在中国快速城市化进程中出现了大量的城市病,导致城市品质下降和区域发展失衡。城市活力为研究城市问题提供了一个新的视角,而大数据的蓬勃发展为城市活力研究提供了准确高效的新手段。本文采用人口热力、兴趣点（POI）和街景活力感知3个指标构建评价体系,对重庆主城的城市活力进行定量评价。结果发现：重庆主城区的城市活力分布呈现多中心的分布特征,与其“多中心、组团式”的城市结构相契合。3个指标评价出的城市活力在空间格局上具有相似性,城市活力高的区域主要分布在重庆内环内侧,尤其是解放碑主中心及活力相对较高的沙坪坝、杨家坪、观音桥、南坪副中心,而外围活力高的区域对应茶园、西永等内环以外的副中心和城市组团,这一结果与街景活力感知得分较为符合。研究认为,基于大数据对重庆城市活力进行客观识别和评价,为城市活力定量研究提供了一种新的思路和视角,弥补了传统定性分析方法的不足。针对山地城市的研究,同时弥补了已有文献在山地城市活力研究方面的不足,对空间规划有一定的参考价值。

With the rapid urbanization in China, the emergence of urban diseases has led to the decline of urban quality and urban vitality. Urban vitality provides a new perspective for the study of urban issues. The use of big data provides an accurate and efficient means for the study of urban vitality. Based on the dimensions of population heat map and point of interest (POI) distribution, this study quantitatively evaluated the urban vitality in the central city of Chongqing, and validated the derived vitality values based on the score of street view perception. The results show that the spatial distribution of urban vitality in Chongqing is characterized by multi-center distribution, which is consistent with its multi-center and clustered urban structure. The urban vitality evaluated by three indicators is similar in spatial pattern. It shows that the areas with high urban vitality are mainly located in the inner ring of Chongqing, especially in the main center of Jiefangbei and sub-centers, such as Shapingba, Yangjiaping, Guanyinqiao, and Nanping. The areas with high urban vitality correspond to the sub-centers of Chayuan and Xiyong outside the inner ring. This result is also consistent with the score of street view perception. The study confirms that the evaluation of urban vitality based on big data can compensate for the deficiency of traditional qualitative analysis methods and provide a new way of thinking and perspective for the quantitative study of urban vitality. The study of a mountainous city also addresses the deficiency of the existing studies on mountain city vitality and can help policy making in spatial planning.

城市地理与城乡规划一直关注建成环境对城市活力的影响，但鲜有研究揭示该影响的时空异质性。城市活力表现为居民在实体空间上的分布及其活动，并呈现时间动态变化特征。通过采集广州市中心城区新浪微博签到数据以及建成环境大数据，在1 km×1 km方格网、2 h时间段的时空单元上可视化城市活力的时空动态变化特征，基于时空地理加权回归模型 (GTWR) 揭示区位、功能混合度与密度对城市活力影响的时空异质性，并对比工作日与双休日的差异。研究发现：① 广州中心城区城市活力呈现东西带状的“多节点”空间格局，在24 h内经历“分散-集聚-进一步集聚-分散”的时空动态变化。② 区位、功能混合度与密度对城市活力的边际效应表现出空间和时间双重维度的不稳定性。③ 由于居民活动的时空约束不同，城市活力时空特征及其建成环境影响在工作日与双休日存在差异。

