The Traj2Vec model to quantify residents’ spatial trajectories and estimate the proportions of urban land-use types

ABSTRACT The formulation of mixed urban land uses is not only intended to find the ideal scenario of land use but also regarded as a way toward sustainable urban development. We propose a geo-semantic mining approach Traj2Vec to quantify the trajectories of residents as high-dimensional semantic vectors. Then, a random forest (RF) method is used to model the relationship between the semantic vectors and mixed urban land uses. The proposed Traj2Vec approach can obtain the highest accuracy (OA = 0.7733, kappa = 0.7245) in urban land-use classification and a high average proportion accuracy (64.0%) in capturing the proportions of urban land-use types. Diversity analysis indicates that Shenzhen has a high degree of mixed urban land use at the scale of a street block. By analyzing the mixing index and the travel distance, we find a weak but significant negative correlation between them ( ), which not only confirms the conclusion that an increase in the degree of mixing will reduce the travel distances of residents but also verifies the mixing index. This suggests that urban planning should focus on mixed urban land uses, which can reduce the travel distances of residents, reduce energy consumption, and make cities more compact.

[1]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[2]  Zili Zhu,et al.  Real options analysis for land use management: Methods, application, and implications for policy. , 2015, Journal of environmental management.

[3]  R. Cervero.  MIXED LAND-USES AND COMMUTING: EVIDENCE FROM THE AMERICAN HOUSING SURVEY , 1996 .

[4]  Michael Batty,et al.  Inferring building functions from a probabilistic model using public transportation data , 2014, Comput. Environ. Urban Syst..

[5]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[6]  Krzysztof Janowicz,et al.  From ITDL to Place2Vec: Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts , 2017, SIGSPATIAL/GIS.

[7]  Liangpei Zhang,et al.  Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Yao Shen,et al.  Urban function connectivity: Characterisation of functional urban streets with social media check-in data , 2016 .

[9]  Xingjian Liu,et al.  Automated identification and characterization of parcels (AICP) with OpenStreetMap and Points of Interest , 2013, ArXiv.

[10]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[11]  Xiang Yu,et al.  Discovering functional zones using bus smart card data and points of interest in Beijing , 2015, ArXiv.

[12]  Chenghu Zhou,et al.  A new insight into land use classification based on aggregated mobile phone data , 2013, Int. J. Geogr. Inf. Sci..

[13]  Xiaoping Liu,et al.  Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method , 2017 .

[14]  Yu Shi,et al.  Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs , 2019, Comput. Environ. Urban Syst..

[15]  Enrique Frías-Martínez,et al.  Comparing and modelling land use organization in cities , 2015, Royal Society Open Science.

[16]  David H. Wolpert,et al.  An Efficient Method To Estimate Bagging's Generalization Error , 1999, Machine Learning.

[17]  Hui Xiong,et al.  Discovering Urban Functional Zones Using Latent Activity Trajectories , 2015, IEEE Transactions on Knowledge and Data Engineering.

[18]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[19]  Bei Zhao,et al.  Scene classification via latent Dirichlet allocation using a hybrid generative/discriminative strategy for high spatial resolution remote sensing imagery , 2013 .

[20]  Dave Kolas,et al.  Enabling the geospatial Semantic Web with Parliament and GeoSPARQL , 2012, Semantic Web.

[21]  Chao Li,et al.  Discovering Urban Spatio-temporal Structure from Time-Evolving Traffic Networks , 2014, APWeb.

[22]  Xiaoping Liu,et al.  Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model , 2017, Int. J. Geogr. Inf. Sci..

[23]  Yu Liu,et al.  Integrating multi-source big data to infer building functions , 2017, Int. J. Geogr. Inf. Sci..

[24]  Gérard Biau,et al.  Analysis of a Random Forests Model , 2010, J. Mach. Learn. Res..

[25]  Qingquan Li,et al.  Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data , 2016, Geographies of Mobility.

[26]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[27]  Yuan Zhang,et al.  Application of GPS Trajectory Data for Investigating the Interaction between Human Activity and Landscape Pattern: A Case Study of the Lijiang River Basin, China , 2016, ISPRS Int. J. Geo Inf..

[28]  Yu Liu,et al.  A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data , 2018, Int. J. Digit. Earth.

[29]  Qingquan Li,et al.  Exploring changes in the spatial distribution of the low-to-moderate income group using transit smart card data , 2018, Comput. Environ. Urban Syst..

[30]  Wei Tu,et al.  Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns , 2017, Int. J. Geogr. Inf. Sci..

[31]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[32]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[33]  A. Stewart Fotheringham,et al.  Analysis of human mobility patterns from GPS trajectories and contextual information , 2016, Int. J. Geogr. Inf. Sci..

[34]  Shihong Du,et al.  A Linear Dirichlet Mixture Model for decomposing scenes: Application to analyzing urban functional zonings , 2015 .

[35]  E. Holden,et al.  Three Challenges for the Compact City as a Sustainable Urban Form: Household Consumption of Energy and Transport in Eight Residential Areas in the Greater Oslo Region , 2005 .

[36]  G. Foody,et al.  Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps , 2017 .

[37]  Peter Nijkamp,et al.  Mobile phone usage in complex urban systems: a space–time, aggregated human activity study , 2015, Journal of Geographical Systems.

[38]  Xiaoping Liu,et al.  Classifying urban land use by integrating remote sensing and social media data , 2017, Int. J. Geogr. Inf. Sci..