Discovering regions of different functions in a city using human mobility and POIs

By Jing Yuan, Yu Zheng, Xing Xie. Knowledge Discovery and Data Mining, 2012

1,212 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/12e04dad4c983662d21ec499ef45fc5761c4ea75

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Citations in the corpus

Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “[74][76] infer the functional regions in a city using road network data, points of interests, and human mobility learned from a large number of taxi trips.”
Trajectory Data Mining (2015, methodology): “traffic anomalies [62][15] and urban functional regions [122][130].”
CityTransfer (2018, methodology): “As discussed earlier, similar to previous urban computing studies [37, 40], we divide the city (both source and target) into a set of location grids, and extract a set of features to characterize each grid.”
Revealing daily travel patterns and city structure with taxi trip data (2013, background): “Keywords: GPS-enabled taxi data, intra-urban human mobility, urban structure, land use condition, Shanghai”
Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, methodology): “Later on, Yuan, Zheng, and Xie (2012) employed both POI type information and the temporal patterns of taxi pick-ups/drop-offs in segmented map regions, utilized a topic modeling method based on latent Dirichlet allocation and Dirichlet multinomial regression techniques, and discovered various urban…”
Mining point-of-interest data from social networks for urban land use classification and disaggregation (2015, methodology): “Beyond using VGI from social networks, other studies also correlate it with sensor data such as mobile phone activity (Toole, Ulm, González, & Bauer, 2012) or taxi GPS traces (Yuan, Zheng, and Xie, 2012) to detect land use or find functional regions in the urban area.”
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment (2015, methodology): “Similarly, it is used to reﬂect how important the POI of a speciﬁc type is in our analysis, which has been proposed in existing works, i.e., Yuan et al. [27] provided a TF-IDF-based method to cluster regions of diﬀerent functions, which solely uses the POI data.”
Discovering Urban Functional Zones Using Latent Activity Trajectories (2015, methodology): “This paper is an extension of our previous paper [1], in which we presented a topic-modeling-based approach for discovering region functions and intensity of functionality using POIs and human mobility.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “…mobility as a critical need for broad-domain applications (e.g., ranging from simple home preheating and sending dinner coupons to epidemic control [221], [222], urban planning [223]–[225], trafﬁc forecasting systems [226]–[228], resource management of mobile communications [3], [229], [230]).”
U-Air: when urban air quality inference meets big data (2013, background): “Human mobility, however, implies useful information, such as land use of a location, traffic flow, and function of a region (like residential or business areas) [11], which could contribute to air quality inference.”
Mapping Urban Land Use by Using Landsat Images and Open Social Data (2016, background): “Within the spatial extent of a parcel, there may be a variety of POIs of different types, which can be regarded as having compound functions instead of a single function [40].”
Diagnosing New York city's noises with ubiquitous data (2014, background): “Second, human mobility indicates the traffic volume and function of a region [25][26].”
Inferring land use from mobile phone activity (2012, methodology): “Other data sources such as points of interest (POIs) as well as GPS data collected from taxi ﬂeets have been combined with unsupervised learning algorithms to identify the rich structure of diﬀerent functional sections of a Beijing [14].”
Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model (2017, background): “people’s lifestyles, which cannot be stereotyped and are continuously changing with further urban development (Yuan et al. 2012).”
Classifying urban land use by integrating remote sensing and social media data (2017, background): “(2012) proposed a POI-based semantic analysis model named DRoF to map urban functional zones (Yuan et al. 2012).”
Where You Like to Go Next: Successive Point-of-Interest Recommendation (2013, neutral): “…personalized POI via the probability that user u will visit location l at time t , which is calculated by Based on only the ﬁrst-order Markov chain property, the probability can be calculated by where p ( l ∈ L tu | i ∈ L t − 1 u ) is the probability for user u moves from location i to location l .”
Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021, background): “…source of information for decision making in various applications, such as natural disaster detection [5], agricultural survey, urban planning [6], nat-ural resources monitoring, weather forecasting, land-cover/ The associate editor coordinating the review of this manuscript and approving it…”
Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, background): “Suggested in earlier studies, each of these geographical regions is usually associated with a spe-ciﬁc function such as education, business, entertainment and so on, considering the category information of the locations in the region [4, 29].”