Our Basemap Service
At Clockwork Micro we have long been surprised that there are so few providers of basemaps. In particular, there are not many basemaps offered by small geospatial companies. Google Maps dominated the field for many years, and now Mapbox is a common alternative. Other alternatives include ESRI, Bing and HERE, but these are all very large providers. Apple and Meta have also recently made their own maps.
We’ve wanted built our own basemap for a long time, and are excited to release the first version. Our goal is to make the most intuitive, easy-to-understand and aesthetically pleasing map possible. We think our first version is a very good start, and we’ll continue to work on it in the years to come.
We ask potential users, especially small businesses like ours, to have a look and use the map as part of your service. Tell us what you think and share your ideas for improvements.
How The Basemap Service Works
The covers the entire globe. The map may be embedded using a URL to offer a dynamic map that a user may scroll and pan. Alternatively, a software customer may request the individual tiles in either raster or vector format and incorporate them into another map. For example, a user building a map with Leaflet can call the raster endpoint and use the Clockwork Micro map within Leaflet.
Why Choose Clockwork Micro
Why use the Clockwork Micro map? Because it’s beautiful and built by a small team that wants to improve and expand the map. Our basemap loads fast and is less expensive than the typical large providers. You can also help us improve our basemap service by using it and sharing your feedback.
Case Studies / Examples of Uses
HowLoud is an environmental noise map of the United States. It tells users how loud various sources of environmental noise are, such as vehicle or air traffic. HowLoud’s data is viewed millions of times a day through embeddings on partner websites, such as real estate websites. HowLoud is one of the first users of the Clockwork Micro basemap. The Clockwork Micro basemap has the amount of detail that allows viewers to get a picture of a neighborhood or recognize a location without being cluttered with too many details. That puts HowLoud’s proprietary data in context and makes it easy to understand.