Customized map applications with data-driven styling

In the summer of 2022, Google Maps Platform enabled the customization of polygons, lines and points for the first time, and since the summer of 2023 it has also been possible to import, save and manage your own datasets via the Maps Datasets API. We developed two demos for Google Maps Platform that present the many possibilities of the new features.

Data-driven Styling for Boundaries

While developing our first data-driven styling demo for Google, we were able to get a good overview of the new functions. The most important new features are summarized here:

• Administrative area polygons are now available: Administrative areas can be selected, colored and clicked on the map. The Data-driven styling feature provides access to the same boundary polygons that appear in Google Maps.
• Detailed administrative areas: Country boundaries for most of the world are available and in selected countries additional administrative boundary types such as city districts and postal code areas are available.
• Customizable design of the map style: The administrative boundaries can be individually restyled - from the line width to the transparency. The labels on the map are never covered, although the administrative areas are the second data level above the basemap.
• Enhanced user experiences: By assigning polygons to the Places ID, areas can be combined with other data. In order for users to easily see whether an entry (e.g. an apartment) is located in a certain area, polygons around the searched entry provide further context for orientation.
• Interactive elements: Users can click on interactive areas on the map and the API provides additional information like name and PlaceID that can be used for display.
• Choropleth display: Administrative areas can be colored based on your own datasets. For example, postcode areas can be colored according to sales figures or federal states according to house prices.

Data-driven Styling for Datasets

In the summer of 2023, we were asked to build a second demo for the new features of data-driven styling for datasets, which enables users to style their own data or open data individually. For this demo, we used data from Seattle Open Data. We took a hard look at the new possibilities and compiled a list of the most important features:

• With the Maps Datasets API, your own geospatial data can be imported, stored and managed server-side can import, store and manage your own geodata on the server side. By using the Google Cloud Console, infrastructure costs can be reduced, data access can be managed and geodata can be updated anytime and anywhere.
• Geospatial data can be visualized in GeoJSON, CSV and KML formats.
• Data-driven styling for datasets can be connected to Google's boundaries. Users see the context around a specific point, from country boundaries to zip code areas.

Use cases of Data-driven styling

The ability to highlight geographical boundaries on a map was a long-awaited feature from our customers. It enables many new use cases where you previously had to worry about challenging technical aspects and costly data acquisition. Google Maps Platform is a great source here, because it provides high-quality polygons.

Data-driven styling has use cases in various industries such as retail, financial services, government, non-profit, education, media, tourism or real estate. Every company that wants to collect, analyze or output data on country, city or even district levels has a use for it. Whether it's hotel groups or real estate companies - data visualization based on different geographic areas - from country to district - offers many opportunities for great user experiences.

• Real estate portals can display the area that apartments are located in and highlight the district accordingly. The choropleth display is suitable for displaying the output of internal analyses, such as the coloring of postcode areas according to sales revenue. From logistics to tourism - with Data-driven styling, geo-based information can be visualized quickly and easily.
• In retail, choropleth maps can be created that use data sets to style different country or city boundaries based on foot traffic, land sales and much more.
• The media industry can use data-driven styling to create visualizations that transform their own data and Google's data into exciting visual stories and thus attract users to their own platforms.

Learn how to get started

If you want to learn more about Data-driven styling, just let us know how we can help.