Sensing Mobility and Routine Locations

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Cláudia Rodrigues	--	1923	2023-09-14 10:36:55	\|
2	references update	Fanny Huang	-31 word(s)	1892	2023-09-18 07:18:58	\|

This entry is adapted from the peer-reviewed paper 10.3390/ijgi12080308

The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom been carried out using mobile phone data and linked to the effects of the pandemic.

mobile phone data routine locations crowdsourced data covid-19 trajectory analysis

1. Introduction

The COVID-19 pandemic was declared a global pandemic in March 2020 ^[1]. During the first wave, in March and April of 2020, Portugal entered a total lockdown. People were strongly encouraged to stay at home, curfews were put in place, and private and public facilities and workplaces were temporarily closed. The way of transmission of the virus has been close contact and contaminated surfaces, therefore, most governments decided to minimize social contact ^[2]. With people’s mobility being a prerequisite for social interaction, the containment and mitigation measures focused on restricting mobility and spatial behavior ^[3]^[4]. The imposed restrictions reduced the movements of the population at a large scale and changed the existing flows of movements. In April 2020, after the first wave, the Portuguese Ministers approved a plan to release the containment measures. Some measures were dropped and with that, schools, private and public facilities, and workplaces started to open. In September 2020, people were encouraged to return to their normal lives with some limitations and, overall, with some special care ^[5]. In 2021 Portugal and other countries started promoting vaccination. As of the end of the year, about 86% of the Portuguese population was fully vaccinated. Since then, the world has experienced several waves due to different variants of COVID-19 ^[6].

While the world governments’ actions to overcome the pandemic’s initial waves played a crucial role in reducing the number of cases, they severely affected the global economy ^[6]. The pandemic also dealt an extreme blow to the Portuguese economy. In the second quarter of 2020, companies’ activity significantly declined. However, the gradual easing of containment measures since the beginning of May, with a phased reopening in retail and services, led to a gradual and differentiated improvement across sectors ^[7]. This crisis had and continues to have tremendous impacts on societies both in the short and long term, including lowering physical activities, increasing working from home, and reducing public transportation usage ^[6]. Such changes might result in a revolution in the general economy and mobility.

Human mobility has become a prominent research field over the last decades, mostly due to the growing need to understand how people move and use urban space ^[8]. The access to mobile phone datasets offers the possibility to predict and study mobility patterns ^[9]. Overall, these ubiquitous data are often used for location analytics to characterize various aspects of human mobility ^[10]. The data types can vary widely and be acquired by several means, including via Global System for Mobiles (GSM), Wi-Fi, Bluetooth, or Global Positioning System (GPS) ^[11]^[12]. In particular, Call Detail Records (CDRs) are a type of GSM data often collected by mobile operators and used to study human movements and social networks. This data type is generated every time a client interacts with the network, providing knowledge on the sent and received calls and text messages ^[13]. Moreover, CDRs also give information on the antenna that received/transmitted the communication, enabling the inference of the client’s location at an antenna level. However, contextual information lack in mobile phone data and in some situations it is important to add to the general analysis information about the circumstances under which the movement or activity happened. As mobile phone data only provides locations at an antenna level, crowdsourced data can contain features of the geographical area providing a more meaningful way to understand the semantics of the places (land use or human use of land) and knowledge of the context of the activity ^[14]^[15]. This data is often collected through digital platforms from large groups to gain insights on multiple subjects. Such databases as Facebook Places or Foursquare contain Points of Interest (POIs) that can provide features for area classification and detect the semantic meaning of meaningful places (those that are frequently visited, such as home and work and other regularly visited places ^[16]) ^[8]. Compared with typical survey data, with this data, phenomena can be monitored continuously for a long time at a lower cost.

