scholarly journals Radiofrequency Exposure Levels from Mobile Phone Base Stations in Outdoor Environments and an Underground Shopping Mall in Japan

2021 ◽  
Vol 18 (15) ◽  
pp. 8068
Author(s):  
Teruo Onishi ◽  
Miwa Ikuyo ◽  
Kazuhiro Tobita ◽  
Sen Liu ◽  
Masao Taki ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Areas ◽  
Human Exposure ◽  
Large Scale ◽  
Time Course ◽  
Base Stations ◽  
Shopping Mall ◽  
Exposure Monitoring ◽  
Guideline Values ◽  
Exposure Levels

Recent progress in wireless technologies has made human exposure to electromagnetic fields (EMFs) increasingly complex. The situation can increase public concerns related to possible health effects due to EMF exposure. Monitoring EMF exposure levels and characterizing them are indispensable for risk communications of human exposure to EMFs. From this background, a project on the acquisition, accumulation, and applications of EMF exposure monitoring data in Japan was started in 2019. One of the objectives of this project is to obtain a comprehensive picture of EMF exposure in actual daily lives. In 2019 and 2020, we measured the electric field (E-field) strength from mainly mobile phone base stations in the same areas as those in measurements conducted in 2006 and 2007 by the Ministry of Internal Affairs and Communications (MIC), Japan, and compared the data to investigate the time-course of the EMF environment. The number of measured points was 100 (10 × 10 grids) in an area of 1 km × 1 km in two urban and two suburban areas, and that in an underground shopping mall was 158. This large-scale study is the first in Japan. As a result, we found that the measured E-field strengths tended to be higher in 2019 and 2020 than those in 2006 and 2007, especially in the mall. However, the median ratios to the Japanese radio wave protection guideline values for urban areas and malls are lower than −40 dB.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiuyang Huang ◽  
Yongjian Yang ◽  
Yuanbo Xu ◽  
En Wang ◽  
Kangning Zhu
Keyword(s):  
Machine Learning ◽  
Mobile Phone ◽  
Disease Transmission ◽  
Large Scale ◽  
Population Distribution ◽  
Base Stations ◽  
Human Origin ◽  
Safety Control ◽  
Fine Grained ◽  
Flow Prediction

The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

Mobile Phones as a Lens into Slum Dynamics

2012 ◽  
pp. 334-352
Author(s):  
Amy Wesolowski ◽  
Nathan Eagle
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Urban Areas ◽  
Large Scale ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Location Data ◽  
Planning Decisions ◽  
Large Scale Data ◽  
Slum Dwellers

The worldwide adoption of mobile phones is providing researchers with an unprecedented opportunity to utilize large-scale data to better understand human behavior. This chapter highlights the potential use of mobile phone data to better understand the dynamics driving slums in Kenya. Given slum dwellers informal and transient lifetimes (in terms of places of employment, living situations, etc.), comprehensive longitude behavioral data sets are rare. Working with communication and location data from Kenya’s leading mobile phone operator, the authors use mobile phone data as a window into the social, mobile, and economic dimensions of slum dwellers. The authors address questions about the functionality of slums in urban areas in terms of economic, social, and migratory dynamics. In particular, the authors discuss economic mobility in slums, the importance of social networks, and the connectivity between slums and other urban areas. With four years until the 2015 deadline to meet the Millennium Development Goals, including the goal to improve the lives of slum dwellers worldwide, there is a great need for tools to make development and urban planning decisions more beneficial and precise.

scholarly journals Health disturbances and exposure to radiofrequency electromagnetic fields from mobile-phone base stations in French urban areas

2021 ◽  
Vol 193 ◽  
pp. 110583
Author(s):  
Sylvie Martin ◽  
Pascal De Giudici ◽  
Jean-Christian Genier ◽  
Etienne Cassagne ◽  
Jean-François Doré ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Electromagnetic Fields ◽  
Urban Areas ◽  
Base Stations ◽  
Radiofrequency Electromagnetic Fields

Estimating shopping center visitor numbers based on a new hybrid fuzzy prediction method

2021 ◽  
pp. 1-14
Author(s):  
Cagatay Ozdemir ◽  
Sezi Cevik Onar ◽  
Selami Bagriyanik ◽  
Cengiz Kahraman ◽  
Burak Zafer Akalin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Large Scale ◽  
Prediction Method ◽  
Base Stations ◽  
Shopping Mall ◽  
Multi Criteria Decision Making ◽  
Shopping Malls ◽  
Data Production ◽  
Telecommunications Services

Companies started to determine their strategies based on intelligent data analysis due to stagey enhance data production. Literature reviews show that the number of resources where demand estimation, location analysis, and decision-making technique applied together with the machine learning method is low in all sectors and almost none in the shopping mall domain. Within this study’s scope, a new hybrid fuzzy prediction method has been developed that will estimate the customer numbers for shopping malls. This new methodology is applied to predict the number of visitors of three shopping malls on the Anatolian side of Istanbul. The forecasting study for corresponding shopping malls is made by using the daily signaling data from indoor base stations of large-scale technology and telecommunications services provider and the features to be used in machine learning models is determined by fuzzy multi criteria decision making method. Output revealed by the application of the fuzzy multi criteria decision making method enables the prioritization of features.

scholarly journals Personal Exposure to Radio Frequency Electromagnetic Fields among Australian Adults

2018 ◽  
Vol 15 (10) ◽  
pp. 2234 ◽  
Author(s):  
Berihun Zeleke ◽  
Christopher Brzozek ◽  
Chhavi Bhatt ◽  
Michael Abramson ◽  
Rodney Croft ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Electromagnetic Fields ◽  
Urban Areas ◽  
Personal Exposure ◽  
Time Of Day ◽  
Base Station ◽  
Frequency Bands ◽  
Wide Range ◽  
Mobile Phone Base Station ◽  
Exposure Levels

The measurement of personal exposure to radiofrequency electromagnetic fields (RF-EMFs) is important for epidemiological studies. RF-EMF exposure can be measured using personal exposimeters that register RF-EMFs over a wide range of frequency bands. This study aimed to measure and describe personal RF-EMF exposure levels from a wide range of frequency bands. Measurements were recorded from 63 participants over an average of 27.4 (±4.5) hours. RF-EMF exposure levels were computed for each frequency band, as well as from downlink (RF from mobile phone base station), uplink (RF from mobile phone handsets), broadcast, and Wi-Fi. Participants had a mean (±SD) age of 36.9 ± 12.5 years; 66.7% were women; and almost all (98.2%) from urban areas. A Wi-Fi router at home was reported by 61 participants (96.8%), with 38 (61.2%) having a Wi-Fi enabled smart TV. Overall, 26 (41.3%) participants had noticed the existence of a mobile phone base station in their neighborhood. On average, participants estimated the distance between the base station and their usual residence to be about 500 m. The median personal RF-EMF exposure was 208 mV/m. Downlink contributed 40.4% of the total RF-EMF exposure, followed by broadcast (22.4%), uplink (17.3%), and Wi-Fi (15.9%). RF-EMF exposure levels on weekdays were higher than weekends (p < 0.05). Downlink and broadcast are the main contributors to total RF-EMF personal exposure. Personal RF-EMF exposure levels vary according to day of the week and time of day.

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