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24 results for “mobile phone data”
Mobile phone data for forests in Szklarska Poreba and Swieradow Forest District
<p><strong>Mobile phone data: </strong>Data were collected for forest in 395 base fields (750 m × 750 m). The scope of data collected covers the period from January 1, 2019 to December 31, 2019. Unique user visits were counted in the base fields. A unique visit to the base field was considered to be a visit that occurred on a specific day in a different time period. There are 5 time periods separated: 6:00 - 10:00, 10:00 - 14:00; 14:00 - 18:00, 18:00 - 22:00, 22:00 - 6:00. Mobile phone data were collected to determine the spatial distribution of social activities in forest areas.The fully anonymized data was acquired from Selectivv. It collects information about mobile phone users (over 20 million users in Poland). The scope of data collected by Selectivv includes: user locations; timestamps; data from applications (350,000 applications) and websites (about 17 million pages), where users consent to data collection for better content profiling.</p> <p> </p> <p><strong>Data description:</strong> type - vector layer, column N - number of visits, coordinate system - 2180</p>
Mobile phone data
<p><strong>Phone communication data</strong></p> <p>Phone communication data from multiple phone extractions. The file NodeList.csv contains the nodes of the multivariate graph and contains the following columns: </p> <p>_nodeID : unique identifier for each entity of the graph<br> _nodeType : specification of node type (phone, person, contact or identifier)<br> _viewIcon: tulip-specific attribute which defines the icon used for each node<br> _viewShape: tulip-specific attribute which defines the shape for each node</p> <p>The file EdgeList.csv contains all edges between the above specified nodes and contains the following columns:<br> _sourceID : unique identifier of source node<br> _targetID : unique identifier of target node<br> _relType : specification of the type of relation (communication, contact association, user account association)<br> _nbCommunications : number of communications exchanged between two given nodes. Attribute present only for edges of type ‘communication’.</p> <p>Nodes and relations can be imported into Tulip using the ‘import csv’ option. In order to create metanodes, only edges describing associations (contacts, user accounts) are used in a first step in order to use the algorithm ‘connected componants’. After importing the communication edges, metanodes can then be created based on the metric value calculated beforehand.</p> <p> </p> <p> </p> <p> </p>
A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland
<p><strong>Related article:</strong> Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. <a href="https://www.nature.com/articles/s41597-021-01113-4"><em>Scientific Data</em> 9, 39</a>.<br> </p> <p><strong>In this dataset:</strong></p> <p>We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning. </p> <p><strong>Please cite this dataset as:</strong><br> <br> Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4<br> </p> <p><strong>Organization of data</strong></p> <p>The dataset is packaged into a single Zipfile <em>Helsinki_dynpop_matrix.zip</em> which contains following files:</p> <ol> <li> <em>HMA_Dynamic_population_24H_workdays.csv</em> represents the dynamic population for average workday in the study area.</li> <li> <em>HMA_Dynamic_population_24H_sat.csv</em> represents the dynamic population for average saturday in the study area.</li> <li> <em>HMA_Dynamic_population_24H_sun.csv</em> represents the dynamic population for average sunday in the study area.</li> <li><em>target_zones_grid250m_EPSG3067.geojson</em> represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.</li> </ol> <p><strong>Column names</strong></p> <ol> <li><em>YKR_ID </em>: a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.</li> <li><em>H0, H1 ... H23 </em>: Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. <br> The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)</li> </ol> <p>In order to visualize the data on a map, the result tables can be joined with the <em>target_zones_grid250m_EPSG3067.geojson</em> data. The data can be joined by using the field <em>YKR_ID</em> as a common key between the datasets.</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p> <p><strong>Related datasets</strong></p> <ul> <li>Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612</li> <li>Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564</li> </ul> <p><br> </p>
Data from: Robust single-image tree diameter estimation with mobile phones
