Register now for the March 2016 Spatial Data Science Bootcamp at UC Berkeley!
We live in a world where the importance and availability of spatial data are ever increasing. Today’s marketplace needs trained spatial data analysts who can:
- compile disparate data from multiple sources;
- use easily available and open technology for robust data analysis, sharing, and publication;
- apply core spatial analysis methods;
- and utilize visualization tools to communicate with project managers, the public, and other stakeholders.
To help meet this demand, International and Executive Programs (IEP) and the Geospatial Innovation Facility (GIF) are hosting a 3-day intensive Bootcamp on Spatial Data Science on March 23-25, 2016 at UC Berkeley.
With this Spatial Data Science Bootcamp for professionals, you will learn how to integrate modern Spatial Data Science techniques into your workflow through hands-on exercises that leverage today's latest open source and cloud/web-based technologies. We look forward to seeing you here!
To apply and for more information, please visit the Spatial Data Science Bootcamp website.
Limited space available. Application due on February 19th, 2016./h3>
Found by Natalie:
Tuck, Sean L., Helen RP Phillips, Rogier E. Hintzen, Jörn PW Scharlemann, Andy Purvis, and Lawrence N. Hudson. "MODISTools–downloading and processing MODIS remotely sensed data in R." Ecology and evolution 4, no. 24 (2014): 4658-4668. And it is Open Access!
Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particu- lar, products from NASA’s MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for eco- logical applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This auto- mation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between spe- cies richness across multiple higher taxa observed at 526 sites in temperate for- ests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal vari- ability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R2 values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/ seantuck12/MODISTools).
Awesome new (ish?) R package from the gang over at rOpenSci
Tired of searching biodiversity occurance data through individual platforms? The "spocc" package comes to your rescue and allows for a streamlined workflow in the collection and mapping of species occurrence data from range of sites including: GBIF, iNaturalist, Ecoengine, AntWeb, eBird, and USGS's BISON.
There is a caveat however, since the sites use alot of the same repositories the authors of the package caution to check for dulicates. Regardless what a great way to simplify your workflow!
Find the package from CRAN: install.packages("spocc") and read more about it here!/span>
Turf provides you with functions like calculating buffers and areas. Other common functions include aggregation, measurement, transformation, data filtering, interpolation, joining features, and classification. The detailed explanations of these functions can be found on this page.
Turf seems like a cool tool to try out if you want to provide spatial analysis functions on your webGIS applications.
Technical Tidbits From Spatial Analysis & Data Science. This nice blog highlights many technical aspects of web mapping: Leaflet, D3, R... lots of neat examples.