Analyzing the Spatial Heterogeneity of the Built Environment and Its Impact on the Urban Thermal Environment—Case Study of Downtown Shanghai

Ongoing urban expansion has accelerated the explosive growth of urban populations and has led to a dramatic increase in the impervious surface area within urban areas. This, in turn, has exacerbated the surface heat island effect within cities. However, the importance of the surface heat island effect within urban areas, scilicet the intra-SUHI effect, has attracted less concern. The aim of this study was to quantitatively explore the relationship between the spatial heterogeneity of a built environment and the intra-urban surface heat island (intra-SUHI) effect using the thermally sharpened land surface temperature (LST) and high-resolution land-use classification products. The results show that at the land parcel scale, the parcel-based relative intensity of intra-SUHI should be attributed to the land parcels featured with differential land developmental intensity. Furthermore, the partial least squares regression (PLSR) modeling quantified the relative importance of the spatial heterogeneity indices of the built environment that exhibit a negative contribution to decreasing the parcel-based intra-SUHI effect or a positive contribution to increasing the intra-SUHI effect. Finally, based on the findings of this study, some practical countermeasures towards mitigating the adverse intra-SUHI effect and improving urban climatic adaption are discussed.


Introduction
Since the era of the industrial revolution, driven by capital flows and labor transfers, urban agglomerations and metropolitan areas have become the preferred destinations for urban-rural and cross-border migrants worldwide [1][2][3]. At present, global urban areas house more than 50% of the total population and 70-90% of all economic activities [4], resulting in unprecedented urban expansion and explosive growth of urban populations. The United Nations Department of Economic and Social Affairs has reported that about 55% of the global population lives in urban areas. By 2050, global urban residents will increase by 2.5 billion, of which 255 million will be in China, such that the urban population will account for 68% of the country's total population [5].
During urban expansion, impervious surfaces, such as buildings, roads, squares, bridges, and parking lots, dominate the land-use structure of the built environment. They consequently occupy and replace the area proportion of the pre-development landscapes (e.g., water bodies and vegetation) [6]. The intensive human activities in cities lead to severe ecological degradation [7], which has the consequence of artificial modification of the urban climate; in particular, the urban heat island (UHI) effect and its influences on human health have been of wide concern. To restore the urban natural environment and that the recent literature emphasizing such relationships is relatively scarce. Moreover, the robustness and applicability of such approaches in practice need further testing in a variety of case studies.
In this study, Shanghai-one of the fast-growing megacities suffering from extreme summertime heat events-was taken as a case study. Our research goals were (1) to quantitatively analyze the spatial heterogeneity of the built environment and its impact on the summertime intra-SUHI effect within the city, and (2), based on the findings of this study, to provide the operational choices for decision making towards enhancing urban planning practices, mitigating the intra-SUHI effect, and improving urban climate adaption.

Study Area
Shanghai is located between latitudes 30 • 40 N-31 • 53 N and longitudes 120 • 52 E-122 • 12 E, in the front of the alluvial plain of the Yangtze River Delta (see Figure 1). The whole city is low-lying and flat, dominated by plains (with an area of 93.91%) and an average altitude of 2.19 m. Shanghai is located in the north sub-tropical monsoon climate zone, with abundant sunshine, abundant rain, and four obvious seasons. Local vegetation types are dominated by evergreen broad-leaved forest and evergreen deciduous broad-leaved mixed forest [25]. At present, the wetland stock is 464,600 hectares, and the forest coverage rate of the whole city reaches 17.6%. In this study, four typical urban functioning zones (UFZs), which represent the socio-economic features and urban land-use patterns within downtown Shanghai (Table 1), were used to investigate the relationship between the urban built environment and intra-SUHI effect. SUHI effect [23,24]. Such an approach can provide a better understanding of the relationship between the spatial heterogeneity of the built environment and the urban thermal environment, considering that the recent literature emphasizing such relationships is relatively scarce. Moreover, the robustness and applicability of such approaches in practice need further testing in a variety of case studies. In this study, Shanghai-one of the fast-growing megacities suffering from extreme summertime heat events-was taken as a case study. Our research goals were (1) to quantitatively analyze the spatial heterogeneity of the built environment and its impact on the summertime intra-SUHI effect within the city, and (2), based on the findings of this study, to provide the operational choices for decision making towards enhancing urban planning practices, mitigating the intra-SUHI effect, and improving urban climate adaption.

