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

Yao, Yao; Xia, Li; Liu, Xiaoping; Liu, Penghua; Liang, Zhaotang; Zhang, Jinbao; Mai, Ke

doi:10.1080/13658816.2016.1244608

Cited by 366 publications

(234 citation statements)

References 42 publications

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“…Previous studies have demonstrated that different POI types have distinctive semantic signatures (Janowicz, ) (i.e., spatial, temporal, and thematic distributions) based on crowd‐sourced location‐based social media data analysis, in analogy to spectral bands in remote sensing (McKenzie, Janowicz, Gao, Yang, & Hu, ). There is a growing trend of using location‐awareness sensing data (e.g., trajectories from mobile phones), POI data, and social media feeds to study the spatial and social structure of urban environments (Hu et al, ; Jiang, Alves, Rodrigues, Ferreira, & Pereira, ; Liu et al, ; McKenzie et al, ; Pei et al, ; Steiger, Westerholt, & Zipf, ; Yao et al, ). However, few studies have investigated the latent relationships among different types of POIs and how they spatially interact with each other to support urban functions, such as education, business, and shopping.…”

Section: Introductionmentioning

confidence: 99%

Extracting urban functional regions from points of interest and human activities on location‐based social networks

Gao

Janowicz

Couclelis

2017

Transactions in GIS

361

185

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Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co‐occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check‐in activities on location‐based social networks. Using a large corpus of about 100,000 Foursquare venues and user check‐in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K‐means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non‐adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity‐based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities.

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Section: Introductionmentioning

confidence: 99%

Extracting urban functional regions from points of interest and human activities on location‐based social networks

Gao

Janowicz

Couclelis

2017

Transactions in GIS

361

185

View full text Add to dashboard Cite

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“…Of the various sources of auxiliary social information that are available for the extraction of urban land use information, mobile phone data, points of interest (POIs), and human trajectories have been used for functional urban area mapping [25][26][27][28]. Specifically, Pei at el.…”

Section: Introductionmentioning

confidence: 99%

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

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Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

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“…POIs are often used for navigations, and they refer to the semantic points that contain locations and land-use categories. In cities, they mainly include scenic points, companies, residential buildings, educational institutions, public services, and commercial services, which symbolize diverse functional zones and are important for functional-zone analysis [4,47]. Usually, each urban functional zone concentrates on one or two kinds of POIs.…”

Section: Segmentation Evaluationmentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities-Beijing, Putian, and Zhuhai-and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

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

Cited by 366 publications

References 42 publications

Extracting urban functional regions from points of interest and human activities on location‐based social networks

Extracting urban functional regions from points of interest and human activities on location‐based social networks

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

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