This event is part of the Harvard Affiliate Only Spatial Data Science Workshop Series.

Date: 1/21/2022Time: 12:00 PM ETLength: 2 hours of presentation with an hour for open discussion/office hours afterwards

 

Registration

 

A link to the ArcGIS Pro project for the first workshop is now available here: https://www.arcgis.com/home/item.html?id=acad3ad96591417695ca51c359dddccb

 

Panelists:

Lauren Bennett

Alberto Nieto

Lynne Buie

Ankita Bakshi

Cheng-Chia Huang

Eric Krause

Jie Liu

Kevin Butler

Xiaodan Zhou

Course Description:

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.

Learning Objectives:

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

Prerequisites:

Basic experience in ArcGIS Pro’s functionality, including the ability to create and open projects, load layers, and use geoprocessing tools.

Target Audience:

Anyone that wants to visualize and analyze continuous data spatially

Anyone that wants to use maps and spatial data in their work

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