Study Area
Shanghai is located between latitudes 30°40′ N-31°53′ N and longitudes 120°52′ E-122°12′ E, in the front of the alluvial plain of the Yangtze River Delta (see Figure 1). The whole city is low-lying and flat, dominated by plains (with an area of 93.91%) and an average altitude of 2.19 m. Shanghai is located in the north sub-tropical monsoon climate zone, with abundant sunshine, abundant rain, and four obvious seasons. Local vegetation types are dominated by evergreen broad-leaved forest and evergreen deciduous broadleaved mixed forest [25]. At present, the wetland stock is 464,600 hectares, and the forest coverage rate of the whole city reaches 17.6%. In this study, four typical urban functioning zones (UFZs), which represent the socio-economic features and urban land-use patterns within downtown Shanghai (Table 1), were used to investigate the relationship between the urban built environment and intra-SUHI effect.

Materials
Landsat 8 OLI/TIRS (Level 1T) and Quickbird imagery were used as the major data sets. Excluding the cloud contaminations, two cloud-free Landsat 8 OLI/TIRS images (path/row: 118/038, cloud cover < 10%) acquired during typical summer days (dated 13 August 2013 and 3 August 2015) were used for the retrieval of the LST and further exploration of the pattern of the urban thermal environment. Quickbird commercial highresolution imagery covering the four UFZs was used to classify the fine-scale land-use structure. The auxiliary data sets included a commercial vector map data of Shanghai city (roads, buildings, land-uses, and so on) and an aerial remote sensing atlas of the Shanghai central area. A standard digital map of Shanghai downtown (Beijing Digital Space Technology Co., LTD, 2015, Beijing, China), an aerial atlas of Shanghai (Shanghai Academy of Surveying and Mapping 2015, Shanghai, China), Google Earth, and Baidu Map were used as further auxiliary data sets.

Land-Use Classification
To better depict the fine-scale land-use characteristics from the Quickbird highresolution imagery, the object-oriented classification (OOC) method, which has higher classification accuracy than regular methods, such as spectral feature extraction and classification and regression trees (CART) [26], was used for land classification within the four UFZs.
For the Quickbird high-resolution imagery covering the four UFZs, classification with the OOC method was performed with the use of PIE-Basic ® (version 6.0) software by PIESAT International Information Technology Limited. The overall accuracy of classification was 80.03%, and the accuracy of the post-classification was determined to be 93.01% with a manual check using Google Earth and Baidu Map layers, as well as a field survey. The validated land-use classification product and its classification scheme are shown in Figure A1 and Table A1, respectively.
In this study, considering the simplicity, representability, and availability of the data sets, the spatial heterogeneity of the built environment was measured using the two-and three-dimensional indices of urban morphology and land surfaces. Table 2 lists several heterogeneity indices that may positively or negatively contribute to the urban thermal environment (for details, see Table A2). According to the National Standard for Urban Residential Planning and Design of China (GB50180-2018) [27], building heights, distances between buildings, and the SVF were calculated from data collected through in situ measurements. The land surfaces, including the impervious surfaces and BGS, were extracted from the land-use classification maps. Specifically, for BGS, the parcelbased land-use information was used to calculate the class-level pattern indices-namely, the mean patch size (MPS), the number of patches (NP), the largest patch index (LPI), and the SPLIT index-using Fragstats 4.2.1 software [28]. The three-dimensional green volume (3DGV) of the BGS, which refers to the total volume of vegetation with stratified layers, was estimated using several empirical models, ground measurements, and aerial photogrammetry [29].

