{"id":11978,"date":"2024-03-07T18:21:31","date_gmt":"2024-03-07T16:21:31","guid":{"rendered":"https:\/\/www.geopostcodes.com\/en-GB\/?p=11978"},"modified":"2026-04-15T12:13:31","modified_gmt":"2026-04-15T10:13:31","slug":"zip-code-map-power-bi","status":"publish","type":"post","link":"https:\/\/www.geopostcodes.com\/en-GB\/blog\/postcode-map-power-bi\/","title":{"rendered":"Power BI Postcode Map : How to create it? [Video Tutorial]"},"content":{"rendered":"\n

Power BI is a powerful tool for creating Business Intelligence dashboards. It can import data from various sources, from files to databases. Among the elements you can include in your dashboards are maps. <\/p>\n\n\n\n

This comes particularly handy to visualize your performance, capacity, or potential per region or review your territory mapping<\/a>, for instance.<\/p>\n\n\n\n

In this article, we will see how you can create maps in your Power BI dashboards. We will start with the possibilities the connection between Power BI and Bing maps offers. This enables you to build maps of fields representing geographical entities such as States, Provinces, or Cities. <\/p>\n\n\n\n

\ud83d\udca1 Use accurate data to create a postcode map. We offer the most comprehensive and up-to-date international postcode data for enterprises.\u00a0Browse GeoPostcodes datasets for free and download a sample here.<\/strong><\/a><\/p>\n\n\n\n

We will then analyze the limitations of that automated mapping: coverage, quality, user-friendliness, and control. To overcome them, we will finally see how you can import custom polygons<\/a> in Power BI. Step-by-step examples will illustrate all the points.<\/p>\n\n\n\n

If you’re looking to discover more ways to leverage Postcode data for enhanced business insights and efficiency, explore how to add geocoded Postcodes to Salesforce<\/a>, ensure accuracy with address validation in Microsoft Dynamics 365<\/a>, create impactful visuals by mapping Postcodes in Tableau<\/a>, and optimize logistics by calculating distances between Postcodes using Python<\/a>.<\/p>\n\n\n\n

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Leveraging the connection with Bing Maps<\/strong><\/h2>\n\n\n\n
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Power BI can handle different data types and categories. Most categories refer to a geographical entity (address, place, city, county, state or province, postal code, country, continent). <\/p>\n\n\n\n

When a data field is labeled as one of those categories, Power BI queries Bing Maps (which relies on TomTom) to retrieve its associated coordinates.<\/p>\n\n\n\n

There are essentially 2 types of Maps you can build that way with Power BI: punctual data (\u201dMap\u201d feature of Power BI) and polygons (\u201dFilled Map\u201d feature of Power BI, corresponding to what is technically referred to as a choropleth map). <\/p>\n\n\n\n

We will focus on the latter throughout this article.<\/p>\n\n\n\n

Let\u2019s illustrate that by showing the population per state in the USA. Follow these steps to set it up:<\/p>\n\n\n\n

Step 1:<\/strong> Download the population per state data from Census.gov<\/a>. <\/p>\n\n\n\n

Step 2: <\/strong>In a new Power BI project, Select \u201cGet Data -> Text\/CSV\u201d and point to the file you have just downloaded<\/p>\n\n\n\n

Step 3: <\/strong>Open the data view tab (1), select the \u201cStates\u201d field (2), and modify its data category to \u201cState or Province\u201d (3)<\/p>\n\n\n\n

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Step 4: <\/strong>Go back to the report view (4), select the \u201cFilled Map\u201d visualization (5), and drag your \u201cState\u201d column to the \u201cLocation\u201d well (6)<\/p>\n\n\n\n

