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The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules. [ABSTRACT FROM AUTHOR]

Many developed cities in the world put forward a spatial development strategy in their construction planning. Analyzing the development level of the urban spatial structure and the influence of driving factors has become a hot topic. Based on 709,232 points of interest data in Beijing, China, this paper integrates nighttime light data and population density data to select eight key driving factors from three perspectives: urban function configuration, economic activity intensity and population spatial distribution. Geodetector is used to optimize data discreteness and highlight the spatial heterogeneity of the development level. We use the technique for order of preference by similarity to ideal solution (TOPSIS) model improved with the entropy weight method to grade the spatial differentiation characteristics of the comprehensive development level. The driving factors and their effects on space are further discussed using Geodetector. The results are as follows: (1) The quartile method can achieve the optimal dispersion of all urban functions. The standard deviation can achieve the optimal dispersion of economic activity intensity and population spatial distribution; (2) A comparison with the "Beijing Urban Master Plan (2016–2035)", finds that the optimized evaluation system can effectively reflect the spatial heterogeneity of urban spatial structure development. It verifies the rationality of the evaluation methods and factors; (3) The driving force of the population under single-factor driving is 0.8428. The dual-factor driving force with population participation ranges from 0.8992 to 0.9550. The results of the study are significant and reflect the prominent role of population on the development level of spatial structure in Beijing. This paper aims to provide a new idea for the study of the interior space planning of large inland cities. [ABSTRACT FROM AUTHOR]

The pedestrian detection model has a high requirement on the quality of the dataset. Concerning this problem, this paper uses data cleaning technology to improve the quality of the dataset, so as to improve the performance of the pedestrian detection model. The dataset used in this paper is obtained from subway stations in Beijing and Nanjing. The data images' quality is subject to motion blur, uneven illumination, and other noisy factors. Therefore, data cleaning is very important for this paper. The data cleaning process in this paper is divided into two parts: detection and correction. First, the whole dataset goes through blur detection, and the severely blurred images are filtered as the difficult samples. Then, the image is sent to DeblurGAN for deblur processing. 2D gamma function adaptive illumination correction algorithm is used to correct the subway pedestrian image. Then, the processed data is sent to the pedestrian detection model. Under different data cleaning datasets, through the analysis of the detection results, it is proved that the data cleaning process significantly improves the detection model's performance. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area's inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH. [ABSTRACT FROM AUTHOR]

With the maturity of big data technology, analyzing residents' travel habits and tracks has become an important research direction in the field of intelligent transportation study. In this paper, based on the subway and bus ride data, a subway-bus double-layer network model was established using complex network theory, taking the optimal traffic efficiency as the goal, the structure of intelligent bus network optimization method is proposed, and an empirical study is conducted on the Beijing bus network. In the empirical study, by adding or deleting bus station in the network, obtain an efficient network structure, the goal of optimal operation efficiency of the bus network was realized, and the theoretical and practical research on solving the problem of transportation line network planning with big data of traffic travel was enriched. [ABSTRACT FROM AUTHOR]

Background: Classic locational principles predict a picture of urban intensity decaying from the city center to the periphery under ideal assumptions. However, various exogenous factors can influence the real-world urban intensity and often deviate from the theoretical pattern. The specific mechanisms are worthy of exploration and are of potential theoretical and practical significance. Methods: In this paper, we consider two city districts with typical urban locations, namely, Changping and Chaoyang in Beijing, and construct mechanistic models of the status quo urban intensity (floor area ratio, FAR) utilizing multisource spatiotemporal big data. We further compare these models with the "theoretically ideal" FAR patterns as would be predicted by applied locational theories. Results: We find that the status quo FAR distribution generally conforms to the theoretical predictions but still exhibits regional deviations that can be explained by historical inertia and influence from particular policies. Conclusion: We conclude this paper with discussions on the findings' methodological and practical implications for urban planning institutions, especially in a transition economy context. [ABSTRACT FROM AUTHOR]

With the fast expansion and controversial impacts of short-term rental platforms such as Airbnb, many cities have called for regulating this new business model. This research aims to establish an approach to understand the impact of Airbnb (and similar services) through big data analysis and provide insights potentially useful for its regulation. The paper reveals how Airbnb is influencing Beijing's neighbourhood housing prices through machine learning and GIS. Machine learning models are developed to analyse the relationship between Airbnb activities in a neighbourhood and prevailing housing prices. The model of the best fit is then used to analyse the neighbourhood price sensitivity in view of increasing Airbnb activities. The results show that the sensitivity is variable: there are neighbourhoods that are likely to be more price sensitive to Airbnb activities, but also neighbourhoods that are likely to be price robust. Finally, the paper gives policy recommendations for regulating short-term rental businesses based on neighbourhood's price sensitivity. [ABSTRACT FROM AUTHOR]