Generation of Thermally Sharpened LST and Cross-Validation
The process for generating the thermally sharpened LST consisted of four steps. First, the 10 gray value thermal band Landsat 8 Level 1T data was converted to the top of atmosphere (TOA) radiance using the rescaling factors in the MTL file [30].
Secondly, co-Kriging interpolation was employed to generate the high-resolution TOA radiance by combining the Quickbird high-resolution land-use classification products and the raw TOA radiance [31]. Given the different resolutions of these two data sets, the high-resolution land-use classification products were resampled with multiple resolutions (1-9 m) and set as base maps to overlap and delimit the raw TOA radiance layer. We Sustainability 2021, 13, 11302 5 of 17 assumed that the same or similar surfaces would have the same or similar radiance values. Then, for each scene of the TOA radiance layer, tedious sampling of random points (ranging between 300 and 6000 points per km 2 ) was carried out in order to generate the co-Kriging interpolation results. By comparing the pairwise bias curve between the raw and interpolated TOA radiance values, we found that the threshold of 3000 points was reasonable, as the curve flattened and was nearly unchanged when the number of points was over 3000 [32]. Subsequently, the interpolated TOA radiance layer was resampled to multiple resolutions (1-9 m). Third, based on the multiple-resolution land-use maps, a surface emissivity correction for the land surfaces was performed according to empirical studies and laboratory testing [33,34]. The multiple-resolution interpolated TOA radiance and corrected surface emissivity layers were used to retrieve the thermally sharpened LST using the Range Transfer Equation (RTE) [35], which requires atmospheric correction for the thermal band [36].
Finally, cross-validation of the thermally sharpened LST products was performed by comparing the pixel-based root-mean-square error (RMSE) between the target LSTs (sharpened) and referencing LSTs [37]. To do so, all the thermally sharpened LST products were resampled to the same resolution as the unsharpened LST products (30 m). Then, by overlapping the 30 m unsharpened LST products and the LST products resampled from the sharpened products, the pixel-to-pixel 10-fold RMSEs were used for cross-validation of the thermally sharpened LST products. As shown in Figure A3, there were no significant differences in the RMSEs of the LST products (resampled from the 1-9 m sharpened LST products) and the original 30 m LST products; however, as they exhibited the best visual quality and the lowest RMSE, the 1 m resolution sharpened LST products were used for further analysis.

Calculation of Intra-SUHII
Rather than using the generalized concept of the UHI effect as measured by the LST difference between urban and rural areas, the intra-SUHI intensity in this study is defined as the LST difference between the impervious surfaces and the BGS (vegetated land and water bodies) in a given land parcel. The parcel-based intra-SUHII is calculated as follows [38]: where the unit of intra-SUHII is Kelvin (K), LST IS represents the average LST of the impervious surfaces, and LST BGS represents the average LST of the BGSs. At the land parcel level, according to the statistical description and analysis method, the intra-SUHIIs were divided into six levels, according to the percentile threshold on the cumulative probability curve of their normal distribution, as follows: Level 1-very low (≤5%); Level 2-low (5-25%); Level 3-low to slightly high (25-50%); Level 4-mediumhigh (50-75%); Level 5-high (75-95%); Level 6-very high (≥95%).

Statistical Analysis
This section mainly describes hierarchical cluster analysis (HCA) and partial least squares regression (PLSR) modeling. The former was used for the overall characterization of the land parcel clusters with differential land developmental intensity, while the latter was used for exploring the relationship between the intra-SUHII and multiple independent variables, particularly in the case of multi-collinearity. Essential data analysis was performed, including a normality test, outlier detection, Box-Cox transformation for skewed data, and Pearson's correlation. With the indices described in Table 2, HCA was performed using the Euclidean distance method. Seven typical land parcel clusters representing differential land developmental intensity were obtained (see Table A3).
The statistically significant correlation coefficients (see Table 4) revealed the existence of multi-collinearity between the independent variables. According to the result of Pearson correlation analysis, as there are many possible forms of PLSR models that involve the independent variables, it is time-consuming to establish the PLSR models. To avoid overfitting and determine a reasonable model, the leave-one-out (LOO) method was adopted, setting 90% of the randomly selected data as the training data and the rest as the test data. Finally, the optimal PLSR model, satisfying the highest determination coefficient (R 2 ) and minimum root mean square error (RMSE), was selected. The PLSR model, indicating the relationship between the intra-SUHII and the impervious surfaces, was written as follows: where α 1 is the intercept/constant item, β 1 -β 5 are the partial coefficients; X 1 -X 5 are the height, distance, SVF, ImperSurf, and parcel area, respectively; ε 1 is the error term.
Similarly, the PLSR model, indicating the relationship between the intra-SUHII and the BGS, was written as follows: where α 1 is the intercept/constant item, β 1 -β 7 are the partial coefficients; X 1 -X 7 are the 3DGV, BGS, MPS, NP, LPI, SPLIT, and parcel area, respectively; ε 1 is the error term.
In this study, all the essential statistical processes were performed using R statistical software 4.0.3 (developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues), and PLSR modeling was performed using the library 'pls' [39].

