Zhou, Ryan Zhenqi, Hu, Yingjie, Sun, Kai, Muldoon, Ryan, Clark, Susan, and Joseph, Kenneth. Explainable GeoAI and statistical analysis reveal complementary insights about disparities of 311 help requests during the 2022 Buffalo blizzard. Retrieved from https://par.nsf.gov/biblio/10628402. International Journal of Disaster Risk Reduction 126.C Web. doi:10.1016/j.ijdrr.2025.105635.

Zhou, Ryan Zhenqi, Hu, Yingjie, Sun, Kai, Muldoon, Ryan, Clark, Susan, & Joseph, Kenneth. Explainable GeoAI and statistical analysis reveal complementary insights about disparities of 311 help requests during the 2022 Buffalo blizzard. International Journal of Disaster Risk Reduction, 126 (C). Retrieved from https://par.nsf.gov/biblio/10628402. https://doi.org/10.1016/j.ijdrr.2025.105635

Zhou, Ryan Zhenqi, Hu, Yingjie, Sun, Kai, Muldoon, Ryan, Clark, Susan, and Joseph, Kenneth. "Explainable GeoAI and statistical analysis reveal complementary insights about disparities of 311 help requests during the 2022 Buffalo blizzard". International Journal of Disaster Risk Reduction 126 (C). Country unknown/Code not available: Elsevier. https://doi.org/10.1016/j.ijdrr.2025.105635. https://par.nsf.gov/biblio/10628402.

@article{osti_10628402,
place = {Country unknown/Code not available}, title = {Explainable GeoAI and statistical analysis reveal complementary insights about disparities of 311 help requests during the 2022 Buffalo blizzard}, url = {https://par.nsf.gov/biblio/10628402}, DOI = {10.1016/j.ijdrr.2025.105635}, abstractNote = {}, journal = {International Journal of Disaster Risk Reduction}, volume = {126}, number = {C}, publisher = {Elsevier}, author = {Zhou, Ryan Zhenqi and Hu, Yingjie and Sun, Kai and Muldoon, Ryan and Clark, Susan and Joseph, Kenneth}, }