Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model

Yao Yao · Xia Li · Xiaoping Liu · Penghua Liu · Zhaotang Liang · Jinbao Zhang +1 more

490 citations0/0 files storedInternational Journal of Geographical Information Systems

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Remote Sensing and Land Use Geographic Information Systems Studies Human Mobility and Location-Based Analysis

Abstract

Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithm by considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes.

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References

2012Discovering regions of different functions in a city using human mobility and POIs 2015Social Sensing: A New Approach to Understanding Our Socioeconomic Environments 2015Mining point-of-interest data from social networks for urban land use classification and disaggregation 2015Automated identification and characterization of parcels with OpenStreetMap and points of interest 2013Toward mapping land-use patterns from volunteered geographic information 2010Evaluation of plan implementation in the transitional China: A case of Guangzhou city master plan 2012Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of Beijing 2015Real options analysis for land use management: Methods, application, and implications for policy

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