<p>Ground-based forest inventories are a key element of forest carbon monitoring, reporting, and verification schemes and a cornerstone of forest ecology research. Recent work using LiDAR-equipped mobile phones to automate parts of the forest inventory process assumes that tree trunks are well-spaced and visually unoccluded, or else requires manual intervention or offline processing to identify and measure tree trunks.</p> <p>In this paper, we design an algorithm that exploits a low-cost smartphone LiDAR sensor to estimate trunk diameter automatically from a single image in complex and realistic field conditions. We implement our design and build it into an app on a Huawei P30 Pro smartphone, demonstrating that the algorithm has low enough computational cost to run on this commodity platform in near real-time.</p> <p>We evaluate our app in three different forests across three seasons and find that in a corpus of 97 sample tree images, our app estimates trunk diameter with RMSE of 3.7 cm (R<sup>2</sup> = .97; 8.0% mean error) compared to manual DBH measurement. It achieves a 100% tree detection rate while reducing surveyor time by up to a factor of 4.6.</p> <p>Our work contributes to the search for a low-cost, low-expertise alternative to Terrestrial Laser Scanning that is nonetheless robust and efficient enough to compete with manual methods. We highlight the challenges that low-end mobile depth scanners face in occluded conditions and offer a lightweight, fully automatic approach for segmenting depth images and estimating trunk diameter despite these challenges. Our approach lowers the barriers to in situ forest measurement outside of an urban or plantation context, maintaining a tree detection and accuracy rate comparable to previous mobile phone methods even in complex forest conditions.</p>
supplementary data Chemical and Microbial Leaching of Valuable Metals from PCBs and Tantalum Capacitors of Spent Mobile Phones
<p>Table S1: Chemical composition of waste PCBs and tantalum capacitors (VICs)without HF precious metals; Table S2: Leaching with organic acids; Table S3: Leaching with inorganic acids; Table S4: Bacterial leaching of PCBS varying pulp density; Table S5: Bacterial leaching of PCBS varying ferrous iron concentration; Table S6: Bacterial leaching of PCBS and tantalum capacitor scrap varying particle size; Table S7: Fungal leaching of metals by <em>A</em>. <em>niger</em> spores and filtrate.</p>
Data from: Robust single-image tree diameter estimation with mobile phones
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Data - Mobile phones and their impact on the socioeconomic development of rural women in Peru
<p>Project database Mobile phones and their impact on the socioeconomic development of rural women in Peru</p>
Data from: Quantifying crowd size with mobile phone and Twitter data
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Data from: Mobile phones as monitors of personal exposure to air pollution: is this the future?
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Data from: Prediction limits of mobile phone activity modelling
Thanks to their widespread usage, mobile devices have become one of the main sensors of human behaviour and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using 10 months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions. We show examples how some of the outliers can be related to external factors such as specific social events.
Figure 9 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 9 - Timeline. We will prepare and update the Web app (Aim 2) as we develop it for use in annotating gold standard audio data (Aim 1) and as we get feedback on its use in connection with Amazon's Mechanical Turk (Aim 2). Year 2 will consist primarily of testing the aggregation of annotated audio data for further analysis (Aim 2), to train an automated approach (Exploratory Aim), and to publish and present our findings.
Figure 2 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 2 - Mockup of audio recording annotation tool – Step 1: Selection. This figure shows a mockup of what an audio annotation Web application tool could look like. In this first step, (A) the Worker presses the Play icon to listen to the voice recording, (B) selects a problematic segment by clicking and dragging the mouse over the waveform, and (C) replays the recording if necessary and selects other problematic segments.
Figure 5 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 5 - DARPA-funded seedling project. This schematic represents our DARPA-funded seedling project to assess the feasibility of collecting phone voice recordings from PD patients for use in a competition.
Figure 4 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 4 - Audio recording annotation tool – Step 3: Rating. Following Figures 2 and 3, here the Worker rates how serious the problem is that is affecting the highlighted segment of the recording. In this example, the Worker indicates that the background noise (wind) is not good, but that it doesn't interfere with his/her ability to hear the voice in the recording.
