Spatial disparity in tract-level associations between mental illness mortality and greenspace exposure in the Pacific Northwest
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Updated
Dec 5, 2022 - R
Spatial disparity in tract-level associations between mental illness mortality and greenspace exposure in the Pacific Northwest
Few graphs/plots and maps as outputs of movement ecology research (GPS telemetry)
Preprocessing Scripts for VIC-CropSyst Modeling
[AAAI] A spatiotemporal embedding framework for geographical entities
Harmonize heterogenous spatiotemporal gridded agriculture-related datasets. Part of a larger ongoing project to monitor land and water use by combining irrigation and gridded data via remote sensing data with machine learning.
Spatiotemporal variability in the association between mental illness and substance use mortality and unemployment in the contiguous US
SpatioTemporal Features eXtractor: a tool for change detection on spatiotemporal phenomena
SpatioTemporal Features eXtractor: a tool for change detection on spatiotemporal phenomena
Extracts low speed segments from spatiotemporal trajectories using moving median of speed. Fast and robust. Adaptively determines the parameters from the data, instead of setting objective, arbitrary parameters. Each trajectory in a set of trajectories will have unique subjective parameters.
Columbia University Data Science Master Capstone Project. The goal of this project was to cluster trajectories by shape for later optimization.
This repository introduces Deep Particulate Matter Network with a Separated Input model based on deep learning by using ConvGRU, which can simultaneously analyze spatiotemporal information to consider the diffusion of particulate matter.
An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution
The official repository for "Unveiling the Role of Climate in Spatially Synchronized Locust Outbreak Risks"
[KDD 2023] Official implementation of "Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM".
Bayesian Neural Field models for prediction in large-scale spatiotemporal datasets
[KDD 2022] Official implementation of "SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data".
Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.
Estimation, analysis and decomposition of brainwave spatiotemporal dynamics
Attention Feature Fusion base on spatial-temporal Graph Convolutional Network(AFFGCN)
Given a shapefile with time-annotated vector objects (e.g., building footprints + construction year), this script will automatically create an animated GIF illustrating the dynamics for a user-specified period of time
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