Green space is the main public space in the city and plays an important role in urban environmental health and residents' physical and mental health. The outbreak of COVID-19 has put forward new requirements for urban green space construction in many aspects. Based on the population vitality data of comprehensive parks in the central urban area of Beijing, this paper evaluated urban comprehensive parks in the context of COVID-19 epidemic to reveal the changing process of their use and visit characteristics, and proposed suggestions and strategies to deal with the epidemic in comprehensive parks in the central urban area of Beijing. [ABSTRACT FROM AUTHOR]

Under the background of urban renewal, this paper re-explores the tourism development mode of Nanluoguxiang historical and cultural block based on online review data, and puts forward corresponding development strategies. As a cultural label of a city, historical and cultural blocks should be updated first in order to achieve sustainable development. By using multi-source big data review and qualitative research methods, the perception evaluation of tourists in Nanluoguxiang is obtained, and the shortcomings of current tourism development mode are analyzed. Furthermore, corresponding improvement strategies and suggestions are put forward, in order to provide some effective ideas for the sustainable development of historical and cultural blocks in the future. [ABSTRACT FROM AUTHOR]

In the first-tier cities, subway has become an important carrier and life focus of people's daily travel activities. By studying the distribution of POIs of public service facilities around Metro Line 10, using GIS to quantitatively analyze the surrounding formats of subway stations, discussing the functional attributes of subway stations, and discussing the distribution of urban functions from a new perspective, this paper provided guidance and advice for the construction of service facilities. [ABSTRACT FROM AUTHOR]

Commercial and residential spaces are two core types of geographical objects in urban areas. However, these two types of spaces are not independent of each other. Spatial associations exist between them, and a thorough understanding of this spatial association is of great significance for improving the efficiency of urban spatial allocation and realizing scientific spatial planning and governance. Thus, in this paper, the spatial association between commercial and residential spaces in Beijing is quantified with GIS spatial analysis of the average nearest neighbor distance, kernel density, spatial correlation, and honeycomb grid analysis. Point-of-interest (POI) big data of the commercial and residential spaces is used in the quantification since this big data represents a comprehensive sampling of these two spaces. The results show that the spatial distributions of commercial and residential spaces are highly correlated, maintaining a relatively close consumption spatial association. However, the degrees of association between different commercial formats and residential spaces vary, presenting the spatial association characteristics of "integration of daily consumption and separation of nondaily consumption". The commercial formats of catering services, recreation and leisure services, specialty stores, and agricultural markets are strongly associated with the residential spaces. However, the development of frequently used commercial formats of daily consumption such as living services, convenience stores, and supermarkets appears to lag behind the development of residential spaces. In addition, large-scale comprehensive and specialized commercial formats such as shopping malls, home appliances and electronics stores, and home building materials markets are lagging behind the residential spaces over a wide range. This paper is expected to provide development suggestions for the transformation of urban commercial and residential spaces and the construction of "people-oriented" smart cities. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyze the factors influencing the behaviors of government officials during the implementation of open government data (OGD). By identifying and understanding the key factors that determine government officials' adoption of OGD in China, this study can create a valuable reference for other countries and their decision-making regarding government implementation of OGD. Design/methodology/approach: This research collected data by in-depth interviews with government officials in Chinese OGD departments. Through these interviews, the authors consulted 15 administrators from departments that are responsible for the information tasks in Beijing and other cities on their opinions about OGD. The authors also interviewed senior executives from information technology (IT) companies, as well as open data policy scholars from big data alliance and research institutions. Findings: This paper provides insights about how to promote government officials in OGD implementation, including (1) strengthen social supervision for the environment, through developing and publishing OGD technology roadmaps, then attracting the public to actively participate in the implementing of OGD; (2) establish an OGD assessment mechanism for government officials, with bonus motivations, position promotion incentives, as well as spiritual incentives via regional or sector rankings; (3) alleviate the risks of officials' OGD decisions in actual practice, using the institution construction of OGD to guide its direction and strengthen security protection. Originality/value: This paper fulfils an identified need to study how government officials' behavior can be motivated on OGD implementation. [ABSTRACT FROM AUTHOR]

How to efficiently identify traffic bottlenecks in the expressway network and implement targeted measures is an important issue to mitigate traffic congestion. In recent years, with the proliferation of smartphones, smartphone-based navigation applications are receiving the worldwide popularity, which are being treated as an extremely rich source of traffic data. In this paper, we are devoted to proposing a novel approach to investigate traffic state and identify recurrent bottlenecks quickly and accurately from macroscopic network perspective, through fusing the data sources of fixed detectors and mobile navigation apps. First of all, this paper plots flow-speed fundamental diagram to derive critical speed, which is treated as the criterion to determine traffic state. Once the criterion has been established, typical bottlenecks in the entire expressway network can be efficiently identified through only utilizing smartphone-based probe speed data. Secondly, three pioneering indicators are put forward to quantify oversaturated traffic state and classify bottleneck patterns on urban expressway network. Applying this methodology, we take Beijing expressway network as a case to identify different patterns of bottlenecks, which are validated to comply with the reality. Moreover, the relationship between critical speed and the associated road segment features is explored and shows the possibility of predicting the critical speed even without flow data. As an end, some recommendations are proposed for improving different types of traffic bottlenecks. [ABSTRACT FROM AUTHOR]

