Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs

Zhai, Wei; Bai, Xueyin; Shi, Yu; Han, Yu; Peng, Zhong-Ren; Gu, Chaolin

doi:10.1016/j.compenvurbsys.2018.11.008

Cited by 164 publications

(120 citation statements)

References 41 publications

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“…Considering the distribution of different POI tags in a particular district makes it possible to discover something new by combining these scores in the district. Previous studies showed that the k-means clustering algorithm had good performance in classifying urban districts, which is well known for its efficiency in clustering large data sets [1,7,11,13,31,32]. However, in our study, k-means could not run a proper result with clear boundary between clusters.…”

Section: K-modes Clustering Algorithmcontrasting

confidence: 60%

“…Just like the Silhouette Coefficient, higher scores mean that better clusters are obtained. Figure 3 shows the results of the two measures, evaluating how many clusters make the k-modes clustering perform better by setting the range of value k from 2 to 20 [11,31]. Fortunately, both results indicate that the scores peak at the highest value when k = 4.…”

Section: Classification Of Urban Districtsmentioning

confidence: 99%

“…Increasing amounts of data on points of interest (POIs) are becoming available online. Researchers [1,[6][7][8][9][10][11] have conducted many studies on urban functions and districts that employ POIs, which are able to lead to a better understanding of individual-level and social-level utilization of urban space. Additionally, POI data can help to understand land use planning, not only at the semantic level, but also at the quantitative level.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

Yang

Liu

et al. 2019

IJGI

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Urban areas involve different functions that attract individuals and fit personal needs. Understanding the distribution and combination of these functions in a specific district is significant for urban development in cities. Many researchers have already studied the methods of identifying the dominant functions in a district. However, the degree of collection and the representativeness of a function in a district are controlled not only by its number in the district but also by the number outside this district and a number of other functions. Thus, this study proposed a quantitative method to identify urban functions, using Fisher’s exact test and point of interest (POI) data, applied in determining the urban districts within the Sixth Ring Road in Beijing. To begin with, we defined a functional score based on three statistical features: the p-value, odds-ratio, and the frequency of each POI tag. The p-value and odds-ratio resulted from a statistical significance test, the Fisher’s exact test. Next, we ran a k-modes clustering algorithm to classify all urban districts in accordance with the score of each function and their combination in one district, and then we detected four different groups, namely, Work and Tourism Mixed-developed district, Mixed-developed Residential district, Developing Greenland district, and Mixed Recreation district. Compared with the other identifying methods, our method had good performance in identifying functions, except for transportation. In addition, the Coincidence Degree was used to evaluate the accuracy of classification. In our study, the total accuracy of identifying urban districts was 83.7%. Overall, the proposed identifying method provides an additional method to the various methods used to identify functions. Additionally, analyzing urban spatial structure can be simpler, which has certain theoretical and practical value for urban geospatial planning.

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Section: K-modes Clustering Algorithmcontrasting

confidence: 60%

Section: Classification Of Urban Districtsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

Yang

Liu

et al. 2019

IJGI

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“…In geography-related fields, the use of embeddings started to be explored very recently. Attempts were performed on modeling vector representations based on spatial proximity between points of interest in cities for place type similarity analysis [26,27] and functional region identification [28]. Regarding embeddings generated from human motion, their study is primarily focused on urban road systems and city-level mobility [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

Crivellari

Beinat

2019

IJGI

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The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral representation of places based on how people move between them, ignoring geographical coordinates and spatial proximity. Inspired by natural language processing techniques, we generate and explore vector representations of locations, traces and visitors, obtained through an unsupervised machine learning approach, which we generically named motion-to-vector (Mot2vec), trained on large-scale mobility data. The algorithm consists of two steps, the trajectory pre-processing and the Word2vec-based model building. First, mobility traces are converted into sequences of locations that unfold in fixed time steps; then, a Skip-gram Word2vec model is used to construct the location embeddings. Trace and visitor embeddings are finally created combining the location vectors belonging to each trace or visitor. Mot2vec provides a meaningful representation of locations, based on the motion behavior of users, defining a direct way of comparing locations’ connectivity and providing analogous similarity distributions for places of the same type. In addition, it defines a metric of similarity for traces and visitors beyond their spatial proximity and identifies common motion behaviors between different categories of people.

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“…POIs can effectively reflect the functional characteristics of different geographical locations [31]. Due to this benefit, POI data have been widely used to analyze the spatial distributions of urban functions in previous studies [32][33][34]. Compared with statistical data (e.g., population and housing prices), large volumes of POI data can be easily achieved in real time [35], which enables researchers to accurately capture the dynamics of urban function evolution.…”

Section: Points-of-interest (Pois) and Their Links With Urban Functionsmentioning

confidence: 99%

Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data

et al. 2020

Sustainability

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Urban spatial structure has a significant impact on the sustainable development of cities. An important step of urban spatial structure analysis is the identification of urban centers. From the perspective of urban function distribution, this study developed a theoretical framework of three layers for urban center identification. In the first layer, point-of-interest data were collected from geospatial databases and utilized to capture the spatial distribution of urban functions. In the second layer, the density-based spatial clustering of application with noise (DBSCAN) algorithm was employed to group points of interest into urban centers according to their inter-distances and urban functions. In the third layer, the spatial distribution of the identified urban centers was visualized by the ArcGIS platform. This framework was applied in the urban center analysis of Beijing. The results showed that Beijing is in the process of transitioning from monocentric to polycentric with urban functions distributed unevenly throughout the city. To facilitate this transition, strategies such as the construction of super-large communities, the development of branch campuses and higher-education parks, and the enhancement of modern culture industries should be considered for the 11 new towns of Beijing.

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Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs

Cited by 164 publications

References 41 publications

Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data

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