Objective: To explore the factors affecting Aedes surveillance by the mosquito ovitrap method in a grid mode, so as to provide a scientific basis for the surveillance and control of Aedes mosquitoes and related mosquito-borne infectious diseases. Methods: Three adjacent residential areas with similar areas, building ages, and greenery ratios were selected as surveillance points in Jing'an District, Shanghai, China. Each residential area was divided into secondary surveillance blocks (about 90 m × 60 m) in a 3×3-grid mode. On-site monitoring was conducted in each surveillance block using the mosquito ovitrap method and the human landing catch method, three times one month from July to September 2021. By comparing the surveillance results of different residential areas, different surveillance blocks, and different environmental characteristics, the factors influencing the positive rate of the mosquito ovitrap method were determined. Excel 2016 and SPSS 16.0 were used to process the data. The Kruskal-Wallis rank sum test, tow-way analysis of variance, and Spearman correlation analysis were performed. Results: A total of 30 secondary surveillance blocks were designated. Eight times of surveillance were completed, and 131 mosquito ovitraps were set each time. The mosquito ovitrap index (MOI) in residential areas 1, 2, and 3 were 8.71, 12.38, and 11.97, respectively, with no significant difference (χ²=2.750, P=0.253). There were significant differences in the MOI among different blocks of residential areas 1 and 2 (F=2.135, P=0.047; F=2.168, P=0.044). In residential areas, the positive rate was 12.24 in living areas and 5.76 in community school areas, with a significant difference (χ²=6.657, P=0.010). The MOI was 14.10 for green areas on the house side, 8.87 for concentrated green areas, and 7.98 for green areas on the road side, with a significant difference (χ²=8.372, P=0.015). During the surveillance period, the MOI was 13.28 when the days of rainfall was < 2 d, and 8.79 when the days of rainfall was ≥2 d, with a significant difference (χ²=4.218, P=0.047). In residential area 1, the average MOI was 8.69, and the average landing index was 3.33 mosquitoes/person·h. In residential area 2, the average MOI was 12.45, and the average landing index was 8.58 mosquitoes/person·h. In residential area 3, the average MOI was 11.88, and the average landing index was 6.50 mosquitoes/person·h. The ratio of the MOI to the landing index was distributed between 1∶1 and 3∶1. Pearson correlation analysis showed that the MOI was highly correlated with the human landing index in each block (r=0.549, P=0.005). Conclusions: The density of Aedes mosquitoes may differ greatly in different areas of large residential areas due to differences in greening types, functional zoning, and other factors. The mosquito ovitrap method has the advantages of simple operation and high specificity compared with other surveillance methods for Aedes mosquitoes, and it is highly consistent with the human landing catch method. The mosquito ovitrap method with grid-based surveillance point distribution can be used in actual practice, which can effectively avoid deviations caused by point selection and fully reflect the density of Aedes mosquitoes.