Spatial Distribution Characteristics of the Urban Thermal Environment
Overall, Figure 2 reveals the spatial distribution of the urban thermal environment indicated with parcel-based LSTs and intra-SUHII levels on two summer days of 2013 and 2015, respectively. Figure 2a-h shows the remarkable pixel-based LST difference between the land parcels dominated with impervious surfaces and BGS. Figure 2 (a-1-h-1) shows the variations of the parcel-based intra-SUHII levels, which exhibit the similar spatial patterns shown in Figure 2a-h. Table 3 shows the statistics of parcel-based LST associated with seven typical land parcel types featured with different land developmental intensities (see Table A3). As shown, Type I and VII parcels exhibited the lowest and second-lowest mean LSTs, followed by Type IV parcels. Apparently, these Type I, IV, and VII parcels, which featured dominant BGS and lower impervious surfaces, exhibited much lower mean LSTs than the other land parcel types. It is noteworthy that Type III, V, and VI parcels exhibited much higher mean LSTs, indicating that the adverse thermal effect is related to higher developmental intensity and lacking BGS. However, for Type V parcels, their temporally highest mean LSTs could be alleviated if newly BGS are created in their later management.  Table 4 shows the significant positive and negative correlations between the spatial heterogeneity indices of the built environment, indicating the complicated relationships between the BGS and urban morphological characters. As shown, a competitive land-use structure exists between the ImperSurf and BGS across the land parcels. The significant negative coefficients between the ImperSurf and the BGS, LPI, MPS, and 3DGV indicate that the dominance of the ImperSurf is inclined to decrease the BGS, LPI, MPS, and 3DGV. The significant positive coefficient between the ImperSurf and the SPLIT indicates that the ImperSurf is inclined to increase the SPLIT since the dominance of the ImperSurf usually causes the absence of BGS and, consequently, an uneven pattern of BGS. On the other hand, the significant positive coefficients between the building distances and BGS, LPI, MPS, and 3DGV indicate that, to a certain extent, wider distances between buildings help shape a better BGS landscape configuration since the former provides available space for the   Table 5 quantifies the relationship between the impervious surfaces' two-and threedimensional indices and parcel-based intra-SUHII on two summer days. Herein, considering the independent variables were measured in different units, the standardized regression coefficients (S-Coefs) were used to interpret the results of the PLSR models. As shown, the PLSR models account for approximately 48.7-49.8% of the variance of parcel-based intra-SUHII in response to the independent variables. The positive and negative S-Coefs indicate their relative importance or strength in determining the variance of parcel-based intra-SUHII. The positive S-Coefs of ImperSurf indicate it exerted a much higher influence on increasing the parcel-based intra-SUHII. When controlling the other independent variables, each standard deviation increase in ImperSurf resulted in a 0.455~0.480 standard deviation increase in parcel-based intra-SUHII on two typical summer days. In contrast, the small positive S-Coefs of the parcel area indicate its very weak contribution to the increase in the parcel-based intra-SUHII. The negative S-Coefs of distance, height, and SVF indicate their descending ordinal of relative importance in negatively contributing to the variance of parcel-based intra-SUHII, as each standard deviation increase measured in these indices   Table 6 shows the PLSR models account for approximately 41.7-43.1% of the variance of parcel-based intra-SUHII in response to the independent variables. The positive S-Coefs of SPLIT, parcel area, and MPS indicate their influence on increasing the parcel-based intra-SUHII. When controlling the other independent variables, each standard deviation increase in the SPLIT and parcel-area resulted in 0.186~0.198 standard deviation increases and 0.079~0.105 standard deviation increases in parcel-based intra-SUHII, respectively. In contrast, the smaller S-Coefs of MPS indicate its very weak importance in increasing the parcel-based intra-SUHII. Meanwhile, the descending sequence of negative S-Coefs of LPI, BGS, NP, and 3DGV indicate their differential relative importance in negatively contributing to the variance of parcel-based intra-SUHII, as each standard deviation increase measured in these indices caused −0.169~−0.159, −0.142~0.137, −0.064~−0.028, and −0.047~−0.026 standard deviation decreases in the parcel-based intra-SUHII, respectively. Note: Coef and S-Coef represent unstandardized partial regression and standardized partial regression coefficients, respectively.

