Best practices for storing temporal data
Based on your needs, temporal data can be stored in a variety of ways. Here are some best practices that you can follow when storing your temporal data for use in ArcGIS.
Store temporal data in a row format
For using temporal data in ArcGIS, you should store the time values associated with individual features in a row format. Each feature or row in a table can have either time values in one field representing a time instant or time values in two fields representing the beginning and end of the observation.
Depending on whether the attributes of your data change over time or the shape of each feature changes over time, you can choose to store your temporal data in a single table or in multiple tables.
Often you'll have time represented in columns in your attribute table, for instance, medical costs per county for 1990, 1991, and 1992. To visualize this data through time, the table must be reformatted such that the time values are in row format.
Store time values in a date field
It is recommended that you store the time values of your temporal data in a date field. This is a special database field type specifically for storing time and date information. It is most efficient for query performance and supports more sophisticated database queries than when storing time in a numeric or string field.
Depending on your needs, you can also store the time values in your data in string or numeric fields. For example, yearly data can be stored as 2000, 2001, and so on. For such cases, you should store your data using one of the supported formats.
You can choose to use the Convert Time Field geoprocessing tool to convert a string or numeric field containing time values into a date field.
Index fields that contain time values
For improving time visualization and query performance, it is recommended that you index the fields containing the time values.
Use standard time
For temporal data collected in regions where time is adjusted for daylight saving, you should try and store the time values in your data in standard time. Data collected with daylight saving time can be hard to maintain. Daylight saving can vary from region to region, and the rules defining the daylight saving adjustments can change over time.
Storing the time values in standard time prevents any loss or overlaps of data during data compilation and allows time visualization during the transition hours without any ambiguity.