Next Article in Journal
Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get?
Next Article in Special Issue
Towards Increasing Residential Market Transparency: Mapping Local Housing Prices and Dynamics
Previous Article in Journal
Types of Crime, Poverty, Population Density and Presence of Police in the Metropolitan District of Quito
Open AccessArticle

Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

1
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
2
Department of Parks, Recreation, and Tourism Management, North Carolina State University, Raleigh, NC 27695, USA
3
Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 559; https://doi.org/10.3390/ijgi8120559
Received: 22 October 2019 / Revised: 14 November 2019 / Accepted: 1 December 2019 / Published: 5 December 2019
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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. View Full-Text
Keywords: GIS; space-time cube; geovisualization; KDE; urban dynamics; georeferencing; pedestrian density GIS; space-time cube; geovisualization; KDE; urban dynamics; georeferencing; pedestrian density
Show Figures

Graphical abstract

MDPI and ACS Style

Petrasova, A.; Hipp, J.A.; Mitasova, H. Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams. ISPRS Int. J. Geo-Inf. 2019, 8, 559.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop