Sigma offers powerful mapping capabilities that allow users to visualize geographic data effectively. Whether you’re analyzing regional trends, plotting locations, or visualizing complex geographical data, Sigma Maps can help you gain valuable insights.Â
In this blog, we will cover how to use maps in Sigma Computing.Â
Why Maps are important in Sigma Computing
1. Geographic Insights
Maps allow users to visualize data geographically, providing insights that are not easily gleaned from tables or charts. This spatial context can reveal patterns, trends, and correlations that are crucial for making informed decisions.
2. Enhanced Data Understanding
By mapping data points, users can better understand the distribution and density of data across different regions. This is particularly useful for businesses that operate in multiple locations, as it helps identify areas of high performance or those needing improvement.
How to Use Maps in Sigma ComputingÂ
Getting Started with Maps
Maps are visual tools that can be crafted within the PAGE ELEMENTS section of your workbook’s editor panel or directly from a pre-existing data element. Data visualizations will not appear on the page canvas until you have defined all the necessary plot fields. To fill open fields, you can either use the + menu of the field or drag and drop the column into it.
Sigma supports three distinct map types: Region, Point, and Geography. Select the type of map according to the data you have and the outcomes you aim to achieve.
Log in to Sigma Computing and click on the Create New option. Start by opening your workbook in Sigma Computing.Â
1) Map - Region
Click on the Data pane then Table to select the Tables and Datasets option and from there, under connections, we can select Sigma sample database.
For creating the map visual we are going to use Census table and Census schema and then click on Add button. This table is having country, state and its population for different years.
Now this table is added as a dataset and we can create visualization by creating a child element as chart from it.
After that, the following interface will appear, where you can select the chart type to Map – Region.
In Region section add the State column as our data contains US States but you can choose other options based on Countries, US Zip codes likewise as per your data availability. For colors select the dropdown to By Scale and add Population 2000 measure as sum.
From the above image we can easily interpret that California state with dark blue color has the highest population among all the other states for the year 2020.
2) Map - Point
To create a map point visualization, we will connect to the below datasource from Sigma’s sample database, which contains the required latitude and longitude data in JSON format.
Click on explore and then select the down arrow beside state json field and select the extract columns option to parse the LAT and LONG with other columns.
After pressing Confirm, all selected columns are now parsed from the JSON field. Now, create a child element as a chart from it.
Select the chart type as Map – Point and add LAT field in the Latitude option , LONG field in Longitude option. It will plot all the points on the map visual shown as below.
3) Map - Geography
To represent complex geographical data beyond simple latitude/longitude columns, you can use WKT (Well-Known Text) or GeoJSON formats.
In Sigma’s Map – Geography chart, the Geography field requires a column formatted as Geography or a Variant data type. Convert text-formatted WKT or GeoJSON data (column ‘abc’) into a geographic type using the Geography() or Variant() function.
For GeoJSON to render correctly:
The coordinates must be nested under a
geometrykey.Coordinates must follow the
[longitude, latitude]order, which is standard in both GeoJSON and Sigma’s geography type.
For this we are using D_GEOJSON_US_STATES dataset under PLUGS_ELECTRONICS in which state GeoJSON column is there which can be used to plot geographic map.
After selecting the explore option, we need to use the state GeoJSON column and create a new column using the Geography function as below:Â
State = Geography([State Geojson])
Now we need to create a child element from it and select the chart option.
In the child element’s chart properties, click the dropdown menu and select Map – Geography from the list. In the Geography property, click Add column and select an option from the menu:
To map objects from an existing column, search or scroll the Select geography/variant column list and select the column as State.
If the GeoJSON file does not have a geometry key, we can use the Sigma inbuilt function called CallVariant to prepend the geometry key. The syntax is as follows:
CallVariant("object_construct", "geometry", Json([Column with GeoJSON text]))
Best Practices
Here are some key best practices to consider:
Geospatial Data Types: Ensure your data contains proper geographic fields (latitude/longitude coordinates or standard geographic identifiers like country codes, state names, or postal codes
Choose the Right Map Type: Point maps for specific locations. Choropleth (filled) maps for regional data comparisons.
Set Proper Zoom Levels: Configure default zoom levels to show the most relevant geographic scope.
Closing
In summary, Maps in Sigma Computing unlock powerful geospatial insights, transforming raw location data into actionable visualizations. By following these best practices—from proper data preparation to thoughtful design and interactivity—you can create maps that enhance your analytics and drive better decision-making.
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FAQs
What is a map in data visualization?
Maps in data visualization transform location-based data into graphical formats, making it easier to identify patterns, trends, and insights tied to physical geography.
What are 3 uses of maps?
Geographic Trend Analysis – Maps reveal regional patterns in data, such as sales hotspots, customer density, or service gaps, helping businesses allocate resources effectively.
Location-Based Decision-Making – By visualizing data like store performance, delivery routes, or market penetration on a map, teams can make informed operational and strategic choices.
Spatial Relationships – Maps highlight correlations between geography and other variables, uncovering insights that tables or charts might miss.
