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Contents Influence of Beijing spatial morphology on the distribution of urban heat island The impact of air pollution on urban vibrancy and its built environment heterogeneity: An empirical analysis based on big data Identification and influencing factors of residential environment deprivation in a river valley city: A case study of Lanzhou city Impact of the COVID-19 pandemic on population heat map in leisure areas in Beijing on holidays Changes of employment spatial structure and location pattern of Nanjing metropolitan area Big-data Oriented Commuting Distribution Model and Application in Large Cities Research on the Influence of Functional Diversity of Business District on Its Vitality Spatio-temporal Pattern of Surface Albedo in Beijing and Its Driving Factors based on Geographical Detectors 1 Influence of Beijing spatial morphology on the distribution of urban heat island  LIU Yonghong, XU Yongming, ZHANG Fangmin, SHU Wenjun Acta Geographica Sinica, 2021, 76(7): 1662-1679 Abstract: Exploring the influence of urban spatial morphology layout on the urban heat island (UHI) at the urban scale is of great significance for the improvement of ventilation environment and the ecological and livable urban planning. Taking Beijing, China as an example, this study analyzed the UHI spatial characteristics using the hourly temperature data of high-density automatic weather stations in 2009-2018 and the 2018 NPP/VIRRS night-light satellite data. Using 1:2000 basic geographic information data and Landsat8 satellite remote sensing data in 2017, based on remote sensing and GIS technology and morphological models, we extracted eight morphological parameters in the main urban area of Beijing, namely, building height (BH), building density (BD), building standard deviation (BSD), floor area ratio (FAR), frontal area index (FAI), roughness length (RL), sky view factor (SVF), fractal dimension (FD) and three land surface parameters consisting of vegetation coverage (VC), impervious cover (IC), albedo (AB). The relationship between these morphological parameters and UHI was further examined at the urban scale using the spatial statistical method. Results show that the downtown area of central Beijing has presented a relatively fixed distribution pattern of UHI at annual scale, four seasons, and 02:00 at nighttime in the past 10 years. The UHI of the annual, spring, summer, autumn, winter, 14:00, and 02:00 are 1.81℃, 1.50℃, 1.43℃, 2.16℃, 2.17℃, 0.48℃, and 2.77℃, respectively. The eight spatial morphological parameters have obvious spatial correlations with UHIs for most of the year, and the correlations are stronger in winter than in other seasons, and stronger at 02:00 am than at 14:00 pm. The top three parameters are SVF, FAR, and BD. There are spatiotemporal changes in the impact of different spatial morphological parameters and land surface parameters on UHI. Spatial morphological parameters have become important drivers of UHI change and the individual contributions of the eleven parameters to UHI changes are 13.7% to 62.2%. The spatial morphological parameters that contribute the most in summer, winter, and the whole year are BD (43.7%), SVF (62.2%), and SVF (43.0%), respectively; and the corresponding largest land surface parameters are VC (42.6%), AB (57.1%), and VC (45.4%), respectively. The comprehensive contribution of multiple parameters to UHI changes in summer, winter, and the whole year are 51.4%, 69.1%, and 55.3%, respectively; and the dominant influencing factors are BD, SVF, and BD. Keywords: urban heat island; floor area ratio; building density; sky view factor; vegetation coverage; albedo
UHIdistribution of different periods for Beijing's main urban area in 2009-2018
The impact of air pollution on urban vibrancy and its built environment heterogeneity: An empirical analysis based on big data WANG Bo, ZHEN Feng, ZHANG Shanqi, HUANG Xuefeng, ZHOU Liang GEOGRAPHICAL RESEARCH, 2021, 40(7): 1935-1948 Abstract: Urban vibrancy describes people’s interactions with urban space. And enhancing urban vibrancy is important for urban sustainable development, and thereby attracts the interest of both geography and urban researchers and policy makers. Although evidence suggests that air pollution may influence people’s out- of- home activity, few studies have quantitatively measured how air pollution depresses urban vibrancy. On the basis of Sina Weibo check-in data and daily weather and air quality data in Guangzhou in 2019 and the built environment of this city, this study compiles samples of city vibrancy in 150 neighbourhoods and 365 days, forming a strongly balanced panel dataset. By Standard Deviational Ellipse (SDE) analysis and both general and spatial panel regression models, this study examines how air pollution negatively influences urban vibrancy and the heterogeneity role of the built environment in this depression effect. Our findings demonstrate that urban vibrancy space varies across different levels of air quality index (AQI). Specifically, the size of SDEs of urban vibrancy when AQI is between 50- 150 and 150- 200 is about 80% and 30% of that when AQI is no more than 50. After we control the spatial dependence (i.e., spatial autocorrelation), spatial panel regression results reveal that air pollution significantly lowers urban vibrancy. The daily activity intensity decreases 0.10 times per ten kilometers with a one-unit increment in AQI. More seriously, once AQI is above 150, this depression effect grows to 0.14 times per ten kilometers with a one-unit increment in AQI. We also test the heterogeneity role of the built environment in this depression effect. The results indicate that while POI density and distance to city center increase the depression effect, the density of metro stations and interactions and land- use diversity decrease the depression effect. It is evident that the depression effect of air pollution on