Session 07: Spatial Analytics

Working with Geographic Data in Tableau

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Spatial Analytics

Spatial analytics focuses on analyzing data that contains a geographic component.

Unlike traditional visualizations, spatial analysis allows us to understand how data behaves in relation to location, distance, and geographic structures. It enables answering questions such as where events occur, how locations interact, how movement happens across space, and how metrics vary across regions.

Maps are powerful because they allow patterns to be understood visually. They are especially useful when geography is not just a visual element, but a key part of the analysis.

Geographic Data Formats

Geographic data can exist in multiple formats, and understanding these formats is essential for correct analysis.

Spatial files such as Shapefile (.shp), GeoJSON, KML
Flat files such as Excel or CSV
Databases with spatial or location-based fields

Spatial files store both:

Geometry → the shape (point, line, polygon)
Attributes → descriptive data

When a spatial file is connected, Tableau automatically creates a Geometry field, which can be directly used for mapping.

Location-based data (like City, Country, Latitude, Longitude) does not contain shapes. Tableau uses geocoding to convert these values into map coordinates.

Dataset for Spatial Analysis

In this session, we use:

Citi Bike dataset (trip-level data with coordinates)
NYC District GeoJSON file (polygon-level data)

The Citi Bike dataset contains:

Start and End coordinates
Time information
Station details

Since the dataset is split across multiple CSV files, we combine them using Union.

The GeoJSON dataset contains:

District boundaries (MultiPolygon)
Geographic shapes for mapping

Spatial Relationships

We have already seen how spacial relationships work during the SQL and Python sessions. Spatial relationships are used when datasets do not share a common key but still need to be analyzed together.

Steps to Create Spatial Relationship

Load the Citi Bike dataset
Combine all CSV files using Union
Add the GeoJSON file to the data model
Create a calculated field in both datasets:

'New York'

Use this field to create a relationship between the datasets

This approach allows Tableau to connect the datasets logically without physically joining them, preserving flexibility and avoiding duplication.

Now we have a working relationship between two tables.

Spatial Joins

Spatial joins combine datasets based on geographic relationships rather than keys.

The most common spatial join is:

INTERSECTS → checks if two geometries overlap

Steps to Create Spatial Join:

In the data source tab, switch to the physical layer
Add the Citi Bike dataset
Create calculated fields:

MAKEPOINT([Start Lat], [Start Lng])

MAKEPOINT([End Lat], [End Lng])

Drag the GeoJSON dataset next to the Citi Bike dataset
Choose Left Join
Set join condition:
- Spatial field (point) INTERSECTS polygon geometry

This will match each trip to a district based on location.

Troubleshooting Spatial Joins

Common issue:

Geometry is incompatible with geography

Solution:

Convert geometry to geography
Use coordinate system EPSG:4326
Ensure coordinates follow Longitude, Latitude order

Spatial Functions

Spatial functions enable advanced geographic calculations directly within Tableau. They allow you to create, transform, and analyze spatial objects such as points, lines, and polygons. These functions are especially important when working with coordinate-based datasets, as they convert raw latitude and longitude values into map-ready objects and allow analytical operations such as distance measurement, movement analysis, and spatial comparison.

Spatial functions are commonly used together with mapping and spatial joins. They help bridge the gap between raw data and geographic insight, making it possible to answer questions about proximity, interaction, and movement.

