Where are the people? Counting people in millions of street-level images to explore associations between people's urban density and urban characteristics

Francisco Garrido-Valenzuela*, Oded Cats, Sander van Cranenburgh

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
28 Downloads (Pure)

Abstract

A thorough understanding of how urban space characteristics, such as urban equipment or network topology, affect people's density in urban spaces is essential to well-informed urban policy making. Hitherto, studies have primarily examined how the characteristics of the urban space impacts the number of people visiting different parts of the urban area (e.g., the city center). However, these studies almost without exception have used relatively small data sets, targeting specific neighborhoods or places. As a result, their findings are confined to specific areas and it is unclear to what extent their findings generalize to other urban areas. This study addresses this gap. We propose a new computer vision-based method to study how the urban space is associated with people's urban density in outdoor urban spaces. Specifically, our method uses a pre-trained object detection model to identify and count people as well as urban-related objects, such as presence of cars, and benches in millions street-level images collected throughout the Netherlands. Importantly, each street-level image is geo-located. Therefore, for each detected person and object its location is known. In turn, we regress urban space characteristics and urban-related objects on the number of people identified as a proxy for density in urban spaces. Our results show that higher numbers of people tend to be observed in places with smaller blocks, suggesting that compact urban development may be an effective way to increase people's density. Moreover, we find that the presence of food places and bicycles is associated with more people, indicating that urban planners could study the location of these amenities to attract more visitors to urban spaces and exploring the causality effects in this relationship. Our methodology offers a complementary way to monitor how the urban space is used over the time and to assess the effectiveness of urban interventions and policies.
Original languageEnglish
Article number101971
Number of pages12
JournalComputers, Environment and Urban Systems
Volume102
DOIs
Publication statusPublished - 2023

Keywords

  • Computer vision
  • Human detection
  • Object detection model
  • Street-level images
  • Urban design
  • Urban space

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