with an average change of 0.485°C. The influence of location factors on LST widely varies.

Urban roads have a significant positive impact on LST (Table 8). Herein, roads in Nanjing are divided into three levels according to the classification standards of urban roads: Arterial Road, Secondary Road, and Access Road. Arterial Road ranges from -0.42 to 1.41, with an average value of 0.495, Secondary Road ranges from -0.34 to 1.53, with an average value of 0.595, and Access Road ranges from 0.76 to 1.97, with an average value of 1.365. These results show that in urban roads, branch roads have a greater impact on LST. In other words, under the circumstance that the influence of other factors remains unchanged, the LST around a branch road (Access Road) would be approximately 1.365°C higher than that in the surrounding areas. Therefore, the density of urban roads will also affect LST.

Table 8

Statistical description of MGWR coefficients.

Variable	Min	Max	Median	Std	Mean
Intercept	-0.49	1.46	0.485	0.975	0.485
Arterial Road	-0.42	1.41	0.495	0.915	0.495
Secondary Road	-0.34	1.53	0.595	0.935	0.595
Access Road	0.76	1.97	1.365	0.605	1.365
BuildingHeight.1	-0.14	0.21	0.035	0.175	0.035
BuildingHeight.2	0.45	3.01	1.73	1.28	1.73
BuildingHeight.3	-0.6	3.76	1.58	2.18	1.58
Urban Green Space	-1.42	0.26	-0.58	0.84	-0.58
Compact Highrise	-0.43	0.67	0.14	0.55	0.12
Open Midrise	-0.52	3.68	1.55	2.1	1.58
Lightweight Rise	0.29	1.2	0.741	0.455	0.745
POI	-0.72	1.6	0.47	1.16	0.44
DEM	-2.5	-0.25	-1.381	1.125	-1.375

Referring to the literature [41], quantitative indicators of local climate zones, and the actual situation of Nanjing, the study area was divided into low-density low-rise building areas, medium-density high-rise building, areas and high-density super high-rise building area (Fig 4E), and the relationships between BH, BD, and LST were analyzed. Low-rise building areas (BuildingHeight.1) showed LST values ranging from -0.14 to 0.21, with an average value of 0.035. High-rise building areas (BuildingHeight.2) showed LST values ranging from 0.45 to 3.01, with an average value of 1.73. Super high-rise building areas (BuildingHeight.3) showed LST values ranging from -0.6–3.76, with an average value of 1.58. This further shows that there is a significant positive correlation between BH and LST. From the perspective of the absolute value of the coefficient, the area of middle-rise buildings has the greatest impact on LST. In terms of BD, the value of the impact of high-density high-rise buildings on LST ranged from -0.43 to 0.67, with an average value of 0.12°C. The value for medium-density high-rise building areas ranged from -0.52 to 3.68, with an average value of 1.58°C. The value for low-density low-rise buildings ranged from 0.29 to 1.2, with an average value of 0.745°C. The absolute value of the influence coefficient shows that high-rise buildings and medium-density mid- and high-rise buildings have the greatest influence on LST. Previous studies suggested that increases in BH will increase LST. In contrast, this study shows that increases in BH do not necessarily lead to an increase in LST. This can be attributed to increased shadow areas generated by high-rise buildings, leading to lower temperatures within a certain range [42].

Urban green space and LST in Nanjing exhibit a negative correlation, with an impact coefficient of -0.58°C, indicating that increases in urban green space will decrease LST. Under the condition that other factors remain unchanged, the LST of urban green space is lower than that of surrounding areas by 0.58°C. In contrast, POI has a positive correlation with LST. The higher the intensity of human activities, the higher the LST, but its impact coefficient is 0.44, which is moderate. The DEM coefficient ranged from -2.5 to -0.25. This implies that the LST of areas at higher altitudes is lower than that of areas at lower altitudes by 1.375°C on average.

Conclusion

In this study, the MGWR model was used to determine the relationship between urban form and surface temperature for the first time. Combining POI data, building data, and remote sensing data of Nanjing, the spatial differentiation of urban form (two-dimensional landscape pattern and three-dimensional structure form) and LST and its influencing factors were analyzed. The following main conclusions can be drawn:

(1) The spatial differentiation of LST in Nanjing is significant, and the distribution of LST hotspots exhibits a distinct spatial consistency with the level of urban construction. The highest LSTs in Qixia District (37.121°C), Jiangning District (36.274°C), and Pukou (35.846°C) are higher than those in other areas, and the lowest LSTs in Qixia District (19.923°C), Lishui District (20.204°C), and Luhe District (20.666°C) are lower than those in other areas.

(2) Compared with classic GWR, MGWR supports the analysis of multiple influencing factors or variables at different scales and provides better fitting effect than GWR. In this study, some factors exhibited significant differences in their effects on LST. Except for the aspect and slope, the other influencing factors all showed significant spatial correlation with LST. In terms of the two-dimensional landscape pattern, the higher the altitude, the lower the LST; the higher the POI concentration, the higher the LST. In terms of the three-dimensional structure, BH and LST are positively correlated.

Advantages and limitations

The conclusions of this research and existing research results are mutually confirmed [39, 43, 44]. In plain areas with low altitude and gentle slopes, the development of human activities is less difficult, human activities are frequent, and construction land is concentrated. Areas with concentrated secondary and tertiary industries are more likely to have higher LST, whereas areas with mountains and agricultural land are likely to have lower LST due to several factors such as altitude and vegetation [45, 46]. It is noteworthy that, unlike the findings of previous studies, an obvious positive correlation was found between urban BH, BD, and LST. However, the LST of high-rise and super high-rise building areas was found to be lower than that of mid-rise building areas. This could be explained by the expansion of shadow areas generated by super high-rise buildings; similar phenomena were also observed between medium-density building areas and high-density building areas [42, 47].

Based on the existing research, this paper determines the impact of different human activities on the urban thermal environment, and further proves that urban green space can help alleviate the urban heat island effect [22, 48]. The process of urbanization has led to overpopulation and excessive industrial concentration, causes a change in the nature of heat exchange at the bottom, and aggravating the formation and development of the urban heat island effect, which requires the attention of urban planning agencies [49]. In addition, based on the results of this article, strategies to reduce heat stress by addressing the urban heat island effect, (e.g., control the scale of urban built-up areas, optimize urban spatial structure, increase urban green areas, alleviate urban population concentration and other measures). We should also cooperate with commercial real estate developers to control the height and density of new buildings, optimize the design of future urban parks, increase the construction of urban ventilation corridors and green spaces, and further alleviate the urban heat island effect [8].

Advantages

This study integrates POI data, building data, urban road data sets and other data to analyze the factors that affect the urban thermal environment. First, in order to assess the human activities that may be responsible for the model described here, we compare land use data with population density data, etc., and conclude that the POI data represents the geographic information and utilization characteristics of various facilities. Secondly, for the classification of building height in Nanjing, after many field investigations and analysis of historical remote sensing images, a more reasonable density classification (limited to 40%) is finally determined. This research will have greater practical value. Third, this study uses a multi-dimensional perspective (two-dimensional and three-dimensional structure) to study the current status of Nanjing’s thermal environment, and explores the current status of Nanjing’s thermal environment from a more specific plane dimension and a deeper perspective. In view of the multi-dimensional perspective of this study, MGWR is used instead of GWR to explore non-stationary relationships in the modeling space [22, 44].

Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. Compared with the traditional GWR model, The MGWR model has advantages in acquiring the ability of different scales [18]. The MGWR model can effectively analyze the multi-scale relationship between the urban thermal environment and its influencing factors, and has a positive effect on urban dynamic development and urban thermal environment management.

Limitations

The MGWR model facilitated multi-factor analysis of LST. However, due to issues such as data availability and collinearity, the application of the MGWR model has certain limitations. The urban landscape is a complex dynamic system composed of infrastructure, human activities, and social connections. Changes in urban surface temperature need to be observed from a more micro perspective [8, 50]. Urban ground monitoring data have not been fully disclosed, which limits the study. In addition, street view data were used in the study of the urban thermal environment, and the number of street scenes in this area requires further investigation [51]. In the future, the interaction between different influencing factors should be considered, and the influencing factors of LST should be analyzed in more detail to provide a more comprehensive perspective for urban or regional environmental governance and planning.

6 Mar 2021

PONE-D-21-03097

Relationship between urban morphology and land surface temperature – a case study of Nanjing City

PLOS ONE

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Additional Editor Comments:

Reviewer 1

This paper is overall interesting. However, the major revisions are required in terms of English, references, logic and structure.

Please see the detailed comments in the attachment. My pleasure to see your revised verison.

Reviewer 2

Summary: The paper presents an application of MGWR to Landsat-derived LST across Nanjing. This is potentially useful to other researchers as an application case study that can inform future work on similar problems. I recommend "major" revisions primarily b/c I'm asking for a bit of additional statistical analysis. But I am confident that the authors can address my comments.

Section 3.2: Since the purpose of this paper is to compare MGWR to GWR, it would be useful if the authors could expand this section to provide a bit more context. Has MGWR been applied to similar problems elsewhere? What is the theoretical motivation for applying MGWR to the challenge of LST prediction in an urban environment?

Section 4.1: The first long paragraph is mostly unnecessary b/c the results are summarized in a table. I suggest that the authors remove this paragraph in favor of a short paragraph that highlights any particularly interesting features of these results. A similar comment applies to the Section 4.2 text that presents the numbers preported in Tables 4 and 5.

Section 4.3: Is MGWR better than GWR by a statistically significant amount? A test of statistical significance could be added to "Table 4" of this section (which I think is actually Table 7 in the manuscript).

