This two-day course (3/28/22 and 3/30/22) will be offered via Zoom. Attendance is required as the course will not be recorded.

Course Summary:
Urbanization has been a fundamental trend of the past two centuries and a key force shaping the development of the modern world. While urbanization in rapidly growing nations is helping lift hundreds of millions of people out of poverty, it is also creating immense societal challenges by increasing greenhouse-gas emissions, destabilizing fragile ecosystems and creating new demands on public services and infrastructure that pose significant challenges on the environment. Despite the importance of understanding the drivers of urban growth, we are still unable to quantify the magnitude and pace of urbanization in a consistent manner at a high resolution and global scale.

The revolution in geospatial data has transformed how we study cities. Since the 1970s, terrestrial Earth observation data have been continuously collected in various spectral, spatial and temporal resolutions. As improved satellite imagery becomes available, new remote-sensing methods and machine-learning approaches have been developed to convert terrestrial Earth-observation data into meaningful information about the nature and pace of change of urban landscapes and human settlements. But until recently, most remote sensing studies focused on local settings. Mapping land cover at a national or regional scale is challenging because of the lack of high-resolution global imagery, the heterogeneous and complex spectral characteristics of land, the small and fragmented spatial configuration of many cities, and importantly, computational constrains (for storage and processing). Emerging cloud-based computational platforms now allow for scaling analysis across space and time. Google Earth Engine (GEE) is one platform that leverages cloud-computing services to achieve planetary-scale utility. GEE leverages cloud-computational services for planetary-scale analysis and consists of petabytes of geospatial and tabular data, including a full archive of Landsat, Sentinel-2, Sentinel-1, and MODIS, together with a JavaScript, Python based API (GEE API), and algorithms for supervised image classification.

This hands-on course will focus on the use of Google Earth Engine (GEE) for urban research applications. It will demonstrate how free and open-source satellite imagery – including electro-optical (EO) and Synthetic Aperture Radar (SAR) imagery – can be utilized to map urban areas and urbanization trends and patterns, across space and time, and to perform a qualitative analysis of the impacts of urban expansion on the landscape. In addition to analyzing existing classification schemes of urban areas to understand how cities expand and evolve, the course will provide a brief introduction to concepts in Remote Sensing Machine Learning, with a focus on supervised pixel-based image classification. Students will learn how to automatically map built-up land cover based on publicly available satellite imagery (e.g., Landsat and Sentinel). In addition, the course will demonstrate recent innovations in the use of remotely sensed nighttime light observations to understand variations in economic activity within and between cities – all utilizing data and tools that are available in GEE. The course will include PowerPoint slides, group hands-on coding (in JavaScript) and short exercises. Prior coding knowledge is not required.

Instructor: Ran Goldblatt
Ran Goldblatt, Ph.D. is the Chief Scientist of New Light Technologies Inc. and is a Geographic Information System (GIS) and Remote Sensing expert. His work focuses on the complex interrelations between the physical and social environment and on utilizing geospatial data analysis for a more sustainable urban development.

Registration Fees
- UNC-CH Students: $0, with a $35 deposit to hold your spot (deposit is refundable upon your attendance for at least 66% of the course)
- UNC-CH Faculty/Staff/Postdoc: $95
- Non UNC-CH: $145

Additional Course Registration
- Registration will close at 12:01 am on 3/25/2022. Once registration closes, no late registrations will be accepted, no exceptions.
- Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
- Zoom link for this course will be sent prior to the course. Registration must be made at least 3 days prior to the course date to receive the Zoom link.
- For questions regarding the status of this class, please contact Jill Stevens at This email address is being protected from spambots. You need JavaScript enabled to view it..

Register

This semester UNC-Chapel Hill is offering monthly virtual mental health seminars addressing the intersection of mental health with related topics such as faith, addition/substance use, intimate partner violence, and vulnerable populations.

The second seminar, Mental Health, Alcohol, and Drugs: How Do They Connect?, will be offered on Monday, March 28, from 3:30 p.m. until 5:00 p.m.

The conversation will be framed by a diverse panel of experts discussing the role that alcohol and drugs play in mental health.

Attendees will have the opportunity to listen, learn, and contribute thoughts and questions during the event. The seminar will be recorded for viewing by those who cannot attend.

More Info

This two-day (3/29 AND 3/31) course will be offered via Zoom only and will not be recorded.