The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system. [ABSTRACT FROM AUTHOR]

Data fusion is an efficient way to achieve an improved accuracy and more specific inferences by fusing and aggregating data from different sensors. However, due to the increasing complexity of spatial data with massive and multi-source heterogeneous characteristics, the existing methods cannot satisfy quite well the requirement for the integrity of data and the accuracy of fusion results in some specific situations. By considering the geographical properties of spatial data, a multi-source heterogeneous spatial big data fusion method based on multiple similarity and voting decision (SDFSV) is proposed in this paper, which develops a three-step record linking algorithm to improve the quality of entity recognition for the incremental fusion of massive data. Then, a one-time voting algorithm is introduced into the proposed method, so that the data conflicts can be significantly reduced and thus the accuracy of the data fusion can be improved. And a relation deduction method based on rule and entity recognition is presented to enhance the data integrity. In addition, in order to promote traceability and interpretability of fusion results, it is necessary to construct a data traceability mechanism. Experimental results show that SDFSV has an improved performance by using the data of Beijing Medical Institutions collected from 10 data sources. [ABSTRACT FROM AUTHOR]

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Based on mobile positioning big data, this study analyzes the quantity of visitation changes of leisure tourism facilities under the impact of COVID-19, which is a direct way to evaluate the impact of COVID-19 on the leisure tourism industry. The quantity of visitation to the four types of leisure facilities (scenic spots, shopping services, sports services, science and cultural education services) in the three Chinese cities (Beijing, Shanghai, and Qingdao) decreased by 63.06% and 44.74% respectively on the Tomb Sweeping Day and the Labor Day in 2020 compared with 2019. The biggest decreasing was in science and cultural education services, followed by scenic spots and sports services, and finally shopping services. The curve of hourly visitations to leisure facilities in 2020 shows a smooth feature, indicating that the public has reduced the risk of pandemic through peak shifting leisure. The analysis of influencing factors shows that the influence of the floor area on visitation is different in different phases. From Tomb Sweeping Day to Labour Day in 2020, the influence of area on leisure behaviour changes from negative to positive. This paper provides a technical framework for assessing the impact of crisis event, and expands the protection motivation theory. [ABSTRACT FROM AUTHOR]

The paper goes into detail about what psychological barriers are and how they can affect performance on the instrument for particular individuals. Furthermore, it provides some practical steps that can be taken to fix or avoid these issues. The study is mainly conducted to analyze the effectiveness of transmitting values to the next generation. The study is done by taking a quantitative research method. The instrument used to collect information was a questionnaire. The question is distributed among 100 participants who belong to Beijing to China. The data is analyzed by using SPSS software. It is revealed that the intergenerational approach plays an active role in transmitting knowledge, economic support and spiritual values to the next generation. It is suggested that a sound parenting system can provide excellent benefits for the next generation. [ABSTRACT FROM AUTHOR]

Air pollution is a growing problem and poses a challenge to people's healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management. In this paper, a novel PM2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is constructed by deep learning techniques based on the principles related to spatial big data. This model consists of the convolutional block attention module (CBAM), the convolutional neural network (CNN), and the bi-directional long short-term memory neural network (Bi LSTM). CBAM is applied to the extraction of feature relationships between pollutant data and meteorological data and assists in deeply obtaining the spatial distribution characteristics of PM2.5 concentrations. As the output layer, Bi LSTM obtains the variation pattern of PM2.5 concentrations from spatial data, overcomes the problem of long-term dependence on PM2.5 concentrations, and achieves the task of accurately forecasting PM2.5 concentrations at multiple sites. Based on real datasets, we perform an experimental evaluation and the results show that, in comparison to other models, CBAM-CNN-Bi LSTM improves the accuracy of PM2.5 concentration prediction. For the prediction tasks from 1 to 12 h, our proposed prediction model performs well. For the 13 to 48 h prediction task, the CBAM-CNN-Bi LSTM also achieves satisfactory results. [ABSTRACT FROM AUTHOR]

In the era of big data, multiple data sources have been employed in the study of land use and transportation for urban and regional planning purposes. This paper offers an example of how multiple data sources (e.g., mobile signalling data, taxi trips and transit trips from smartcard data) can be used to estimate the spatial pattern of commute mode preference in Beijing, China. The comparative analysis investigates the spatial pattern of commute mode preference by taxi at a fine resolution in Beijing. This work indicates how the preference for taxis can be seen in the north-east of the inner city, but not around employment centres. Equally, a complementary relationship is found between a preference for taxis and public transit that provides useful insights into modal choice at an intra-urban scale. These findings are useful in urban planning and transport management. [ABSTRACT FROM AUTHOR]

