Mining point-of-interest data from social networks for urban land use classification and disaggregation

By Shan Jiang, Ana Oliveira Alves, Filipe Rodrigues, Joseph Ferreira Jr., Francisco Câmara Pereira. Computers, Environment and Urban Systems, 2015

330 citations recorded by the source corpus.

Abstract Over the last few years, much online volunteered geographic information (VGI) has emerged and has been increasingly analyzed to understand places and cities, as well as human mobility and activity. However, there are concerns about the quality and usability of such VGI. In this study, we demonstrate a complete process that comprises the collection, unification, classification and validation of a type of VGI—online point-of-interest (POI) data—and develop methods to utilize such POI data to estimate disaggregated land use (i.e., employment size by category) at a very high spatial resolution (census block level) using part of the Boston metropolitan area as an example. With recent advances in activity-based land use, transportation, and environment (LUTE) models, such disaggregated land use data become important to allow LUTE models to analyze and simulate a person’s choices of work location and activity destinations and to understand policy impacts on future cities. These data can also be used as alternatives to explore economic activities at the local level, especially as government-published census-based disaggregated employment data have become less available in the recent decade. Our new approach provides opportunities for cities to estimate land use at high resolution with low cost by utilizing VGI while ensuring its quality with a certain accuracy threshold. The automatic classification of POI can also be utilized for other types of analyses on cities.

Subfields: Geography, Planning and Development, Transportation

View Paper: https://www.semanticscholar.org/paper/ebc56f80ae237005b198f23bce359cbf9d62c174

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geography, Planning and Development

In Transportation

Citations in the corpus

Spatial Explicit Assessment of Urban Vitality Using Multi-Source Data: A Case of Shanghai, China (2019, methodology): “Using POI-based mixed use to measure urban function that can identify developmental activities inside a land parcel is more precise than using land-based mixed use [40].”
Social Media data: Challenges, opportunities and limitations in urban studies (2019, background): “However, other LBSNs, such as Foursquare and Google Places have demonstrated their relevance as supplementary georeferenced data sources (Jiang et al., 2015; Milne, Thomas, & Paris, 2012; Serrano-Estrada, Marti, & Nolasco-Cirugeda, 2016; Van Canneyt, Schockaert, et al., 2012a).”
Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, methodology): “Jiang et al. (2015) utilized the Yahoo online POI data together with publicly available aggregated employment data from census at the block group level to derive fine resolution disaggregated land use estimates (i.e., employment by category) at the city block level.”