Typeface Reveals Spatial Economical Patterns

Abstract

Understanding the socioeconomic and demographic characteristics of an urban region is vital for policy-making, urban management, and urban planning. Auditing socioeconomic and demographic patterns traditionally entails producing a large portion of data by human-participant surveys, which are usually costly and time consuming. Even with newly developed computational methods, amenity characteristics such as typeface, color, and graphic element choices are still missing at the city scale. However, they have a huge impact on personalized preferences. Currently, researchers tend to use large-scale street view imagery to uncover physical and socioeconomic patterns. In this research, we first propose a framework that uses deep convolutional neural network to recognize the typeface from street view imagery in London. Second, we analyze the relationship between 11 typefaces and the average household income in 77 wards of London. The result show that the typefaces used in the neighborhood are highly correlated with economic and demographic factors. Typeface could be an alternative metric to evaluate economic and demographic status in large-scale urban regions. More generally, typeface can also act as a key visual characteristic of a city.

A gridded establishment dataset as a proxy for economic activity in China

“…Because of the limitations of official sources, researchers often seek to use alternative data as proxies for existing measures of economic output [6]. Some 'big data' sourcese.g., nighttime lights [7,8], streetview imagery [9,10,11], mobile phone data [12,13], social media data [14], and restaurant data [15,16] -have been examined by investigating their relationships with traditional indicators of socioeconomic activity (e.g., population, GDP, and income). However, the time span covered by most big data sources is very limited; therefore, the relevant research mainly deploys cross-sectional analysis, and it is difficult to track temporal changes of a small area.…”

Linguistic Landscape Research on the Relationship of Urban Language and Commerce Based on Large-scale Street View Images

“…Urban linguistic landscape research can reveal socio-economic information, such as the place identity of minority groups (Leeman & Modan, 2009;Manan et al, 2015) ,the localization processes of exotic language varieties (Backhaus, 2006;Manan et al, 2017) and the degree of gentrification (Lou, 2010;Papen, 2012;Shcherbakov & Bagirova, 2020). However, most studies mainly utilize qualitative analysis or small-scale image acquisition without integrating urban street views and socioeconomic quantitative analysis (Backhaus, 2007;Ma et al, 2019).…”

Estimation of Regional Economic Development Indicator from Transportation Network Analytics

“…Traditional inference approaches to economic status mainly rely on official reports and census surveys, which usually take a long period and are labor intensive. With the rapid development of information, communication and technology (ICT), new data sources of human activities 1 and vehicle movement flow [2][3][4] , air transport flow 5,6 , financial flow 7 , information flow 8 , communication flow [9][10][11][12] , and others 13 have become available for better understanding and monitoring the status of our socioeconomic environments 14,15 . Liu et al 1 found that online social activity could reflect the macro economic status of 282 prefecture-level cities in China.…”