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Step 5: <\/strong>Click on the \u201cFormat your visual\u201d menu (7), then expand the \u201cFill colors\u201d tab (8) and click on the \u201cfx\u201d icon (9). Choose \u201cgradient\u201d in format style and \u201cPopulation\u201d under \u201cWhat field should we base this on?\u201d and finally \u201cSum\u201d as \u201cSummarization\u201d. You can also play with the Lowest (13) and Highest (14) Value colors, and add a middle color (15), if you want.<\/p>\n\n\n\n

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Step 6: <\/strong>After clicking \u201cok\u201d to close the Default Colours tab, dropping the population to the tooltip dwell (16), and choosing \u201cSum\u201d as the aggregation method for the tooltip, your map should now look like the following one, displaying the State and population number when you hover any state.<\/p>\n\n\n\n

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Common Challenges Encountered in Power BI Postcode Mapping<\/strong><\/h2>\n\n\n\n
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The limitations of the embedded polygons in Power BI<\/strong><\/h3>\n\n\n\n
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Although at first sight this looks promising, and you may even find exactly what you need off the shelf, the approach suffers from several limitations:<\/p>\n\n\n\n

Obscurity<\/strong>: no list exists explaining which geographic levels are (or are not) available from Bing Maps. You need to discover it by yourself by trying and looking at the results. <\/p>\n\n\n\n

For instance, postal boundary polygons exist for the USA, Spain, and Belgium but not for Argentina. Furthermore, Power BI uses its own unified terminology to denote the administrative divisions (State or Province, County, Place), and it\u2019s not straightforward to understand which official level has been mapped to those Bing levels. <\/p>\n\n\n\n

For example, Spanish Provinces are mapped to \u201cCounties\u201d in Power BI, not \u201cState or Province.\u201d You will hence have to explore several combinations to determine whether Power BI does or does not provide the geographical data you are looking for.<\/p>\n\n\n\n

Connection<\/strong>: stating the obvious, as Power BI queries Bing Maps to retrieve location information, this only works when you have an internet connection. You can\u2019t work on your maps offline if they rely on the relationship with Bing.<\/p>\n\n\n\n

Confusions<\/strong>: several places, administrative divisions, or postal codes can have the same name or values in different parts of the world. For names, there can even be several instances within the same country (for instance, there are 7 towns named \u201cBoston\u201d in the USA). <\/p>\n\n\n\n

You will hence have to encode some form of hierarchy to help Bing narrow down its search to the target locations. This is illustrated in the figure below, which is meant to display the population per county in Washington state, USA. <\/p>\n\n\n\n

If we only use the counties as geographical key, Bing Maps use the homonyms from other regions of the world for certain counties (for example, \u201cClark\u201d county shows the county with the same name in Nevada, while \u201cLincoln\u201d even points to its homonym in Great Britain). <\/p>\n\n\n\n

You need to encode the geographical hierarchy (country, state, county) for Bing Maps to retrieve the correct polygons<\/a>.<\/p>\n\n\n\n

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This can prove complicated and at least diminishes the solution’s user-friendliness: you can\u2019t just plug your source address data<\/a> in and see it appear on the Power BI map; you have to tweak the mapping to create a useful map. Note that an alternative solution to help Bing identify your targets is to provide their coordinates<\/a>.<\/p>\n\n\n\n

But if you have all the coordinates, you better directly leverage them through custom maps (following the steps explained later in this article), without invoking Bing.<\/p>\n\n\n\n

Slowliness<\/strong>: pinging Bing servers to geocode<\/a> every point in your data is time-consuming. Likely, Power BI will rapidly become unresponsive, leading to frustrating idle periods before being able to resume your work. Power BI also offers an alternative to Filled Maps, called Shape Maps, in preview mode. <\/p>\n\n\n\n

You need to use this map to import custom maps (see in the next chapter). It also includes the top administrative level for 12 countries (Australia, Austria, Brazil, Canada, France, Germany, Ireland, Italy, Mexico, Netherlands, United Kingdom, United States). In that case, Bing is not queried.<\/p>\n\n\n\n

