Marc Weber's Projects
Accumulation Scripts to allocate landscape data and accumulate
Allocation and accumulation tools that utilize open source tools and pure Python for rapid and highly customizable functionality. Originally intended as a workaround for the NHD Plus tool CA3TV2.
Repository contains base function library array_tools.py, and a set of example applications.
R data package for AWRA 2020 spatial workshop
Material for AWRA2022 Geospatial R and Python Workshop
Material for AWRA 2020 R Spatial Workshop
An exhaustive reference to problems seen in real-world data along with suggestions on how to resolve them.
An absolutely minimal bookdown example
Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/.
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
Repository for data science course
The Leek group guide to data sharing
Python Library for NASA Earthdata APIs
Overview of EE slides
Environmental justice analysis tools for R
An R package for accessing elevation data
This R package can be used to compile and evaluate Water Quality Portal (WQP) data for samples collected from surface water monitoring sites on streams and lakes. It can be used to create applications that support water quality programs and help states, tribes, and other stakeholders efficiently analyze the data.
this repo is an ESRI toolbox and tool(s) that export ESRI Feature Classes to open data formats, CSV, JSON, and GeoJSON
Example scripts originally derived from Prof. Dana Tomlin's handouts for his course on Geospatial Software Design. Shared with his permission.
Plotting Assignment 1 for Exploratory Data Analysis
Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources
A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets
Python tools for geographic data
Lightning talk for GIS 2023 Workshop
Web pages for GIS in Action R spatial workshop spring 2017 at PSU
The Open Source Data Science Masters
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
source for IALE Workshop: An Open Science and Reproducible Research Primer for Landscape Ecologists