Discussion
The findings of this study show that, to an extent, the goal of alleviating the intra-SUHI effect can be achieved via optimizing land parcel design, for instance, by increasing the building distance/spacing and SVF, increasing the area proportion of BGS and 3DGV, and improving the spatial configuration of BGS. Taking the Cui-hu-tian-di (CHTD) modern residence (well planned) and the neighboring old residence (poorly planned) as examples, Figures 3 and 4 show the contrasting intra-SUHI effects between these two land parcels at the Urban Core UFZ. As can be seen, the CHTD modern residence, with higher building spacing (averaged 46 m) and higher BGS cover (39%), exhibits overall lower LSTs (ranging between 307 K and 318 K, and averaging 309 K). Moreover, together with the trees, the higher buildings with wide spacing help create shadow areas and ventilation corridors for cooling. In contrast, the neighboring old residence, with poorly planned building spacing (averaging 5 m) and lacking BGS, exhibits much higher LSTs (ranging between 309 and 330 K, and averaging 317 K). Obviously, for such an old residence, if future urban regeneration is performed with the land parcel design attributes of the CHTD modern residence, then the intra-SUHI effect will be substantially enhanced. . In the sense of urban resilience and urban planning, our findings exemplify the relative importance of land parcel design attributes in positive or negative contributions to the intra-SUHI effect. However, there are some shortcomings of this study. Firstly, due to the 16-day revisiting interval of the Landsat 8 satellite and cloud contamination [30], the TIRS data captured within the instantaneous field of view (IFOV) could not provide sufficient a time series of thermal images for studying the seasonal variation of the intra-SUHI effect. Secondly, until now, the satellite-borne thermal sensors could not generate the ~m resolution data. We used thermally sharpened LSTs, which lack in situ measurements for validation since there are no weather stations or long-term observation sites of the four UFZs in downtown Shanghai [6,37]. Thirdly, the focus of this study was on the possible linkage between solar radiation and the thermal effect of land surfaces in built environments, regardless of the influence of the micro-climatic conditions. Thus, the interpretation power of the PLSR models is somewhat low. Fourthly, the findings of this study ignore the influence of anthropogenic heat emission, considering the complicated relationship that exists between the LST-based intra-SUHI effect and the AT-based UHI effect at a small scale.
Given the above shortcomings, future research should focus on the multi-point distribution of data monitoring and improve the empirical research system. Data assimilation processes, including three-dimensional modeling, computational fluid dynamics (CFD), machine learning methods and other technologies, the long-term in situ observation data, thermal infrared remote sensing data, and numerical simulation results, can provide ensured outputs with cross-validation and improve the accuracy of the prediction modeling. . In the sense of urban resilience and urban planning, our findings exemplify the rela tive importance of land parcel design attributes in positive or negative contributions to the intra-SUHI effect. However, there are some shortcomings of this study. Firstly, due to the 16-day revisiting interval of the Landsat 8 satellite and cloud contamination [30], th TIRS data captured within the instantaneous field of view (IFOV) could not provide suf ficient a time series of thermal images for studying the seasonal variation of the intra SUHI effect. Secondly, until now, the satellite-borne thermal sensors could not generat the ~m resolution data. We used thermally sharpened LSTs, which lack in situ measure ments for validation since there are no weather stations or long-term observation sites o the four UFZs in downtown Shanghai [6,37]. Thirdly, the focus of this study was on th possible linkage between solar radiation and the thermal effect of land surfaces in buil environments, regardless of the influence of the micro-climatic conditions. Thus, the in terpretation power of the PLSR models is somewhat low. Fourthly, the findings of thi study ignore the influence of anthropogenic heat emission, considering the complicated relationship that exists between the LST-based intra-SUHI effect and the AT-based UH effect at a small scale.
Given the above shortcomings, future research should focus on the multi-point dis tribution of data monitoring and improve the empirical research system. Data assimila tion processes, including three-dimensional modeling, computational fluid dynamic (CFD), machine learning methods and other technologies, the long-term in situ observa tion data, thermal infrared remote sensing data, and numerical simulation results, can provide ensured outputs with cross-validation and improve the accuracy of the prediction modeling. In the sense of urban resilience and urban planning, our findings exemplify the relative importance of land parcel design attributes in positive or negative contributions to the intra-SUHI effect. However, there are some shortcomings of this study. Firstly, due to the 16-day revisiting interval of the Landsat 8 satellite and cloud contamination [30], the TIRS data captured within the instantaneous field of view (IFOV) could not provide sufficient a time series of thermal images for studying the seasonal variation of the intra-SUHI effect. Secondly, until now, the satellite-borne thermal sensors could not generate the~m resolution data. We used thermally sharpened LSTs, which lack in situ measurements for validation since there are no weather stations or long-term observation sites of the four UFZs in downtown Shanghai [6,37]. Thirdly, the focus of this study was on the possible linkage between solar radiation and the thermal effect of land surfaces in built environments, regardless of the influence of the micro-climatic conditions. Thus, the interpretation power of the PLSR models is somewhat low. Fourthly, the findings of this study ignore the influence of anthropogenic heat emission, considering the complicated relationship that exists between the LST-based intra-SUHI effect and the AT-based UHI effect at a small scale.
Given the above shortcomings, future research should focus on the multi-point distribution of data monitoring and improve the empirical research system. Data assimilation processes, including three-dimensional modeling, computational fluid dynamics (CFD), machine learning methods and other technologies, the long-term in situ observation data, thermal infrared remote sensing data, and numerical simulation results, can provide ensured outputs with cross-validation and improve the accuracy of the prediction modeling.

