Re-examining urban region and inferring regional function based on spatial–temporal interaction

Tao, Haiyan; Wang, Keli; Li, Zhuo; Li, Xuliang

doi:10.1080/17538947.2018.1425490

Cited by 29 publications

(12 citation statements)

References 29 publications

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“…In recent years, with the popularization of mobile application technology and taxi GPS devices, a large amount of data related to individuals’ positions and trajectories has spring up [ 9 ]. All these provide an important scientific basis for the refined study of the spatial and temporal distribution characteristics of residents’ taxi travel.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of the mixed degree of urban functions on the taxi travel demand

et al. 2021

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As an important service industry in cities, taxis provide people with an all-weather travel mode. And its demand is greatly affected by the internal functions of the city. It is very important to understand the relationship between the mixed degree of urban internal functions and the residents’ taxi travel demand to alleviate traffic congestion and formulate corresponding urban traffic strategies. This paper combined two heterogeneous data in the main urban area of Xi’an, urban points of interest (POIs) and taxi GPS. Firstly, a spatial information entropy model was constructed to quantitatively evaluate the mixed degree of functions in different spaces within the city. Secondly, the kernel density estimation method was used to analyze the spatial distribution evolution characteristics of residents’ taxi travel demand. A geographically weighted regression (GWR) model was further used to study the spatial and temporal influences of the mixed degree of urban internal functions on taxi travel demand. Results indicate that there is an obvious spatiotemporal pattern in the impact of the mixed degree of urban functions on taxi travel demand. And the GWR model is used to study the impact is superior to the ordinary least squares (OLS). In more developed areas, improving the mixed degree of urban functions will be more attractive than backward areas. It is also found that although the single function of the city has an impact on the taxi travel demand, the result of the single function is not ideal. This study can provide a reference for the optimal combination of basic units of urban space in urban planning, promote the balance of supply and demand of urban taxis, rationalize urban taxis’ operation and allocation, and solve the problems of urban transportation systems.

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Section: Literature Reviewmentioning

confidence: 99%

Impact of the mixed degree of urban functions on the taxi travel demand

et al. 2021

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“…The taxi trip is a significant mode of transportation in urban areas due to its flexible door-to-door service and 24-7 operation [26]. With information about when and where a customer is picked up or dropped off by a taxi, meaningful dynamic patterns of a city can be obtained by data mining approaches and models [27], [28].…”

Section: Literature Review a Spatial And Temporal Analysis Of Tamentioning

confidence: 99%

“…If the cluster size is large, the risk of overfilling may increase. If the cluster size is too small, the clusters in the latent space are not adequately depicted and determined [28], which may disregard the complex spatio-temporal interdependence of the trip data. In this case, the number of timestamp-clusters was set to 4 after testing values from 4 to 8 in the initialization process of the BBAC_I algorithm because the co-clustered results always returned this number of timestamp-clusters, which means the loss function of BBAC_I reached minimum with this value.…”

Section: B Determination Of the Cluster Sizementioning

confidence: 99%

A Spatio-Temporal Co-Clustering Framework for Discovering Mobility Patterns: A Study of Manhattan Taxi Data

et al. 2021

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Research on clustering spatio-temporal data to extract mobility patterns requires further development, as most existing studies do not simultaneously integrate data along both spatial dimensions and temporal dimensions but instead focus on only one dimension or separate the dimensions in analyses and applications, which could lead to discoveries that are not representative of the overall data or are dificult to interpret. To simultaneously reveal the spatial and temporal patterns of urban mobility datasets, we propose an analytical framework that is based on co-clustering and enables mobility behaviors to be distinguished in spatial and temporal dimensions. We use one month of taxi GPS data from the Manhattan area to explore spatio-temporal co-occurrence patterns. The spatial and temporal dimensions of taxi trip data were coclustered by using the Bregman Block Average co-clustering algorithm with I-divergence (BBAC_I). We performed this process on weekdays and holidays and compared the mobility differences between these two periods. The experimental results demonstrated the effectiveness of this analytical framework, with which we can reveal the spatial patterns and their temporal dynamics as well as temporal patterns and their spatial dynamics in mobility data.INDEX TERMS Mobility patterns, co-clustering, spatio-temporal co-occurrence, taxi trip.

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“…A significant body of work relies on human mobility data, such as commuting flows or taxi trajectories to identify functional regions defined in a slightly more specialized manner than our definition, as discussed at the beginning of this article. A recent indicative example is the work of Tao et al [23], which used taxi GPS trajectory data to analyse urban regions and infer region functions in Guangzhou, China. Data was used to construct a probability tensor decomposition model, which, in turn, was used to extract temporal patterns and spatial distribution of trajectories.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality

Papadakis

Gao

Baryannis

2019

IJGI

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The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, using data to train machine learning models, which can discover similar regions. Both methodologies face limitations, with knowledge-based approaches being criticized for scalability and transferability issues and data-driven approaches for lacking interpretability and depending heavily on data quality. To mitigate these disadvantages, we propose a novel framework that fuses a knowledge-based approach using design patterns and a data-driven approach using latent Dirichlet allocation (LDA) topic modeling in three different ways: Functional regions discovered using either approach are evaluated against each other to identify cases of significant agreement or disagreement; knowledge from patterns is used to adjust topic probabilities in the learning model; and topic probabilities are used to adjust pattern-based results. The proposed methodologies are demonstrated through the use case of identifying shopping-related regions in the Los Angeles metropolitan area. Results show that the combination of pattern-based discovery and topic modeling extraction helps uncover discrepancies between the two approaches and smooth inaccuracies caused by the limitations of each approach.

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Re-examining urban region and inferring regional function based on spatial–temporal interaction

Cited by 29 publications

References 29 publications

Impact of the mixed degree of urban functions on the taxi travel demand

Impact of the mixed degree of urban functions on the taxi travel demand

A Spatio-Temporal Co-Clustering Framework for Discovering Mobility Patterns: A Study of Manhattan Taxi Data

Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality

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