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Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities. [ABSTRACT FROM AUTHOR]

Urban commercial centers play a critical role in the development of cities, and it is of significant relevance to research the influencing variables of the urban vitality of commercial centers to improve the quality of urban commercial centers. This study employs big data to construct a multiple linear regression model in order to uncover the spatial-distribution characteristics of urban vitality and commercial sectors in commercial centers within the primary urban region of Guangzhou. The findings indicate that the commercial sectors of life, business, finance, and leisure have a substantial influence on the fluctuation of pedestrian flow in commercial centers throughout the day. Conversely, public service commercial sectors do not have a significant impact on pedestrian flow. Furthermore, the effect of various commercial sectors on the vibrancy of urban commercial centers varies, and their performance differs on weekdays and holidays. Additionally, the level of integration among commercial sectors affects the vitality of the city's commercial space. This research presents empirical facts that can be used to optimize the logical allocation of urban commercial resources in urban planning. [ABSTRACT FROM AUTHOR]

Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. [ABSTRACT FROM AUTHOR]

• Developing a novel strategy with multisource data to identify the land-use patterns. • Proposing a practical land-use classification model that requires less labelling. • Characterizing the mix land-use patterns from diverse views. The cognition of up-to-date urban land-use patterns has important guiding significance for grasping the current status of urban development, promoting sustainable urban planning and social development. Despite the emergence of various methods for identifying land-use patterns with the advent of geographic big data and related technologies, several issues, such as the suitability of the data used for exploring land-use patterns and the provision of sufficient information to characterize the land functions, are worthy of further discussion. This paper establishes a framework that fuses visual and semantic features to identify the up-to-date urban land-use patterns based on multisource geospatial data. The proposed framework mainly consists of two components: the first component is a fine-grained land-use type identification model with deep learning networks that employ an Inception-based feature extractor to obtain visual features in remote sensing (RS) images and a BERT-based feature extractor to extract semantic features from human activity-related data; the second component is the evaluation of the land-use patterns at the parcel level from diverse perspectives. The experiments on real-world data demonstrate the effectiveness and superiority of the proposed method over alternative methods for identifying the up-to-date urban land-use patterns. [ABSTRACT FROM AUTHOR]

Urban vitality is a key indicator for measuring urban development. This topic has been trending in urban planning and sustainable development, and significant progress has been made in measuring single indicators of urban vitality based on parcel or block units. With the continuous development of smart sensing technology, multisource urban data are becoming increasingly abundant. The application of such data to measure the multidimensional urban vitality of street space, reflecting multiple functions of an urban space, can significantly improve the accuracy of urban vitality analyses and promote the construction of people-oriented healthy cities. In this study, streets were taken as the analysis unit, and multisource data such as the trajectories of taxies and shared bicycles, user reviews and cultural facility points of interest (POIs) in Chengdu, a city in southwestern China, were used to identify spatial patterns of urban vitality on streets across social, economic and cultural dimensions. The correlation between the built environment factors and the multidimensional urban vitality on the street was analyzed using a multiple regression model. The spatial distribution of the different dimensions of urban vitality of the street space in Chengdu varies to a certain extent. It is common for areas with high social vitality to have production and life centers nearby. High economic vitality centers are typically found along busy streets with a high concentration of businesses. Areas with high cultural vitality centers tend to be concentrated on the city's central streets. Land use, transportation, external environment, population and employment are all closely linked to urban vitality on streets. The crowd counting and POI density have the greatest impact on multidimensional urban vitality. The crowd and the level of service facilities profoundly affect social interaction, trade activities and cultural communication. The goodness of fit (R2) of the regression models for social, economic and cultural vitality are 0.590, 0.423 and 0.409, respectively. Using multisource urban data, our findings can help stakeholders better understand the spatial patterns and influencing factors of multidimensional urban vitality on streets and provide sustainable urban planning and development strategies for the future. [ABSTRACT FROM AUTHOR]

Real-time population data are vital for urban planning and resource management for sustainable development. To complement satellite-based population estimation methods, geospatial social media data provide additional opportunities to estimate the distribution of population with high levels of efficacy and accuracy. Thus, this study attempts to assess the performance of various sensing data to disaggregate population data in China; the tested data include Tencent location-based service (LBS) data (about 0.8 billion users), satellite-derived land use/cover data, and nightlight imagery data. With the use of census data for validation, the experimental results show that Tencent LBS data are much better than satellite-derived land use/cover data and nightlight satellite data for mapping the population distribution. The overall mapping accuracy at the city level using Tencent LBS data was 88.9%, whereas the accuracy using land use/cover data was 87.1% and that using nightlight satellite data was 85.5%. The experimental results also indicate that LBS data and remote sensing data could both be well integrated to map the population distribution in China. Thus, a population spatialization model was further developed using all of the tested indicators; this model allowed the overall population estimation accuracy at the city level to reach 90.4%. This model could help determine the population distribution on various spatial scales quickly and efficiently, and the developed tool and the provided population estimates may be vital for the sustainable development of cities and regions for which population data are lacking. • Population mapping in China using social user and remote sensing data. • Comparison of different indicators in mapping population. • Performance of different models in estimating population. • Implications of social user data and remote sensing data in mapping population. [ABSTRACT FROM AUTHOR]

Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps. [ABSTRACT FROM AUTHOR]

Understanding the balance between the supply and demand of leisure services (LSs) in urban areas can benefit urban spatial planning and improve the quality of life of residents. In cities in developing countries, the pursuit of rapid economic growth has ignored residents' demand for LSs, thereby leading to a high demand for and short supply of these services. However, due to the lack of relevant research data, few studies have focused on the spatial mismatch in the supply and demand of LSs in urban areas. As typical representatives of multisource geographic data, social sensing data are readily available at various temporal and spatial scales, thus making social sensing data ideal for quantitative urban research. The objectives of this study are to use openly accessible datasets to explore the spatial pattern of the supply and demand of LSs in urban areas and then to depict the relationship between the supply and demand by using correlation analysis. Therefore, taking Beijing, China, as an example, the LS supply index (SI) and societal needs index (SNI) are proposed based on open data to reflect the supply and demand of LSs. The results show that the spatial distribution of the LS supply and demand in Beijing varies with a concentric pattern from the urban center to suburban areas. There is a strong correlation between the supply and demand of commercial and multifunctional services in Chaoyang, Fengtai, Haidian and Shijingshan, but there is no obvious correlation between the supply and demand of ecological and cultural services in Beijing. Especially in Dongcheng and Xicheng, there is no obvious correlation between the supply and demand of all services. The proposed approach provides an effective urban LS supply and demand evaluation method. In addition, the research results can provide a reference for the construction of "happy cities" in China. [ABSTRACT FROM AUTHOR]