urban vibrancy is not evenly distributed, varying across neibourhoods with different built environment characteristics. Compared to the city center, the outskirts bear a larger depression effect. Therefore, urban vibrancy space may be more polarized when air quality deteriorates and thus, challenging urban spatial restructuring development. Our spatial panel data analysis at the neighourhood scale improves our understanding of the mechanism among air pollution, built environment, and urban vibrancy, which provides evidence- based support for built environment planning and management at the neighbourhood scale to decrease the depression effect of air pollution on urban vibrancy. Keywords: human behaviour; built environment; spatial dependence; air quality; Guangzhou Identification and influencing factors of residential environment deprivation in a river valley city: A case study of Lanzhou city LUO Zhanfu, LEI Dehong, LI Xiaohui,YUAN Yue GEOGRAPHICAL RESEARCH, 2021, 40(7):1949-1962 Abstract: Improving the quality of living environment is an important part of high- quality urban development. The imbalance, accessibility and selectivity of facilities have a significant impact on the quality of life and emotional needs of residents. This study takes the main urban area of Lanzhou as the research object, uses the methods of mathematical modeling and ArcGIS analysis, takes POI of various urban facilities as the data source, constructs the evaluation system of residential environment deprivation to quantitatively identify the degree of deprivation, and analyzes the influencing factors. The results show that: (1) The comprehensive deprivation of residential environment in the narrow East-West River Valley presents a "core- periphery" distribution pattern with the center of gravity eastward. The high deprivation areas are concentrated in the valley edge area and the western urban area, while the low deprivation areas are distributed in valley center area and eastern urban areas. (2) The positive dimension of living environment basically presents a similar "core edge" structure in space, that is, the positive dimension of residential environment presents a trend of low deprivation in the central area of the valley and high deprivation in the marginal area of the valley, while the negative dimension is spatially opposite to the positive dimension, but there are still large differences in local areas. From the overall difference of space, the difference between medical services and urban crime environment is the largest, while that in traffic noise environment is the smallest. (3) The residential environment deprivation pattern of valley city is the result of multiple factors such as urban morphology, residential location, urban dynamic expansion, residential density and policy planning. Among them, the spatial accessibility of urban morphology restricts the development sequence of urban space, and then affects the spatial allocation level of facilities. The location of residential area restricts the selectivity of facilities and environmental interference of residential area. The dynamic expansion of city leads to the different development cycle of the valley city, and then leads to the difference of facilities allocation between new and old urban areas; the density of residential area has a certain guiding effect on the allocation of urban facilities; policy planning can reconstruct the pattern of deprivation of living environment. Influencing factors and mechanism of residential environment deprivation pattern 1959 Impact of the COVID-19 pandemic on population heat map in leisure areas in Beijing on holidays Changes of employment spatial structureand location pattern of Nanjing metropolitan area Keywords: employment spatial structure; identification of employment centers; locational patterns; mechanism analysis; Nanjing metropolitan area Big-data Oriented CommutingDistribution Model and Application in Large Cities  LIU Yunshu, ZHAO Pengjun, LV Di Journal of Geo-information Science,2021, 23(7): 1185-1195 Abstract: In recent years, big data has been widely applied in traffic analysis. However, they are mostly used for data visualization and phenomenon description. There is a lack of big-data oriented transport modeling, which leads to limited application of big-data in transportation planning. In this study, we propose a Location-Space Dependent Indicator (LSDI) based on the time-space interaction between transportation and land use. Based on this indicator, the urban commuting distribution model is developed, which improves the traditional gravity model. Taking Beijing as a study case, the developed model is applied and verified using mobile phone signaling big data derived from the communication service of an operator in September 2017. Travel generation and distribution models are constructed and verified respectively. Our results show that: (1) For the travel generation model simulations, commuter population and resident population show a good linear relationship. This model generates a significant prediction with a goodness of fit of 0.84; (2) For the travel distribution model simulations, a comparison analysis is conducted between gravity model, radiation model, and modified model with LSDI. The gravity model corrected by real commuting data performs best in regression analysis with a goodness of fit of 0.94. But large errors occur in the probability density distribution. The radiation model performs normal in regression analysis with a goodness of fit of 0.37. It has a better accuracy in the probability density distribution. The modified gravity model with LSDI has the best overall performance. The underestimation phenomenon is optimized in the commuter population distribution with a highest goodness of fit (0.85). Our findings provide new insights in developing big-data oriented transport prediction models and contribute to promote the application of big data in transport planning.