Urban vitality reflects human activities and their interactions with spatial entities, which exhibits both spatial and temporal variations. However, the spatio-temporal variations have not been well addressed in existing research on the impacts of the built environment on urban vitality. Based on a collection of Sina Weibo check-in records and POI-based built environment data in central Guangzhou, this research visualizes the urban vitality at the scale of 1 km×1 km grid and 2 h period over one day. Geographically and temporally weighted regression (GTWR) are used to unveil the influence of location, density, and diversity on the urban vitality and their spatiotemporal heterogeneity. The difference between urban vitality and the impacts of the built environment on weekdays and on weekends have also been examined. The results show that: 1) the urban vitality in central Guangzhou extends along the east-west strip, with White Goose Pool, Beijing Road, Shangxiajiu Road, Zhuajing Newtown, Pazhou as the agglomerations. In one day, the urban vitality experiences dynamic changes from relatively scattered to agglomeration during the daytime and keep further agglomeration till midnight. 2) Although the location, density, and diversity significantly influence urban vitality, their impacts vary across space and time over a 24-h period. Generally, distance to the city center is negatively associated with urban vitality, while diversity and the density of leisure, housing, transport, and other facilities are positively associated with urban vitality. 3) Due to a lower activity constraint of space and time on weekdays and on weekends, their urban vitality and the spatio-temporal impacts of the built environment are obviously different.

采用城市POI、手机信令数据及夜间灯光遥感数据，以深圳市为例，构建城市活力度量指标分析城市活力的空间特征。构建“5D”指标体系度量城市建成环境，并运用地理探测器，探究建成环境对城市活力的影响机制。结果表明：1）深圳市城市综合活力显示出“南强北弱，西强东弱”的空间分异特征，空间分布极不均衡。城市综合活力最主要反映的是社会活力。2）城市综合活力、经济活力、社会活力、文化活力均受到建成环境的显著影响。高POI密度、高建筑密度、高平均建筑层数和高土地利用混合度对城市活力具有显著促进作用。3）居住人口密度与路网密度、土地利用混合度与路网密度等交互作用对城市活力产生更为积极的影响。4）在影响机制中，建筑密度、平均建筑层数为底层支撑因素，POI密度、土地利用混合度为直接驱动因素，居住人口密度为诱发因素，距行政区中心距离、交叉口密度、路网密度、地铁站点密度和公交站点密度为调节因素。

As a key index to evaluate whether a city is attractive or has a healthy, comprehensive, and sustainable development potential, urban vitality is critical to the future development and construction of a city. Exploring the mechanism of how the built environment influences urban vitality from the perspective of urban morphology can be conducive to future urban space planning and design and can provide city planners with quantitative and scientific decision-making support. This study uses urban POI, cellular signaling, and nighttime light remote sensing data from Shenzhen to measure its cultural, social, and economic vitality, which constitutes a comprehensive vitality assessment of the city. Spatial features of these urban vitalities were then analyzed to further illustrate their distribution patterns. Using Bivariate Moran's I method, the spatial autocorrelations between economic, social, and cultural vitality were calculated. From the five dimensions of Density, Design, Diversity, Distance to Transit, and Destination Accessibility, the 5D detection index system was constructed to measure the urban built environment. This study also used GeoDetector to detect and analyze the spatial differentiation between various built environmental factors and comprehensive, economic, social, and cultural vitality and to explore the mechanism of the influence of the built environment on urban vitality. The results show that: 1) The spatial distribution of comprehensive vitality is extremely uneven in Shenzhen, with a higher vitality in the southern and western regions of Shenzhen city and a lower vitality in the northern and eastern regions. Areas with higher comprehensive vitality usually developed along the main roads and metro lines and are distributed in strips along the coastline of central-southern and western Shenzhen. The high-value areas of the urban comprehensive vitality center are primarily concentrated in city- or district-level commercial centers, and employment centers. Comprehensive vitality in Shenzhen reflects social vitality. 2) Urban comprehensive, economic, social, and cultural vitality are significantly affected by the built environment, as indicated by the 5D system. High POI and building density, average building floors, and land use entropy, have significant positive impacts on urban vitality. However, residential population density did not have a significant impact on each aspect of urban vitality. 3) Some interactions between built environment factors had a greater impact on urban comprehensive vitality, such as residential population density, road network density, land use entropy, and road network density. Overall, the interaction between building density, average building floors, land use entropy, and other factors, have a more positive impact on each aspect of urban vitality. 4) The presentation of urban comprehensive vitality is the result of multiple dimensions and factors. In the influencing mechanism of urban comprehensive vitality, building density and average building floors were the fundamental factors. POI density and land use entropy were the direct driving factors. Residential population density was an inducing factor. Distance from the administrative center, intersection density, road network density, metro station density, and bus station density were adjustive factors. The research results therefore provide valuable suggestions for the optimal allocation of resources and the rational layout of urban functional facilities in Shenzhen.

