How To Use Open Data
Do you want to find out which cities in the County had the most tree maintenance requests in 2016? In this example, we will be using the MC311 Service Requests dataset to answer this question. You will be introduced to a few tools and concepts in dataMontgomery to get there.
Step 1: Searching for a dataset
One quick way to find a dataset is by using the search box in dataMontgomery as in this example: 'Service Requests.'
Step 2: Search results
The search results will include a list of datasets, views, maps, and other presentations of datasets.
Select the dataset: MC311 Service Requests.
Note: You could continue browsing the datasets using the options in the left panel. These options help you filter datasets by categories, view types, and tags.
Step 3: Review the dataset primer view
Upon selecting the dataset, you will land on the dataset's primer, a description page in which you can answer questions such as:
- "Is this the data that I'm looking for?"
- "How up to date is this data?"
- "What is the data being used for now?"
Scroll down to the bottom of the primer. You will find a description for each data element in the dataset, and a preview of a few rows containing actual data.
Click on 'View Data' to access the actual dataset and analysis tools.
Step 4: View the dataset
You will now see the full dataset containing over 2 million rows collected over many years!
You can sort each column using a column menu.
You have a series of tools you can use to prepare and view the dataset in many ways.
Step 5: Narrow the data using filters
We will narrow down the amount of information by looking at data only for 2016 and a particular type of service request. Select the blue icon 'Filter' to open the panel to add your filtering criteria. Click on 'Add a New Filter Condition.' Open the first drop-down menu and select 'Opened' as the date column to filter by. Open the second drop-down menu and select 'is between.' Enter 1/1/2016 in the first date field, and 12/31/2016 in the second date field.
To narrow the data even further, you can add a second filter by selecting 'Add a New Filter Condition' again. In this case, we opted to narrow down the services by 'Area' to 'Tree Maintenance.'
Now, we will proceed to present the information visually. Select the green 'Visualize' button on the top right-hand corner.
Note: To learn more about filtering?: Watch a short tutorial video.
Step 6: Select the visualization tool
Scroll to the Chart section and under 'Visualization Type', select the 'Launch New Visualization' option.
Choose a name for your new visualization and then select "Create Visualization".
Step 7: Visualize your data
This is the visualization canvas where you can create graphs, charts or maps with your data to 'Visualize' the information in various ways.
Now you need to decide how you want to visualize your data by selecting a type of chart.
Alternatively, we could have started by selecting a data dimension on the left part of the canvas.
To answer our question, we need a horizontal bar graph.
You can try other types of charts to see what happens.
Next, you need to decide what data dimension you want to group your data visually. For this example, we need to aggregate the data by the City 'Dimension.'
The final step would be to select a measure option. Count of Rows will count the number of tree maintenance requests per City.
Step 8: Saving your work
Below the data table preview you will notice the option to insert your visualization.
In the top left corner, select 'Save' and provide a name for it. Note that to be able to save your work, you need to be signed-in to your dataMontgomery account.
Note: To retrieve your saved views, visit your profile.
This is the resulting visualization obtained in this example. You can select the View Source Data link below to try it yourself!
Try this visualization challenge!
Create a pie chart with the types of MC311 Service Requests in your city.
- Use the MC311 Service Requests data set to create the visualization.
- Hint: Filter data set by your city name, and use the ‘Area’ column for aggregation.