Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

Anna Petrasova,Helena Mitasova,J Aaron Hipp

doi:10.3390/ijgi8120559

Anna Petrasova, Helena Mitasova + Show 1 more

Open Access

https://doi.org/10.3390/ijgi8120559

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Abstract

Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate pedestrians in the captured images is a promising technique for analyzing pedestrian activity. However, it is challenging to efficiently transform the time series of pedestrian locations in the images to information suitable for geospatial analytics, as well as visualize data in a meaningful way to inform urban design or decision making. In this study, we propose to use a space-time cube (STC) representation of pedestrian data to analyze the spatio-temporal patterns of pedestrians in public spaces. We take advantage of AMOS (The Archive of Many Outdoor Scenes), a large database of images captured by thousands of publicly available, outdoor webcams. We developed a method to obtain georeferenced spatio-temporal data from webcams and to transform them into high-resolution continuous representation of pedestrian densities by combining bivariate kernel density estimation with trivariate, spatio-temporal spline interpolation. We demonstrate our method on two case studies analyzing pedestrian activity of two city plazas. The first case study explores daily and weekly spatio-temporal patterns of pedestrian activity while the second one highlights the differences in pattern before and after plaza’s redevelopment. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time series of pedestrian occurrences captured by webcam images.

Highlights

Understanding the spatio-temporal distribution of pedestrian volume in urban environments is essential for informing urban management and planning decisions to create livable and thriving city centers as well as mitigating negative effects including increased traffic and crime rates
Given the importance of up-to-date and reliable data, researchers and urban planners studying the use of public spaces have been testing and applying different types of data collection methods varying in the type and geographic extent of information they provide, cost of their implementation, and privacy issues they raise
The challenges of asking people to actively wear the different sensors and privacy issues associated with telecommunication data, together with data and participant inaccessibility for research has led researchers to take advantage of crowdsourced, often publicly available, big data coming from social media networks, such as Twitter, Flickr, and Instagram [10]

Summary

Introduction

Understanding the spatio-temporal distribution of pedestrian volume in urban environments is essential for informing urban management and planning decisions to create livable and thriving city centers as well as mitigating negative effects including increased traffic and crime rates. While evidence from public health [1], transportation [2], environmental sciences [3], and environmental psychology [4] can provide theoretical basis for informing urban design, planners are increasingly using data-driven methods to identify opportunities for infrastructure improvements, analyze the use of design features, and evaluate the impacts of special events or public space redevelopment. Given the importance of up-to-date and reliable data, researchers and urban planners studying the use of public spaces have been testing and applying different types of data collection methods varying in the type and geographic extent of information they provide, cost of their implementation, and privacy issues they raise. Strava—a network for tracking athletic activity—provides even more geospatially rich data which has been used to investigate cycling behavior [15], cycling infrastructure [16], and air pollution exposure of commuting cyclists [17]

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