Abstract
This paper explored the correlation between the visual proportion of urban street cultural landscape and crowd aggregation. In the study, the relevant theoretical assumptions and measurement scales were established first; Then the street panoramic images of 535 sampling points were obtained through systematic sampling and field shooting; Easygo and POI (Points of Interest) data of the research area collected every two hours within one week were picked up through big data capture; Finally, the driving force of geographical differentiation was detected by using the geographic detector. The results showed that: (1) in the artificial landscape, the visual proportion of architectural landscape had a significant impact on crowd aggregation and the explanatory power q was 0.15. Neither the visual proportion of roadway landscape nor that of sidewalk landscape had significant impact on crowd aggregation; (2) In the natural landscape, both the visual proportion of greenery landscape and that of sky landscape had significant impact on crowd aggregation and the explanatory power q was 0.09 and 0.05 respectively; (3) The interaction between the visual proportion of architectural landscape and that of greenery landscape or between the former and that of sky landscape showed a two-factor enhancement and the interaction between the visual proportion of greenery landscape and that of sky landscape showed non-linear enhancement; (4) There were significant two-factor enhancement effects in the interactions among the the visual proportion of architectural landscape, that of greenery landscape, of sky landscape and aggregation of POI facilities, of which the biggest q value was 0.76.
Highlights
At present, the urbanization rate of our country has reached more than 50%, and the urbanization rate in some areas has even reached more than 75%
The six independent variables were the grading of the points of interest (POI) aggregation, grading of visual proportion of architectural landscape (AL_1), grading of visual proportion of roadway landscape (AL_2), grading of visual proportion of sidewalk landscape (AL_2), grading of visual proportion of greenery landscape (NL_1) and grading of visual proportion of sky landscape (NL_2)
The results showed that the P values of the points of interest (POI) aggregation, grading of visual proportion of architectural landscape (AL_1), grading of visual proportion of greenery landscape (NL_1) and grading of visual proportion of sky landscape (NL_2) were all 0.000
Summary
The urbanization rate of our country has reached more than 50%, and the urbanization rate in some areas has even reached more than 75%. In order to improve the quality of urban space, enhance the attraction of cities and gather popularity [5,6] This will greatly change the cultural landscape pattern of the city's original streets, and influence people's spatial perception and behavior through sensual, intellectual and rational perception [15]. [19] Some scholars have studied the correlation characterization of crowd aggregation, Zhang Yan and others, for example analyzed three kinds of indicators such as job-housing balance, job-housing separation and commuting behavior, and identified the types and characteristics of job-housing space in Beijing based on multi-source data fusion such as mobile phone signaling. For example, Duan Yaming et al finely identified Chongqing’s “multi-center, cluster-like” urban structure using kernel density analysis and other methods based on Easygo heat data for a continuous week. [18] Wu Zhiqiang et al studied the crowd aggregation, location and population center of gravity and other indicators in downtown Shanghai by using the Baidu Heat Map. [19] Some scholars have studied the correlation characterization of crowd aggregation, Zhang Yan and others, for example analyzed three kinds of indicators such as job-housing balance, job-housing separation and commuting behavior, and identified the types and characteristics of job-housing space in Beijing based on multi-source data fusion such as mobile phone signaling. [20] in general, there are more literature on the application methods of big data in urban crowd activities, and fewer on the driving force behind it, so it is of theoretical value to explore the correlation between urban cultural landscape and crowd gathering activities
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