Section 4.3: Are the R2 statistics provided here for data that were used in fitting the MGWR and GWR, or for holdout data? While AIC is useful, it would be better to see how the models compare in out-of-sample prediction. Also, why is R2 and adjR2 the only metric used? What about MSE, or some other indicator of magnitude of error?

Section 5: It is unusual to have a "Conclusions" section between Results and Discussion. Perhaps this could be restructured to be a Section 4.4 "Summary" of results?

p. 26, top line: there's an incomplete sentence here.

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear authors,

This paper is overall interesting. However, the major revisions are required in terms of English, references, logic and structure.

Please see the detailed comments in the attachment. My pleasure to see your revised verison.

Reviewer #2: Summary: The paper presents an application of MGWR to Landsat-derived LST across Nanjing. This is potentially useful to other researchers as an application case study that can inform future work on similar problems. I recommend "major" revisions primarily b/c I'm asking for a bit of additional statistical analysis. But I am confident that the authors can address my comments.

Section 3.2: Since the purpose of this paper is to compare MGWR to GWR, it would be useful if the authors could expand this section to provide a bit more context. Has MGWR been applied to similar problems elsewhere? What is the theoretical motivation for applying MGWR to the challenge of LST prediction in an urban environment?

Section 4.1: The first long paragraph is mostly unnecessary b/c the results are summarized in a table. I suggest that the authors remove this paragraph in favor of a short paragraph that highlights any particularly interesting features of these results. A similar comment applies to the Section 4.2 text that presents the numbers preported in Tables 4 and 5.

Section 4.3: Is MGWR better than GWR by a statistically significant amount? A test of statistical significance could be added to "Table 4" of this section (which I think is actually Table 7 in the manuscript).

Section 4.3: Are the R2 statistics provided here for data that were used in fitting the MGWR and GWR, or for holdout data? While AIC is useful, it would be better to see how the models compare in out-of-sample prediction. Also, why is R2 and adjR2 the only metric used? What about MSE, or some other indicator of magnitude of error?

Section 5: It is unusual to have a "Conclusions" section between Results and Discussion. Perhaps this could be restructured to be a Section 4.4 "Summary" of results?

p. 26, top line: there's an incomplete sentence here.

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: NJC2(authors)-with comments.docx

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14 Sep 2021

Dear Roland Paile Bendaña

Thank you for sending the feedback. Overall, the manuscript has been extensively revised to address the concerns raised by the editor. The specific comments and changes are listed as follows in a point-to-point manner.

Response to Editor Comments:

1. Thank you for uploading your figures as individual figure files. Before we can proceed, please still remove the images from the manuscript file, and upload an updated version of this doc.

Response #1: We have removed the pictures in the manuscript and submitted a new manuscript.

Thanks again for the comments and valuable suggestions to improve our manuscript.

Kind regards,

Shusheng Yin.

Submitted filename: Responses to Reviewers.docx

Click here for additional data file.

5 Oct 2021

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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PLOS ONE

Additional Editor Comments (if provided):

After reading through authors revision, I found authors have made most revisions. However, it cannot meet the requirements.

1. For instance, several abbrevations have been used in the abstract, but it does not provide the full name.

2. Authors have inserted the solid references to support their argument in the context. However, such references have not been updated in the last Reference section. Authors have to address this problem. I will further check this in the next round of review.

3. Authors may be interested in the following references: Yang, J., Wang, Y., Xue, B., Li, Y., Xiao, X., Xia, J. C., & He, B. (2021). Contribution of urban ventilation to the thermal environment and urban energy demand: Different climate background perspectives. Science of The Total Environment, 795, 148791.

Luo, X., Yang, J., Sun, W., & He, B. (2021). Suitability of human settlements in mountainous areas from the perspective of ventilation: A case study of the main urban area of Chongqing. Journal of Cleaner Production, 310, 127467.

4. Moreover， authors are required to provide a document of 'responses to reviewers' in which authors' responses are provided point-by-point..

5. Therefore, please reply to me in the next round based on the document of 'responses to reviewers'. Both the first-round and second-round responses should be provided.

6. Section 6 should be shortened to provide key information.

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: After reading through authors revision, I found authors have made most revisions. However, it cannot meet the requirements.

1. For instance, several abbrevations have been used in the abstract, but it does not provide the full name.

2. Authors have inserted the solid references to support their argument in the context. However, such references have not been updated in the last Reference section. Authors have to address this problem. I will further check this in the next round of review.

3. Authors may be interested in the following references: Yang, J., Wang, Y., Xue, B., Li, Y., Xiao, X., Xia, J. C., & He, B. (2021). Contribution of urban ventilation to the thermal environment and urban energy demand: Different climate background perspectives. Science of The Total Environment, 795, 148791.

Luo, X., Yang, J., Sun, W., & He, B. (2021). Suitability of human settlements in mountainous areas from the perspective of ventilation: A case study of the main urban area of Chongqing. Journal of Cleaner Production, 310, 127467.

