This event is part of the Harvard Affiliate Only Spatial Data Science Workshop Series.
Time: 12:00 PM ET
Length: 2 hours of presentation with an hour for open discussion/office hours afterwards
A link to the ArcGIS Pro project for the first workshop is now available here: https://www.arcgis.com/home/item.html?id=acad3ad96591417695ca51c359dddccb
- Lauren Bennett
- Alberto Nieto
- Lynne Buie
- Ankita Bakshi
- Cheng-Chia Huang
- Eric Krause
- Jie Liu
- Kevin Butler
- Xiaodan Zhou
Whenever we look at a map, we naturally organize, group, differentiate, and cluster what we see to help us make better sense of it. This workshop will explore powerful spatial statistics techniques designed to do just that in space and time. We’ll start with statistical cluster analysis methods, such as Hot Spot Analysis and Cluster and Outlier Analysis. We will then present advanced space-time pattern mining techniques, including aggregating and visualizing temporal data into a Space Time Cube and running an Emerging Hot Spot Analysis. Through discussions and demonstrations, we will learn how these techniques work, the types of questions each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. The workshop will focus on a use case that examines racial disparities in police stops.
Prior to the workshop attendees will be provided an ArcGIS Pro project with demo data that will be used throughout the workshop. Attendees will be invited to follow along during demonstrations using the provided data, though this is not required.
- To understand the challenges encountered due to the subjectivity in maps, and the opportunities to use spatial analysis to mitigate these impacts
- To understand how spatiotemporal data is converted to a space time cube for use in space-time pattern mining tools in ArcGIS
- To learn to make appropriate decisions about bin dimensions and mitigating temporal bias when aggregating spatiotemporal data
- To learn how to visualize and interpret the results of space time pattern mining tools
To learn how the following spatial and spatiotemporal statistical analysis tools work and how to apply them in their own work:
- Hot Spot Analysis (Getis-Ord Gi* statistic)
- Cluster and Outlier Analysis (Anselin Local Moran’s I Statistic)
- Emerging Hot Spot Analysis
- Basic experience in ArcGIS Pro’s functionality, including the ability to create and open projects, load layers, and use geoprocessing tools.
- Anyone that wants to visualize and analyze continuous data spatially
- Anyone that wants to use maps and spatial data in their work