With the advent of the big data era, the use of computers has spread to all walks of life, and the finance and taxation industry is also in the middle of it. The current taxation system is huge and complex, and different tax types are inevitably linked to different economic indicators at a deep level, so tax forecasting requires personalised forecasting analysis for different tax types. This paper selects several tax types that account for a large proportion of tax revenue for prediction analysis, respectively, and conducts fusion research on multi-source big data, including business tax, corporate income tax, and personal income tax. Based on the multi-source big data fusion method, the prediction research on fiscal taxation tax types is conducted, and experiments are conducted with the taxation data of Beijing from 1995 to 2020 to predict the three tax types from 2017 to 2020. The results show that the deviation of the forecast data from the real tax data is small, controlling the forecast deviation to within 14%, indicating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Public management service is the key to urban intelligent construction. This paper proposes an analysis method and model based on Spark big data framework and takes resident income, happiness index, urban planning, and ecological environment as the indicators of Spark big data. From the high difficulty of Spark big data cluster analysis of urban public management, we build the index weight by the entropy weight method, optimize the similarity calculation, and achieve the rapid understanding of urban public management. Subsequently, the Spark big data public management platform is applied to the public management of Beijing. The results indicate that the public management platform based on Spark big data framework can improve the public management level of the city and help to build an intelligent city. [ABSTRACT FROM AUTHOR]

In order to cope with the rapid growth of spatiotemporal big data, data organization models based on discrete global grid systems have developed rapidly in recent years. Due to the differences in model construction methods, grid level subdivision and coding rules, it is difficult for discrete global grid systems to integrate, share and exchange data between different models. Aiming at the problem of information fusion between a GeoSOT grid and global hexagonal equal area grid system, this paper proposes the GeoSOT equivalent aggregation model (the GEA model). We establish a spatial correlation index method between GeoSOT grids and global hexagonal equal area grids, and based on the spatial correlation index, we propose an interoperable transformation method for grid attributes information. We select the POI (points of interest) data of Beijing bus and subway stations and carry out the transformation experiment of hexagonal grid to GeoSOT grid information so as to verify the effectiveness of the GEA model. The experimental results show that when the 17th-level GeoSOT grid is selected as the particle grid to fit the hexagonal grid, the accuracy and efficiency can be well balanced. The fitting accuracy is 95.51%, and the time consumption is 30.9 ms. We establish the associated index of the GeoSOT grid and the hexagonal grid and finally realized the exchange of information. [ABSTRACT FROM AUTHOR]

Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing's air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

Research on multimodal accessibility under uncertain travel time has become a significant issue. Existing studies on accessibility lack a direct integration of multi-source data accessibility evaluation methods. This paper develops a multimodal random accessibility model (MR model). Multi-source data is integrated with a built-in joint calculation method of walking time, waiting time, and transit time while considering the effects of both the travel time budget in the time dimension and distance friction parameter in the spatial dimension. Taking Beijing as an example, accessibility generally shows a downward trend from the center of the city to the suburbs, especially along the subway lines, and there is a positive correlation between traffic flow and accessibility. The low-accessible high-flow area is mainly distributed in areas away from the city center and at the end of subway lines. These results could help transport planners formulate more reasonable public transport planning policies. [ABSTRACT FROM AUTHOR]

Although zoning has great potential in coordinating regional development and destination management, it is challenging to derive an optimal zoning method in the historic district. To address this challenge, this paper proposes a zone division method attempting to balance tourists' and residents' spatial demands and realizing urban historic area sustainable development. A 3-zones (Tourist active zone, Local community zone, and Buffer zone) plan is put forward based on a case study of the Qianmen area in Beijing, China. Then zone extraction is conducted with two kinds of big data, Points of Interests (POI) and Mobile Phone Signal (MPS). The Thiessen polygon-based method to divide the tourists' concentrated area and a fishnet-based density method to distinguish the area of residents mainly living are used to help identify borderlines of different zones. A survey map for tourists and residents is used to verify the zone extraction results. Subsequently, zone management strategies are suggested for different zones improvement. This discussion contributes to overcoming the traditional methods' defects in subarea-scale research with scientifically sound and practical. [ABSTRACT FROM AUTHOR]

Urbanization and related construction activities generate massive construction and demolition (C&D) waste, which poses considerable risks to the environment and human health. However, it is difficult to assess the significance of these issues without a quantitative understanding of spatial patterns of C&D waste generation (source), transportation (flow), and treatment (sink). This paper maps source, sinks, and flows of C&D waste by coupling a geographic information system and material flow analysis. The methodology is new in the field of C&D waste quantification at the city level, especially downscaling to 5 km × 5 km grids. The results showed that a total of 37.72 million metric tonnes (Mt) of C&D wastes were generated in Beijing, 2019, and ~72% of them were reused and recycled. In space, C&D waste generation in suburban and rural districts (28.73 Mt) was over three times more than that in the downtown area (8.99 Mt). However, the downtown area was the net source region and transported massive amounts of C&D waste to suburban and rural districts. In comparison, several suburban and rural districts had self-sufficient treatment capacity. Our study highlights that a series of C&D waste maps on multiple spatial scales are of great help to design effective policies for waste management by providing spatial details of magnitude and components, and explicitly recognizing primary source and sink areas in cities. [ABSTRACT FROM AUTHOR]

