Do you want to bring in different data sources to one single work bench? Do you want to look at interactive maps and charts at the same time? Do you want to gain more “insights” with your data? If you answered yes to any of the three questions above, then this post is for you. Insights for ArcGIS has transformed how we traditionally performed spatial analysis.
With the latest December updates in ArcGIS Online, Insights for ArcGIS is now accessible to ArcGIS Online users as a premium app. Previously, Insights was only available through ArcGIS Enterprise. If you haven’t seen Insights for ArcGIS, it is a web-based, data analytics application that allows you to perform more in-depth data analysis. You can now work in an environment where visualization and analysis happen at the same time. With the capability to work with both spatial and tabular data, Insights allows us to compare maps and charts right beside each other to discover unique trends and patterns. With its connected demographic data source through ArcGIS Online, Insights enables us to answer questions that we wouldn’t be able to otherwise with our current datasets.
In this blog post, I will walk you through my six favourite functionalities in Insights to jumpstart your new spatial analysis journey. To help you understand better, I’m using patients records and community health data in Winnipeg as an example for each functionality.
1. Add data from a variety of data sources
First and foremost, Insights allows you to add data from different sources. You can add the following data types:
- Hosted or registered feature layers from your content, groups or organization
- Feature layers from
- Excel files (.xlsx)
- Comma-separated value files (.csv)
In my analysis, I’m bringing in Winnipeg dissemination areas from a hosted feature layer. The feature layer has four layers. Insights allows you to select one or more layers to add to your workbook. Similarly, you can select one or more tables within an Excel file. Both spatial and tabular data are added to the data pane with spatial data automatically displayed on your page as a map card.
Insights for ArcGIS allows you to add data from different sources
Learn more about adding data to Insights:
2. Enable locations on non-spatial datasets
Insights for ArcGIS identifies and uses icons to indicate field roles. The field role also determines the default statistic type applied to each field in a visualization. Insights makes it very easy to change field role from one to another.
A spatial layer has at least one location field. If you look at my patient records, it’s a table that does not have a location field. Don’t panic just yet -- it contains fields with coordinates information. Insights can use this data to enable location of the patient records. In addition to coordinates, you can enable location with addresses or geographies. This functionality allows us to transform our tabular data into a spatial format and get started on our spatial analysis.
Enable location with information from the tabular data
Learn more about the different kinds of visualization you can create and how you can change a field role here.
3. Create relationship between spatial and tabular datasets
If your tabular dataset does not have any geographic information, you can still display it on a map card. This can be done by creating a relationship between your spatial and non-spatial datasets using a common identifier. The demo below shows how I created a relationship between the Community Health Data and the Dissemination Areas using the dauid field. This unique identifier field exists in both datasets. After the two datasets are joined together, I can visualize information in our Community Health Data on map cards.
Create relationship between two datasets using a common ID
4. Create geographic relationship between two non-related datasets
What if your tabular data does not share a common identifier with your spatial data but you still want to examine the relationship between the two? No worries. Insights allows you to use geography as the bridge to create a relationship between two non-related datasets.
Use geography as a bridge to associate two non-related spatial datasets
The clip above demonstrates how I get the distribution of patients in each dissemination area through a simple drag-and-drop. You will notice, Insights figures out the input parameters for the aggregation analysis. I rarely need to click more than three times when performing analysis in Insights.
5. Answer new set of questions by reaching out to demographic datasets
One column in my Community Health dataset indicates the number of people who reported having diabetes in each dissemination area. I wonder if this number has a correlation with socioeconomic factors such as education, unemployment rate and money spent on pharmaceutical products and prescriptions. However, I don’t have these data available in my current dataset. What Insights allows me to do is to reach out to hundreds of demographic variables through ArcGIS Online using the Enrich Data tool. It uses the Esri GeoEnrichment services to give you demographic data associated with your point, line or data locations. You can browse through different data categories or search for the variables you are looking for. Within a few clicks, I grabbed all the socioeconomic variables I needed.
Reach out to ArcGIS Online demographic data to strengthen your analysis
6. Use chart statistics to find correlations
When we are working with data, we are often interested in statistics. Insights can help us with that through the Chart Statistics button. For histograms, you can display the mean, median and normal distribution on the chart. You can also display upper quartile and lower quartile with bar charts, column charts, time series graphs and line graphs. In the video below, I’m creating a scatter plot for the number of people who reported having diabetes and the money spent on pharmaceutical products.
Use chart statistics to find the correlations between two values
Using the Chart Statistics button on my card, I can ask Insights to add a best-fit line to the scatter plot. The best-fit line can be linear, exponential and polynomial. It also gives me an R2 value to indicate the correlation between the two variables. The correlation between these two values is not very strong with a R2 value of 0.492. However, I can easily change the x or the y value from the dropdown list and the best-fit line will change accordingly. The correlation becomes much stronger with a R2 value of 0.722 when I change the y-value from money spent on pharmaceutical products to the number of people who reported having an inactive lifestyle.
Hopefully, these tips and tricks can help you jumpstart your spatial analysis in Insights. If you are ready to leverage this powerful web-based application to discover valuable insights embedded in your data, sign up now for a 21-day free trial of this premium app.
Tell us what other Insights functionalities you would like to learn about in the comment section below.
About the AuthorMore Content by Christina Xing