4. Moreover， authors are required to provide a document of 'responses to reviewers' in which authors' responses are provided point-by-point..

5. Therefore, please reply to me in the next round based on the document of 'responses to reviewers'. Both the first-round and second-round responses should be provided.

6. Section 6 should be shortened to provide key information.

Reviewer #2: I thank the authors for their responses to my queries, and I am happy to recommend the manuscript for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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29 Oct 2021

Dear editors and reviewers:

Thank you very much for your letter, and the referees’ reports. Based on your comment and request, we have made extensive modification on the original manuscript. Here, we attached the revised manuscript in the formats of both manuscript and editable words for your approval. A document answering every question from the referees was also summarized and enclosed.

A revised manuscript with the correction sections blue marked was attached as the supplemental material and for easy check and editing purpose. If you have any questions, please contact us without hesitation.

Comment 1:For instance, several abbrevations have been used in the abstract, but it does not provide the full name.

Answers to comment 1:

Thank you very much for referees’ reports. I seriously thought about the reviewer’s opinion and carefully answered the question. Based on your question about the full name of academic term , I checked the manuscript specifically.  For the first

time, the explanation for the POI has appeared in the Line 11 of the abstract section .

Original(L8 - L11 ):

This study investigated the relationship between urban form and land surface temperature (LST) using the Multi-access Geographically Weighted Regression (MGWR) model. A case study on Nanjing City was conducted using building data, point-of-interest (POI) data, land use data, remote sensing data, and elevation data.

Amendment(L8 - L11 ):

This study investigated the relationship between urban form and land surface temperature (LST) using the Multi-access Geographically Weighted Regression (MGWR) model. A case study on Nanjing City was conducted using building data, point-of-interest (POI) data, land use data, remote sensing data, and elevation data.

Comment 2:Authors have inserted the solid references to support their argument in the context. However, such references have not been updated in the last Reference section. Authors have to address this problem. I will further check this in the next round of review.

Answers to comment 2:

Thank you very much for referees’ reports. I seriously thought about the reviewer's opinion and carefully answered the question. During the writing process, I have updated all the cited information in the manuscript after careful inspection. On the premise of following the journal citation rules and tracking the latest research

in 2021, it is an honor and pleasure that some inspiration collided in my brain. I, based on the above, amended some expressions of the manuscript, in addition, and adequately proofread the references many times to ensure the rationality and scientific. Once again, I sincerely thank the referees for their careful reports and predecessors for their painstaking research.

Original(L426 - L432 ):

The conclusions of this research and existing research results are mutually confirmed (Han et al., 2016; Yan et al., 2019). In plain areas with low altitude and gentle slopes, the development of human activities is less difficult, human activities are frequent, and construction land is concentrated. Areas with concentrated secondary and tertiary industries are more likely to have higher LST, whereas areas with mountains and agricultural land are likely to have lower LST due to several factors such as altitude and vegetation.

Amendment(L427 - L440 ):

The conclusions of this research and existing research results are mutually confirmed (Han et al., 2014; Yan et al., 2019;Yang et al.,2020). In plain areas with low altitude and gentle slopes, the development of human activities is less difficult, human activities are frequent, and construction land is concentrated. Areas with concentrated secondary and tertiary industries are more likely to have higher LST, whereas areas with mountains and agricultural land are likely to have lower LST due to several factors such as altitude and vegetation(Luo et al.,2021;Peng et al.,2021). It is noteworthy that, unlike the findings of previous studies, an obvious positive correlation was found between urban BH, BD, and LST. However, the LST of high-rise and super high-rise building areas was found to be lower than that of mid-rise building areas. This could be explained by the expansion of shadow areas generated by super high-rise buildings; similar phenomena were also observed between medium-density building areas and high-density building areas (Meng et al., 2018; Yang et al., 2021).

Originall(L528 - L732 ):

Reference

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33.Milan Gavrilović,Milan Pjević,Mirko Borisov,Goran Marinković,Vladimir M. Petrović. Analysis of Climate Change in the Area of Vojvodina-Republic of Serbia and Possible Consequences[J]. Journal of Geographical Research,2020,02(02):11-19. DOI.10.30564/jgr.v2i2.952.

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39.Ricardo Fonseca, María‐Paz Zorzano‐Mier, Armando Azua‐Bustos, Carlos González‐Silva, Javier Martín‐Torres. A surface temperature and moisture intercomparison study of the Weather Research and Forecasting model, in‐situ measurements and satellite observations over the Atacama Desert [J]. Quarterly Journal of the Royal Meteorological Society, 2019,145(722).

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Amendmentl(L514 - L686 ):

Reference

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2.Cao Jie, Zhou Weiqi, Zheng Zhong, Ren Tian, Wang Weimin. Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature [J]. Landscape and Urban Planning, 2021,206. DOI: 10.1016/j.landurbplan.2020.103979.

