scholarly journals Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects

2021 ◽  
Vol 776 ◽  
pp. 145935
Author(s):  
Stefan Steger ◽  
Volkmar Mair ◽  
Christian Kofler ◽  
Massimiliano Pittore ◽  
Marc Zebisch ◽  
...  
Keyword(s):  
Data Collection ◽  
Landslide Susceptibility ◽  
Data Driven ◽  
Landslide Data

Providing Data-Driven Equitable Palliative and End-of-Life Care for Structurally Vulnerable Populations: A Pilot Survey of Information Management Strategies

2019 ◽  
Vol 37 (4) ◽  
pp. 244-249
Author(s):  
Akshay Rajaram ◽  
Trevor Morey ◽  
Sonam Shah ◽  
Naheed Dosani ◽  
Muhammad Mamdani
Keyword(s):  
Palliative Care ◽  
North America ◽  
Data Collection ◽  
Information Management ◽  
Vulnerable Populations ◽  
Management Strategies ◽  
Data Driven ◽  
Cross Sectional ◽  
Convenience Sample ◽  
Data Infrastructure

Background: Considerable gains are being made in data-driven efforts to advance quality improvement in health care. However, organizations providing hospice-oriented palliative care for structurally vulnerable persons with terminal illnesses may not have the enabling data infrastructure or framework to derive such benefits. Methods: We conducted a pilot cross-sectional qualitative study involving a convenience sample of hospice organizations across North America providing palliative care services for structurally vulnerable patients. Through semistructured interviews, we surveyed organizations on the types of data collected, the information systems used, and the challenges they faced. Results: We contacted 13 organizations across North America and interviewed 9. All organizations served structurally vulnerable populations, including the homeless and vulnerably housed, socially isolated, and HIV-positive patients. Common examples of collected data included the number of referrals, the number of admissions, length of stay, and diagnosis. More than half of the organizations (n = 5) used an electronic medical record, although none of the record systems were specifically designed for palliative care. All (n = 9) the organizations used the built-in reporting capacity of their information management systems and more than half (n = 6) augmented this capacity with chart reviews. Discussion: A number of themes emerged from our discussions. Present data collection is heterogeneous, and storage of these data is highly fragmented within and across organizations. Funding appeared to be a key enabler of more robust data collection and use. Future work should address these gaps and examine opportunities for innovative ways of analysis and reporting to improve care for structurally vulnerable populations.

Mapping landslide susceptibility using data-driven methods

2017 ◽  
Vol 589 ◽  
pp. 250-267 ◽  
Author(s):  
J.L. Zêzere ◽  
S. Pereira ◽  
R. Melo ◽  
S.C. Oliveira ◽  
R.A.C. Garcia
Keyword(s):  
Landslide Susceptibility ◽  
Data Driven ◽  
Using Data

OBACIS Analytics: The Catalogs

2018 ◽  
Author(s):  
Mohamed Ismail
Keyword(s):  
Data Collection ◽  
Continuous Improvement ◽  
Work Flow ◽  
Data Driven ◽  
Reporting Standards ◽  
Integrated Framework ◽  
Order Of Magnitude ◽  
Previous Visit ◽  
Centralized Database

OBACIS is an integrated framework being developed to accelerate the accreditation reporting work-flow, cut down the reporting cost by an order of magnitude, and close the data-driven continuous improvement loop. This paper focuses on creating a centralized database for compiling accreditation data required for accreditation reporting from various resources such as previous visit accreditation reports, academic calendars, course schedules, and a handful of other resources are used to create what we call the Catalogs. Despite the fact that the Catalogs framework has been developed to meet the reporting standards of Canadian Engineering and Accreditation Board (CEAB), The system can be easily adapted to meet other standards such as ABET and EUR-ACE. The Catalogs are supposed to save a sheer amount of time needed for accreditation reporting and should act as an instrumental tool for accelerating accreditation data collection, creating insightful analyses, and identifying gaps for continuous improvement initiatives at both program and faculty levels.

scholarly journals An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

2020 ◽  
Vol 9 (10) ◽  
pp. 561
Author(s):  
Omid Ghorbanzadeh ◽  
Khalil Didehban ◽  
Hamid Rasouli ◽  
Khalil Valizadeh Kamran ◽  
Bakhtiar Feizizadeh ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Data Driven ◽  
Susceptibility Maps ◽  
Normalised Difference Vegetation Index ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Sentinel 2

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

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