Common Use Cases

Converting latitude and longitude into spatial points
Visualizing movement between two locations
Measuring distance between locations
Creating service or coverage areas
Identifying overlapping or interacting regions

Examples

Creating a spatial point from coordinates:

MAKEPOINT([Latitude], [Longitude])

Visualizing movement between two locations:

MAKELINE(
    MAKEPOINT([Start Lat], [Start Lng]),
    MAKEPOINT([End Lat], [End Lng])
)

Calculating distance between two points:

DISTANCE(
    MAKEPOINT([Start Lat], [Start Lng]),
    MAKEPOINT([End Lat], [End Lng]),
    'km'
)

Spatial Functions Summary

Function	Description	Typical Use Case
MAKEPOINT	Converts latitude and longitude columns into a spatial point.	Enabling spatial joins for coordinate-based datasets.
MAKELINE	Creates a line between two spatial points.	Origin–destination maps, mobility analysis, route visualization.
DISTANCE	Calculates the distance between two spatial points using specified units.	Nearest branch analysis, trip distance calculation, proximity analysis.
AREA	Returns the total surface area of a spatial polygon.	Territory size comparison, land coverage analysis.
LENGTH	Returns the total geodetic length of a linestring geometry.	Route length measurement, infrastructure analysis.
BUFFER	Creates a radius around a point, line, or polygon.	Service coverage zones, delivery radius modeling, proximity analysis.
INTERSECTS	Returns True or False indicating whether two geometries overlap.	Spatial joins, containment checks.
INTERSECTION	Returns the overlapping portion between two geometries.	Market overlap analysis, shared service area evaluation.
DIFFERENCE	Subtracts the overlapping area of one polygon from another.	Identifying uncovered or restricted areas.
SYMDIFFERENCE	Removes overlapping portions from both geometries and returns the remaining parts.	Territory comparison and competitive analysis.
OUTLINE	Converts polygon geometry into boundary lines.	Styling borders separately from polygon fill.
SHAPETYPE	Returns the geometry structure as text (Point, Polygon, etc.).	Debugging spatial data issues.
VALIDATE	Confirms whether spatial geometry is topologically correct.	Cleaning corrupted spatial files and preventing join errors.

Geographic Data Visualization

Mapping in Tableau is not only about placing marks on a geographic background.

It is a combination of:

Data modeling
Geocoding
Aggregation logic
Visual design

For a map to function correctly both analytically and visually, we need four foundational components to be configured properly:

Data Type
Data Role
Geographic Role
Geographic Hierarchy

If any of these elements are misconfigured, you may encounter:

Unknown locations
Incorrect aggregation
Missing map rendering
Broken drill-down behavior
Spatial joins that do not work as expected

Data Type | Structural Foundation**

The Data Type determines how Tableau stores and interprets the raw values.

This is the first layer of configuration.

Common Geographic Data Types

Field Type	Required Data Type	Why
Latitude	Number (Decimal)	Must allow precise coordinate plotting
Longitude	Number (Decimal)	Must allow precise coordinate plotting
Country/State/City	String	Needed for geocoding
Postal Code	String	Preserves leading zeros
Geometry (GeoJSON/Shapefile)	Geometry	Native spatial object

Incorrect data types can cause:

Aggregation errors
Loss of leading zeros (postal codes)
Tableau not recognizing geographic information
Failure in map rendering

If Postal Code is stored as Number:

01234 becomes 1234
Geocoding fails

Data Role | Analytical Behavior

The Data Role defines how Tableau treats the field in analysis.

Two primary roles:

Dimension → categorical grouping
Measure → numeric aggregation

Typical Configuration for Mapping

Field	Data Role
Latitude	Measure
Longitude	Measure
Country	Dimension
State	Dimension
City	Dimension
Geometry	Measure

If Latitude/Longitude are set as Dimensions:

Points may not render correctly
Aggregation logic may break

Geographic Role | Geocoding Layer

The Geographic Role connects a field to Tableau’s geocoding engine.

“This field represents a real-world geographic level.”

Common Geographic Roles:

Country/Region
State/Province
County
City
Postal Code
Latitude
Longitude

Once a geographic role is assigned:

Tableau generates Latitude (generated)
Tableau generates Longitude (generated)

These generated fields are automatically used for plotting.

