Related
Mar 12,2019
Announcing Spring 2019 Civic Analytics Fellows
Paper
/ Jul 20,2017
Urban phenology
Toward a real-time census of the city using Wi-Fi data
by
Constantine Kontokosta, Nicholas Johnson
The Civic Analytics program’s NSF EAGER grant, Bias and Discrimination in City Predictive Analytics, was profiled in NSF’s Committee on Equal Opportunities in Science and Engineering 2021–2022 Biennial Report to Congress:
....this project addressed bias in citizen reports data to improve the fairness of data-driven decision-making in the urban context by: (1) building statistical machine learning models to estimate reporting rate biases, (2) providing tools to city decisionmakers, policymakers and planners to understand and visualize the spatial and socioeconomic dependence of reporting behaviors, and (3) developing methods to account for observed biases in responding to resident reports. In other words, this project developed methods to compensate for bias in reporting data to improve allocation of city services.
Please fill out the information below to receive our e-newsletter(s).
*Indicates required.