Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model
Bibliographic Data
| ID | 23348124 |
|---|---|
| Authors | Yao Yao (0000-0002-6723-6152, Sun Yat-sen University), Xia Li (0000-0003-3050-8529, Sun Yat-sen University, corresponding author), Xiaoping Liu (0000-0003-4242-5392, Sun Yat-sen University, corresponding author), Liu Xiao-ping (0000-0003-2524-2957, Sun Yat-sen University), Penghua Liu (0000-0002-8574-891X, Sun Yat-sen University), Zhaotang Liang (0000-0001-9261-5261, Sun Yat-sen University), Jinbao Zhang (0000-0001-8510-149X, Sun Yat-sen University), Ke Mai (0000-0002-3532-9872, Sun Yat-sen University) |
| Year | 2017 |
| Volume | 31 |
| Issue | 4 |
| Pages | 825-848 |
| Publication date | 2017-04-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | International Journal of Geographical Information Systems (JOURNAL) |
| Journal identifiers | ISSN: 0269-3798 • E-ISSN: 1362-3087 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13658816.2016.1244608 |
| OpenAlex | W2534538876 |
| Language | EN |
| Citations received | 115 |
| References cited | 43 |
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.
Cartography · Civil engineering · Data mining · Data science · Geographic information system · Geography · Geospatial analysis · Land use · Point of interest · Remote sensing · Scale (ratio) · Spatial analysis · Urban planning · Word2vec · Artificial Intelligence · Computer Science · Data-Driven Disease Surveillance · Engineering · Geographic Information Systems Studies · Human Mobility and Location-Based Analysis
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Modeling multi-type urban landscape dynamics along the horizontal and vertical dimensions
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Land use optimization modelling with ecological priority perspective for large-scale spatial planning
How does parking availability interplay with the land use and affect traffic congestion in urban areas? The case study of Xi’an, China
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Is ride-hailing a valuable means of transport in newly developed areas under TOD-oriented urbanization in China? Evidence from Chengdu City
Spatiotemporal analysis of bike mobility chain
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Mining the Sequence Pattern of Functional Zones to Analyze the Spatial Layout of Port Cites in Coastal Zones
From human mobility to building functions
Mapping urban villages based on point-of-interest data and a deep learning approach
Relationship between POI configurations and environmental benefits of dockless bike-sharing system
Analysing the dynamics of urban functional areas in a rapidly changing spatial structure
Quantifying the effects of built environment on travel behavior in three Chinese cities during Covid-19
Socioeconomic and functional zoning characterization in a city
The impact of urban mixed land use on urban vitality with multi-source big data
Gender–age inequalities in public service facility demand
An Identification of Industrial Functional Zones Based on NLP
Automatic extraction of urban outdoor perception from geolocated free texts
Mapping urban land type with multi-source geospatial big data
Recovering urban nightlife
Regional development assessment based on POIs and Geotree
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Comparing effectiveness of point of interest data and land use data in theft crime modelling
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Geographic Object-Based Image Analysis – Towards a new paradigm
Automated identification and characterization of parcels with OpenStreetMap and points of interest
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Social Sensing
| Unique citing works | 115 |
|---|---|
| Citations per year | 12,78 |
| Citation span | 2017 - 2026 (10) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 109 |