Categorizing urban space based on visitor density and diversity: A view through social media data

Urban space categorization


Analyses of urban spaces have often stressed the importance of both the density and diversity of the people they attract. However, the diversity of people is a challenging concept to operationalize within the context of urban spaces, which is why many evaluations of urban space have relied primarily on density-based measures. We argue that a focus on only one of the two aspects misses important aspects of the variety of urban spaces in our cities. To address this, we design a methodology that evaluates both the density and diversity of human behavior in urban spaces based on geosocial media data. We operationalize density as the frequency of tweets from visitors to a particular location and diversity as the variety of the home neighborhoods of those visitors. Taking Singapore as a test case, we identify networks between the home neighborhoods of 28k Twitter users based on 2.2 million geolocated tweets collected between 2012 and 2016. Based on these data, we categorize the urban landscape of Singapore into four “performance” categories, namely High-Density/High-Diversity, High-Density/Low-Diversity, Low-Density/High-Diversity, and Low-Density/Low-Diversity. Our findings illustrate that this combined indicator provides useful nuance compared to differentiation between well and less performing spaces based on density alone. By enabling a categorization of urban spaces that fits closer to the diversity of human behavior in these spaces, human mobility data sets, such as the social media data we use, open the door to a practical evaluation of the design and planning of our heterogeneous urban environment.

Environment and Planning B: Urban Analytics and City Science
Qingqing Chen
Qingqing Chen
PhD Candidate | Data Scientist

Ph.D. candidate focuses on critically understanding urban space by leveraging big data, combined with data science and machine learning techniques.