# Data Intensive Tutorials

Scientific programming can be used to efficiently work with many different types of data. Rather than performing tasks manually, you can write code that opens, cleans and processes your data. However, often figuring out how to perform a specific task in R, Python or another programming language can be tricky. In the tutorials below, you will learn how to use R, Python and Javascript programming languages to perform specific tasks including calculating slope in a digital elevation model or using Leaflet to create an interactive map.

If there is a tutorial you’d like to see covered, reach out to us on Twitter @EarthLabCU.

## Tutorials

Tutorials that cover data intensive topics

## Visualizing hourly traffic crime data for Denver, Colorado using R, dplyr, and ggplot

This tutorial demonstrates how to access and visualize crime data for Denver, Colorado.

## Calculating the area of polygons in Google Earth Engine

This tutorial demonstrates polygon creation, perimeter and area calculations, and visualization using the JavaScript interface to Google Earth Engine.

## Introduction to the Google Earth Engine Python API

This tutorial outlines the process of installing the Google Earth Engine Python API client.

## Introduction to the Google Earth Engine code editor

This tutorial introduces the code editor in Google Earth Engine and shows how to use LandSat imagery using the JavaScript API.

## Get Modis sinusoidal tile grid positions from latitude and longitude coordinates in Python

This tutorial demonstrates how to convert Modis sinusoidal tile grid positions to latitude and longitude in Python.

## Convert Landsat 8 path/row to lat/lon coordinates in Python

This tutorial demonstrates how to convert Landsat 8 path/row coordinates to latitude and longitude in Python.

## Using Leaflet and Folium to make interactive maps in Python

This tutorial shows how to make interactive maps in Python with folium.

## Visualizing elevation contours from raster digital elevation models in Python

This tutorial shows how to compute and plot contour lines for elevation from a raster DEM (digital elevation model).

## Calculating slope and aspect from a digital elevation model in Python

This tutorial shows how to compute the slope and aspect from a digital elevation model in Python.

## Introduction to spatial regression in Python

This tutorial outlines how to use PySAL to perform spatial regression in Python.

## Computing raster statistics around buffered spatial points Python

This tutorial shows how to compute raster statistics like the mean and variance around buffered spatial points in Python.

## Acquiring streamflow data from USGS with climata and Python

This tutorial demonstrates how to use climata to acquire streamflow data in and around Boulder, Colorado.

## Computing and plotting 2d spatial point density in R

This tutorial demonstrates how to compute 2d spatial density and visualize the result using storm event data from NOAA.

## Using R and Python in the same Jupyter notebook

This tutorial shows how to use rpy2 in a Jupyter notebook to run both R and Python.

## Acquiring U.S. census data with Python and cenpy

This tutorial outlines the use of the Cenpy package to search for, and acquire specific census data.

## Create rasters from SMAP data in Python

This tutorial demonstrates how to access SMAP data, and how to generate raster output from this data.