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14.Hooker Josh, Duveiller Gregory, Cescatti Alessandro. A global dataset of air temperature derived from satellite remote sensing and weather stations [J]. Scientific Data, 2018,5. DOI:10.1038/sdata.2018.246.

15.Hu Zhaolin, Dai Hui, Hou Fei, Li Erzhu. Spatio-temporal change of urban-rural vegetation phenology and its response to land surface temperature in Northeast China. Acta Ecologica Sinica, 2020, 40(12):4137-4145. DOI:10.5846/stxb201812252806.

16.Li Bin, Wang Huimin, Qin Mingzhou, et al. Comparative study on the correlations between NDVI, NDMI and LST [J]. Progress in Geography, 2017,36(05):585-596. DOI: 10.18306/dlkxjz.2017.05.006.

17.Li Dan, Tian Peipei, Luo Hongying, et al. Impacts of land use and land cover changes on regional climate in the Lhasa River basin, Tibetan Plateau [J]. Science of The Total Environment, 2020,742. DOI: 10.1016/j.scitotenv.2020.140570.

18.Li, Yunfei, Schubert Sebastian, Kropp J. et al. On the influence of density and morphology on the Urban Heat Island intensity[J]. Nature communications, 2020, 11(1):1-9. DOI: 10.1038/s41467-020-16461-9.

19.Liu Lu., Shen Guangrong., Wu Yu., et al. Characteristics of Land Use Transfer and Its Influence on Thermal Environment in County-level Urbanization [J]. Bulletin of Soil and Water Conservation, 2019,39(06):260-266. DOI:10.13961/j.cnki.stbctb.2019.06.038.

20.Liu Shengjie, Shi Qian. Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020,164:229-242 DOI.10.1016/j.isprsjprs.2020.04.008

21.Liu Yanxu , Peng Jian , Wang Yanglin . Efficiency of landscape metrics characterizing urban land surface temperature[J]. Landscape and Urban Planning,2018,180. DOI:10.1016/j.landurbplan.2018.08.006.

22.Liu Yaobin, Leng Qingsong. Urbanization, Population Agglomeration and Haze Changes: Based on Threshold Regression and Spatial Partition [J]. Ecological Economy, 2020,36(03):92-98.

23.Luo Xue,Yang Jun,Sun Wei,et al. Suitability of human settlements in mountainous areas from the perspective of ventilation: A case study of the main urban area of Chongqing[J]. Journal of Cleaner Production,2021,310:DOI: 10.1016/J.JCLEPRO.2021.127467.

24.Mashhoodi Bardia, Stead Dominic, van Timmeren Arjan. Spatializing household energy consumption in the Netherlands: Socioeconomic, urban morphology, microclimate, land surface temperature and vegetation data [J]. Data in Brief, 2020,29. DOI:10.1016/j.dib.2020.105118.

25.Meng Qingyan, Sun Yunxiao, Zhang Jiahui,et al. Assessing Vertical Greenery Distribution and Spatial Allocation based on Multi-source Remote Sening:A Case Study of Székesfehérvár City, Hungary, [J]. Remote Sensing Technology and Application, 2018,33(02):370-376. DOI：10.11873/J.ISSN.1004-0323.2018.2.0370.

26.National Bureau of Statistics http://data.stats.gov.cn.

27.Oke T R. Canyon geometry and the nocturnal urban heat island: comparison of scale model and field observations [J]. International Journal of Climatology, 1981,1(3):237-254.

28.Peng Jian , Dan Yuzhuo , Qiao Ruilin ,et al. How to quantify the cooling effect of urban parks? Linking maximum and accumulation perspectives [J]. Remote Sensing of Environment, 2021,252:112-135. DOI.: 10.1016/j.rse.2020.112135.

29.Qin Zhihao, Zhang Minghua, Arnon Karnieli, Pedro Berliner. Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM6 data [J]. Acta Geographica Sinica, 2001(04):456-466. DOI:10.1142/S0252959901000401.

30.Qiu Lisha, He Yi, Zhang Lifeng, Wang Wenhui, Tang Yuanwei. Spatiotemporal variation characteristics and influence factors of MODIS LST in Qilian Mountains [J]. Arid Land Geography, 2020,43(03):721-737. DOI:10.12118/j.issn.1000-6060.2020.0319.

31.Scarano Mancini. Assessing the relationship between sky view factor and land surface temperature to the spatial resolution [J]. International Journal of Remote Sensing, 2017,38(23):6910-6929. DOI: 10.1080/01431161.2017.1368099

32.Shi Xin, Zhou Maichun, Liu Zhenhua, et al. Comparative Analysis on Three Land Surface Temperature Inversion Algorithm based on Landsat 8 Data over Sanheba Basin [J]. Remote Sensing Technology and Application, 2018,33(03):465-475. DOI:10.11873/j.isn.1004-0323.2018.3.0465.

33.Song Ting, Duan Zheng, Liu Junzhi, et al. Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite [J]. Journal of Remote Sensing, 2015,19( 3) : 451-464. DOI: 10.11834 /jrs. 20154180.

