This article employs two indexes—accessibility and effective service ratio (ESR)—to assess the spatial distribution of urban parks with the consideration of both equity and efficiency. Traditional approaches to calculate these two indexes are often based on the shortest distance to the park within its service radius by network analysis. Such approaches cannot reflect the actual travel behaviors of urban residents and require extensive data collection of road networks and complex parameter setting. To avoid these defects, this study directly acquires travel time data for various travel modes in a specific time period to the park through web mapping API (Application Program Interface), then calculates the accessibility and ESR of urban parks based on these detailed data. This method gets closer to actual park usage situation and avoids the cumbersome process of road network model building. At last, a case study is conducted on the assessment of spatial distribution of main parks in Wuhan, finding that districts with poor park accessibility in Wuhan can be divided into three types: districts with an absence of parks, districts with an insufficiency with parks, and districts separated from parks by traffic. Then, pertinent improvement suggestions are proposed, which provide some bases for decisions on future park planning and construction.

The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents’ travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents’ travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

When assessing urban eco-environmental sensitive areas (Urban Eco-ESAs) by multi-criteria evaluation method, the widely used weighted linear combination method may inevitably lead to some factors of high sensitive value being neutralized by other factors of low sensitive value, resulting in the neglect of some eco-sensitive areas as a consequence, while on the other hand, Boolean OR combination method, which give a result of high sensitive value as long as any factor has the value of high sensitivity and thus ignore the mutual compensation mechanism among ecological factors, can lead to excessively wide ranges of the eco-sensitive areas. To overcome the defects of these two methods, the authors propose an Urban Eco-ESAs synthesized assessment method giving finely controlled priority to bottleneck factors, and relative model based on selective local encouraging variable weights combination. The method is able to increase the weights of some eco-bottleneck factors on the basis of a weight-changing function in synthesizing multiple factor assessments as long as their assessment values break the bottleneck threshold values, which can guarantee the eco-sensitive areas identified by bottleneck factors being embodied in the final result, while also retain the compensation effect of multiple ecological factors, which makes the results of the assessment more reasonable. In order to deliver this empirical research, Taogang Town in China, which is adjacent to Net lake wetland nature reserve area, has been used as an example to confirm the validity of the method and the model.

The outbreak of COVID-19 (coronavirus disease 2019) has become the focus of attention in the field of urban geography. Built environment, such as the layout of public spaces like transportation hubs and urban open spaces, is an important factor affecting the spread of the epidemic. However, due to the different behavior patterns of different age groups, the intensity and frequency of their use of various built environment spaces may vary. Based on this, we selected patients that were infected, with a non-manipulated time period, and the classification of human behavior patterns; we then conducted a regression analysis study on the spatial distribution and building environment of these COVID-19 patients. The results showed that the spatial distribution of young and middle-aged patients (18–59 years old) was more homogeneous, while the spatial distribution of elderly patients (60 years old and above) had a strong clustering characteristic. Moreover, the significant built environment factors exhibited in the two populations were extremely different. More diverse urban facilities and public spaces exhibited influential properties for older patients, while middle-aged and young adults were more influenced by commuting facilities. It can be said that the built environment shows different influences and mechanisms on the transmission of respiratory infectious diseases in different populations. Therefore, the results of this paper can inform decision makers who expect to reduce the occurrence of urban respiratory infectious diseases by improving the urban built environment.

Ensuring that commuting distance remains within a certain range has important effect of residents’ quality of life. Although many studies have investigated the relationship between the built environment and residents’ commuting distance, limited evidence has been provided of the impact of job location. As such, in this study, we used data from the Wuhan Metropolitan Development Area in China and applied Bayesian linear regression (BLR) models to examine the impact of the built environment at both residential and job locations on commuting distances for residents. Our findings showed that, for residential locations, the residential density, land use mix, number of intersections, parking service level, and number of companies have a significant negative effect on commuting distance, whereas the plot ratio, distance to sub-employment centers, number of metro stations, and number of bus stops have a significant positive effect on commuting distance. For employment locations, land use mix, parking service level, and number of companies have a significant negative effect on commuting distance, whereas job density, number of intersections, distance to sub-employment centers, number of metro stations, and number of bus stops have a significant positive effect on commuting distance. By describing the influence of the built environment at both residential and job locations on commuting distance, our findings are conducive to the optimization of land use and the formulation of related policies to reduce commuting distance, which has a positive effect on improving residents’ quality of life and reducing energy emissions and air pollution.

Ride-hailing, a newly emerging mobility service that is popular worldwide, has become an efficient new mode of transportation. Nonetheless, the use and value of ride-hailing remain unclear for newly developed areas in the suburbs. We crawled through the usage data of 10 ride-hailing apps from Wuhan, China, and used Spatial Autocorrelation and Geographic Weighted Regression (GWR) to explore the role of ride-hailing in suburban new towns. We found that: (1) There is variability between urban and suburban in the use of ride-hailing, and residents in suburban new towns are more inclined to complete travel activities by ride-hailing. (2) In suburban new towns, ride-hailing has a complementary effect on public transportation, and this complementary role has differences in regional and demographic attributes. This effect is greater for high-tech industrial areas and is more in women and young people than in men and elderly people. Overall, this study confirms from a geospatial perspective that residents of suburban new towns are more likely to use ride-hailing compared to central urban areas and that ride-hailing can supplement the lack of public transportation services (PTS) in suburban areas and improve transportation services in such new towns where development and construction are not yet complete. Therefore, the integration of online taxis with traditional public transportation is expected to promote multi-modal transportation options in newly developed areas and help realize the development of suburban new towns. In addition, the study also found the effectiveness of using big data from mobile phones in studying residents’ temporal and spatial behavior.