Mapping the pulse of the city: innovative framework for dynamic population insight
en-GBde-DEes-ESfr-FR

Mapping the pulse of the city: innovative framework for dynamic population insight

29.10.2024 TranSpread

The research team from the Department of Geography at SUNY Buffalo developed an innovative framework that uses a combination of 34 models to map monthly population distributions at fine resolutions. By integrating mobile phone data, building area, and detailed residential classifications, they created highly accurate population maps. The most successful model used ordinary least squares (OLS) regression, which incorporated mobile phone location data and a seven-class classification of buildings, such as single-family homes and mixed-use residential buildings. The model demonstrated high accuracy (R² = 0.82) and captured monthly population variations effectively. This approach offers a practical and replicable method for urban planners and researchers to track population dynamics in detail. The study (DOI: 10.34133/remotesensing.0227) was published on August 23, 2024, in the Journal of Remote Sensing.

The framework leverages remote sensing orthoimage, GIS tax parcel data, and SafeGraph home panel data. Remote sensing data sources like LiDAR and Landsat 8 were used to enhance spatial detail by mapping building areas and vegetation cover. Through comparing different models, the research identified building area as a key variable in population distribution. Machine learning models were also tested to further improve accuracy in predicting population trends.

“This framework provides a novel solution to tracking urban population dynamics. By integrating mobile data with remote sensing, we can now create monthly population maps that are more accurate and timely, which is crucial for urban planning and disaster management,” said Le Wang, co-author of the study and professor at SUNY Buffalo’s Department of Geography.

The research employed a two-step hybrid method. First, mobile phone data were combined with population-related variables to update population estimates at the census block group (CBG) level. Then, a weighted layer was created using statistical models and machine learning techniques, refining the population data down to the census block (CB) level. Model validation used random sampling and showed high accuracy, with an R² value of 0.82.

This hybrid approach combining remote sensing and mobile phone data can be applied to track population changes in various cities. Future applications could extend the model to larger regions and integrate additional dynamic data sources, such as real-time traffic or public services data, to further improve prediction accuracy and scalability. This could be a valuable tool for city management, emergency response, and policy-making, providing more detailed and up-to-date population insights.

###

References

DOI

10.34133/remotesensing.0227

Original Source URL

https://doi.org/10.34133/remotesensing.0227

About Journal of Remote Sensing

The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.

Paper title: A Novel Framework for Mapping Updated Fine-resolution Populations with Remote Sensing and Mobile Phone Data
Angehängte Dokumente
  • Flowchart of the proposed hybrid methodology.
29.10.2024 TranSpread
Regions: North America, United States, Asia, China
Keywords: Science, Space Science

Disclaimer: AlphaGalileo is not responsible for the accuracy of news releases posted to AlphaGalileo by contributing institutions or for the use of any information through the AlphaGalileo system.

Referenzen

We have used AlphaGalileo since its foundation but frankly we need it more than ever now to ensure our research news is heard across Europe, Asia and North America. As one of the UK’s leading research universities we want to continue to work with other outstanding researchers in Europe. AlphaGalileo helps us to continue to bring our research story to them and the rest of the world.
Peter Dunn, Director of Press and Media Relations at the University of Warwick
AlphaGalileo has helped us more than double our reach at SciDev.Net. The service has enabled our journalists around the world to reach the mainstream media with articles about the impact of science on people in low- and middle-income countries, leading to big increases in the number of SciDev.Net articles that have been republished.
Ben Deighton, SciDevNet
AlphaGalileo is a great source of global research news. I use it regularly.
Robert Lee Hotz, LA Times

Wir arbeiten eng zusammen mit...


  • BBC
  • The Times
  • National Geographic
  • The University of Edinburgh
  • University of Cambridge
  • iesResearch
Copyright 2024 by DNN Corp Terms Of Use Privacy Statement