建成环境对街道活力的影响是国内外城市研究的热点议题。然而，对于建成环境要素的测度与评估多偏重于二维建成环境指标，尚未充分挖掘三维建成环境指标。以深圳市福田区为例，采用街景数据、路网数据、POI数据及移动互联网位置服务数据，建立周末分时段模型，探讨商业街道和生活街道活力的时空间分布特征及建成环境对其产生的影响。结果表明：① 商业街道上居民全天活动的峰值时段为18:00~20:00，生活街道上居民全天活动的峰值时段为11:00~13:00。② 深圳市福田区街道活力总体呈现多中心结构，随时间变化显现出明显的空间差异。③ 不同的建成环境指标对街道活力的作用时段与影响程度存在差异。提高功能混合度有利于提升商业街道凌晨、上午和夜间的活力及生活街道午后和傍晚的活力；过宽的相对步行宽度对生活街道活力提升有抑制作用，更安全的步行环境对商业和生活街道夜间活力提升有促进作用；界面连续程度高的商业街道午后及夜间的活力更强，界面多样性丰富的生活街道白天活力更强。

The last decades have witnessed a steady increase in studying urban vitality. However, the information that could depict 3D space has not been included in studies. This research attempts to incorporate street view imagery data to elaborate the spatiotemporal variability of vibrancy in Futian District, Shenzhen. As for the street-level physical environment, attributes are extracted from street view images by deep machine-learning algorithms SegNet. 2D built environment indicators of streets were extracts from the Open Street Map and POI points. A multivariate econometric are framed to examine the association between urban environment and the vitality at the street level of commercial streets and residential streets. The results showed that: 1) The peak hours of activity on commercial streets are 18:00 to 20:00, and the peak hours of activity on living streets are 11:00 to 13:00. 2) Futian District Street Vitality Shows Polycentric Structure, whose distribution had shown obvious spatial differences with time. 3) Variations in the temporal and degree effects of different built environment indicators on street vitality. Increasing the mix of facilities contributes to the morning and nighttime vibrancy of commercial streets, as well as the afternoon vibrancy of living streets. More compact streets have a positive impact on commercial streets’ vitality, and safer pedestrian environments have a positive impact on both living and commercial streets’ vitality. Improving the building continuity will promote the vitality of commercial streets. Higher interface richness increases the vitality of living streets in afternoon and evening. An increase in the richness of the interface will promote the daytime vitality of the residential street.

运用大众点评数据、出租车到达数据与文化设施POI数据测度城市活力的经济、社会和文化维度,在街区和街道层面分析城市活力的空间结构特征,建立计量模型分析城市建成环境对城市活力的影响关系。结果表明：①上海中心城区城市活力表现出明显的空间差异,主要表现为由中心向四周递减、浦西优于浦东的总体特征;②街区和街道尺度影响城市活力的建成环境因素存在差异;③街区尺度建成环境对城市社会、经济和文化活力的影响存在差异：提升人口密度对经济活力具有正面影响,过高的人口密度可能不利于社会和文化活力发展;增加POI密度、路网密度和POI混合度有利于城市活力的提升;建筑层数和建筑密度的增加会降低街区的社会活力和文化活力,但会提升街区的经济活力;交通可达性有利于经济活力的提升。

Research on urban vitality has drawn more attention from different disciplines and perspectives such as urban geography, urban planning and urban government. Urban vitality is not only an important symbol of urban sustainable development, but also an important factor for cities to retain high-skilled talents, which could improve the competitiveness of the city. Studying urban vitality is conducive for us to re-understanding the city in a new perspective and improving urban human-oriented planning and management for the government, especially for some megacities and international cities which contain a large population. Urban vitality, consisting of economic vitality, social vitality and cultural vitality, reflects the level of urban development capacity and quality of life, which is also closely related to the citizens. In order to discover the urban vitality in Shanghai, this paper uses multi-source data, including dianping’s data which counts the number of businesses in the studying area to reflect the economic vitality, urban taxi arrival data in one week to reflect the social vitality and cultural facilities data in the studying area to reflect cultural vitality, to measure urban vitality from two scale: both block and sub-district level. Based on this comprehensive measurement, this article intends to analyze the spatial outcome characteristics of urban vitality in different blocks and sub-districts. This article also establishes a regression models for analyzing the impact mechanism of urban built environment which consists of multiple elements on urban vitality. It is found that the urban vitality of Shanghai is higher in the central area, decreasing from the central area to the surrounding area in the city. In the meanwhile, the value of urban vitality in Puxi area is higher than Pudong area, which means the daily activities and communication in Puxi area are more dynamic than these in Pudong area. Apart from differences between Puxi and Pudong areas, there are also distinctions regarding to the factors of built environments effecting the urban vitality between block level and street level. On the block level, it is found that the built environments effect significantly to different types of urban vitality and the comprehensive vitality. What’s more, for each vitality, the effect shows in different ways. Specifically, the increase of population density will influence the urban vitality by firstly in an active way then in a passive one. Especially for social and cultural vitality, the high density of population is not beneficial. For the facilities, increase of POI density and road network density presenting completeness and POI mixing degree presenting the variety of land use will promote all kinds of the vitality and comprehensive vitality. In the respect of the design, the increase of average building layers and building density will reduce the social vitality and cultural vitality, but will enhance the economic vitality and comprehensive vitality of the block at the same time. And in terms of the access to public transportation, the distance to the nearest bus station will increase the economic vitality of the block. According to the discoveries about the spatial pattern and the effecting factors of urban vitality, this article proves improving the built environment is meaningful to the development of urban.

建设充满活力的城市空间得到地理和城乡规划学者的广泛关注。随着空气污染问题的加剧,空气质量影响居民在城市空间中的活动,但鲜有研究考察空气污染与城市活力的定量关系。基于广州市2019年新浪微博签到记录、日气象和空气质量数据、以及建成环境数据,本研究构建以街道为空间单元、以天为时间单元的面板数据,通过标准差椭圆（SDE）以及面板回归模型测度空气污染对城市活力的抑制效应以及该抑制效应在不同建成环境上的异质性。研究得到以下结论：① 城市活力SDE面积随空气质量指数（AQI）上升而收缩,轻度污染和中度污染的城市活力SDE面积仅为空气质量优的约80%和30%。② 运用空间面板回归模型控制街道的空间关联性后,空气质量指数（AQI）对城市活力具有明显负向影响,AQI每增加1个单位,日活动强度减少约0.10次/10 km²;当空气质量恶化到中等污染后,AQI每增加1个单位,日活动强度减少约0.14次/10 km²。③ 空气污染对城市活力的抑制效应在不同建成环境上存在异质性,POI密度、离城市中心距离强化空气污染对城市活力的抑制效应,而地铁站密度、道路交叉口密度、土地利用混合度则弱化空气污染对城市活力的抑制效应。本研究有助于更好厘清空气污染、建成环境与城市活力的关系,并为优化建成环境以缓减空气污染对城市活力抑制效应提供分析支撑。

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.