Conclusions
In this study, we quantitatively examined the spatial heterogeneity of the built environment and its impact on the summertime intra-SUHI effect using the high-resolution land-use classification products and thermally sharpened LSTs. The findings are summarized as follows: 1.
There are remarkable variations of LSTs and intra-SUHII among seven typical land parcels with different land developmental intensities. Overall, land parcels featured with dominant BGS and lower impervious surfaces, particularly parks and recreational landscapes, a university/college campus with a higher green cover, and well-planned modern residences exhibited much lower mean LSTs than the other land parcel types with dense buildings and lacking BGS.

2.
The PLSR models quantitatively revealed the relative importance of the main effect of the urban built environment in determining the variances of the urban thermal environment. The results show that the building distance/spacing, SVF, LPI, and BGS are major negative contributors to decreasing the variance of the parcel-based intra-SUHI effect. In contrast, the ImperSurf and SPLIT are major positive contributors to increasing the variance of the parcel-based intra-SUHI effect.
To sum up, based on the findings, this study provides some practical implications towards alleviating the adverse UHI effects via potentially optimizing the land parcel design attributes, particularly focusing on increasing the two-and three-dimensional indices of BGS and reducing the influence of impervious surfaces. Future urban decisionmaking processes of mitigating UHI effects and improving cities' adaption to climate change should sufficiently embody these key points and produce scientifically sound countermeasures.

Conflicts of Interest:
The authors declare no conflict of interest.   Table A3. Illustrations of seven typical land parcel types with differential land developmental intensity.

Type Introduction
Park and recreational landscape featured with high vegetation cover (≥60%) and low impervious cover (ranging between 5 and 20%).
Park and recreational landscape featured with high vegetation cover (≥60%) and low impervious cover (ranging between 5 and 20%).

