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The school–residence spatial relationship is a key factor in understanding urban spatial structure and travel-to-school behavior of students. Analyzing the change law and the spatial characteristics of travel-to-school distance can provide a basis for improved accessibility of urban educational facilities and enable enrolment of students from the neighborhood. Based on one complete month of mobile phone signaling data for May 2018, the changes in student density with the travel-to-school distance was analyzed using MATLAB and Mann–Kendall Trend Test, and the pattern and the spatial structure of travel-to-school were explored. The results revealed that: (1) With increase in travel-to-school distance, the student density showed a decrease in truncated power law distribution, and it is concentrated within the travel-to-school distance of 5.0 km; (2) According to the sudden change points of the student density growth rate, the threshold distance for travel to kindergartens is 1.30 km, and for primary schools and secondary schools is 1.50 km. The school–residence spatial structure is divided according to the threshold of travel-to-school distance and the scope of attendance; (3) The dominant flow of travel-to-school is generally from urban peripheral and marginal areas to the urban core area, and partly from marginal areas to peripheral areas; (4) The pattern of travel-to-school is polycentric, and the study centers are mainly located in the urban central district north of the Hun River. The urban core area has the strongest attraction of students, while the marginal area has the weakest.

Resilience is a new path to express and enhance urban sustainability. Cities suffer from natural shocks and human-made disturbances due to rapid urbanization and global climate change. The construction of an urban resilient developmental environment is restricted by these factors. Strengthening the comprehensive evaluation of resilience is conducive to identifying high-risk areas in cities, guiding regional risk prevention, and providing a scientific basis for differentiated strategies for urban resilience governance. For this study, taking Shenyang city as a case study, the resilience index system was constructed as an ECP (“exposure”, “connectedness”, and “potential”) framework, and the adaptive cycle model was introduced into the resilience assessment framework. This model not only comprehensively considers the relationship between exposure and potential but also helps to focus on the temporal and spatial dynamics of urban resilience. The results show that the exposed indicators have experienced three spatial evolution stages, including single-center circle expansion, multicenter clustering, and multicenter expansion. The potential index increased radially from the downtown area to the outer suburbs, and the low-value area presented a multicenter pattern. The spatial agglomeration of connectivity indicators gradually weakened. The results reflect the fact that the resilience level of the downtown area has been improved and the resilience of the outer expansion area has declined due to urban construction. The multicenter cluster pattern is conducive to the balance of resilience levels. In terms of the adaptive cycle phases of urban resilience, the first ring has gone through three phases: exploitation (r), conservation (K), and release (Ω). The second and third rings have gradually shifted from the exploitation (r) phase to the conservation (K) phase. The fourth ring has entered the exploitation (r) phase from the reorganization (ɑ) phase. The fifth ring and its surrounding areas are in the reorganization (ɑ) phase. The results provide specific spatial guidance for implementing resilient urban planning and realizing sustainable urban development.