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Urban linguistic landscape studies examine visible written languages in urban areas, revealing socio-economic information, such as the place identity of minority groups and the localization processes of exotic language varieties. However, studies mainly utilize qualitative analysis or small-scale image acquisition without integrating socioeconomic quantitative analysis. Our research aims to expand the quantitative indicators of linguistic landscape in city-wide scale to explore the relationship between detailed quantitative text analysis and consumer prices in spatially differentiated and temporally controlled urban street view images. We examine such correlation through street view images scrapping of historical Baidu Street View images, semantic segmentation machine learning tools, and Optical Character Recognition. Our study reveals a negative correlation between linguistic landscape indicators in street signage and consumption levels. This research provides quantitative methods for large-scale and repeatable study of linguistic landscape, introducing a novel perspective on linguistic landscape evidence for further urban economic development and urban segmentation.