34.Stewart I D , Oke T R . Local Climate Zones for Urban Temperature Studies[J]. Bulletin of the American Meteorological Society, 2012, 93(12):1879-1900.DOI:10.1175/BAMS-D-11-00019.1

35.Stewart I D. A systematic review and scientific critique of methodology in modern urban heat island literature [J]. International Journal of Climatology, 2011,31(2):200-217. DOI.10.1002/joc.2141.

36.Stewart, I D.,Oke, T R. Local Climate Zone for Urban Temperatures Studies[J]. Bulletin of the American Meteorological Society,2012,93(12):DOI:10.1175/BAMS-D-11-00019.1.

37.Tang Chengli, Chen Weiyang, Wu Jiamin.et al. Spatial distribution and industrial agglomeration characteristics of development zones in the Yangtze River Economic Belt. Scientia Geographica Sinica, 2020,40(4):657-664. doi:10.13249/j.cnki.sgs.2020.04.018.

38.Wan K W, Tsang C L, Lam J C. Sensitivity analysis of building energy use in different climates [J]. IFAC Proceedings Volumes, 2010, 43(1):58-62.DOI. 10.3182/20100329-3-PT-3006.00013

39.Wang Hui, Wang Qi. The Pollution Emission and Urbanization in China：Based on Input-output Analysis [J]. Chinese Journal of Population Science, 2011(05):57-66+111-112.

40.Wang Xiaoni., Catherine Prigent. Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements [J]. Remote Sensing, 2020,12(3).DOI: 10.3390/rs12030583.

41.Wu Qiong, Gong Jian, Yang Jianxin. Multi-scale Anaysis of Main Factors of Summer Thermal Field in Wuhan Based on POI Data [J]. Resources and Environment in the Yangtze Basin, 2020,29(01):200-210. DOI:10.11870/cjlyzyyhj202001018.

42.Xue Yayong, Liang Haibin, Zhang Yuan, Wang Xiaofeng. Spatial and Temporal Variations of Land Surface Temperature of the Loess Plateau [J]. Earth and Environment, 2017,45(05):500-507. DOI: 10.14050 /j.cnki.1672-9250.2017.05.002.

43.Yan Guanghua, Su Junru, Guan Dunyi. The Impact of Urban Architectural Vertical Characteristics on Urban Thermal Environment in Zhongshan District, Dalian [J]. Scientia Geographica Sinica, 2019,39(1): 125-130. DOI: 10.13249/j.cnki.sgs.2019.01.014.

44.Yang Chen, Zhan Q, Gao Sihang, et al., Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale[J]. Geo-spatial Information Science,2020,23(4). DOI: 10.1080/10095020.2020.1834882.

45.Yang Jun ,Jin Shanhe ,Xiao Xiangming et al. Local climate zone ventilation and urban land surface temperatures: Towards a performance-based and wind-sensitive planning proposal in megacities[J]. Sustainable Cities and Society,2019,47.DOI: 10.1016/j.scs.2019.101487.

46.Yang Jun,Wang Yichen,Xue Bing,et al. Contribution of urban ventilation to the thermal environment and urban energy demand: Different climate background perspectives[J]. Science of the Total Environment,2021,795:DOI: 10.1016/J.SCITOTENV.2021.148791.

47.Yang Jun.,Sun Jing.,Ge Quansheng.,et al. Assessing the impacts of urbanization-associated green space on urban land surface temperature: A case study of Dalian, China[J]. Urban Forestry & Urban Greening,2017,22:DOI: 10.1016/j.ufug.2017.01.002.

48.Zhang Hui, Yuan Fenghui, Wang anzhi, Guan Dexin, Dai Guanhua, Wu Jiabing. Variation characteristics of NDVI and its response to climatic change in the growing season of Changbai Mountain Nature Reserve during 2001 and 2018 [J]. Chinese Journal of Applied Ecology, 2020,31(04):1213-1222. DOI:10.13287/j.1001-9332.202004.020.

49.Zhang Yujia, Middel Ariane, Turner B L. Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression [J]. Landscape Ecology, 2019. DOI: 10.1007/s10980-019-00794-y.

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Comment 3: Moreover， authors are required to provide a document of 'responses to reviewers' in which authors' responses are provided point-by-point.

Answers to comment 3:

Thank you very much for referees’ reports. I seriously thought about the reviewer's opinion and carefully answered the question. After the revision, I have been more cautious about the content that reviewers focused on. Named as the response to the reviewer, the document, as a separate file, is attached to the response to the response to the manuscript.

Comment 4:Therefore, please reply to me in the next round based on the document of 'responses to reviewers'. Both the first-round and second-round responses should be provided.

Answers to comment 4:

Thank you very much for referees’ reports. I seriously thought about the reviewer's opinion and carefully answered the question. After the revision, I am willing to provide additional documents for the first and second rounds of revision responses. Nominated as the response to the reviewer, the files mentioned above are attached to the response to the manuscript.