从精细尺度上研究城市功能区分布与人口活动规律对于政府和相关部门合理调节城市内部资源配置、安排城市设施布局具有重要意义。以沈阳市中心城区为例,根据核密度估计原理,基于兴趣点（POI）数据探索沈阳市中心功能区分布情况,通过解读多时相百度热力图数据,探索工作日和周末城区人口时空分布规律,即从城市人口活动和城市功能设施分布2个角度解读沈阳市中心城区空间结构与组织形式。借助SPSS 20.0分析人口热度和设施分布的相关性关系,建立多元线性回归模型。主要结论如下：① 沈阳市城市活力区呈现多中心分布模式,大多位于商业中心、金融中心或者城市功能复合中心。② 工作日人口高热区空间分布较周末更为分散,面积较大,早晚波动较大。周末人口高热区主要集中在商业中心和城市复合功能区且白天存在较大波动。③ 功能区分布与人口热度显著相关,其中生活服务设施、金融设施、交通设施、住宿设施和景点设施是工作日人口热度的主要影响因子;教育设施、餐饮设施、交通设施、金融设施和公共服务设施是周末人口热度的主要影响因子。

1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.

城市轨道交通网络的发展在提高居民通勤效率的同时也对其职住平衡状况产生了一定影响。论文以北京市206个轨道站点为例,基于高斯混合模型(Gaussian mixture model, GMM)和一卡通刷卡数据将轨道站点按职住功能进行分类,利用腾讯“宜出行”定位数据考察轨道站点周边的动态人口分布并计算就业居住比。研究发现：① 中心城区的职住状况明显优于中心城区以外区域;② 轨道交通线网末端区域的职住平衡程度较差,仅有少数成规模的高端服务产业集中分布的轨道站点周边形成了区域性就业中心;③ 部分就业-居住较为均衡的城郊地区仍存在一定的职住不匹配现象。随后,通过计算一卡通出进站比和“宜出行”职住比得到出进站均衡度和职住平衡度,利用广义自回归条件异方差(generalized autoregressive conditional heteroskedasticity, GARCH)模型对轨道交通通勤和职住平衡程度进行相关性分析,研究结果表明：① 出进站均衡度与职住平衡度具有非常显著的正向关系,即站点进出站人数越接近,站点周边区域的职住状况越好;② 典型就业地站点与站点周边区域的职住平衡程度显著正相关,而典型居住地站点与站点周边区域的职住状况存在显著的负相关性。这表明,人口稠密的聚居区无法带动同样数量就业岗位的产生,而完善的就业中心能够吸引一定数量的人口在附近居住;③ 轨道站点的区位条件与职住平衡状况存在一定正向关系;④ GMM能够对属性复杂模糊的轨道站点进行有效的聚类分析;⑤ 具有实时性强、精确度高、覆盖度广、获取难度低等优点的“宜出行”数据能够在微观空间尺度下弥补其他捕捉和分析实时人口时空分布特征方法的局限性。

The development of urban rail transit (URT) network improves the commuting efficiency of residents while it has a certain impact on their jobs-housing balance. This study took 206 URT stations in Beijing as an example and classified them according to their jobs-housing functions based on the Gaussian mixture model (GMM) and smart card data. The dynamic population distribution characteristics around URT station were explored and jobs-housing ratio was calculated by "Yichuxing" position data. The study found that: 1) The jobs-housing balance in the central city is obviously better than that outside of the central city. 2) At the ends of the URT network, the jobs-housing balance is worse while only a few stations with concentrated distribution of top service industries have formed regional employment centers. 3) There still exists a certain degree of jobs-housing mismatch in the areas around some suburban stations where employment and residential functions are relatively equal. Station outflow-inflow and jobs-housing balances were calculated by the station egrass-ingrass ratio and the jobs-housing ratio, and the correlation between URT commuting behavior and jobs-housing balance was analyzed by generalized autoregressive conditional heteroskedasticity (GARCH) model. The results of this study indicate that: 1) There is a very strong positive relationship between URT station egrass-ingrass balance and jobs-housing balance. The closer the numbers of URT station outflow and inflow population, the better the jobs-housing balance around the URT station is. 2) There is a strong positive relationship between employment opportunity and jobs-housing balance around a URT station; and there is a strong negative relationship between residential function and jobs-housing balance around a URT station. This suggests that dense settlement will not generate the same quantity of jobs while well-developed employment hubs can attract a certain number of residents to live nearby. 3) There is a positive correlation between locational conditions of URT stations and jobs-housing balance. 4) The GMM can effectively cluster URT stations with complex and unclear attributes. 5) With its advantages of real-time data capturing, high precision, wide coverage, and great accessibility, "Yichuxing" position data can effectively compensate for the limitations of other methods on collecting and analyzing spatial-temporal characteristics of real-time population distribution at the microscopic scale.