Figure 7 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 7 - Example mPower patient voice data. In the mPower app, PD patients are prompted to perform the voice activity three times per day: once before taking their medication, a second time when they feel they are at their best after taking their medication, and a third "random" time. This figure shows example voice data for a single patient on medication (top) and at a "random" time, very likely off medication (bottom). On the left are waveforms, showing the acoustic voice signal over time (0-10 seconds), from which one can clearly see that the patient's voice trailed off to a minimum (bottom left) compared to after medication (top left). On the right are spectrograms, representing signal amplitude at different frequencies (0-5 kHz) over time (0-10 seconds). The spectrogram after medication (top right) has more uniform frequency bands across the recording compared to the rather "muddled" spectrogram recorded at the random time (bottom right).
Figure 3 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 3 - Audio recording annotation tool – Step 2: Annotation. Following Figure 2, here the Worker selects one or more categories describing why the highlighted segment in the audio waveform is problematic. In this example, there was a lot of background noise (wind).
Figure 6 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 6 - Android and iOS Parkinson app screenshots. Top: Android PD app screenshots showing instructions for the phonation (voice) task. Bottom: mPower PD app screenshots. Each participant in the mPower study is prompted to perform a voice activity three times a day. The rightmost screenshot demonstrates the visual feedback that is provided during audio recording, to try to keep the voice at the best amplitude for recording.
Data from: Prediction limits of mobile phone activity modelling
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Data from: Using mobile phones as acoustic sensors for high-throughput mosquito surveillance
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Data from: Mobile phone reminders and peer counseling improve adherence and treatment outcomes of patients on ART in Malaysia: a randomized clinical trial
Background: Adherence to treatment remains the cornerstone of long term viral suppression and successful treatment outcomes among patients receiving Antiretroviral Therapy (ART). Objective(s): Evaluate the effectiveness of mobile phone reminders and peer counseling in improving adherence and treatment outcomes among HIV positive patients on ART in Malaysia. Methods: A single-blind, parallel group RCT conducted in Hospital Sungai Buloh, Malaysia in which 242 adult Malaysian patients were randomized to intervention or control groups. Intervention consisted of a reminder module delivered through SMS and telephone call reminders by trained research assistants for 24 consecutive weeks (starting from date of ART initiation), in addition to adherence counseling at every clinic visit. The length of intended follow up for each patient was 6 months. Data on adherence behavior of patients was collected using specialized, pre-validated Adult AIDS Clinical Trial Group (AACTG) adherence questionnaires. Data on weight, clinical symptoms, CD4 count and viral load tests were also collected. Data was analyzed using SPSS version 22 and R software. Repeated measures ANOVA, Friedman's ANOVA and Multivariate regression models were used to evaluate efficacy of the intervention. Results: The response rate after 6 months follow up was 93%. There were no significant differences at baseline in gender, employment status, income distribution and residential location of respondents between the intervention and control group. After 6 months follow up, the mean adherence was significantly higher in the intervention group (95.7; 95% CI: 94.39–96.97) as compared to the control group (87.5; 95% CI: 86.14–88.81). The proportion of respondents who had Good (>95%) adherence was significantly higher in the intervention group (92.2%) compared to the control group (54.6%). A significantly lower frequency in missed appointments (14.0% vs 35.5%) (p = 0.001), lower viral load (p = 0.001), higher rise in CD4 count (p = 0.017), lower incidence of tuberculosis (p = 0.001) and OIs (p = 0.001) at 6 months follow up, was observed among patients in the intervention group. Conclusion: Mobile phone reminders (SMS and telephone call reminders) and peer counseling are effective in improving adherence and treatment outcomes among HIV positive patients on ART in Malaysia. These findings may be of potential benefit for collaborative adherence planning between patients and health care providers at ART commencement.
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