Exploring Data with Insights

ENV 859 - Geospatial Data Analytics   |   Fall 2025   |   Instructor: John Fay  

Recording link:

ArcGIS Insights is a powerful cloud-based tool for visualizing and analyzing spatial and non spatial data. If you’ve used Tableau, it’s a bit like that, but with more finely tuned spatial analytical capabilities. Its ability to track your workflow and share the results widely make it a quite useful tool to have in your cloud-based spatial analysis toolbelt. One drawback of Insights, however, is its youth. It can do a lot, but it also breaks a lot, with operations often resulting in an unhelpful error message. My recommendation is to become familiar with this tool and keep your eye on it. I could go places, or ESRI could just decide to shelve this idea and migrate its abilities into other apps like Dashboards and Experience Builder.

This document includes a demonstration of the structure and operation of ArcGIS Insights.

Terminology:

  • Workbook - a data analysis project hosted in ArcGIS Insights
  • Model -
  • Dataset - a spatial or tabular dataset that can be added to your workbook for visualization & analysis
  • Card - a visual representation of one or more datasets
  • Page - a set of cards viewed together (a workbook can have several pages access via tabs)
  • Page view - viewing data and cards
  • Analysis view - viewing the schematic of your analysis sequence

Additional Resources:

Objective: Explore spatio-temporal patterns of malaria cases occurring in Loreto, Pero

We have been provided a dataset of malaria cases in the state of Loreto, Peru. While these are actual data obtained by a Peruvian health agency, you should not use these data beyond this demonstration as they have not been approved for analysis – and I’ve guessed/altered a few of the attributes. The two files we’ll use have already been added to ArcGIS Online and shared with the ENV859_2022 group. They are:

  • Loreto_Malara_Data” (LINK): a table of malaria case counts, tagged by date on and the district in which it was recorded, as well as a few climatic variables corresponding to that date and location. (NOTE: These data should not be used in an actual analysis; they are for demonstration purposes only.) Explore these datasets and note the columns included.
  • Loreto (LINK): a feature layer showing the district boundaries for the Loreto province

Getting Started

1. Logging in

ArcGIS Insights is a cloud based application requiring and ArcGIS Online subscription to use. It also comes as a desktop application which is installed on NSOE machines (and also requires an AGOL account to activate), but we’ll stick with the cloud-based version for ease of use.

:point_right: To access ArcGIS Insights:

  • Navigate to https://insights.arcgis.com and sign in as you would your Duke AGOL account, i.e., with your ArcGIS organizational URL, which will authenticate you using Duke’s familiar Shibboleth login. Be sure to set the address to dukeuniv.maps.arcgis.com.
  • Review the on-line tour if you wish. (It can be accessed later via the ? icon in the upper right.)
  • You will be presented with the Insights main page. You don’t likely have any documents here, but we’ll fix that shortly…

2. Creating a new workbook and adding data

  • Create a new workbook
    • Select the Workbook tab, then click New Workbook
  • Add the Loreto Malaria Data dataset:
    • Open the Add Data dialog.
    • Set to search the Duke University connection, setting the subunit to My organization.
    • Search for Loreto Malaria.
    • Add both the Loreto_Malaria_Data and Loreto datasets to your workbook.
  • Rename your workbook: <netID> Malaria Analysis
  • Save your workbook, which should have two datasets and one card displayed.

Exploring your data

1. Data Types

Exploration of a dataset should begin with examining how data were imported into the analytical environment. In particular, it’s important to note what data types are assigned to each column as that can affect how those values can be analyzed.

  • Familiarize yourself with the structure of the data
    • Click the triangle next to the Loreto_malaria_data dataset name to expose the fields and their assigned data types
    • Which fields appear as categories? as numeric? as dates?
    • Which fields, if any, are assigned an incorrect data type?
  • Fix the data type for Ecoregion, setting it to be a category, not a value.
    • Click on the data type icon and set to String
  • Fix the data type for nfalciparum, setting it to numeric. (A bit more challenging…)
    • View the table for the dataset
    • Click on + Field to add a new field
    • Rename the field Falciparum
    • Set the formula to be VALUE(nfalciparum) and run the calculation
  • Repeat for nvivax
  • View the worksheet in Analysis View

2. Visualize temporal patterns your data

  • Close the map card on your workbook (if displayed), making space for a new one

  • Q: What is the distribution of N. falciparum case counts in our dataset?

    → Answer with a histogram of N. falciparum

    • Drag the calculated Falciparum field into the workspace, and drop it onto the Chart>Histogram box that appears
    • How does our distribution appear?
      • Heavily skewed toward zero
      • One count very, very high! An outlier??
    • Flip the card to view stats of the distribution
    • Rename this card to “Distribution of falciparum counts” and save your workbook

  • Q: How have cases of falciparum changed over time?

    → Answer with a line plot, setting the X axis to years:

    • Select both the Date and the Falciparum fields, and then drag them into the workspace
    • Select Time series as the type of plot
    • Do you see any trends over time? Any major peaks or extended dips?

  • Q: Do trends in cases follow a similar pattern to climatic variables?

    → Answer by adding variables to your time series plot:

    • Drag rainMM onto your plot; do you see a trend?

      → This isn’t a great way to compare trends because both value rely on the same y-scale…

    → Answer by building a new line plot:

    • Select Year and Falciparium and drag into the workspace, creating a line plot
    • Drag rainMM on top of the plot, noting that it will add as a Combo plot
    • Change the rainMM variable from bars to a line

  • Q: Are trends in cases similar across the different districts in Loreto?

    → Answer by showing a different trendline for each district:

    • Remove the rainMM item from your line plot
    • Click on the X-axes of your line plot so that the Group by option appears
    • Select district as the grouping variable
    • Can you interpret the results? It’s a bit busy…

    → Add a filter to your workbook:

    • Click the Widgets button in the upper right
    • Select Predefined Filter
    • Add a new filter, selecting district as the field

  • Q: Are there seasonal trends in cases within each year?

    • Answer by plotting cases by month (or epiweek): bar plot, box plot
    • Create a data clock: Select Year, Month, and Falciparum….

3. Visualize Spatial patterns in the data

  • Create a new page for your spatial explorations
    • Add a new page (click the + at the bottom tab), and close the dialog to add new data
    • Navigate back to Page 1 and drag the Loreto_malaria_data and Loreto datasets to the Page 2 tab
    • Open Page 2
  • Adding location to the records in the Loreto_malaria_data:
    • Click the button on the top button bar for Create Relationships
    • Add the Loreto dataset to the dialog, then the Loreto_malaria_data dataset
    • Select the join fields: NOMBDIST <-> and district
    • Click Finish and a new dataset will be added to your project:
  • Create a map of your data

    • Drag the location attribute to the workspace and select Map

    • Drag another attribute, e.g. Population onto the map and the map will show that value
    • There are other means to alter the field shown on the map and how it’s displayed…
    • You can also change the base map of your data
  • Add a filter and link it to your map
    • Add Years as a table to your map
  • Add a second map and sync it to your map
    • Search AGOL for Bioclimatic projections
      • Note the Item ID for one of the results

    • In Insights, search for that Item ID

    • Link this to your Loreto map
  • Add a scatter plot
  • Explore spatial selections

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