Creating Visualizations in ChattaData

If you’ve made it to the final guide then you are in pretty deep. While this guide will continue to be at a beginner level, it will assume that you have read the previous How To Guides on Exploring and Searching. You’ll also want to have Signed Up so you can save your creations

For this guide, we’re going to be creating Visualizations (remember what those are?) based on Service Requests received from citizens through 311. These data contain dates, which are good for charts and timelines, as well as location information, which is needed to make maps.

First thing’s first, pull up the 311 Service Request data by searching for “311 Service Requests” on ChattaData. Select the result with the type ‘Filtered View’ that was updated most recently. If you can’t find it, here’s a link to the dataset. You should wind up here:

Visualization Page Overview

When you go to create a visualization from a dataset or filtered view, you always start on the same ‘Configure Visualization’ screen. Here’s a quick overview of what’s on that page to help you get started creating your own visualizations. This is one of those areas where you will need to explore yourself to truly understand what each option changes but you can visualize your changes as you go!

Configure/Data Selection - The section on the upper left part of the screen changes based on your selections elsewhere on the page, this current view is its default

The buttons on the left panel are as follows:

Data - This is where you chose your options for which data you want to present and how you would like to measure it
Axis - Allows you to make changes to the Axes such as how they are sorted and organized
Presentation - Change colors, add labels and provide Titles and Description
Legends and Flyouts - These control what is seen when a user ‘hovers’ their mouse pointer over an object in the visualization or what is shown on a legend
Map Layers (Maps only) - allows adjustment of most values that will be shown on the map, sort of a condensed version of the previous options but specific to presenting geographic information
Map Settings (Maps only) - more options specific to maps such as what level to zoom to by default and how to cluster multiple points in an area

Visualization Types - The icons across the top of the screen are where you select which type of visualization you want, clicking each of these will change the configuration/selection options explained above.

It’s easier to click through them to see which works best for your data presentation idea but here are some quick pointers:

The preview screen will go red when the mixture of data selection and visualization settings don’t match

You can’t create a map without location information
You can’t create a timeline without dates
You can’t present measures without numerical data

The visualization tool is somewhat basic and limited. If you think you’re doing something wrong because you’ve seen other charts that look a certain way, you may have just reached the limitations of the toolset

This will become more clear as you work through different visualizations but you’ll notice color options for charts are limited and there are no advanced statistics

Filters - these are the filters seen on any visualization but this is the setup step. What you create here is what will be seen by other ChattaData users

Setup filters based on the dataset, you can only filter by existing data and filters may lead to a blank screen if the data goes back to 2015 but you’ve filtered for 2010
Just like how you want your visualization to be easy to read, also be mindful of how the end users will be using your presentation when selecting which filters will be shown

Raw Data - The data shown at the bottom of this page changes based on the filter and affects the look of the visualization too. It will always be the full dataset selected on the previous page or a subset of that data based on your filters.

A “Top 10” pie chart may look terrible if you are previewing 5 years of data but great if you limit it to the last month
A 10-year timeline showing several categories of information will be a mess, but 2 years of data with only certain categories selected will be much easier to read

Now that we’ve gone through an overview of the visualization page, creating the actual visualizations will require some trial and error before you get the hang of things. In order to get started, however, here are a couple of quick examples of data created from 311 Service Requests.

As a side note, you are free to view any of the public data available to you and save any visualizations you create. They belong to you and will show up on your profile. You will also be able to share them with others once they have been saved

Rest assured that changing your data will not affect our data. Go wild.

Creating a Chart

Starting from the previous step, using the 311 Service Requests dataset, click ‘Visualize’ and in the drop-down, select ‘Create Visualization’. For this chart, let’s create a pie chart that shows the top 10 request types for 2020. This is much easier than it looks from the screenshot, we promise

Click ‘Pie Chart’ option
Select ‘Request Type’ for the Dimension
Measure should already say ‘Count of Rows’ but if not, select it
Click ‘Add Filter’ to create a Date Range
Select ‘Create Date’ to filter by (when Service Request was entered)
Click ‘Range’ so you can manually set range
Add date range ‘01/01/2020’ to ‘12/31/2020’
Click ‘Apply’ to save the filter
Click ‘10 Slices’ and uncheck the ‘Group Remaining as Other’ box (this is just to clean things up a bit)

That’s it! You’ve created your first visualization! Now click through all of the other options on the page to make changes as you see fit. Change the colors, add a title, or show the top four instead of top ten. Make it better!

Skip to the end to see how to save and share your visualizations or go to the next section to see how to create a map.

Mapping the Data

Return to the 311 Service Requests dataset and click ‘Visualize’ in the top right corner of the page showing the dataset details. Then, in the drop-down, select ‘Create a Visualization’ and you will be presented with this screen:

Because this dataset is quite large it may take a moment to load, once it does, let’s add a filter.

Select the ‘Map’ visualization type (may need to wait for it to load)
Ensure that ‘Public Location’ is selected for the Geo Column
Click ‘Add Filter’ (another point where it takes time to load)

Add a filter for Created Date

Select ‘Relative Date’
Then select Custom so you have more control over the range
Select ‘3’ and ‘Months’ for Last 3 Months of data

Once you’ve added the filter, zoom in to the Chattanooga area:

Notice that this map is practically unreadable. It shows too much information so it can’t be seen as really showing anything useful. How would you make it easier to read?

You could… change the date filter to only show the last week of data. You could add another filter that was only for ‘Brush Pickup’ requests. You could zoom waaaay into your neighborhood and see only the few requests that happened near you. You can also do some combination of these things and aggregate the data into a heat map instead of showing several separate points. Here’s how:

Click on the ‘Point Aggregation’ tab
Select ‘Heat Map’
I chose to increase the date filter to 10 months because…
I added a new filter to only include ‘Potholes’ Request Types
Zoom into the East Lake area to see areas with most reported potholes

In our line of work, it may be worth sending out a pothole inspector to the areas highlighted in red. When a large number of potholes are reported in the same area, it can often be a sign of major underlying damage rather than just a quick patch job. These maps that help tell our crew members where to go come from citizens reporting potholes. Neat, huh?

As with the walkthrough above about how to create a chart, now it’s your turn. Add/remove filters. Turn off the point aggregation so you can see individual requests and look at the last two weeks of bulk garbage pickup near where you live. Try different options and filters to learn the tools. Then find new datasets other than 311 Service Requests, such as Fire or Police incidents, to create other visualizations for. And finally, learn how to save your work!

Saving and Publishing Your Visualizations

We’ll wrap up quickly with how to save your work after you have created a visualization and if you are finished and ready to share, how to publish it for others to see. Let’s save the map of potholes created in the last section

Click ‘Save Draft’ then give your visualization a name once prompted.

Once saved with a new more informative name, click ‘Save’ on the top right of the page.

If you just want to save the visualization for later edits, then you are done.

If you would like to publish and share it with the ChattaData community, click ‘Publish’

You can find your visualizations and other saved assets under ‘My Profile’ by clicking on your Display Name at the top right of nearly every page of the ChattaData website.