How Tableau Geocoding Works

When using location names:

Tableau references its internal geographic database
Matches names to coordinates
Places marks accordingly

If Tableau cannot match values, you will see:

Unknown locations warning

To resolve:

Click the warning icon
Edit locations
Specify country context
Correct spelling inconsistencies

Geographic Hierarchy | Drill-Down Structure

A Hierarchy defines the logical order of geographic levels.

Country \(\rightarrow\) State \(\rightarrow\) City \(\rightarrow\) Postal Code

Hierarchies allow:

Drill-down navigation
Controlled aggregation
Structured geographic exploration

How to Create a Hierarchy

Right-click a geographic field (e.g., Country)
Select Hierarchy → Create Hierarchy
Drag lower levels into it

Benefits

Enables + / − drill controls
Maintains geographic logic
Improves dashboard interactivity

Mapping

Mapping with Raw Coordinates

If your dataset contains Latitude and Longitude:

Required Configuration:

Data Type → Number (Decimal)
Data Role → Measure
Geographic Role → Latitude / Longitude

Validation Rules:

Longitude range: -180 to 180
Latitude range: -90 to 90
Coordinates must be decimal degrees

Important

Longitude always goes to: Columns (X-axis)
Latitude always goes to: Rows (Y-axis)

If properly configured:

Tableau plots points automatically
No internal geocoding is required

Mapping with Location Names

If your dataset contains names instead of coordinates:

Required Configuration

Data Type → String
Data Role → Dimension
Geographic Role → Appropriate geographic level

Tableau converts names into coordinates using geocoding. In some cases, you may need to specify the country context to resolve ambiguities.

Mapping with Spatial Files (GeoJSON / Shapefile)

Spatial files contain embedded geometry objects.

When imported:

Field Type → Geometry
Data Role → Measure

Characteristics:

Coordinates are embedded
No geocoding required
Supports polygon and line rendering

Enables:

Choropleth maps
Boundary overlays
Spatial joins
Spatial calculations

Map Styles

Once configuration is correct, map styling enhances interpretability.

Background Map Styles are:

Light
Normal
Streets
Satellite

Choose style based on:

Analytical clarity
Contrast with marks
Density visualization

Map Layers

Tableau allows multiple layers:

Polygon layer (district boundaries)
Point layer (stations)
Line layer (routes)

Multi-layer maps enable:

Territory + event visualization
Origin–destination flows
Hotspot analysis

Each layer can have:

Independent mark type
Independent color
Independent size

Basic Map Creation

Now let’s create a simple map showing the distribution of the trips across New York city using the geometry field from the spatial file.

Step 1

As we do not have a column with states we will make a calculated field 'New York' and assign it as a geographic role with the level of State/Province. This will allow us to use the geometry field from the spatial file to plot the map of New York city.

Step 2

Now we can make a hierarchy by right clicking on the newly created field, [State] and choosing Hierarchy → Create Hierarchy and then dragging the [Boroname] field (NYC borough) to the hierarchy. This will allow us to drill down from the state level to the geometry level and see the different districts of New York city which are available in our spatial file.

Step 3

In order to count rides by district, we will drag the Ride ID field to the view and change the aggregation to Count. To show the distribution we can place the Count of Ride ID on the color mark and we will have a choropleth map showing the distribution of the rides across the different districts of New York city.

Step 4

To make districts more visible drag and drop [Boroname] field to the label mark and we will have the name of the district on the map.

Step 5

To make the map more readable, we can also change the background map style by right clicking on the map and choosing Background Layers and then choosing the style that we like. In this case, we will choose the Light style to make the districts more visible and add Background Map Layer by ticked prefferences such as Land Cover and Labels to make the map more informative.

Advanced Map Creation

Step 1

As we have already done the join between the spatial file and the tabular file, we can now build a map using the geometry field from the spatial file. To do that:

First make sure that the fields with geographic roles are correctly assigned
Double click on the geometry field and it will be added to the view

Tableau automatically:

Adds Latitude (generated) to Rows
Adds Longitude (generated) to Columns
Places Geometry on the Marks card, Details

The result is a map of New York City with its districts.

