Research Guides: Research Data Services: Spatial Analysis Working Group

Spatial Analytics Working Group

Building community around spatial analytics for discovery & insight

A community of practice around spatial and geospatial analytics open to faculty, students, staff and scholars.

Our group will include researchers from a variety of disciplines, and have the following:

slack channel for the free exchange of ideas
github repository for spatial and geospatial analysis using code in Python, Leaflet.js and R
links to resources, tutorials, tools and research guides
meetings to discuss advances and share tools and ideas
workshops promoting learning and research tools

Research Facilitation @ Dartmouth Libraries are facilitating this group to foster collaboration collaboration among researchers from diverse fields, including geography, earth sciences, biology, anthropology, history, and classical studies. These researchers use spatial analysis techniques to study patterns in modern and historical data, leading to new insights and discoveries.

The working group aims to bring together all that are interested in spatial analytics.

Tools & resources for spatial and geospatial community practitioners

ESRI / ArcGIS - Online or Desktop (Windows PC) - spatial data tools, transformation and data visualization
ArcGIS Online / Dartmouth Sign-On https://dartmouth.maps.arcgis.com/
ArcGIS StoryMaps / ESRI ArcGIS Online
QGIS - open source, runs on Mac, Linux, Windows PC https://qgis.org/download/
Spatial processing and visualization with R - libraries like sf, leaflet, terra. R map gallery example
Spatial processing and visualization with Python - libraries like geopandas, folium. Python map gallery example
SQL database extraction and loading using geodatabases, Postgres, MySQL
mobile apps, including Survey123, QuickCapture ,Qfield QGIS App
code (R and Python) for spatial tasks: https://github.com/Dartmouth-Libraries/spatial-analytics-working-group/tree/main/code
json / geojson data checker / data creator https://geojson.io
map projection assistant/tool / https://projectionwizard.org/

Join our community!

Visit and join our slack channel

Spatial Analytics Hot Topics

Biological and ecological modeling, habitat suitability
Geography, site selection, spatial patterns, spatial clusters
Earth Sciences
Public Health analysis & planning
Conservation
Sustainability
Big (spatial) data, machine learning and AI with geospatial imagery
Anthropology & Classical studies: imagery and spatial data for anthropological & cultural sites, pattern recognition with big data, LiDAR, machine learning & AI
Economics: impact & location selection for economic development
Spatial & geospatial data visualization including Python & R notebooks, web-mapping & web-publishing of maps using Leaflet.js and html
Geocoding street addresses to latitude and longitude map coordinates
Historical map scanning, data extraction, and transformation (Georeferencing)

Emerging research and trends in Generative AI (GenAI) and the use of Artificial Intelligence, Machine Learning and Deep Learning in geospatial problem-solving for areas such as public health, emergency response, and decision-assistance https://research.google/blog/geospatial-reasoning-unlocking-insights-with-generative-ai-and-multiple-foundation-models/

Additional Resources & Links:

Dartmouth Libraries Research Data Services
Dartmouth Libraries GIS Data and geographic statistics
Geography Department & Earth Sciences Department
Books:
- Geospatial Analysis—A Comprehensive Guide, 6th edition; 2007–2018; de Smith, Goodchild, Longley.
- Geographic Information Systems, Dawsen, Christopher J. New York : Nova Science Publishers, 2011
- Getting to know ArcGIS Desktop by Tim Ormsby
- The ESRI guide to GIS analysis by Andy Mitchell
- Geographic Information Systems and Science by Paul Longley
- GIS Fundamentals by Paul Bolstad
- GIS Tutorial for ArcGIS Pro by Wilpen Gorr and Kirsten Kurland
- GIS Concepts and ArcGIS Methods by David M. Theobald
Leaflet and Folium – https://leafletjs.com/ and https://python-visualization.github.io/folium/latest/index.html - For Python, R and web development (javascript)
Python spatial imports such as Geopandas , Folium/Leaflet and Shapely
R's sp, sf, R Spatial View on the Comprehensive R Network (CRAN)
R & spatial libraries on Github. Contact us to contribute to our Github repos.

