A framework for evacuation hotspot detection after large scale disasters using location data from smartphones

2016 ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Akihito Sudo ◽  
Yoshihide Sekimoto
Keyword(s):  
Large Scale ◽  
Hotspot Detection ◽  
Location Data

Crowding cost estimation with large scale smart card and vehicle location data

2017 ◽  
Vol 95 ◽  
pp. 105-125 ◽  
Author(s):  
Daniel Hörcher ◽  
Daniel J. Graham ◽  
Richard J. Anderson
Keyword(s):  
Smart Card ◽  
Cost Estimation ◽  
Large Scale ◽  
Location Data ◽  
Vehicle Location

scholarly journals Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

2019 ◽  
Vol 116 (38) ◽  
pp. 18962-18970 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein
Keyword(s):  
Protein Dynamics ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Data ◽  
The Cancer Genome Atlas ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Driver Genes ◽  
Mutational Hotspots

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

scholarly journals Automated Method for Delineating Harvested Stands Based on Harvester Location Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2754
Author(s):  
Timo Melkas ◽  
Kirsi Riekki ◽  
Juha-Antti Sorsa
Keyword(s):  
Forest Inventory ◽  
Large Scale ◽  
Field Measurements ◽  
Detailed Comparison ◽  
Satellite System ◽  
Comparison Method ◽  
Aerial Images ◽  
Location Data ◽  
Automated Method ◽  
Good Stand

The data produced by cut-to-length harvesters provide new large-scale data source for event-based update of national forest stand inventory by Finnish Forest Centre. This study aimed to automate geoprocessing, which generates delineations of operated areas from harvester location data. Automated algorithms were developed and tested with a dataset of 455 harvested objects, recorded during harvestings. In automated stand delineation, the location points are clustered, the stand points are identified and external strip roads are separated. Then, stand polygons are produced. To validate the results, automatic delineations were compared to 57 observed delineations from field measurements and aerial images. A detailed comparison method was developed to study the correspondence. Stand polygonization parameter was adjusted and areal correspondence with 1% error on average was obtained for stands over 0.75 ha. Good stand shape agreement was observed. Overall, the automated method worked well, and the operative stand delineations were found suitable for updating the forest inventory data. To modify the operative stands towards forest inventory stands, a balancing algorithm is introduced to create a solid, unique stand boundary between overlapping stands. This algorithm is beneficial for upkeep of stand networks. In addition, the Global Navigation Satellite System (GNSS) accuracy of the harvesters was examined and estimated numerically.

scholarly journals More Than Just Privacy: Using Contextual Integrity to Evaluate the Long-Term Risks from COVID-19 Surveillance Technologies

2020 ◽  
Vol 6 (3) ◽  
pp. 205630512094825
Author(s):  
Jessica Vitak ◽  
Michael Zimmer
Keyword(s):  
Large Scale ◽  
Data Transfer ◽  
Contact Tracing ◽  
Political Values ◽  
Notification System ◽  
Contextual Integrity ◽  
Location Data ◽  
Monitoring Technologies ◽  
Access To Data

The global coronavirus pandemic has raised important questions regarding how to balance public health concerns with privacy protections for individual citizens. In this essay, we evaluate contact tracing apps, which have been offered as a technological solution to minimize the spread of COVID-19. We argue that apps such as those built on Google and Apple’s “exposure notification system” should be evaluated in terms of the contextual integrity of information flows; in other words, the appropriateness of sharing health and location data will be contextually dependent on factors such as who will have access to data, as well as the transmission principles underlying data transfer. We also consider the role of prevailing social and political values in this assessment, including the large-scale social benefits that can be obtained through such information sharing. However, caution should be taken in violating contextual integrity, even in the case of a pandemic, because it risks a long-term loss of autonomy and growing function creep for surveillance and monitoring technologies.

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.

Revealing temporal stay patterns in human mobility using large‐scale mobile phone location data

2021 ◽  
Author(s):  
Xiping Yang ◽  
Zhixiang Fang ◽  
Yang Xu ◽  
Ling Yin ◽  
Junyi Li ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Location Data

scholarly journals Close Relationships in Close Elections

Social Forces ◽  
2020 ◽  
Author(s):  
Byungkyu Lee
Keyword(s):  
Cell Phone ◽  
Close Relationships ◽  
Large Scale ◽  
Political Discussion ◽  
Political Dissent ◽  
Location Data ◽  
Political Views ◽  
2016 Election ◽  
Egocentric Network ◽  
Kinship Ties

Abstract Close elections are rare, but most Americans have experienced a close election at least once in their lifetime. How does intense politicization in close elections affect our close relationships? Using four national egocentric network surveys during the 1992, 2000, 2008, and 2016 election cycles, I find that close elections are associated with a modest decrease in network isolation in Americans’ political discussion networks. While Americans are more politically engaged in close elections, they also are less likely to be exposed to political dissent and more likely to deactivate their kinship ties to discuss politics. I further investigate a potential mechanism, the extent of political advertising, and show that cross-cutting exposure is more likely to disappear in states with more political ads air. To examine the behavioral consequence of close elections within American families, I revisit large-scale cell phone location data during the Thanksgiving holiday in 2016. I find that Americans are less likely to travel following close elections, and that families comprised of members with strong, opposing political views are more likely to shorten their Thanksgiving dinner. These results illuminate a process in which politicization may “close off” strong-tied relationships in the aftermath of close elections.

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