Type Introduction
Type II: Mixture of high-density residential and commercial area Park and recreational landscape featured with high vegetation cover (≥60%) and low impervious cover (ranging between 5 and 20%).
Mixture of high-density low-rise, multi-story residential and commercial areas. Building cover varies between 49 and 60%.
Old residential areas with a construction age of more than 50 years because the overall area has not been properly planned (e.g., in terms of roads, greening, housing). Mainly includes two types, 2-3 high-density old residential areas with a building density of 50-88% (averaged 61%), or single-story bungalows or 3-6 floors of old residential apartments with a building density of 46-66% (approximately averaged 56%).
Type IV: Modern residential area Modern residences with scientific and complete overall planning concepts, large distances between high-rise buildings, high vegetation coverage, and complete public service facilities. The building density varies between 25 and 35% (approximately averaged 26%).
Mixture of high-density low-rise, multi-story residential and commercial areas. Building cover varies between 49 and 60%.
Type III: Poorly planned old residential areas Park and recreational landscape featured with high vegetation cover (≥60%) and low impervious cover (ranging between 5 and 20%).
Mixture of high-density low-rise, multi-story residential and commercial areas. Building cover varies between 49 and 60%.
Old residential areas with a construction age of more than 50 years because the overall area has not been properly planned (e.g., in terms of roads, greening, housing). Mainly includes two types, 2-3 high-density old residential areas with a building density of 50-88% (averaged 61%), or single-story bungalows or 3-6 floors of old residential apartments with a building density of 46-66% (approximately averaged 56%).
Type IV: Modern residential area Modern residences with scientific and complete overall planning concepts, large distances between high-rise buildings, high vegetation coverage, and complete public service facilities. The building density varies between 25 and 35% (approximately averaged 26%).
Old residential areas with a construction age of more than 50 years because the overall area has not been properly planned (e.g., in terms of roads, greening, housing). Mainly includes two types, 2-3 high-density old residential areas with a building density of 50-88% (averaged 61%), or single-story bungalows or 3-6 floors of old residential apartments with a building density of 46-66% (approximately averaged 56%). Mixture of urban development land, including land to be demolished for reconstruction, land used for demolition and reconstruction, and land under construction. After the original building is demolished, temporary construction site housing is often built to facilitate construction.
In the mixed area of medium-density residential communities and commercial districts, the building density varies between 27 and 51% Modern residences with scientific and complete overall planning concepts, large distances between high-rise buildings, high vegetation coverage, and complete public service facilities. The building density varies between 25 and 35% (approximately averaged 26%). Mixture of urban development land, including land to be demolished for reconstruction, land used for demolition and reconstruction, and land under construction. After the original building is demolished, temporary construction site housing is often built to facilitate construction.
In the mixed area of medium-density residential communities and commercial districts, the Mixture of urban development land, including land to be demolished for reconstruction, land used for demolition and reconstruction, and land under construction. After the original building is demolished, temporary construction site housing is often built to facilitate construction.

Type Introduction
Type VI: Mixture of medium-density residential and commercial area land to be demolished for reconstruction, land used for demolition and reconstruction, and land under construction. After the original building is demolished, temporary construction site housing is often built to facilitate construction.
In the mixed area of medium-density residential communities and commercial districts, the building density varies between 27 and 51% (approximately averaged 40%).
Except for parks, areas with high green coverage, mainly low-rise, multi-story buildings, with building density varying between 15 and 30%.
In the mixed area of medium-density residential communities and commercial districts, the building density varies between 27 and 51% (approximately averaged 40%).
Type VII: University campus with high green cover and low-to medium-density buildings land to be demolished for reconstruction, land used for demolition and reconstruction, and land under construction. After the original building is demolished, temporary construction site housing is often built to facilitate construction.
In the mixed area of medium-density residential communities and commercial districts, the building density varies between 27 and 51% (approximately averaged 40%).
Except for parks, areas with high green coverage, mainly low-rise, multi-story buildings, with building density varying between 15 and 30%.
Except for parks, areas with high green coverage, mainly low-rise, multi-story buildings, with building density varying between 15 and 30%.