GIS Fundamentals by Paul Bolstad
ISBN: 1593995520

Publication Date: 2019-07-01

New and updated Sixth Edition of GIS Fundamentals, 6th Edition - the bestselling GIS textbook by Paul Bolstad.

Geographic Information Systems (GIS) are computer-based tools for the entry, maintenance, and analysis of spatial data. GIS are critical for effective resource management, and have been applied across a wide range of science, business, and government endeavours.

This book provides an introduction to the theory and application of GIS. It is written for use in a first-year GIS class and as a reference for the GIS practitioner. This sixth edition balances theoretical and practical material, so that students may apply knowledge of GIS in the solution of real-world problems. Improvements over the previous editions are included in each chapter. Topics treated include an introduction to GIS, spatial data models, map projections, data entry, image data, GPS, digital data, database systems in GIS, general spatial analysis, raster analysis, terrain modeling, metadata, standards, and accuracy assessments.
GIS for Science, Volume 2 by Dawn J. Wright (Editor); Christian Harder (Editor); Jared Diamond (Foreword by)
ISBN: 9781589485877

Publication Date: 2020-12-22

Merging the rigor of the scientific method with the technologies of GIS GIS for Science, Volume 2: Applying Mapping and Spatial Analytics brings to life a continuing collection of current, real-world examples of scientists using geographic information systems (GIS) and spatial data science to expand our understand of the world. Co-edited by Esri Chief Scientist Dawn Wright and Esri Technology Writer and Information Designer Christian Harder and with a foreword by Pulitzer Prize winner Jared Diamond, these case studies are part of a global effort to find ways to sustain a livable environment for all life on this planet. The contributors of GIS for Science, Volume 2: Applying Mapping and Spatial Analytics represent a cross section of scientists who employ data gathered from satellites, aircraft, ships, drones, and myriad other remote-sensing and on-site technologies. This collected data is brought to life with GIS and the broader realm of spatial data science to study a range of issues relevant to our understanding of planet Earth--including epidemiology in light of the COVID-19 pandemic; sustainable precision agriculture; predicting geological processes below the surface of the earth; leveraging GIS near-realtime disaster response, recovery, resilience and reporting; the latest innovations in monitoring air quality; and more. Their stories also show in very practical terms how ArcGIS software and the ArcGIS Online cloud-based system work as a comprehensive geospatial platform to support research, collaboration, spatial analysis, and science communication across many settings and communities. A rich supplementary web site--gisforscience.com--includes actual data along with additional maps, videos, web apps, story maps, workflows and snippets of computer code, including Python notebooks, for readers curious to learn more. Written for professional scientists, the swelling ranks of citizen scientists, and anyone interested in science and geography, GIS for Science, Volume 2: Applying Mapping and Spatial Analytics offers wonderful examples of our impulse to dream, discover, and understand, as coupled with the rigor and discipline of the scientific method and the foundation of geography. See how scientists from a variety of disciplines are solving some of the world's most pressing problems using geographic information systems--GIS.
Applied Geospatial Data Science with Python by David S. Jordan
ISBN: 9781803238128

Publication Date: 2023-02-28

Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in PythonThe book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book Description Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you'll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You'll learn how to read, process, and manipulate spatial data effectively. With data in hand, you'll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you'll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you'll be able to tackle random data, find meaningful correlations, and make geospatial data models. What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.
An Introduction to R for Spatial Analysis and Mapping by Chris Brunsdon; Lex Comber
ISBN: 1526428490

Publication Date: 2019-01-10

This is a new edition for anyone using R for the first time, for use in spatial statistical analysis, geocomputation and digital mapping. The authors, once again, take readers from 'zero to hero', updating the now standard text to further enable practical R applications in GIS, spatial analyses, spatial statistics, web-scraping and more. Revised and updated, each chapter includes: example data and commands to explore hands-on; scripts and coding to exemplify specific functionality; self-contained exercises for students to work through; embedded code within the descriptive text. The new edition includes detailed discussion of new and emerging packages within R like sf, ggplot, tmap, for all researchers collecting and using data with location attached.This is an introduction to the use of R for spatial statistical analysis, geocomputation, and GIS for all researchers - regardless of discipline - collecting and using data with location attached.