Excel Spreadsheet Column Headers
Map Data from Excel with the Right Column Headers
When you create a map from Excel data, we need to be able to recognize that data. Therefore, we need you to get the right column headers. This article explains how to format your data so that it comes into Mapline correctly.
How to Create a Map with Excel Data Successfully
The first row of the data that you paste into Mapline should contain the titles of each column. These are the “headers.” You can have as many columns of data as you like, however, there are certain ones which we need in order to be able to recognize your addresses and plot the locations correctly. The following headers are recognized by Mapline:
This is the column with the name of your location. When you hover over a location in the map, the Name will appear to let you know which location you are viewing. This is not a required field, but it is a key field we look for.
This is the address of your location. This can either contain the full address (street, city, state, postal code), or this my just contain the street.
This is the city of your location.
This is the state or providence of your location.
Zip Code or Postal Code
This is the postal code or zip code of your location. This is a semi-required field.
Latitude and Longitude
You can also have columns for Latitude and Longitude values. This will speed up the processing of your locations, but don’t worry if you don’t have these as long as you have other address columns. If you do include latitude and longitude coordinates, be sure that they’re in decimal format, i.e., 35.5486465, -52.8976423, so that Mapline can read them properly.
Mapline requires that you have AT LEAST ONE column with address-type information (Address, City, State, or Postal Code). To reduce any issues with your data, label the appropriate columns with the header names listed above. We have tried to anticipate any variations you may have to these key headers listed above.
Ready to create your map? Discover Mapline’s features and get started analyzing your data.