Step 2

Now we can add the trips data to the map. To do that let’s create calculated fields for the start and end locations of the trips using MAKEPOINT() function as explained in the spatial joins section. Then we can add these calculated fields to the view to show the trip start and end locations on the map.

Step 3

Having the trip start and end locations we can now make a flow map to show the movement of the trips between the start and end locations. To do that we will use MAKELINE() function to create a line between the start and end points of each trip. Then we can add this line to the view to visualize the flow of trips across the city.

Step 4

In map visualizations, we can use different geometry types adding map layers to show different aspects of the data. For example, we can draw routes to the polygon layer by dragging the line geometry to the view and we can also show the start and end points of the trips by dragging the point geometry to the view. This allows us to create a multi-layered map that shows both the routes and the locations of trip starts and ends.

Proportional Symbol Map

Proportional symbol maps use sized marks (usually circles) to represent the magnitude of a measure at specific locations. The size of each mark is proportional to the value it represents.

Step 1

As in previous examples, we will start by creating a map using the geometry field [Boroname] from the spatial file to show the districts of New York city.

Step 2

Add [Starting point] to the view to show the start locations of the trips on the map.

Step 3

Now we can add the Count of Ride ID to the size mark to show the number of trips starting at each location. This will create a proportional symbol map where the size of each mark corresponds to the number of trips starting at that location.

Step 4

Add the Count of Ride ID to the color mark to show the distribution of the trips across the city. From color marks activate borders.This will allow us to quickly identify areas where there are more trips starting based on the color intensity of the marks on the map.

Step 5

Add [Starting Point ID] to the detail mark to show the individual starting points of the trips on the map.

Step 6

From the Marks card, we can also change the mark type to Circle and adjust the size and color to make the map more visually appealing and easier to interpret. This will allow us to quickly identify areas with high trip activity based on the size of the circles on the map.

Step 7

Add [Boroname] to the filter shelf for interactive filtering by district. This will allow users to select specific districts and see the corresponding trip data on the map, enabling more detailed analysis of trip patterns within different areas of New York City.

This analysis can help identify which districts have the highest demand for Citi Bike trips, and can inform decisions about where to add more bike stations or increase bike availability.

Density Map

Density maps use color intensity to represent the concentration of points in a given area. They are useful for visualizing patterns of activity across a geographic space.

Step 1

Bring [Make route] calculated field, [Start Station ID] and [End Station ID] to the view to show the routes of the trips on the map.

Step 2

Change the mark type to Density to create a density map that shows the concentration of trips across the city. The color intensity will indicate areas with higher or lower trip activity.

Step 3

Adjust map style to Streets to make the trips density patterns more visible on the street map background. This will allow us to better understand the spatial distribution of trips in relation to the city’s street layout.

Step 4

Make density color intensity and opacity adjustments to enhance the visibility of high-density areas. This will help us quickly identify hotspots of trip activity across New York City.

This type of analysis can be useful for understanding where the highest demand for Citi Bike trips is located, and can inform decisions about where to focus resources for bike station placement or maintenance.

Step 5

Untick Aggregate Measures on the top pane Analysis section to show the individual trip routes on the map. Density maps calculate intensity based on the number of marks in a geographic area, so if we want to see the actual routes of the trips, we need to turn off aggregation.

Analysis → Aggregate Measures in Maps

ON (default) → Measures are summarized (SUM, AVG, etc.).
Use for choropleth maps, KPIs by region, and territory comparison.
OFF → Each row becomes an individual mark.
Use for point distribution maps, density maps, and event-level analysis.

Rule of thumb:
Use aggregation for regional summaries.
Turn it off for raw spatial events and clustering analysis.

Resources

Github Repo

Tableau Course Code Repository for this session is available in the GitHub repository linked above. It includes:

Tableau workbook with all examples
Sample datasets