April 22, 2013 -- Fortunately, and unfortunately, humans are creatures of habit. The same can be said for all of us when using GIS data. The common practice is to use what we always use. It seems like the safe choice and, (historically) in some cases, has been the only choice.
Things have changed... today, there's a big difference in GIS data provided by the different data vendors. The big difference is not usually the quality or accuracy, as just about everyone has and uses the same sources. The difference is price, quantity of layers, delivery and ease of implementation.
Take land grid data as an example. Does the land grid data you use provide you the full picture - including lots and quarters, or does your land grid provider give you just the sections and townships? It's a little like getting the hot dog without the bun. Imagine if that hot dog vendor then told you it costs an extra $4 for the bun (sections), and $2 for the ketchup (quarters). Your expectation should be to get the complete hot dog... you should expect to get the complete land grid.
Other important questions you have probably already experienced include: Is it easy to implement into company projects? Is it seamless and contiguous? How much does it cost your organization? Is it leased or owned? How is it delivered?
As a side note, accuracy is important. The beauty of technology, such as streaming topographic maps and streaming imagery have made this a must do, but easier task. As long as you know the source of your data (source: "the original" from which the data was derived eg. USGS 1:24k topos) you have the tools to make informed decisions based on the science behind GIS. Texas land grid can be a different animal, but the principles are the same. In Texas, some vendors use a proprietary land grid (not tied to a legal source but based off imagery), while others use the government regulated RRC and the GLO as their source.
The same principles hold true for other datasets, such as oil and gas wells and tax parcels.
Tax parcels tend to be based off of imagery, with the original source (usually) being the county tax assessor's office. Variance between data vendors tends to be very, very minimal.
Oil and gas wells, however, vary vastly from vendor to vendor. Luckily, like land grid, there are key things to look for when determining the quality and completeness of your oil and gas wells. Imagery is a great place to check accuracy (most oil and gas wells are spotted based from footage calls). Much like the hot dog analogy earlier, just like land grid, you need to ask whether you have the complete picture (well headers, bottom holes, formations, directionals, etc)? And lastly, does it follow a consistent data model (consistent attribute or field data) across the different states?
There should be no mystery behind your spatial data. If you remember the science behind that point or polygon, you will always be able to check the source and accuracy. Apart from possibly saving you large amounts of money from bad habits, it provides the backbone to accurate and complete map projects. The best part is that you will have confidence in your data, and most importantly, your maps.
Below are some tips you can use to make sure you are getting the full picture from your GIS data:
- Check the accuracy using streaming USGS topos (+-40 feet accuracy, this is what most vendors use in the PLSS states)
- Check the accuracy using streaming imagery
- Are you getting all the layers (lots, surveys, quarters, etc - headers, bottom holes, formations, etc)
- Is it seamless and contiguous (run a dissolve across the states for polygons)
- When was it last updated
- Does it have a common data model within its own dataset and across the other related datasets
- Is it easy to integrate
- Don't be fooled by claims that one is better than the other - compare each to the source
There are alternatives.
Some important things to remember:
- A ground survey is always more accurate than digital data provided by data vendors. This type of data should always be treated as the primary
- Some data vendors lease their data. Once subscription is cancelled, they may ask you to remove the data
- Most subscription datasets are set on auto-renew. Don't be caught out paying for something you don't need