April 15, 2019
SRC-SRO Researchers at the Federal Computer Assisted Survey Information Collection Workshops
The Federal Computer Assisted Survey Information Collection (FedCASIC) workshops will be in Washington DC April 16-17 at the Bureau of Labor Statistics. Gina Cheng, Cheng Zhou, and Raphael Nishimura of Survey Research Operations will be presenting.
Tuesday, April 16
1:00-2:15, Gina Cheung will be a panelist on Top Three Management Challenges in Survey Technology Programming Agencies and Organizations Face Today, in Room 1.
Panelists will identify the top challenges facing their agencies or organizations today given the changing survey technology, data systems, and programming environments. Projects today often include innovative survey technologies, the use of specialized programming customizations, incorporated administrative and extant data sources, and the integration of different devices and technologies to support data collection. The panelists will discuss the ways that their organizations are dealing with the environmental changes that they have identified, and offer examples and lessons learned in addressing these challenges.
Wednesday, April 17
Survey Research Operation (SRO) in University of Michigan developed a coding application back in 2002. The tool has outdated with modern technology and also it was designed to tightly couple with one data collection software. In the presentation, we will discuss and demo a new coding application we have developed, which incorporates the new development of software, database, and machine learning technology with the aim to achieve efficiency, consistency, enhanced reporting functions, low maintenance and versatility. The new application is suitable to do any kind of coding activities and it is independent from any data collection software.
12:45-2:15, Raphael Nishimura will present Predicting Segments and Household Characteristics Using Satellite and Street View Images in Room 3.
Many face-to-face surveys need to screen households to identify eligible respondents for their studies, which tends to be very time- and cost-intensive. We can improve the efficiency of such efforts by using auxiliary information about the sampled units to predict their eligibility and use these predictions to assist the sample design, with stratification and differential selection probabilities, for instance. For this purpose, public-available satellite and street view images can be used as auxiliary information to feed machine-learning algorithms. In this presentation we will show initial results of such attempts. We initially trained a deep learning algorithm to predict household eligibility based on Google Street View images from geo-coded sampled households, which we know their eligibility status from previous surveys. Then, we evaluate the results of the algorithm using a validation dataset of households with their corresponding Google Street View images. This same approach is also applied to predict sampled segments using satellite images.