2. Sensing Mobility and Routine Locations

Planning and managing urban spaces are essential topics to promote smart cities, which depend on reliable and updated data ^[17]. Mobile phone data have been used to sense urban dynamics, helping urban planners and designers to cope with urban growth ^[14]. Furthermore, various social issues have been endorsed and resolved by analyzing mobile phone data, including CDRs, generated as a result of the pervasiveness of mobile phones. Although CDRs’ temporal density has increased due to the popularity of smartphones in the last decades, these data present some challenges, such as spatial and temporal sparseness, as they are only generated if the individual receives or makes a call or SMS. As a consequence, the amount of in-depth research conducted into mobility and behavior patterns is low compared to studies using GPS data ^[18]. However, although GPS data can provide spatio-temporal information with higher frequency and accuracy than CDRs, the high power consumption and the collection and analysis of these detailed data can be overwhelming. Besides the overhead of the collection and analysis of CDRs being inferior, these data do not require any API, battery, memory, or permission of the individual to be collected and have the advantage of being available for all groups of the population ^[8]^[13].

Despite providing large amounts of spatial data, the limitation in the accuracy of the temporal and spatial dimensions raises some questions about the quality of the data. Although the problem of low sampling remains in CDRs, several authors proved that they can be used to identify patterns of human mobility ^[19]^[20]. An analysis from Ranjan et al. ^[10] observed that CDRs allow the correct identification of meaningful places that account for 90% of the individual’s activity. Additionally, many researchers and institutions are aware of CDRs’ potential in reflecting human mobility and identifying important places ^[10]^[14]. In fact, multiple studies, such as from Rodrigues et al. ^[13] and Zhang et al. ^[21], proved that CDRs can be effective in the identification of meaningful places such as home, work, or second home locations.

However, to understand the movements, it is necessary to understand the reasons behind the visit. Certain types of crowdsourced data can be useful to understand how urban space is organized and used. These data are created voluntarily by users, most of the time through mobile applications to provide useful and powerful sources of data for multiple domains. Some types of data include POIs, social media data, taxi trajectories, cell phone usage, check-in activities from Location-Based Social Networks (LBSNs), and even text messages ^[17]. Information such as the POIs are often used to give semantic meaning to the locations found. Nevertheless, the quality of the information provided by these data can be strongly influenced by who uses the platforms, APIs, and Social Media.

Graells-Garrido et al. ^[14] used CDRs to identify citizens’ meaningful places and to understand land use, using floating population flows in Chile. CDRs were used to identify home and work locations and recreate the population distribution as well as commuting trips. To contextualize their findings, crowdsourced geographical information containing POIs was used to explain the daily rhythms, allowing them to cluster areas of the city from a land use perspective. Ferreira et al. ^[8] also used CDRs to identify routine locations and a POIs dataset to explain and classify the locations found.

In a pandemic analysis context, several authors used mobile phone data to understand the short-term effects of the virus on mobility and vice-versa. Satamaria et al. ^[22] presented a mobile indicator derived from mobile position data that captures information about mobility patterns of the European population, which can be used to analyze the impact of COVID-19 confinement measures. Results showed that the increase in the contact rate after lifting the lockdown, demonstrated by the increase in mobility, was not automatically reflected in an increase in the number of infections. Also, a study from Heiler et al. ^[23] demonstrated the relevance of mobility data for epidemiological studies in real-time, as the usage of mobile phone data permit the moment-by-moment quantification of mobility behavior for a whole country. The results of the study showed that the announcement of restrictions led to a dramatic reduction in mobility and that between an infection in the region of study and the detection of the disease in a new area, 8 days passed.

Regarding the analysis of changes in both mobility and routine locations caused by the pandemic, Sevtsuk et al. ^[24] used GPS positioning data from smartphone users to analyze the impact of COVID-19 on trips to urban amenities. The study traces the changes in amenity visits in Somerville, MA from before the pandemic to after the first wave of the pandemic (January 2019 to December 2020). Their findings suggest that amenity-visiting preferences significantly diverged from expected patterns in the first few months of the pandemic. Even though overall trip volumes remained lower than normal levels throughout the remainder of the year, preferences towards amenity-visiting preferences mostly returned to the expected levels by the end of 2020.

