Spatial Statistics for GIS Using R

With increasing amounts of spatial data generated each year, spatial analysis is becoming an increasingly useful tool across public health research, ecological modeling, and econometrics. The spatial statistical analysis looks beyond the map to the data mapped and to inquire about the patterns observed. In this tutorial, participants will explore point pattern analysis, spatial autocorrelation statistics, and geostatistical interpolation to estimate values across continuous and discrete distributions.

Objectives:

• Representing geographical data in R
• Finding non-randomness in point maps using spatstat
• Detection and measurement of spatial autocorrelation in lattice data using spdep
• Creating contour-type maps and semivariance using inverse distance weighting and geostatistical methods

• Level: Intermediate
• Duration: 2 hours
• Prerequisites: Basic knowledge of statistics and some previous exposure to working with R is assumed.
• Git repository: https://github.com/rcc-uchicago/SpatialStatistics_R

Tue. April 20, 2021, 2:00-4:00 p.m.

Tue. Feb 9, 2021, 2:00-4:00 p.m.

Introduction to Spatial Analysis with R

The size of spatial data sets continues to grow, and advanced tools for their analysis are needed. R has become a widely used open-source tool for spatial data analysis and visualization. This training course introduces SF, an R package for handling geographic data. Participants will be provided with basic knowledge about spatial data manipulation and map visualization using R, as well as the fundamental principles and the specific methods for creating interactive maps using R. Participants will be provided with the necessary information for applying R's powerful suite of geographical tools to their own problems.

Objectives:

• The structure of spatial objects in R
• Loading and interrogating spatial data with sf
• Data manipulation with spatial data using dplyr
• Spatial operations such as intersections
• Interactive maps with leaflet

• Level: Introductory
• Duration: 2 hours
• Prerequisites: Basic knowledge of statistics and some previous exposure to working with R is assumed.
• Git repository: https://github.com/rcc-uchicago/SpatialAnalysis_R

Introduction to ArcGIS and Arcpy

This workshop is intended for those needing to learn ArcGIS Pro and explore its full potential. Attendees will learn automation of basic geoprocessing, and introductory level GIS programming with Python. This is a hands-on style workshop with exercises designed to teach basic geoprocessing operations used in spatial data science and visualization.

Objectives:

• Introduction to ArcGIS Pro
• GIS analysis using Geoprocessing tools
• Automation using ModelBuilder & Python
• Introduction to Arcpy and ArcGIS Notebook
• Dynamic visualization in 2D and 3D

• Level: Introductory
• Duration: 2 hours
• Prerequisites: Participants are encouraged to bring a laptop with a Windows operating system as ArcGIS only works on Windows. Mac users can use UChicago Virtual Lab (vLab) to access the software https://academictech.uchicago.edu/vlab/. An RCC account is not required.

Tue. Nov 10, 2020, 2:00-4:00 p.m.

Introduction to GDAL/OGR

Geospatial Data Abstraction Library (GDAL/OGR) is the most widely used library for raster and vector geospatial data formats. GDAL supports over 50 raster formats and OGR over 20 vector formats. It provides the primary data access engine for many applications such as MapServer, GRASS, and QGIS and has been utilized by major packages such as FME, and ArcGIS. This workshop introduces GDAL through some examples to manipulate raster and vector files for Geographic Information Systems and Remote Sensing analysis.

Objectives:

• Understand what GDAL is, and how it is used
• Write single line GDAL commands to process raster and vector data
• Know how to interpret the help dialog for issuing GDAL commands
• Write GDAL batch scripts to automate many common geoprocessing operations such as merging, cropping and re-projecting, mosaicing, and filtering images.

• Level: Intermediate
• Duration: 1.5 hours
• Prerequisites: Basic knowledge of Geographic Information Systems and previous experience of working with the command line is assumed.
• Github repository: https://github.com/rcc-uchicago/gdal_introduction

Tue. Nov 24, 2020, 3:00-4:30 p.m.

Introduction to HPC for GeoComputation

This workshop will introduce attendees to performing Geocomputation analysis using a High-Performance Computing (HPC) cluster. Methods for parallel computing will be presented in the context of geospatial analysis. Participants will be introduced to geospatial tools available on the midway cluster. Most of the hands-on training could be done on any operating system; however, some parts of the hands-on will be demonstrated using the Midway2 cluster.

Objectives:

• Introduction to single-threaded and multithreaded geospatial tools such as GDAL/OGR, arcpy, and simple features (SF), raster, and stars packages in R
• Implicit and explicit parallelization
• Introduction to Slurm
• Writing simple Bash scripts for raster and vector processing

• Level: Intermediate
• Duration: 2 hours
• Prerequisites:Basic knowledge of Geographic Information Systems and previous experience of working with the command line is assumed.
• Git repository: https://github.com/rcc-uchicago/GeoComputation
• Registration URL: https://www.eventbrite.com/e/introduction-to-hpc-for-geocomputation-tickets-110021976694

Tue. Aug 13, 2020, 3:00-5:00 p.m.

Introduction to ArcGIS

This workshop introduces the underlying principles and methods of Geographical Information Systems (GIS) and provides opportunities for the development of practical skills in processing data using an industry-standard GIS software package. The workshop comprises presentations and computer-based practical sessions using ESRI’s ArcGIS software, with example data sets. Topics covered include data management, data visualization; data quality and analysis; georeferencing; data presentation and reporting.

Objectives:

• What a GIS is; what spatial data is; raster and vector data models
• The core tasks involved in a GIS analysis e.g. data acquisition and input; data storage and management; data manipulation and analysis; and data presentation and output
• Importing data from various sources, including scanned paper maps
• Handling tables including selections and queries
• Basic geoprocessing tasks e.g. buffering and clipping

• Level: Introductory
• Duration: 2.5 hours
• Shared Drive: Introduction

Tue. Feb 4, 2020, 2:00-4:30 p.m. Kathleen A. Zar Room, The John Crerar Library.

Intermediate ArcGIS

This course builds on participants existing knowledge of the underlying principles and methods of Geographical Information Systems (GIS). The workshop comprises presentations and computer-based practical sessions using ESRI’s ArcGIS software, with example data sets. Topics covered include geodatabases, using model builder, GIS customization with Python, extensions, online data, manipulating coordinate systems and spatial analysis/statistics tools.

Objectives:

• Working with geodatabases including importing existing data sets
• Basic automation using ModelBuilder and Python
• Introduction to arcpy and ArcGIS API
• The basics of some ArcGIS extensions (Spatial Analyst and 3D Analyst) are demonstrated
• Online mapping and sharing data
• Spatial statistics concepts and tools

• Level: Intermediate
• Duration: 2.5 hours
• Shared Drive: Intermediate

Tue. Feb. 11, 2020, 2:00-4:30 p.m. Kathleen A. Zar Room, The John Crerar Library.

Exploring Geospatial Raster Images

October 22, 2019

Introduction to ArcGIS Insights

November 13, 2019

Introduction to Satellite Imagery for Social Sciences

November 20, 2019

Introduction to ggplot

June 28, 2019

Introduction to GDAL

Aug 20, 2019

Introduction to Geocoding

May 21, 2019

Introduction to GIS for historical data

April 30, 2019

Introduction to Raster processing with R

April 18, 2019