城中村作为带着农民特色和阶级混杂重塑的空间,为弱势劳动群体融入城市化体系、实现反资本而不反市场的城镇化进程提供了保障。探讨建成环境对居民通勤方式选择的影响机制,开展城中村建成环境改造,对于解决弱势群体通勤和就业困难意义重大。论文基于武汉市2018年LBS(Location Based Services)数据,以公共交通通勤方式选择作为二元分类因变量,采用梯度提升决策树(gradient boosting decision tree,GBDT)分析建成环境与城中村居民通勤方式选择的非线性关系与阈值效应,结果显示：普遍长距离通勤的城中村居民对公共交通系统依赖性强,建成环境的非线性影响和阈值效应明显,弥补了传统线性模型在可解释性上的不足;其中公交站可达性预测贡献度最大(19.89%),阈值分别在300 m和1000 m左右,在城中村改造时应尽量满足直线距离300 m公交站点可达,保证1000 m内存在公交线路,其余变量也有相似的可解释性。研究得到的建成环境影响的优先层次和最有效的空间属性范围将为提升城中村居民就业和通勤公平,实现区域包容、协调的城镇化提供参考。

As a space with farmers' characteristics and reshaped by mixed classes, urban villages provide a guarantee for the vulnerable groups to integrate into the urbanization system and realize the anti-capital but not anti-market urbanization process. Exploring the impact mechanism of the built environment on residents' commuting mode choices and carrying out inclusive renovation of the built environment of urban villages will be of great significance for addressing the commuting and employment difficulties of vulnerable groups. Based on the location based services (LBS) data of Wuhan City in 2018 and taking the choice of commuting mode as binary dependent variable, this study used the gradient boosting decision tree to explore the nonlinear relationship between the built environment and commuting mode choices of urban village residents and threshold effect. The results show that the residents of urban villages who commute for a long distance generally depend on the public transport system, and the nonlinear influence and threshold effect of the built environment are obvious, which make up for the insufficient interpretability of the traditional linear model. The contribution of bus station accessibility is the largest (19.89%) in prediction, and the thresholds are about 300 m and 1000 m respectively. In urban village reconstruction, bus station within a Euclidean distance of 300 m should be satisfied as far as possible, there must be bus lines within 1000 m, and other variables have similar interpretability. The impact priority level and the most effective spatial attribute range of the built environment obtained from this study will provide a reference for improving the employment and commuting equity of urban village residents and for realizing regional inclusive and coordinated urbanization.

借助新浪微博,引入位置服务大数据,以南京市为例,从时间、空间、活动3个方面分析城市活动空间的动态变化,并在掌握变化规律的基础上进一步划分城市活动区域。研究发现：传统的作息规律仍然支配着人们的签到活动,时间与活动内容间的对应关系仍然存在;居民活动在工作日、休息日与节假日,以及主城与外围地区存在差异;城市活动空间在一天内经历了相对分散-集聚-进一步集聚-分散-相对集聚的动态变化;城市活动区域可以划分为就业活动区、居住活动区、休闲活动区、夜生活活动区,及综合活动区;活动功能区呈现出混合化与边界模糊化的特征。

