Using 311 data to develop an algorithm to identify urban blight for public health improvement

Training Computers to See the Built Environment Related to Physical Activity: Detection of Microscale Walkability Features Using Computer Vision

The study purpose was to train and validate a deep learning approach to detect microscale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep learning models for eight microscale features guided by the Microscale Audit of Pedestrian Streetscapes Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train−correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N = 512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between microscale features and GIS-measured and participant-reported neighborhood macroscale walkability. Classifier precision, recall, and overall accuracy were all over >84%. Total microscale was associated with overall macroscale walkability (r = 0.30, p < 0.001). Positive associations were found between model-detected and self-reported sidewalks (r = 0.41, p < 0.001) and sidewalk buffers (r = 0.26, p < 0.001). The computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

Harmful algal bloom-related 311 calls, Cape Coral, Florida 2018-2019

Harmful algal blooms (HABs) can adversely impact water quality and threaten human and animal health. People working or living along waterways with prolonged HAB contamination may face elevated toxin exposures and breathing complications. Monitoring HABs and potential adverse human health effects is notoriously difficult due to routes and levels of exposure that vary widely across time and space. This study examines the utility of 311 calls to enhance HAB surveillance and monitoring. The study focuses on Cape Coral, FL, USA, located along the banks of the Caloosahatchee River and Estuary and the Gulf of Mexico. The wider study area experienced a prolonged cyanobacteria bloom in 2018. The present study examines the relationship between weekly water quality characteristics (temperature, dissolved oxygen, pH, microcystin-LR) and municipal requests for information or services (algal 311 calls). Each 1 μg/L increase in waterborne microcystin-LR concentrations corresponded with 9% more algal 311 calls (95% confidence interval: 1.03-1.15, p = 0.002). The results suggest water quality monitoring and the 311 dispatch systems may be further integrated to improve public health surveillance.