Summary: Ground‐penetrating radar (GPR) technology is being widely used for urban roadbed damage detection. In this paper, we present the development of a cloud‐based difference detection method using big GPR data for roadbed damage detection. Unlike many processing methods relying on standalone servers, the proposed method can achieve high accuracy and processing efficiency by distributing the computation over a server cluster. The method includes a difference detection algorithm for GPR image enhancement and damage extraction, the Hadoop Distributed File System, and MapReduce distributed parallel computing framework for big GPR data parallel processing and interpretation. Moreover, to automatize roadbed damage classification, a criterion with seven grades is established based on the wave group shape, amplitude, phase, and attenuation characteristics. We validate the proposed method through a detection experiment that was conducted on the Fourth Ring Road in Beijing, China. The detection revealed 187 damage from the efficient processing of big GPR data, thus suggesting the ability of the proposed method to provide timely support for road safety in cities. [ABSTRACT FROM AUTHOR]

Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi, ride-hailing service, subway, regular bus, airport bus, etc.), we accurately extract candidate stations, which are very popular with passengers and convenient for parking and transfer, through public transportation demand level calculation, iterative clustering and POI matching. Then, the candidate stations need to be partitioned appropriately by selecting suitable features and calculating the similarity of candidate stations, so as to make the stations within each group a moderate size and have a consistent spatial orientation. Finally, a line generation and vehicle scheduling algorithm, which is compatible with multi-vehicle, high success rate of ride-sharing matching and low cost, is designed to realize accurate and rapid operation scheduling within each group according to the situation of passengers booking tickets. We have carried out experiments in Wangjing and Yayuncun, and the results show that our method can satisfy passenger demand fast and accurately. [ABSTRACT FROM AUTHOR]

With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing's 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method. [ABSTRACT FROM AUTHOR]

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the key issues relating to sustainability and livability in many large cities. In particular, the situation in the growing megacities of developing countries has been worsening and is now attracting considerable attention from researchers and politicians. An understanding of the spatio-temporal patterns of this congestion is necessary in order to formulate effective policies to relieve it. Much of the research to date has focused on single districts for relatively short periods (days or weeks) using GPS, while long-term analysis of spatial and temporal patterns of traffic congestion at the city level has been rare. The aim of this paper is to help fill this gap in the literature by applying a big data analytic approach to a sample of 10.16 million records of traffic congestion indexes for 233 roads in the Beijing area over a six-month period. This analysis revealed four typical traffic congestion patterns in Beijing, which can be described as the weekend mode, holiday mode, weekday mode A, and weekday mode B. Each of these patterns possesses unique spatial and temporal characteristics. Compared with working days, on which congestion is regular and agglomerated, weekends and holidays are characterized by long-lasting congestion peaks throughout the day. Non-commuting travel on weekends and holidays, including trips for tourism, shopping, entertainment, and children's after-school activities, are major contributors to traffic congestion of the weekend and holiday mode. Owing to poor jobs-housing balance, the suburban new towns and job centres had relatively higher congestion than other areas. These findings shed significant light on geographical patterns of traffic congestion in growing megacities. • Traffic congestion is a key issue relating to urban sustainability. • This paper apples a big data analytic approach to a sample of 10.16 million records of traffic congestion. • There are four typical traffic congestion patterns in Beijing. • No-commuting travels on weekends and holidays are major contributors to traffic congestion. • The suburban new towns and job centres had relatively higher congestion. [ABSTRACT FROM AUTHOR]

Urban renewal has become the application hotspot of urban remote sensing development in recent years. However, scientific distribution of weight relationship between indicators reflecting progress and effectiveness of different urban renewal goals and tasks, and comprehensive evaluation of effectiveness of policies in the existing indicator system require further in-depth research. In particular, there is no clear standard for application caliber of remote sensing data of different cities in spatial indicators. Taking Beijing's dual control policy as an example, this paper measures effectiveness of mega-city renewal policies represented by Beijing based on analytic hierarchy process, and analyzes types of remote sensing data suitable for urban renewal. In addition, the suitability scale of urban remote sensing data application is given through case studies. By analyzing the actual validity of Beijing's dual-control policy implementation, it is concluded that the application scale of urban remote sensing data for urban renewal suitability is remote sensing image big data > remote sensing data artificial intelligence algorithm > remote sensing image historical data > remote sensing big data. It is necessary to focus on automatic identification and exploration of spatial changes based on remote sensing, design and calculation of spatial indicators of urban physical examination based on remote sensing in the future. Furthermore, work needs to be carried out to further improve evaluability, accuracy and objectivity of practical application of remote sensing data in different cities for urban renewal, and explore establishment of a clear urban physical examination system and remote sensing data application system guidelines. [ABSTRACT FROM AUTHOR]

