About preparing your data for mapping

MapPoint North America 2004 SDK

When you map your data, MapPoint interprets the information contained in each of the fields or columns, and then uses any location information that it identifies to place your data on the map. Formatting your data correctly will help MapPoint match each of the records in your data set to the map quickly and easily. Here are some things to keep in mind when formatting your data:

Organization. Your data must be consistently organized. For instance, if you are importing a set of addresses, check that the city name for each record is always in the same column or field.

Column headings. The more meaningful the headings are for each column or field in your data the better. MapPoint can recognize certain headings such as Name, Address, City, and ZIP Code. Using headings such as these helps MapPoint identify your data correctly.

Location data. If your data contains different kinds of location information (for instance, a mixture of records with complete addresses and records with only ZIP Codes), although MapPoint will try to match the mixed records, it is best to create different files for each type of record (one for records with complete addresses and one for those with ZIP Codes), and then import or link to each file separately.

Address format. Instead of having an entire address in a single column or field, create individual columns or fields for each class of information (such as Street Address, City, ZIP Code, State, Country, and so on).

Clearly define location information. For example, if you are going to match records to Counties or Census Districts, include a State column. The same name can exist in numerous states, so by including the state, you are minimizing the number of records that will need to be disambiguated (matched to the map).

Multi-country data. If you are including records from more than one country, make sure that your data contains one column or field containing all the different country names that pertain to your records.

Unique identifier. If you are linking to data to map it, each record in your data set must have a unique ID to be used as the primary key. The primary key is used to uniquely identify each record so that your data can be updated even if the name of the record changes. The primary key can be a customer ID or other piece of unique information. If your data does not contain a primary key or a unique field, you can easily create one by adding a field or column to your data and inserting a unique number for each record. You can quickly do this by using the Autofill feature in Microsoft Excel, or by formatting a field as AutoNumber in Microsoft Access. It is best to avoid 5-digit primary keys because MapPoint will identify these as ZIP Codes.

Tip  It is recommended that you structure your data source to have the primary key be the first column or data field.

Separator characters. If your data consists of a text file (*.txt, *.csv, *.asc, or *.tab format), use a tab, semicolon, or comma as a separator character (also called delimiter) to distinguish each field of your data.

Currency format. When importing data from a Microsoft Excel spreadsheet with currency-formatted cells, make sure that the entire column, not just individual cells, is formatted as currency.

Hyperlinks. If you want to enable hyperlinks after you created your map, make sure that you include a column that includes hyperlink information and set this column to <Other Data>.

Related topics

About supported data sources

Create data maps

About using hyperlinks in MapPoint

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