Since 1990s, information and communication technologies (ICTs) have experiecned a rapid development over the world. Information and communication devices have almost penetrated into each aspect of people’s daily life, and thus become the necessities in the modern world. Under this advancement, the inner relationship among activities, time and location has been theoretically changed because of a serious of the responding tele-activities. However, we still have little knowledge from the empirical studies due to the lack of data. Besides, as a part of urban social space analysis, a dynamic analysis of urban activity space is even scarce, albeit with its importance in understanding the undergoing and future changes in the E-society. Especially in China where is under the socio-economic transition, understanding residents’ own need and their real-time activity as well as the influence of ICTs on activity space are quite necessary for the future urban management and planning. With the aid of Location-based service (LBS), Global Positioning System (GPS) and other applications, ICTs helps to record people’s real space-time activity, which is one of the main sources of big data in recent research. Among them, given by the soaring popularity of online social network such as Sina micro-blog (the most influential social networking platform in China) and the LBS check-in application, the check-in data undoubtedly provide a real-time big data for the study of urban activity space. Based on this understanding, this article tries to analyze the dynamic changes of urban space-time activity focused on activity, time, and location, using the LBS checking-in data from Sina micro-blog. The results show that: 1) although some research argued that residents’ activity schedule would be more flexible by the help of ICTs, in this study we find that the traditional routine still dominate in residents’ check-in activities, and thus the rhythm of check-in activities could be used to reflect the corresponding relationship between time and activity; 2) there are differences of residents’ check-in activity between on working days, weekends, and holidays, as well as between in downtown and in outskirts, which reveals the difference of daily activities in reality; 3) in a day urban activity space experience dynamic changes, specifically, varied from relative disperse to agglomeration in the morning (6-12 O’clock) and keeps further agglomeration till the afternoon (12-18 O’clock); while encounters dispersion in the evening (18-24 O’clock), though with a relative agglomeration in the night (0-6 O’clock); 4) according to the dynamic changes, activity zones are subdivided into office area, bedroom area, leisure area, nightlife area, and multifunctional area; 5) these activity zones could be generally characterized as hybrid, within a blurred boundary.

城市地理与城乡规划一直关注城市活力与建成环境的关系。论文以广州中心城区为例，通过采集百度热力图、建筑矢量数据、路网数据、兴趣点数据等多源空间大数据，应用梯度提升决策树模型，探究建设强度、功能性质和交通可达性3个维度的建成环境要素对城市活力影响的非线性关系和阈值效应，并对比工作日白天与夜间的影响差异。研究发现：① 容积率对城市活力塑造的相对重要性最高，其次是休闲设施密度与公交密度，且白天与夜间的差异不显著。合理的开发建设强度、集聚的休闲与办公设施、公交导向交通发展，更有助于塑造充满活力的城市。② 各建成环境要素与城市活力之间均存在非线性关系和阈值效应，且部分建成环境要素白天与夜间的差异较明显。研究结论可为精细化的建成环境规划与治理以促进城市活力提供一定的政策启示。

The relationship between urban vitality and the built environment has always been a subject of intense research in urban geography and planning. Traditionally, global regression techniques have been mostly developed to analyze their quantitative relationship based on a linear consumption. However, the results from existing studies are rather mixed, indicating that their relationship may not be global. Therefore, it is necessary to explore the local characteristics of the associations between urban vitality and the built environment. Based on a collection of multi-source datasets including Baidu Heat Map data, building data, road network data, and point of interest data in central Guangzhou City—including Liwan, Yuexiu, Tianhe and Haizhu districts, this study applied the gradient boosting decision tree model to unveil the nonlinear associations of built environment characteristics (including intensity, function, and accessibility) with urban vitality and threshold effects. The differences in the impacts of the built environment between daytime and nighttime on weekdays have also been examined. The results show that: 1) During both the daytime and the nighttime, floor area ratio contributes the greatest to urban vitality, followed by the intensity of recreation and public transport facilities. Compared to diversity, reasonable development intensity, concentration of leisure and office facilities, and public transport oriented development have larger collective contributions to urban vitality. 2) Although differences exist between daytime and nighttime, all built environment variables have nonlinear associations with urban vitality. The threshold value and gradient of key built environment variables are recognized in the nonlinear shapes of associations. Urban planners and local governments are recommended to meticulously disentangle the complicated built environment associations to make informed and targeted interventions for fostering and maintaining urban vitality.