Course Summary:
Usability testing in survey research allows in-depth evaluation of how respondents and interviewers interact with questionnaires, particularly web and mobile surveys. A respondent may understand the survey question and response options but may be unable to select their answer accurately on the small screen of a smartphone. Although there is a growing body of literature on best practices for web surveys and mobile devices, not all design guidelines work equally well for all surveys and all survey populations. In addition, it is clear that the capabilities of computerized surveys are constantly emerging. Examples are the use of images, videos, maps and GPS, interactive features, and mobile devices. As a result, it is critical for researchers to have the necessary tools to evaluate, test, and modify surveys to incorporate user-centered design in an iterative method as part of the survey pretesting process.

Instructors: Emily Geisen and Jennifer Romano
Emily Geisen is a Senior Experience Management scientist at Qualtrics. She manages a team of 10 Project Managers in Qualtrics’ Sample Science and Delivery unit. She specializes in improving data quality and reducing respondent burden in web surveys by employing best practices in questionnaire design, visual design, and user experience.

Jennifer Romano is a Staff UX Researcher and Manager at Google, an instructor at UC Berkeley Extension, an instructor at the University of Maryland, and a UXR Coach. She specializes in efficient applications of research methods to ensure scientific rigor is not compromised while working fast to gain actionable results.

Registration Fees
- UNC-CH Students: $65
- UNC-CH Faculty/Staff/Postdoc: $90
- Non UNC-CH: $90

Additional Course Registration
- Registration will close at 12:01 am on 3/26/2022. Once registration closes, no late registrations will be accepted, no exceptions.
- Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
- Zoom link for this course will be sent prior to the course. Registration must be made at least 3 days prior to the course date to receive the Zoom link.
- For questions regarding the status of this class, please contact Jill Stevens at This email address is being protected from spambots. You need JavaScript enabled to view it..

Register

Join a live training session for the N3C Data Enclave. Users of all types can learn how to navigate the N3C, utilize the Enclave and resources it provides, and learn how to better achieve their analytical goals. This orientation is split into 2 sessions – Session A and Session B. These orientations are offered on a monthly basis. Session A is recommended before attending Session B.

Session B is for analysts, statisticians, data scientists, or anyone who wants to gain a broader understanding of the tools needed to work with the data.

Topics include:

• Focus on technical aspects of working with data in the secure N3C Enclave, including use of OMOP concept sets and N3C-specific tooling, such as the Concept Set Browser.
• Introduce commonly used analysis tools, such as Contour and Code Workbooks and corresponding workflows for simple analyses.
• Introduce the Enclave Knowledge Store, a mechanism for sharing and using community-developed code and data across projects.

Register

This two-day course (3/28/22 and 3/30/22) will be offered via Zoom. Attendance is required as the course will not be recorded.

Course Summary:
Urbanization has been a fundamental trend of the past two centuries and a key force shaping the development of the modern world. While urbanization in rapidly growing nations is helping lift hundreds of millions of people out of poverty, it is also creating immense societal challenges by increasing greenhouse-gas emissions, destabilizing fragile ecosystems and creating new demands on public services and infrastructure that pose significant challenges on the environment. Despite the importance of understanding the drivers of urban growth, we are still unable to quantify the magnitude and pace of urbanization in a consistent manner at a high resolution and global scale.

The revolution in geospatial data has transformed how we study cities. Since the 1970s, terrestrial Earth observation data have been continuously collected in various spectral, spatial and temporal resolutions. As improved satellite imagery becomes available, new remote-sensing methods and machine-learning approaches have been developed to convert terrestrial Earth-observation data into meaningful information about the nature and pace of change of urban landscapes and human settlements. But until recently, most remote sensing studies focused on local settings. Mapping land cover at a national or regional scale is challenging because of the lack of high-resolution global imagery, the heterogeneous and complex spectral characteristics of land, the small and fragmented spatial configuration of many cities, and importantly, computational constrains (for storage and processing). Emerging cloud-based computational platforms now allow for scaling analysis across space and time. Google Earth Engine (GEE) is one platform that leverages cloud-computing services to achieve planetary-scale utility. GEE leverages cloud-computational services for planetary-scale analysis and consists of petabytes of geospatial and tabular data, including a full archive of Landsat, Sentinel-2, Sentinel-1, and MODIS, together with a JavaScript, Python based API (GEE API), and algorithms for supervised image classification.