Comment 5:Section 6 should be shortened to provide key information.

Answers to comment 5:

Thank you very much for referees’ reports. After intense internal brain-storming, we seriously reviewed the sixth section. Strongly and humbly, we agreed with and adopted the comments of the reviewers. Based on the above internal-external joint contributions, we have made the following changes to the parts that reviewers focused on.

On the one hand, based on the selection of impact factors, two-dimensional plane combined with three-dimensional multi-dimensional research perspectives, and research methods, we have re-written the advantage section. By comparing the latest research progress, the manuscript expanded the research perspective of LST, starting from a two-dimensional plane and extending it to a three-dimensional space, further quantifying the impact of different spatial organization types on LST. It is necessary to mention that the manuscript in an earlier period combined MGWR model with LST research , which further proved the correlation of urban spatial structure on LST.

On the other hand, in the limitation section, instead of the previous expressions that may have caused readers' misunderstanding, we have rewritten some expressions that are more likely to arouse readers' discussion. As MGWR model is more widely used in thermal environment research, we look forward to receiving more feedback. In addition to calling on people to pay more attention to the relevance of their own development and environmental changes, we are more inclined to seek solutions, whether it is economic policies or law regulations. Once again, I sincerely thank the referees for their careful reports and predecessors for their painstaking research.

Originall(L425 - L503 ):

6. Advantages and Limitations

The conclusions of this research and existing research results are mutually confirmed (Han et al., 2016; Yan et al., 2019). In plain areas with low altitude and gentle slopes, the development of human activities is less difficult, human activities are frequent, and construction land is concentrated. Areas with concentrated secondary and tertiary industries are more likely to have higher LST, whereas areas with mountains and agricultural land are likely to have lower LST due to several factors such as altitude and vegetation. It is noteworthy that, unlike the findings of previous studies, an obvious positive correlation was found between urban BH, BD, and LST. However, the LST of high-rise and super high-rise building areas was found to be lower than that of mid-rise building areas. This could be explained by the expansion of shadow areas generated by super high-rise buildings; similar phenomena were also observed between medium-density building areas and high-density building areas (Meng et al., 2018; Peng et al., 2021).

A city is a complex system where residents provide certain infrastructure and social relations. The urban thermal environment is the result of a combination of human activities and other factors under certain natural conditions (Zhao et al.,2020; Chen et al.,2019). Due to the problem of data acquisition, the research on the relationship between surface temperature and its influence at home and abroad mostly adopts top-down methods, to a certain extent Ignore the influence of human activities and urban form.

Based on the existing research, this paper determines the impact of different human activities on the urban thermal environment, and further proves that urban green space can help alleviate the urban heat island effect. (Zhou et al.,2018; Zhao et al.,2020). The process of urbanization has led to overpopulation and excessive industrial concentration, causes a change in the nature of heat exchange at the bottom, and aggravating the formation and development of the urban heat island effect, which requires the attention of urban planning agencies. In addition, based on the results of this article, strategies to reduce heat stress by addressing the urban heat island effect, (e.g., control the scale of urban built-up areas, optimize urban spatial structure, increase urban green areas, alleviate urban population concentration and other measures). We should also cooperate with commercial real estate developers to control the height and density of new buildings, optimize the design of future urban parks, increase the construction of urban ventilation corridors and green spaces, and further alleviate the urban heat island effect (Liu et al.,2020).

6.1 Advantages

Based on research of urban thermal environment effect for a single data source influencing factors, and the problem of analysis method of single with higher level of urbanization in Nanjing as the research object, combined with digital elevation data urban building land use type data and POI data set multi-source spatial information data, comprehensive utilization of quantitative inversion and MGWR analysis methods, such as, on the basis of comprehensive study and the urban thermal environment effect and influence factors, further enrich the research of urban thermal environment research perspective Studies have proved that Geography Weighted Regression(GWR) has been broadly used in various fields to model spatially non-stationary relationships(Liu et al.,2018;Yang et al.,2020).Multi-scale Geographically Weighted Regression(MGWR) is a recent advancement to the classic GWR model. Compared with the traditional GWR model, The MGWR model has advantages in acquiring the ability of different scales (Jin et al.,2021). The MGWR model can effectively analyze the multi-scale relationship between the urban thermal environment and its influencing factors, and has a positive effect on urban dynamic development and urban thermal environment management.

6.2 Limitations

This study integrated POI data, building data, urban road data sets and other data to analyze the factors affecting the urban thermal environment. However, it should also be noted that POI data represents the geographic information and utilization characteristics of various facilities, and can only represent a certain Whether the utilization characteristics of each time node can represent the historical utilization characteristics and intensity still needs to be studied. Secondly, as for the classification of building height in Nanjing, this study is based on the established unified standard. However, if we can carry out extensive discussion based on the actual situation of the study area, and finally determine the most reasonable height classification, the research will have more practical value. Thirdly, existing researches mostly focus on the status quo of urban thermal environment, and there is still a lack of in-depth research on the prediction and analysis of urban thermal environment evolution trends, the construction of a heat island effect warning mechanism, and urban space optimization strategies.