Short-term prediction of origin–destination (OD) flow is a primary but complex assignment to urban rail companies, which is the basis of intelligent and real-time urban rail transit (URT) operation and management. The short-term prediction of URT OD flow has three special characteristics: data lag, data dimensionality, and data malconformation, distinguishing it from other short-term prediction tasks. It is essential to propose a novel prediction algorithm that considers the special characteristics of the URT OD flow. For this purpose, based on deep learning methods and multi-source big data, a modified spatial–temporal long short-term memory (ST-LSTM) model is established. The proposed model comprises four components: (1) a temporal feature extraction module is devised to extract time information within network-wide historical OD data; (2) a spatial correlation learning module is introduced to address the data malconformation and data dimensionality problems, which provides an interpretable spatial correlation quantization method; (3) an input control-gated mechanism is originally proposed to solve the data lag problem, which combines the processed available OD flow and real-time inflow/outflow; (4) a fusion module combines historical spatial–temporal features with real-time information to achieve accurate OD flow prediction. We also further discuss the interpretability of the model in detail. The ST-LSTM model is evaluated by sufficient experiments on two large-scale actual subway datasets from Nanjing and Beijing, and the experimental results demonstrate that it can better learn the spatial–temporal correlations and exceed the rest benchmarking methods. [ABSTRACT FROM AUTHOR]

The use of Internet data to carry out geographic study has become an academic hotspot in recent years. We referred to the "Zhongke Beauty Index" and crawled 530 thousand POIs (POI: Point of interest) to carry out regional construction evaluation in Beijing-Tianjin-Hebei region. The results showed that: (1) Geographic big data provided detailed information for fine-grained regional research, and the POI evolution tree model revealed the shortcomings of regional development. (2) The Beijing-Tianjin-Hebei comprehensive beauty index in 2019 was 0.21, and Beijing and Tianjin are far ahead of Hebei in terms of ecology and culture. Specifically, the POI types of eco-environment construction of research area were single; the industrial development construction presented a trend of multi-center gathering; the social harmony construction has formed a gathering circle in Beijing; the eastern and central regions were the dominant regions in terms of institutional improvement construction; and the cultural inheritance construction was the shortest board in research area. (3) The evaluation ranking was sensitive to the population and the size of the area in Beijing-Tianjin-Hebei region. When evaluating, the uncertainty caused by the characteristics of indicators and POIs should be fully considered. This article indicated the shortcomings in five dimensions of Beijing-Tianjin-Hebei region and can provide reference for the evaluation of regional construction. [ABSTRACT FROM AUTHOR]

Abstract With the wide application of big data technology and the continuous innovation of the financial industry, the development of Internet finance has become irreversible. The new financial model has improved the efficiency of financial services and formed new ways of payment and transaction. The traditional credit information system has been unable to meet the needs of the development of the financial industry. The research methods adopted in this paper mainly include literature research, case analysis and comparative analysis. The development process, current situation and characteristics of China's personal credit system, as well as the development process and current situation of China's personal credit system under Internet finance are analyzed. It also points out the main problems faced by China's personal credit reporting system under the Internet finance. Then, aiming at the specific problems faced by China's personal credit reporting system under Internet finance, this paper makes case studies of representative market-oriented credit reporting institutions such as Beijing, Tianjin and Hebei, and uses convolutional neural network to analyze the specific practices of these individual credit reporting institutions and achieves results. At the same time, it compares how different credit agencies make up for the shortcomings of personal credit reporting system. Finally, it draws the conclusion of perfecting the personal credit system of our country under the Internet finance, points out the new problems that may be brought by the superposition of various solutions, and puts forward the countermeasures and suggestions of perfecting the personal credit system of our country under the Internet finance. [ABSTRACT FROM AUTHOR]

Advanced techniques of multi-temporal InSAR (MT-InSAR) represent a valuable tool in ground subsidence studies allowing remote investigation of the behavior of mass movements in long time intervals by using large datasets of SAR images covering the same area and acquired at different epochs. The settlement in Beijing is very serious, producing adverse environmental impacts. Excessive groundwater withdrawal is the most direct cause of land subsidence.In this paper, a spatial-temporal analysis of the land subsidence in Beijing was performed using one of the MT-InSAR techniques, referred to as Small Baseline Subset (SBAS). This technique allows monitoring the temporal evolution of a deformation phenomenon, via the generation of mean deformation velocity maps and displacement time series from a data set of acquired SAR images. 52 C-band ENVISAT ASAR images acquired from June 2003 to August 2010 were used to produce a linear deformation rate map and to derive time series of ground deformation. The results show that there are three large subsidence funnels within this study area, which separately located in Balizhuang-Dajiaoting in Chaoyang district, Wangjing-Laiguangying Chaoyang district, Gaoliying Shunyi district. The maximum settlement center is Wangsiying-Tongzhou along the Beijing express; the subsidence velocity exceeds 110 mm/y in the LOS direction. In particular, we compared the achieved results with leveling measurements that are assumed as reference. The estimated long-term subsidence results obtained by SBAS approach agree well with the development of the over-exploitation of ground water, indicating that SBAS techniques is adequate for the retrieval of land subsidence in Beijing from multi-temporal SAR data. [ABSTRACT FROM AUTHOR]

