Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

Gang, Yuan; Chen, Yanyan; Sun, Lishan; Lai, Jun; Li, Tongfei; Liu, Zhuo

doi:10.1155/2020/8956910

Cited by 14 publications

(14 citation statements)

References 41 publications

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“…The y -axis is the normalized values of the activity pattern computed dividing all vector components by the value of the maximum component. Overall, these patterns are consistent with those reported on previous work using other data sources (see, for example, [ 26 , 33 ]).…”

Section: Resultssupporting

confidence: 92%

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

Muñoz-Cancino

Ríos

Goić

et al. 2021

IJERPH

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In this paper, we propose and validate with data extracted from the city of Santiago, capital of Chile, a methodology to assess the actual impact of lockdown measures based on the anonymized and geolocated data from credit card transactions. Using unsupervised Latent Dirichlet Allocation (LDA) semantic topic discovery, we identify temporal patterns in the use of credit cards that allow us to quantitatively assess the changes in the behavior of the people under the lockdown measures because of the COVID-19 pandemic. An unsupervised latent topic analysis uncovers the main patterns of credit card transaction activity that explain the behavior of the inhabitants of Santiago City. The approach is non-intrusive because it does not require the collaboration of people for providing the anonymous data. It does not interfere with the actual behavior of the people in the city; hence, it does not introduce any bias. We identify a strong downturn of the economic activity as measured by credit card transactions (down to 70%), and thus of the economic activity, in city sections (communes) that were subjected to lockdown versus communes without lockdown. This change in behavior is confirmed by independent data from mobile phone connectivity. The reduction of activity emerges before the actual lockdowns were enforced, suggesting that the population was spontaneously implementing the required measures for slowing virus propagation.

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Section: Resultssupporting

confidence: 92%

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

Muñoz-Cancino

Ríos

Goić

et al. 2021

IJERPH

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“…The TAZ is a unit based on geographic information or mobile data. Due to the limitation of data sources, the application scope of this unit is limited [41,58].…”

Section: The Basic Unit Of Urban Functional Zone Identificationmentioning

confidence: 99%

“…It mainly relies on processing platforms such as ArcGIS or SPSS to realize fast and efficient classification of urban functional zones [56]. However, since most of them only consider single data (POI), there is room for improvement in the accuracy of these division methods [38,58].…”

Section: Urban Functional Zone Division Based On Density Analysismentioning

confidence: 99%

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Liu

Deng

et al. 2021

Applied Sciences

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Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

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“…Spatiotemporal mobility patterns and mobile phone activity patterns have also been widely extracted from mobile phone data for other practical applications, such as capturing individuals’ activities associated with urban zones, transportation, and the COVID-19 pandemic. The authors of [ 8 , 9 , 10 , 11 , 12 ], among others, explored or captured human activity patterns from mobile phone data to infer land use types based on spatiotemporal call volume patterns. This feature represents the total call volume (the number of incoming calls received, and outgoing calls made from all smartphones) managed by a given base transceiver station (BTS) within a given period of time.…”

Section: Introductionmentioning

confidence: 99%

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Okmi

Por

Ang

et al. 2023

Sensors

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Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people’s mobility patterns as well as communication (incoming and outgoing calls) data, revealing people’s social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.

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Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

Cited by 14 publications

References 41 publications

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Mobile Phone Data: A Survey of Techniques, Features, and Applications

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