The MGWR model facilitated multi-factor analysis of LST. However, due to issues such as data availability and collinearity, the application of the MGWR model has certain limitations. The urban landscape is a complex dynamic system composed of infrastructure, human activities, and social connections. Changes in urban surface temperature need to be observed from a more micro perspective (Cao et al., 2021; Li et al., 2020). Urban ground monitoring data have not been fully disclosed, which limits the study. In addition, street view data were used in the study of the urban thermal environment, and the number of street scenes in this area requires further investigation (Zhang et al., 2019). In the future, the interaction between different influencing factors should be considered, and the influencing factors of LST should be analyzed in more detail to provide a more comprehensive perspective for urban or regional environmental governance and planning.

Amendmentl(L426 - L490 ):

6. Advantages and Limitations

The conclusions of this research and existing research results are mutually confirmed (Han et al., 2014; Yan et al., 2019;Yang et al.,2020). In plain areas with low altitude and gentle slopes, the development of human activities is less difficult, human activities are frequent, and construction land is concentrated. Areas with concentrated secondary and tertiary industries are more likely to have higher LST, whereas areas with mountains and agricultural land are likely to have lower LST due to several factors such as altitude and vegetation(Luo et al.,2021;Peng et al.,2021). It is noteworthy that, unlike the findings of previous studies, an obvious positive correlation was found between urban BH, BD, and LST. However, the LST of high-rise and super high-rise building areas was found to be lower than that of mid-rise building areas. This could be explained by the expansion of shadow areas generated by super high-rise buildings; similar phenomena were also observed between medium-density building areas and high-density building areas (Meng et al., 2018; Yang et al., 2021).

Based on the existing research, this paper determines the impact of different human activities on the urban thermal environment, and further proves that urban green space can help alleviate the urban heat island effect. (Yang et al.,2017;Zhao et al.,2020). The process of urbanization has led to overpopulation and excessive industrial concentration, causes a change in the nature of heat exchange at the bottom, and aggravating the formation and development of the urban heat island effect, which requires the attention of urban planning agencies. In addition, based on the results of this article, strategies to reduce heat stress by addressing the urban heat island effect, (e.g., control the scale of urban built-up areas, optimize urban spatial structure, increase urban green areas, alleviate urban population concentration and other measures). We should also cooperate with commercial real estate developers to control the height and density of new buildings, optimize the design of future urban parks, increase the construction of urban ventilation corridors and green spaces, and further alleviate the urban heat island effect (Liu et al.,2020).

6.1 Advantages

This study integrates POI data, building data, urban road data sets and other data to analyze the factors that affect the urban thermal environment. First, in order to assess the human activities that may be responsible for the model described here, we compare land use data with population density data, etc., and conclude that the POI data represents the geographic information and utilization characteristics of various facilities. Secondly, for the classification of building height in Nanjing, after many field investigations and analysis of historical remote sensing images, a more reasonable density classification (limited to 40%) is finally determined. This research will have greater practical value. Third, this study uses a multi-dimensional perspective (two-dimensional and three-dimensional structure) to study the current status of Nanjing's thermal environment, and explores the current status of Nanjing's thermal environment from a more specific plane dimension and a deeper perspective. In view of the multi-dimensional perspective of this study, MGWR is used instead of GWR to explore non-stationary relationships in the modeling space (Liu et al., 2018; Yang et al., 2020).

Multi-scale Geographically Weighted Regression(MGWR) is a recent advancement to the classic GWR model. Compared with the traditional GWR model, The MGWR model has advantages in acquiring the ability of different scales (Jin et al.,2021). The MGWR model can effectively analyze the multi-scale relationship between the urban thermal environment and its influencing factors, and has a positive effect on urban dynamic development and urban thermal environment management.

6.2 Limitations

The MGWR model facilitated multi-factor analysis of LST. However, due to issues such as data availability and collinearity, the application of the MGWR model has certain limitations. The urban landscape is a complex dynamic system composed of infrastructure, human activities, and social connections. Changes in urban surface temperature need to be observed from a more micro perspective (Cao et al., 2021; Li et al., 2020). Urban ground monitoring data have not been fully disclosed, which limits the study. In addition, street view data were used in the study of the urban thermal environment, and the number of street scenes in this area requires further investigation (Zhang et al., 2019). In the future, the interaction between different influencing factors should be considered, and the influencing factors of LST should be analyzed in more detail to provide a more comprehensive perspective for urban or regional environmental governance and planning.

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5 Nov 2021

Relationship between urban morphology and land surface temperature – a case study of Nanjing City

PONE-D-21-03097R2

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21 Jan 2022

PONE-D-21-03097R2

Relationship between urban morphology and land surface temperature –– a case study of Nanjing City

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