This hands-on course will focus on the use of Google Earth Engine (GEE) for urban research applications. It will demonstrate how free and open-source satellite imagery – including electro-optical (EO) and Synthetic Aperture Radar (SAR) imagery – can be utilized to map urban areas and urbanization trends and patterns, across space and time, and to perform a qualitative analysis of the impacts of urban expansion on the landscape. In addition to analyzing existing classification schemes of urban areas to understand how cities expand and evolve, the course will provide a brief introduction to concepts in Remote Sensing Machine Learning, with a focus on supervised pixel-based image classification. Students will learn how to automatically map built-up land cover based on publicly available satellite imagery (e.g., Landsat and Sentinel). In addition, the course will demonstrate recent innovations in the use of remotely sensed nighttime light observations to understand variations in economic activity within and between cities – all utilizing data and tools that are available in GEE. The course will include PowerPoint slides, group hands-on coding (in JavaScript) and short exercises. Prior coding knowledge is not required.

Instructor: Ran Goldblatt
Ran Goldblatt, Ph.D. is the Chief Scientist of New Light Technologies Inc. and is a Geographic Information System (GIS) and Remote Sensing expert. His work focuses on the complex interrelations between the physical and social environment and on utilizing geospatial data analysis for a more sustainable urban development.

Registration Fees
- UNC-CH Students: $0, with a $35 deposit to hold your spot (deposit is refundable upon your attendance for at least 66% of the course)
- UNC-CH Faculty/Staff/Postdoc: $95
- Non UNC-CH: $145

Additional Course Registration
- Registration will close at 12:01 am on 3/25/2022. Once registration closes, no late registrations will be accepted, no exceptions.
- Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
- Zoom link for this course will be sent prior to the course. Registration must be made at least 3 days prior to the course date to receive the Zoom link.
- For questions regarding the status of this class, please contact Jill Stevens at This email address is being protected from spambots. You need JavaScript enabled to view it..

Register

This two-day (3/29 AND 3/31) course will be offered via Zoom only and will not be recorded.

Course Summary:
Usability testing in survey research allows in-depth evaluation of how respondents and interviewers interact with questionnaires, particularly web and mobile surveys. A respondent may understand the survey question and response options but may be unable to select their answer accurately on the small screen of a smartphone. Although there is a growing body of literature on best practices for web surveys and mobile devices, not all design guidelines work equally well for all surveys and all survey populations. In addition, it is clear that the capabilities of computerized surveys are constantly emerging. Examples are the use of images, videos, maps and GPS, interactive features, and mobile devices. As a result, it is critical for researchers to have the necessary tools to evaluate, test, and modify surveys to incorporate user-centered design in an iterative method as part of the survey pretesting process.

Instructors: Emily Geisen and Jennifer Romano
Emily Geisen is a Senior Experience Management scientist at Qualtrics. She manages a team of 10 Project Managers in Qualtrics’ Sample Science and Delivery unit. She specializes in improving data quality and reducing respondent burden in web surveys by employing best practices in questionnaire design, visual design, and user experience.

Jennifer Romano is a Staff UX Researcher and Manager at Google, an instructor at UC Berkeley Extension, an instructor at the University of Maryland, and a UXR Coach. She specializes in efficient applications of research methods to ensure scientific rigor is not compromised while working fast to gain actionable results.

Registration Fees
- UNC-CH Students: $65
- UNC-CH Faculty/Staff/Postdoc: $90
- Non UNC-CH: $90

Additional Course Registration
- Registration will close at 12:01 am on 3/26/2022. Once registration closes, no late registrations will be accepted, no exceptions.
- Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
- Zoom link for this course will be sent prior to the course. Registration must be made at least 3 days prior to the course date to receive the Zoom link.
- For questions regarding the status of this class, please contact Jill Stevens at This email address is being protected from spambots. You need JavaScript enabled to view it..

Register

ICD-Pieces: Improving Care for CKD, Diabetes and Hypertension in Health Systems (Miguel A. Vazquez, MD; George (Holt) Oliver, MD, PhD)

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Miguel A. Vazquez, MD
Professor of Medicine
University of Texas Southwestern Medical Center
Dallas, TX
George (Holt) Oliver, MD, PhD
Vice President Clinical Informatics
Parkland Center for Clinical Innovation
Dallas, TX

Learn More