新时期人本导向的社区生活圈规划转型关注居民的日常活动对社区空间的利用情况,从而反映出居民对社区公共服务设施的使用需求。但目前研究较少关注不同类型居民的差异化时空行为特征,不能有效应对社区居民的差异化需求。论文结合行为地理学的理论,利用手机信令数据,基于居民出行活动的频率和密度测度其核心活动空间和弹性活动空间,分析不同类型人群日常活动特征。以南京市沙洲和南苑街道为例,划定不同社区中,青少年、青年、中年和老年群体的日常活动空间,并从日常活动范围和活力的视角分析居民对社区空间利用的基本特征及群体间差异。研究结果表明,居民的日常活动空间表现出层次性、共享性和方向性,且不同年龄群体的日常活动空间存在显著差异;进一步利用相关性检验分析不同群体社区活力的影响因素,提出考虑不同群体活动空间和设施需求的公共服务设施差异化供给策略。研究有助于完善基于居民时空间行为测度社区日常活动空间的方法,丰富对社区居民差异化行为需求的认知。实证研究的结论对基于居民差异化空间和设施需求的社区公共服务设施配置具有一定指导意义。

In the new era of China's development, people's needs for living space have become diversified. At the community level, the transformation of the planning of a human-oriented community life circle needs to pay attention to residents' daily activities to use the community space, thereby reflecting residents' use of community public service facilities. This demands to identify the daily activity space of residents. However, current research has paid little attention to the differentiated spatial and temporal behavior characteristics of different types of residents, and cannot effectively respond to the diverse needs of community residents. Based on the theory of behavioral geography, this research proposed a method of using mobile phone signaling data to measure the core activity space and flexible activity space based on the frequency and density of residents' travel activities, and then analyze the characteristics of daily activities of different types of people. Taking Shazhou and Nanyuan streets in Nanjing City as examples, the daily activity space of adolescent, youth, middle-aged, and elderly resident groups in different communities was delineated, and the basic characteristics of residents'use of community space were analyzed from the perspective of daily activity range and vitality differences among groups. The research results show that the daily activity space of residents shows hierarchical, sharing, and directional features, and there are significant differences in the daily activity space of different age groups. Further, by using the correlation test to analyze the influencing factors of the vitality of different groups of the communities, we proposed a differentiated supply strategy for public service facilities that considers the activity space and facility needs of different groups. This research is helpful for improving the method of measuring the daily activity space of community based on the spatiotemporal behavior of residents, and enriching the understanding of the diverse behavior needs of community residents. The conclusions of the empirical research have certain guiding significance for the allocation of community public service facilities based on residents' diverse space and facility needs.

The study of spatial distribution of population and economic situations is important for government policy making. County-level agriculture statistical data in 2000 and Beijing's second economic census data in 2008 were collected in order to explore the hot spots' scale effects. First, China's county-level agriculture statistical data and Beijing's second economic census data were aggregated to different scales based on certain aggregation rules. Second, hot spots detection was implemented based on G value at each scale respectively. Third, the changes of hot spots at different scales were analyzed. Fourth, factors affecting the changes were identified by employing Logistic Regression Model and a prediction model was built. Results show that, space hot spots explored by G value have significant MAUP effects. The higher the aggregation level, the greater the spatial scale, the less the number of hot spots. The number of units in a hot spot on the confidence level of 99.9% has a significant effect on the changes of hot spots. The mean G value of a hot spot on the confidence level of 98% has a significant effect on the changes of hot spots. Hot spots will become less susceptible to MAUP when they have more units and a larger G value. When the hot spot distribution is already known in the fine scale, changes of a hot spot can be predicted based on the model we built, which depends on unit number that the hot spot contains and mean G value of the hot spot. The prediction accuracy of China's county-level agriculture statistical data can reach 93.8% and that of Beijing's second economic census data can reach 94.2%. The consistent conclusion of the two datasets shows that scale effects on the detection of spatial hot spots have nothing to do with variables and study areas.