Notice these columns with abbreviations that seem very specific to this dataset
and a few values that we might recognize, but mostly quite a lot of names and
values that won't be understandable without some additional information
explaining what they mean.
To find out, we can look inside the metadata by going to the item description
section, accessible by right clicking on a layer in the table of contents.
And a window will pop up describing the data set, its sources, its fields and
values, and so on.
So long as the author filled that information in.
Now let's talk for a moment about what good metadata should contain.
In general, metadata should include any information
required to effectively interpret or use the data.
To be more specific, metadata should describe the data sources or
the data collection instruments.
So if I'm collecting points on a GPS, I should put in the metadata the model
of the GPS that collected it and the date and time the data was collected,
as well as the person who collected the data, at the very least.
I should also describe important individuals connected to this data in
the contact information, so if someone who receives this dataset has a question,
they know who to reach out to for an answer.
Metadata should also define any fields in the data and their potential values and
what they mean.
Abbreviations should be well-defined, as well as anything specific or
proprietary to this dataset.
Again, metadata should include any information required
to effectively interpret or use the data.
That doesn't mean just the list of information I just gave you,
because your project may be different and
have other information necessary to interpret or use your data.
As part of this, your metadata should also describe
the actual meaning of the data in addition to the basics of the data.
In the end, you're really trying to help someone else or even yourself
in the future after you've forgotten the answers to work with the data and
interpret the data.
You should record in your metadata anything necessary to make that possible.
One important aspect of this is your choice of metadata type.
There are numerous standards that you can use for metadata.
The basic one that ArcGIS uses by default has just a few options for
what to fill in that allow you to describe your data.
That's fine, but it's often easy to forget important things when using that format,
because it doesn't really guide you through the process very much.
Instead I like to use the FGDC CSDGM metadata format.
I know that's a mouthful.
FGDC stands for Federal Geographic Data Committee, and
they are a group responsible for
coordinating all things spatial in the Federal Government of the United States.
The metadata standard described by the FGDC is robust and explicit.
It forces me to fill in all the important information I need in order to have
complete metadata.
To switch ArcGIS into this mode, I need to go into the options area,
either in ARC map or ARC catalog.
If I go to the metadata tab, I can select the metadata style from the drop down.
The default is item description, but I'll select FDGC CSDGM now.
If I go edit a dataset's metadata now, I get numerous options for
information that could be added to my data.
I can include citations, topics and keywords, use constraints,
points of contact, information about data quality and
lineage, explicit information about the fields and the dataset and their values.
And ArcGIS will even attach a history of geoprocessing for
me, to help provide context for how the dataset was created.
Just like you would expect based on things like photos,
some information is automatically populated in your metadata.
Basic information about your fields,
your projection, the extent of the data, etc, are all populated
automatically based upon the spatial information that ArcGIS can read for you.