The accessibility of rail transit station areas is an important factor affecting the efficiency of rail transit. Taking the Beijing rail transit station area as our research object, this paper took a 15 min walking distance as the index of station area accessibility, and investigated the status quo and influencing factors of the unbalanced distribution of rail transit station area accessibility in Beijing. In this paper, the data of Beijing rail transit stations were obtained from the Amap open platform, and the accessibility of the station area was calculated using the path planning service provided by the Amap API. The Getis–Ord Gi* method was used to analyze the overall distribution characteristics of the accessibility of the Beijing rail transit station area, then the high accessibility area and the low accessibility area were determined. To explore the factors influencing domain accessibility, multi-source data were obtained, a total of 11 indicators were constructed, and the random forest model was used to identify feature importance. Using the eight selected influencing factors, the OLS regression model, GWR model, and MGWR model were used to study the spatial heterogeneity of influencing factors. By comparison, it was concluded that the MGWR model can not only effectively analyze the spatial heterogeneity of rail transit station accessibility, which can automatically mediate the bandwidth of different influencing factors, and then reflect the spatial changes of the influencing factors of rail transit station accessibility more truly. The results show that (1) the accessibility of the Beijing rail transit station area shows obvious spatial agglomeration characteristics in space. The accessibility of the station area in the fourth ring is higher than that outside of the fourth ring road, and the accessibility near the south and north fifth ring road is higher than that of the east fifth ring road and the west fifth ring road. (2) The basic influencing factors of rail transit station accessibility include road density and functional mixing degree.

Visually impaired individuals face substantial challenges in kitchens, where identifying objects accurately is crucial yet difficult due to the complexity and variability of the environment. Traditional object detection1 methods fall short in these settings, struggling with the assortment of items. This research highlights the need for advanced, kitchen-specific solutions that leverage deep learning to improve detection accuracy and offer real-time, interactive guidance through speech technologies. By focusing on the unique demands of kitchen environments, the proposed system aims to significantly enhance the autonomy and safety of visually impaired users, presenting a notable advancement in assistive technology. The effectiveness of this approach is assessed by its ability to accurately identify kitchen items for visually impaired individuals.

In order to promote the development of the basic public service system, it also needs to enhance the affordability and fairness of surrounding communities of rail transit stations. Based on this, the example for this research has been selected as the area within the 6 th Ring Road of Beijing. This paper is aimed at finding out the difference between the openness of the environment around the rail transit station and the population density. This simulation and examination on rail transit station regions’ accessibility employed the Gaussian-based two-step floating catchment area approach and the Gaode Map service Application Programming Interface (API) four transport modes: walking, cycling, driving a car, and taking public transport. By combining the calculation results with population distribution data, the current state of accessibility equity is assessed. The IPA analysis method is employed to determine the priority order for different area modifications and to propose corresponding suggestions for optimization. The findings are as follows: (1) Within the urban core and urban sub-center area, accessibility is higher compared to other regions across different travel modes; (2) Within the study area, there is a significant positive correlation between the accessibility of rail transit station areas and the travel mode; (3) Within the maturely developed areas, there are notable differences in the equity and accessibility of rail transit station areas across different travel modes. According to the Gini coefficient, accessibility is most equitable when traveling by car or bicycle, and least equitable when traveling on foot or by public transit; (4) Local bivariate spatial correlation analysis indicates that cycling and driving can improve the accessibility equity within the urban core compared to walking and public transit; (5) Importance-Performance Analysis (IPA) results show that the area from western fringe area of the urban core has the highest priority for redevelopment, and the accessibility equity northern fringe of the city can be improved by promoting cycling to enhance accessibility of walking and public transit. The findings of this study can be applied to urban rail transit accessibility improvement projects.

Accessibility is an important index for evaluating the efficiency of rail stations. In view of the imbalance between the supply and demand of rail station settings and population distribution, this paper takes the Shijingshan District in Beijing as an example. Based on the Gaussian two-step floating catchment area method and Gaode map’s service interface, the accessibility of rail stations is simulated and analyzed in terms of both walking and riding. Combined with the calculation results, supply and demand relationship and trip time, the current characteristics and causes are analyzed, and the corresponding optimization suggestions are put forward. The main conclusions are as follows: (1) The accessibility distribution of rail stations in the Shijingshan District is relatively coordinated with the population distribution. The effectiveness of the accessibility assessment of rail stations can be further improved by improving the causal evaluation model with traditional calculation data; (2) The change of trip mode has a small impact on the accessibility of large stations, while small stations and areas with uneven station distribution can be improved by riding; (3) According to the K-value clustering method, the results of the two calculation methods are divided into five categories, and each category of demand units has different accessibility characteristics and causes; (4) Comprehensive accessibility is positively correlated with road density and population density.

The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China’s policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this paper, we conduct a refined COVID-19 risk prediction and identification of its influencing factors in Beijing based on neighborhood-scale spatial statistical units. We obtained geographic coordinate data of COVID-19 cases in Beijing and quantified them into risk indices of each statistical unit. Additionally, spatial autocorrelation was used to analyze the epidemic risk clustering characteristics. With the multi-source data, 20 influencing elements were constructed, and their spatial heterogeneity was explored by screening 8 for Multiscale Geographically weighted regression (MGWR) model analysis. Finally, a neural network classification model was used to predict the risk of COVID-19 within the sixth ring of Beijing. The MGWR model and the neural network classification model showed good performance: the R2 of the MGWR model was 0.770, and the accuracy of the neural network classification model was 0.852. The results of this study show that: (1) COVID-19 risk is uneven, with the highest clustering within the Fifth Ring Road of Beijing; (2) The results of the MGWR model show that population structure, population density, road density, residential area density, and living service facility density have significant spatial heterogeneity on COVID-19 risk; and (3) The prediction results show a high COVID-19 risk, with the most severe risk being in the eastern, southeastern and southern regions. It should be noted that the prediction results are highly consistent with the current epidemic situation in Shijingshan District, Beijing, and can provide a strong reference for fine-grained epidemic prevention and control in Beijing.