In this study, we focus on the quality of street space which has attracted high attentions. We discover associations between the quality of street space and built environment attributes through an ordered logistic model using massive street view pictures (SVPs) and data on street location, form, function and attributes. Before ascertain which built environment factors influence the quality of street space, we checked the concordance of the experts’ scores, as well as correlations between different dimensions through Kappa analysis and drew the distribution map of street space quality. We found that the value of intersection over union is 85.61% for scoring the street space quality by different people. The spatial quality of more than 75% streets are in the middle level with no obvious polarisation observed in the central area of Qingdao. In addition, for street quality index, all variables are statistically significant. The sequence is as follows: near-line rate > D/H ratio > slope > length of street > distance to administrative center > POIs diversity. The D/H ratio, near-line rate, slope length of street, distance to administrative center and POIs diversity have various associations on every dimension of street quality. They can prove useful for drafting more appropriate policy measures aimed at improving street quality.

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Zones, cells, and parcels have long been regarded as the main units of analysis in urban modeling. However, only limited attention has been paid to street-level urban modeling. The emergence of fine-scale open and new data available from various sources has created substantial opportunities for research on urban modeling at the street level, particularly for modeling the spatiotemporal process of urban phenomena. In this paper, the street is adopted as the spatial unit of an urban model, and a conceptual framework for such modeling based on cellular automata is proposed. The validity of the proposed framework is verified by an empirical application to the urban space within the Fifth Ring Road in Beijing from 2014 to 2018. The results show that the density of points of interest simulated by the cellular automata model for 2018 is basically consistent with the actual distribution according to direct observation, and there is no significant difference in the proportion of high, medium, and low points of interest density streets between different ring roads. In addition, the deviation rate and Kappa index are 0.1171 and 0.97, respectively, indicating the proposed model can replicate historical patterns well and predict the transition of points of interest density at the street level. Subsequently, we considered three scenarios, adopting 2018 as the base year and using the proposed model to simulate the distribution of points of interest density in 2022 and the changes in points of interest density from 2018 to 2022. The conceptual framework and empirical application also provide support for urban planning and design based on the integration of linear public space and big data.