Variable quality<\/strong>: even when a layer is available in Power BI, it may be poor quality. For instance, postal boundaries<\/a> are available for Spain, but as shown in the figure below, there are many holes and overlaps in the Granada Province. <\/p>\n\n\n\n

Unfortunately, we have faced similar issues when building choropleth maps based on postal polygons in many areas we have explored (e.g., New York, Belgium), etc.<\/p>\n\n\n\n

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Lack of control<\/strong>: even if you manage to set up your custom map, initially, you will probably face issues down the road as you have no control over the geographical source. <\/p>\n\n\n\n

If Bing changes its data, it may break your dashboard. You will be forced to work with outdated geographic areas if it does not. If you have new data points, you have no guarantee that Power BI will properly capture them. <\/p>\n\n\n\n

And if you\u2019re not happy with some polygons rendered by Power BI (because they are outdated or maybe because you would like to introduce some custom groupings to reflect your own operations), you just can\u2019t do anything about it.<\/p>\n\n\n\n

Limitations in number of rows<\/strong>: Shape Maps are limited to 1,500 datapoints, Maps 3,500, and Filled Maps 1,000. Azure Maps has a maximum of 30,000 data points. <\/p>\n\n\n\n

Beyond those limits, you must divide and conquer: create different dashboards\/views for high-level insights (e.g., country, states) and granular analyses (e.g., counties or postcodes within one state). This is possible thanks to the geo-hierarchy feature explained below.<\/p>\n\n\n\n

Unknown cost<\/strong>. Geocoding through the Power BI service is free for the moment, but it looks like that will change with Azure Maps, and it could become quite expensive (4,5$ for 1,000 geocoding requests)<\/p>\n\n\n\n

Expertise needed<\/strong>: as explained above, the data pulled from Bing Maps is sometimes erroneous or inaccurate. But you won\u2019t know unless you have some expertise in the field. And if you find something odd, you can\u2019t just ask Microsoft to explain or release a quick patch.<\/p>\n\n\n\n

No automatic aggregation<\/strong>: frequently, you will have data at a very granular level (e.g., addresses, postal codes, or localities), and you will want to show aggregations at higher levels (e.g., revenue per County). <\/p>\n\n\n\n

Power BI does not offer any intermediary data to aggregate your granular data. Although this is not inherently a drawback of the Power BI embedded polygons, it\u2019s important to remember that aspect when you start your project.<\/p>\n\n\n\n

The solution to all those issues is to push your geographical data to Power BI. Let\u2019s see how you can do that in a couple of clicks.<\/p>\n\n\n\n

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Importing custom polygons in Power BI<\/strong><\/h3>\n\n\n\n
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Let\u2019s now see how to import your polygons to Power BI to create choropleth maps you have full control over. To do so, you must have your polygons\/boundaries<\/a> data in topojson format. <\/p>\n\n\n\n

It\u2019s the only file format accepted by Power BI for custom Shape Maps. Tools exist to convert other geographical file formats to topojson, like MapShaper.<\/p>\n\n\n\n

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Linking geometries to other data<\/h4>\n\n\n\n
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The way Power BI links polygon data to other uploaded datasets is a bit obscure. It seems it can use keys with the same column name in the topojson polygon file (as a variable attribute) and the rest of your data.<\/p>\n\n\n\n

As we will see in the examples below, it indeed works when you want to link postcode polygons, with an attribute called \u201cpostcode\u201d, to a table containing population data per postcode<\/a> (also called \u201cpostcode\u201d). <\/p>\n\n\n\n

It also works when you have \u201cID\u201d fields in both your polygon file and your value file. Unfortunately, we have not seen the possibility of actually deciding which keys to involve in the join.<\/p>\n\n\n\n

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Step-by-step example: population per postcode in South Korea<\/h4>\n\n\n\n
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In the following video, I will teach you how to create a Choropleth map showing the population per postcode in Busanjin County, South Korea.<\/p>\n\n\n\n

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