scholarly journals Identifying Urban Residents’ Activity Space at Multiple Geographic Scales Using Mobile Phone Data

2020 ◽  
Vol 9 (4) ◽  
pp. 241
Author(s):  
Lunsheng Gong ◽  
Meihan Jin ◽  
Qiang Liu ◽  
Yongxi Gong ◽  
Yu Liu
Keyword(s):  
Built Environment ◽  
Mobile Phone ◽  
Human Life ◽  
Activity Space ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Activity Spaces ◽  
Point Of Interest ◽  
The Relationship

Residents’ activity space reflects multiple aspects of human life related to space, time, and type of activity. How to measure the activity space at multiple geographic scales remains a problem to be solved. Recently, the emergence of big data such as mobile phone data and point of interest data has brought access to massive geo-tagged datasets to identify human activity at multiple geographic scales and to explore the relationship with built environment. In this research, we propose a new method to measure three types of urban residents’ activity spaces—i.e., maintenance activity space, commuting activity space, and recreational activity space—using mobile phone data. The proposed method identifies the range of three types of residents’ activity space at multiple geographic scales and analyzing the relationship between the built environment and activity space. The research takes Zhuhai City as its case study and discovers the spatial patterns for three activity space types. The proposed method enables us to achieve a better understanding of the human activities of different kinds, as well as their relationships with the built environment.

scholarly journals An ethno-linguistic dimension in transnational activity space measured with mobile phone data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Veronika Mooses ◽  
Siiri Silm ◽  
Tiit Tammaru ◽  
Erki Saluveer
Keyword(s):  
Mobile Phone ◽  
Activity Space ◽  
Mobile Phone Data ◽  
Minority Population ◽  
Mobile Phone Use ◽  
Linguistic Differences ◽  
Activity Spaces ◽  
Cross Border ◽  
Mobility Data ◽  
Majority Population

Abstract In addition to permanent migration, different forms of cross-border mobility were on the rise before the COVID-19 pandemic, ranging from tourism to job-related commuting. In this paper ethno-linguistic differences in cross-border mobility using the activity space framework are considered. New segregation theories emphasise that segregation in one part of the activity space (e.g. in residential neighbourhood) affects the segregation in other parts of the activity space (e.g. in workplace), and that spatial mobility between activity locations is equally important in the production and reproduction of ethnic inequalities. Until now, segregation in activity spaces has been studied by focusing on daily activities inside one country. In reality, an increasing number of people pursue their activities across different countries, so that their activity spaces extend beyond state borders, which can have important implications for the functioning of ethno-linguistic communities and the transfer of inequalities from one country to another. This study takes advantage of mobility data based on mobile phone use, and the new avenues provided for the study of ethno-linguistic differences in temporary cross-border mobility. Such data allow the study of different cross-border visitor groups—tourists, commuters, transnationals, long-term stayers—by providing the means to measure the frequency of visits and time spent abroad, and to link together the travel of each person over several years. Results show that members of the ethno-linguistic minority population in Estonia make more trips than members of the ethno-linguistic majority, and they also have higher probability of being tourists and cross-border commuters than the majority population, paying frequent visits to their ancestral homelands. The connections between ethno-linguistic background and temporary cross-border mobility outlined in this study allows for future discussion on how (in)equalities can emerge in transnational activity space and what implications it has for segregation.

scholarly journals THE RELATIONSHIP BETWEEN LIVEABILITY AND PRICE OF RESIDENTIAL HOUSES – A CASE STUDY OF SHENZHEN

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 141-144
Author(s):  
L. Liu
Keyword(s):  
Housing Price ◽  
Urban Residents ◽  
Policy Makers ◽  
Four Dimensions ◽  
Point Of Interest ◽  
Using Data ◽  
The Relationship ◽  
The City ◽  
Real World Situation

Abstract. This study proposes an index for cities in China to measure the liveability of real estate. This liveability index combines indicators from four dimensions including education, transportation, living facilities and entertainment, and can be quickly obtained by using data of Point of Interest, based on popular internet maps. Then, using Shenzhen as a sample city, correlation analysis has been adopted to examine the relationship between this liveability index and housing price. The results show that, the liveability index can well reflect the real-world situation of the city. Moreover, a weak but significant relationship can be found between liveability and the housing price. The results of this study not only can be used for urban residents to search a proper housing estate, but also can assistant urban planners and policy makers to get a general map of the spatial structure of the city.

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

Assessing Refugees’ Onward Mobility with Mobile Phone Data—A Case Study of (Syrian) Refugees in Turkey

2019 ◽  
pp. 251-263
Author(s):  
Harald Sterly ◽  
Benjamin Etzold ◽  
Lars Wirkus ◽  
Patrick Sakdapolrak ◽  
Jacob Schewe ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Syrian Refugees

scholarly journals Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure

2018 ◽  
Vol 10 (12) ◽  
pp. 4564 ◽  
Author(s):  
Zhuangbin Shi ◽  
Ning Zhang ◽  
Yang Liu ◽  
Wei Xu
Keyword(s):  
Built Environment ◽  
Topological Structure ◽  
Regression Models ◽  
Closeness Centrality ◽  
Spatiotemporal Variation ◽  
Spatial And Temporal Variation ◽  
Structure Factors ◽  
The Relationship ◽  
Best Fit

Reliable and accurate estimates of metro demand can provide metro authorities with insightful information for the planning of route alignment and station locations. Many existing studies focus on metro demand from daily or annual ridership profiles, but only a few concern the variation in hourly ridership. In this paper, a geographically and temporally weighted regression (GTWR) model was used to examine the spatial and temporal variation in the relationship between hourly ridership and factors related to the built environment and topological structure. Taking Nanjing, China as a case study, an empirical study was conducted with automatic fare collection (AFC) data in three weeks. With an analysis of variance (ANOVA), it was found that the GTWR model produced the best fit for hourly ridership data compared with traditional regression models. Four built-environment factors, namely residence, commerce, scenery, and parking, and two topological-structure factors, namely degree centrality and closeness centrality, were proven to be significantly related to station-level ridership. The spatial distribution pattern and temporal nonstationarity of these six variables were further analyzed. The result of this study confirmed that the GTWR model can provide more realistic and useful information by capturing spatiotemporal heterogeneity.

scholarly journals Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

2021 ◽  
Author(s):  
Alex A Berke ◽  
Ronan Doorley ◽  
Luis Alonso ◽  
Marc Pons ◽  
Vanesa Arroyo ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Compartmental Models ◽  
Mobile Phone Data ◽  
Population Changes ◽  
Study Region ◽  
Testing Program ◽  
Dynamic Population ◽  
Serology Testing

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra's serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic.

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