With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers' behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities. [ABSTRACT FROM AUTHOR]

Online car‐hailing, with its advantages of convenience and efficiency, has quickly become popular among tourists, playing a crucial role in the accessibility of scenic spots. Due to the particularities of tourist travel behaviour and the complexity of travel supply and demand around scenic spots, research on car‐hailing tourists is relatively lacking at this stage. Based on multi‐source data, this study couples the identifying of travel characteristics, by introducing the concept of service dependency degree, with a Bayesian optimization–long short‐term memory–convolutional neural network (BO‐LSTM‐CNN) method to conduct multi‐task online car‐hailing demand forecasting. The evaluation of the dependency degree primarily encompasses the establishment of evaluation indices and the application of the entropy weight method and natural breakpoint method. The BO‐LSTM‐CNN model utilizes Bayesian optimization for hyperparameter tuning, LSTM for temporal variable processing, and CNN for the fusion of multi‐source information related to weather, space, and online car‐hailing attributes. Extracting online car‐hailing tourist travel orders based on spatial–temporal constraints, the proposed methods are applied to 72 scenic spots in Beijing, China. According to their dependency degree, Beijing's scenic spots are categorized into three levels of dependency on online car‐hailing services, from high to low. The outstanding forecasting efficacy of the proposed model for various scenic spots is verified through comparison tests with several benchmark models. Consequent to these findings, mobility service improvement strategies are specifically proposed for each class of scenic spots, which can provide valuable insights for the relevant tourism traffic management personnel. [ABSTRACT FROM AUTHOR]

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3–0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios. [ABSTRACT FROM AUTHOR]

The pervasiveness of location-acquisition and mobile computing techniques has generated massive spatial trajectory data, which has brought great challenges to the management and analysis of such a big data. In this paper, we focus on the sub-trajectory dataset profiling problem, and aim to extract the representative sub-trajectories from the raw trajectory as a subset, called profile, which can best describe the whole dataset. This problem is very challenging subject to finding the most representative sub-trajectories set by trading off the size and quality of the profile. To tackle this problem, we model the features of the trajectory dataset from the aspects of density, speed and the direction flow. Meanwhile we present our two-step method to select the representative trajectories based on the feature model. First, a novel trajectory segmentation algorithm is applied on a raw trajectory to identify the representative segments concerning their feature representativeness and automatically estimate the number of segments and the segment borders. Then, a sub-trajectory profiling method is performed to yield the most representative sub-trajectories in the dataset, based on a local heuristic evolution strategy. We evaluate our method based on extensive experiments by using two real-world trajectory datasets generated by over 12,000 taxicabs in Beijing and Shanghai. The results demonstrate the efficiency and effectiveness of our methods in different applications. [ABSTRACT FROM AUTHOR]

The Catering Service Industry (CSI) experienced profound impacts due to the COVID-19 pandemic. However, the long-term and multi-timepoint analysis using big data remained limited, influencing governmental decision-making. We applied Kernel Density Estimation, Shannon Diversity Index, and the Geographic detector to explore the spatial heterogeneity and determinants of the CSI in Beijing during the pandemic, with monthly granularity. The temporal-spatial dynamics of the CSI presented a "W"-shaped trend from 2018 to 2023, with pivotal shifts aligning with key pandemic stages. Spatial characteristics exhibited heterogeneity, with greater stability in the city center and more pronounced shifts in peripheral urban zones. Districts facing intricate outbreaks showed lower catering income, and Chinese eateries exhibited heightened resilience compared to others. The CSI displayed strong interconnections with living service sectors. Development in each district was influenced by economic level, population distribution, service facilities convenience, and the risk of the COVID-19 pandemic. Dominant factors included total retail sales of consumer goods, permanent population, average Baidu Heat Index, density of transportation and catering service facilities, infection cases and the consecutive days with confirmed cases existing. Consequently, we suggested seizing post-pandemic recovery as an avenue to unlock the CSI's substantial potential, ushering a fresh phase of growth. [ABSTRACT FROM AUTHOR]

Urban perceptions are related to residents’ subjective well-being, activities, settlement choice, and physical and mental health, which are vital for promoting sustainable community development. Landsenses ecology proposes the concepts of physical and psychological perceptions to measure the interaction between humans and the urban environment. However, such perceptions and their quantitative relationships with housing rental prices (HRPs) are still not clear. In this study, a novel urban-perception-oriented hedonic price model (UP-HPM) is proposed based on the construction of a multisource geographic database of Beijing’s rental market with rich information on people-oriented perception metrics, neighborhood environment characteristics, and HRPs. Furthermore, we use advanced machine learning (ML) pipelines to measure residents’ multidimensional urban perceptions and investigate their impact on block-level HRPs and their spatial disparities. We also quantify and compare the contribution of different physical and psychological perceptual attributes to HRPs through UP-HPM. The results show that neighborhood perceived greenery and the use of fences tend to bring about positive psychological perceptions and further affect HRPs. By comparing different combinations of hedonic and perceptual features, the UP-HPM performs well in terms of fitting capacity, spatial description, and interpretability and can inspire humanistic thinking and landsenses planning guidelines for the regulation of the allocation of public rental housing and improvement in block-level physical settings and infrastructure services, thereby providing insights into how best to promote residents’ quality of life and well-being. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors. • Explore seasonal variance in Electric Vehicle (EV) charging demand with big data. • EVs got recharged when their state of charge (SOC) was lower in winter. • The central urban areas tended to have a higher charging demand in winter. • Significant seasonal effects on infrastructure deployment in central and outer areas. • More noticeable seasonal effects with fewer charging infrastructure to be deployed. [ABSTRACT FROM AUTHOR]

Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people's daily activities and trip purposes, and lead to a deeper understanding of human travel patterns. Most existing literature analyses of activity semantics mainly focus on the characteristics of the destination. However, the movement from the origin to the destination can be represented as the flow. The flow can completely represent the activity semantic and describe the spatial interaction between the origin and the destination. Therefore, in this paper, we proposed a two-layer framework to infer the activity semantics of each taxi trip and generalized the similar activity semantic flow to reveal human travel patterns. We introduced the activity inference in the first layer by a combination of the improved Word2vec model and Bayesian rules-based visiting probability ranking. Then, a flow clustering method is used to uncover human travel behaviors based on the similarity of activity semantics and spatial distribution. A case study within the Fifth Ring Road in Beijing is adopted and the results show that our method is effective for taxi trip activity inference. Six activity semantics and four activity semantics are identified in origins and destinations, respectively. We also found that differences exist in the activity transitions from origins to destinations at distinct periods. The research results can inform the taxi travel demand and provide a scientific decision-making basis for taxi operation and transportation management. [ABSTRACT FROM AUTHOR]

• The community livability evaluation is quantitative, practical and fine-grained. • The evaluation criteria span dimensions of environment, traffic and population. • Remote sensing, surface observation and geospatial big data are used. • Most communities are distributed within the central and marginal urban areas. • Communities in urban area tend to have robustly high livability. Effective evaluation of community livability is in urgent need to avoid increased livability at the expense of sustainability. However, studies concerning community livability evaluation were still conceptual, qualitative or conducted at city or regional scales. The availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive livability evaluation at much finer scales. This paper proposed a quantitative and practical method for up-to-date livability evaluation at individual community scale in China. Nine evaluation criteria were identified spanning dimensions of environment, traffic, convenience, and population. These criteria were calculated respectively from remote sensing, surface observation and geospatial big data. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied for community livability evaluation, and the uncertainty and sensitivity of evaluation results were assessed. The livability evaluation in the case study area of Haidian District, Beijing, China demonstrated the practicality and effectiveness of the framework. A total number of 1242 communities in Haidian District were evaluated. Communities in urban area were generally associated with higher evaluation scores and lower uncertainties than those in rural area. The careful selection of criteria weights with high sensitivity, i.e., green space coverage within community and driving time to schools, can potentially significantly reduce the uncertainty of the livability evaluation. The community scale livability evaluation is expected to bridge the gap between theoretical concepts and practical implementations of livability evaluation, and enables the development of more effective and locally specific regulations and policies to improve community livability. [ABSTRACT FROM AUTHOR]

• A graphical method for intuitively illuminate fundamental driving habits of drivers. • Describe the basic and internal driving patterns of drivers based on big data. • A good application in the analysis of driving safety. Due to differences in driving skills and personal characteristics among drivers, the behaviors of drivers when faced with various driving environments differ, causing different levels of driving safety concerns. In past research, the measurement of safety-related driving behavior mostly focused on classification, while few studies were concerned with individual driving behavior characteristics. However, it is important for drivers to recognize and correct their dangerous behaviors and optimize their driving. This paper presents a graphical method for modeling individual driving behaviors, and the results can be used in driving safety analysis. Based on the assumption that drivers have specific driving habits, typical driving patterns during driving are first detected and extracted. These typical driving patterns are then sorted according to their frequencies, forming a driving behavior graph that can directly illustrate each driver's behavior features. Furthermore, a quantitative analysis method for evaluating driving safety based on the behavior graph is provided. To verify the proposed method, a case study focusing on vehicles' longitudinal motion was conducted using GPS data collected from Beijing taxis. The results demonstrated that the graphical method can describe the individual features of a driver's longitudinal acceleration behavior and distinguish differences among drivers. The development of this method can help understand the individual features of driving behaviors and further support measures to optimize driving safety. [ABSTRACT FROM AUTHOR]

The slow traffic system is an important component of urban transportation, and the prerequisite and necessary condition for Beijing to continue promoting "green priority" are establishing a good urban slow traffic system. Shijingshan District of Beijing City is taken as a research object. By analyzing and processing population distribution data, POI data, and shared bicycle data, the shortcomings and deficiencies of the current slow traffic system in Shijingshan District are explored, and corresponding solutions are proposed, in order to provide new ideas and methods for future urban planning from the perspective of data. [ABSTRACT FROM AUTHOR]