Hey there! I am Jack Rummler - a Florida born, Philadelphia residing lover of all things urban and spatial. I currently work as a policy and data analyst at the Defender Association of Philadelphia and a geospatial data contractor for the World Bank's Urban Resilience unit.
My interests lie in using spatial data and urban policy to build communities and spaces that are socially and ecologically resilient.
Click my links below to check out my socials, and keep scrolling for some of my spatial analytics, public policy, and writing works. Thanks for stopping by!
Building a latent bus transit demand model and informational evaluative app to allow Sun Metro transit planners to build more equitable and profit maximizing routes.
Skills used: R, Random Forest, XG Boost, Google Cloud Services
Project Team: Charlie Huemmler, Yingxue Ou, and Jack Rummler
Using deep learning models to classify hurricane damage from post-Hurricane Harvey satellite imagery.
Skills used: Python, convolutional neural networks, VGG-16, residual neural networks
Project Team: Ben Keel, Jack Rummler, and Kate Tanabe
Developing several convolutional neural network models to predict 10 different land cover classifications, with an accuracy of nearly 80%.
Skills used: Keras, Tensorflow, convolutional neural networks
Using a Random Forest Regression to predict home prices with 75% accuracy.
Skills used: Python, Random forest regression
A study of Gainesville, Florida's microtransit pilot and a recommendation guideline for the effectiveness and stability of micromobility services.
Skills used: Literature and practice review, technical memorandums, transit planning
Project Team: Dr. Ruth Steiner, Dr. Siva Srinivasan, Dr. Mehri Mohebbi, Dr. Xiang Yan, Sagar Patni, Larissa Krinos, Juan Suarez, Jack Rummler
A scoping literature review and case study analysis of best practices in urban resilience planning to inform recommendations for Jacksonville, Florida. Mentored by Dr. Azza Kamal.
Skills used: Social sciences and qualitative research, social-ecological resilience practice
Using spatial statistics and different regression methods to investigate relationship between census block level median household income and neighborhood characteristics.
Skills used: R, ArcGIS Pro, GeoDa, spatial lag, spatial error, geographic weighted regression
Project Team: Haobing Liu, Jack Rummler, and Kate Tanabe