Mining point-of-interest data from social networks for urban land use classification and disaggregation. (September 2015)

• Record Type: Journal Article
• Authors: Jiang, Shan; Alves, Ana; Rodrigues, Filipe; Ferreira, Joseph; Pereira, Francisco C.
• Is Part Of: Computers, environment and urban systems. Volume 53(2015)
• Page Start: 36
• Page End: 46
• Publication Date: 2015-09
• Subjects: Information extraction -- Machine learning -- Points of interest -- Land use -- Volunteered geographic information; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2014.12.001
• Languages: English
• ISSNs: 0198-9715

Abstract

Graphical abstract: Highlights: We demonstrate the unification, classification and validation of online POI data. The classified POI data is used to disaggregate land use at a high spatial resolution. The disaggregated land use data is useful for LUTE models and for economic analyses. 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.