Sevtsuk et al. ^[25] also examined changes in mobility between residents of the highest and lowest Socio-Economic Index (SEI) at the Census Block Group (CBG) during the COVID-19 pandemic, in the United States, using GPS positioning data. In general, low-SEI groups traveled shorter distances but visited more city-wide CBGs before the pandemic. Contrary, high-SEI residents universally reduced their mobility to a greater extent during the pandemic. Although high-SEI residents were making more trips to parks and healthcare providers and fewer subsistence trips to retail stores already before the pandemic, COVID-19 significantly widened these differences thereby exacerbating “mobility gaps” between low-SEI and high-SEI groups. In the same context, Huang et al. ^[26] also studied the social inequality exposed by the pandemic using mobile phone data and the United States CBG. The study reflected how COVID-19 disproportionately affected vulnerable populations. The analysis revealed that lower income groups cannot afford to comply with lockdown orders. Results corroborate the findings of Sevtsuk et al. ^[25], which used GPS data: the pandemic exposed social differences between high-SEI and low-SEI, revealing that poor communities tend to show less compliance, as evidenced by their lower levels of time at home and higher rates of mobility than wealthy communities.

Shamshiripour et al. ^[27] used data from a survey to study how and to what extent people’s mobility-styles and habitual travel behaviors have changed during the COVID-19 pandemic in Chicago. They also examined whether these changes will persist afterward or will bounce back to the pre-pandemic situation. The survey incorporates a comprehensive set of questions associated with individuals’ travel behaviors, habits, and perceptions before and during the pandemic, as well as their expectations about the future. The analysis showed significant changes in various aspects of people’s mobility behavior, including habits, predispositions, and higher-level orientations towards online activities (i.e., shopping, meetings, and working from home, etc.) and travels (i.e., long-distance commutes and urban travel mode choice) during and after the COVID-19 pandemic.

