Spatial data

A conceptual graphic of the proposed data analysis pipeline. Each step is represented by a stack of maps of the continental US. The top reads: "For each year, 1981-2023; geographic extent of all maps: NE US". Then, from left to right: "Inputs: Daily mean temperature (source: PRISM)", "Intermediat products: DOY maps for GDD thresholds", "Output: Mean DOY for each threshold, 1991-2020". Pointing downward from the intermediate products is an arrow labeled "Calculation of trends and variation for each thres — A conceptual diagram of the proposed data analysis pipeline

Theresa Crimmins

Estimating Trends in Phenology

Collaborators

Data Science Team

In collaboration with the National Phenology Network, we are creating an analytical pipeline to estimate spatial trends in the time to reach different thresholds of growing degree days in the Northeastern USA.

A map of Arizona where the standard deviation in AGB is represented by color. There is high standard deviation in areas that roughly correspond to mountain ranges and low standard deviation in lower elevations. — Standard deviation in above-ground biomass across 8 data products in Arizona

Estimating Uncertainty in Carbon Stores

Collaborators

Data Science Team

We are working with David More and Yang Li of the School of Natural Resources & the Environment to build an analytical pipeline characterizing uncertainty in estimates of above-ground biomass, to help stakeholders make informed decisions about which data products to use for their needs.

STAC Jupyter Notebook

STAC (Spatial Temporal Asset Catalog) is a specification for JSON files (and an API) which provide descriptions and links to spatial and temporal data.

Christophe Schnaufer

Research Software Engineer

schnaufer@arizona.edu

Pronouns:

he, him, his

Kristina Riemer

Director of Data Science

kristinariemer@arizona.edu

Pronouns:

she, her, hers

Subscribe to Spatial data