Research on the Influence of Functional Diversity of Business District on Its Vitality LUO Wen, KUANG Yaoqiu, ZHOU Mingdan, HE Yeyu, RUAN Zhu Journal of Geo-information Science,2021, 23(7): 1259-1271 Abstract:As the most active area of urban economic and social activities, business district provides a complex and mutually supporting diversity of functions to meet people's needs. Exploring how the functional diversity affect the vitality of business districts can provide the oretical support for the optimization and adjustment of urban land use functional structure and urban renewal, and then promote the mixed-use development and enhance its vitality. Taking the main urban district of Guangzhou as an example, this paper used Point of Interest (POI), google satellite images, high-resolution night light data of luojia No.1, spatial grid method, and hot spot analysis method to quantitatively identify the boundary range of business districts. At the same time, 28 business district were identified on the basis of comprehensive consideration of transportation railways, rivers, mountains, and historical development factors. Taking the business district as the basic research unit, the functional diversity of multiple and multi-dimensional features of the business district was measured using the Hill number model, and then the brightness of the corrected luminous radiation was calculated to represent the vitality of the business district. Finally, the method of partial correlation analysis was used to explore the relationship between single entropy index and functional diversity index with business district vitality. The main conclusions are as follows: (1) It is not enough to use entropy index only to reflect diversity, and it needs to combine with Hill number diversity index to measure the functional diversity of the business district from multiple perspectives to make up for the deficiency of the single entropy index measurement method. (2) The functional diversity index of the business district has a certain effect on business district vitality. Improving the functional richness index of the business district can help increase the complementarity, heterogeneity or mixing of functions, and to have enough diversified functions to stimulate the vitality of the business district. (3) The scale effect of business district can enhance its vitality more than agglomeration effect. The larger scale of the business district, the more stimulating on the vitality, while the functional agglomeration effect of the business district has relatively little effect on improving its vitality.
Spatio-temporalPattern of Surface Albedo in Beijing and Its Driving Factors based onGeographical Detectors LIU Qinqin, TIAN Yichen, YIN Kai, ZHANG Feifei, YUAN Chao, YANG Guang* Journal of Resources and Ecology, 12(5): 609–619 Abstract: Surface albedo directly affects the radiation balance and surface heat budget, and is a crucial variable in local and global climate research. In this study, the spatial and temporal distribution of the surface albedo is analysed for Beijing in 2015, and the corresponding individual and interactive driving forces of different explanatory factors are quantitatively assessed based on geographical detectors. The results show that surface albedo is high in the southeast and low in the northwest of Beijing, with the greatest change occurring in winter and the smallest change occurring in spring. The minimum and maximum annual surface albedo values occurred in autumn and winter, respectively, and showed significant spatial and temporal heterogeneity. LULC, NDVI, elevation, slope, temperature, and precipitation each had a significant influence on the spatial pattern of albedo, yielding explanatory power values of 0.537, 0.625, 0.512, 0.531, 0.515 and 0.190, respectively. Some explanatory factors have significant differences in influencing the spatial distribution of albedo, and there is significant interaction between them which shows the bivariate enhancement result. Among them, the interaction between LULC and NDVI was the strongest, with a q-statistic of 0.710, while the interaction between temperature and precipitation was the weakest, with a q-statistic of 0.531. The results of this study provide a scientific basis for understanding the spatial and temporal distribution characteristics of surface albedo in Beijing and the physical processes of energy modules in regional climate and land surface models. Key words: albedo; spatio-temporal distribution; explanatory factors; geographical detectors (封面图片来源：https://） |
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