References

The Day Everything Changed: A Timeline of March 11. Available online: https://www.nbcsandiego.com/news/coronavirus/the-day-everything-changed-a-timeline-of-march-11-2020/2545558/ (accessed on 12 December 2022).
WHO (World Health Organization). Coronavirus Disease (COVID-19): How Is It Transmitted? Available online: https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-covid-19-how-is-it-transmitted (accessed on 12 December 2022).
Giles, J.R.; Erbach-Schoenberg, E.; Tatem, A.J.; Gardner, L.; Bjørnstad, O.N.; Metcalf, C.J.E.; Wesolowski, A. The duration of travel impacts the spatial dynamics of infectious diseases. Proc. Natl. Acad. Sci. USA 2020, 117, 22572–22579.
Willberg, E.; Järv, O.; Väisänen, T.; Toivonen, T. Escaping from Cities during the COVID-19 Crisis: Using Mobile Phone Data to Trace Mobility in Finland. ISPRS Int. J. Geo-Inf 2020, 10, 103.
Comunicados do Conselho de Ministros. Available online: https://www.portugal.gov.pt/pt/gc22/governo/comunicado-de-conselho-de-ministros?i=368 (accessed on 24 November 2022).
Palma de, A.; Vosough, S.; Liao, F. An overview of effects of COVID-19 on mobility and lifestyle: 18 months since the outbreak. Transp. Res. Part A 2022, 159, 372–397.
Manteu, C.; Monteiro, N.; Sequeira, A. The Short-Term Impact of the COVID-19 Pandemic on Portuguese Companies. Working Papers o202003, Banco de Portugal, Economics and Research Department. 2020. Available online: https://ideas.repec.org/p/ptu/wpaper/o202003.html (accessed on 10 December 2022).
Ferreira, G.; Alves, A.; Veloso, M.; Bento, C. Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data. ISPRS Int. J. Geo-Inf. 2022, 11, 228.
Montero, L.; Ros-Roca, X.; Herranzc, R.; Barcelób, J. Fusing mobile phone data with other data sources to generate input Fusing mobile phone data with other data sources to generate input OD matrices for transport models. Transp. Res. Procedia 2019, 37, 417–424.
Ranjan, G.; Zang, H.; Zhang, Z.; Bolot, J. Are call detail records biased for sampling human mobility? SIGMOBILE Mob. Comput. Commun. Rev. 2012, 16, 33–44.
Yuan, Y.; Raubal, M. Exploring Georeferenced Mobile Phone Datasets – A Survey and Reference Framework. Geogr. Compass 2016, 10, 239–252.
Kasemsan, M.L.K.; Ratsameethammawong, P. Moving mobile phone location tracking by the combination of GPS, Wi-Fi and cell location technology. Commun. IBIMA 2010, 1–4, 979–985.
Rodrigues, C.; Veloso, M.; Alves, A.; Ferreira, G.; Bento, C. Generating the Users Geographic Map Using Mobile Phone Data. In Progress in Artificial Intelligence EPIA 2022; Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2022.
Graells-Garrido, E.; Peredo, O.; García, J. Sensing Urban Patterns with Antenna Mappings: The Case of Santiago, Chile. Sensors 2016, 16, 1098.
Dashdorj, Z.; Sobolevsky, S.; Serafini, L.; Ratti, C. Human activity recognition from spatial data sources. In Proceedings of the MobiGIS ’14: Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, Dallas, TX, USA, 4 November 2014; pp. 18–25.
Tongsinoot, L.; Muangsin, V. Exploring Home and Work Locations in a City from Mobile Phone Data. In Proceedings of the 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Bangkok, Thailand, 18–20 December 2017; Volume 21, pp. 123–129.
Andrade, R.; Alves, A.; Bento, C. POI Mining for Land Use Classification: A Case Study. ISPRS Int. J. Geo-Inf 2020, 9, 493.
Kanno, T.; Sekimoto, Y.; Kanasugi, H.; Shibasaki, R. Real-time passenger location estimation using CDRs and train objects generated from crowdsourced timetables. In Proceedings of the PUbiComp/ISWC’15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7–11 September 2015; pp. 1197–1205.
Thuillier, E.; Moalic, L.; Sid, L.; Caminada, A. Clustering Weekly Patterns of Human Mobility Through Mobile Phone Data. IEEE Trans. Mob. Comput. 2017, 17, 817–830.
Lind, A.; Hadachi, A.; Piksarv, P.; Batrashev, O. Spatio-Temporal Mobility Analysis for Community Detection in the Mobile Networks Using CDR Data. In Proceedings of the 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Brno, Czech Republic, 25–27 October 2017; pp. 250–255.
Zhang, D.; Huang, J.; Li, Y.; Zhang, F.; Xu, C.; He, T. Exploring human mobility with multi-source data at extremely large metropolitan scales. In Proceedings of the MobiCom ’14: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, Maui, HI, USA, 7–11 September 2014; pp. 201–212.
Santamaria, C.; Sermi, F.; Spyratos, S.; Iacus, S.M.; Annunziato, A.; Tarchi, D.; Vespe, M. Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A European regional analysis. Saf. Sci. 2020, 132, 104925.
Heiler, G.; Reisch, T.; Hurt, J.; Forghani, M.; Omani, A.; Hanbury, A.; Karimipour, F. Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic. In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Virtual, 10–13 December 2021; Volume 112, pp. 3123–3132.
Sevtsuk, A.; Hudson, A.; Halpern, D.; Basu, R.; Ng, K.; Jong de, J. The impact of COVID-19 on trips to urban amenities: Examining travel behavior changes in Somerville, MA. PLoS ONE 2021, 16, e0252794.
Sevtsuk, A.; Basu, R.; Halpern, D.; Hudson, A.; Ng, K.; Jong de, J. A tale of two Americas: Socio-economic mobility gaps within and across American cities before and during the pandemic. Cities 2022, 131, 104006.
Huang, X.; Lu, J.; Gao, S.; Wang, S.; Liu, Z.; Wei, H. Staying at Home Is a Privilege: Evidence from FineGrained Mobile Phone Location Data in the United States during the COVID-19 Pandemic. Ann. Am. Assoc. Geogr. 2021, 112, 286–305.
Shamshiripour, A.; Rahimi, E.; Shabanpour, R.; Mohammadian, A. How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transp. Res. Interdiscip. Perspect. 2020, 7, 100216.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Others

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Cláudia Rodrigues

Marco Veloso

Ana Alves

Carlos Bento

View Times: 89

Update Date: 18 Sep 2023

Table of Contents

Video Upload Options

Confirm

1. Introduction

2. Sensing Mobility and Routine Locations

References