PLSS CadNSDI - Metadata at a Glance

This is the second of a series of documents that describe the contents of the PLSS CadNSDI data set.

The Metadata at a Glance is an unlikely first feature class to select to use but the information in this feature class will rapidly show the data stewards and currency of features in the rest of the data set.  As the PLSS CadNSDI expands and more locally sourced control and divisions are incorporated into the data set, there will be increasing numbers of contributors.  This is the feature class that can be used to quickly and easily see updates. 

The Metadata at a Glance is a geographic representation of the data stewards and currency or vintage.  The initial or baseline data set is based on PLSS Townships in PLSS areas. In non-PLSS areas the metadata at a glance is based on a data steward defined polygons such as a city or county or other units. The identification of the data steward is a general indication of the agency that will be responsible for updates and providing the authoritative data sources. In the shared environment of the NSDI the data steward for an area is the primary coordinator or agency responsible for making updates or causing updates to be made. The data stewardship polygons are defined and provided by the data steward.

The standard attributes for this feature class are listed in the table.

FieldName	Type	Length	Description	AliasName
PLSSID	String	50	Concatenation of the principal meridian, township, range, and duplication code that form a unique id.	Township Identifier
TWNSHPLAB	String	25	Township label that is used for cartographic output or web display.	Township Label
STEWARD	String	50	Data steward for the cadastral reference data. The data steward is responsible for the authoritative data	Data Steward
REVISEDDATE	Date	8	The last date of any revision in the data steward defined polygon	Revised Date
STATEABBR	String	2	The two letter postal abbreviation for the state	State Abbreviation
STWRDPLYID	String	50	This is a unique identifier for the stewardship polygon.	Stewardship Polygon ID
STWRDPLYTP	String	50	This is the type of polygon for the metadata at a glance. Examples include PLSS Township, Survey Township, County or City	Stewardship Polygon Type
STWRDLAB	String	50	This is a label for the stewardship polygon for cartographic purposes	Stewardship Label

In the figure below a portion of the Idaho PLSS CadNSDI is shown illustrating an area where there are three data stewards.

The PLSS Township was selected as the initial unit of maintenance for this feature class because typically PLSS data is updated on a Township basis.  

In the image below the polygons are shaded with the Data Steward and labeled with the last revised date.  This figure is from the Mississippi PLSS CadNSDI.

The initial data set reflects the nationwide collection and standardization efforts.  As this data set continues to be updated the analysis and information in the Metadata at a Glance will grow, the unit of maintenance will become better defined, and more granular.  Potential expanded attributes data stewards may consider will be links to contributor geospatial metadata, statements of overall accuracy, and aggregated or compiled date.  Individual users may want to add fields to track download or access dates.  All of these expansions are compliant with the standard intent.

The analysis that can be done and the functions this feature class can support for data set management are numerous, especially in shared maintenance and web service environments.

This feature class is the manifestation of the metadata for aggregated data described in this blog http://nationalcad.blogspot.com/2015/03/metadata-for-aggregated-data-sets.html

PLSS CadNSDI - What’s in the Database

This is the first of a series of documents that will describe the contents of the PLSS CadNSDI data set.

What is the PLSS CadNSDI?

The CadNSDI or the Cadastral Publication Data Standard is the cadastral data component of the NSDI.  This is the publication guideline for cadastral data that is intended to provide a common format, naming conventions, domains, structure and content for cadastral information that can be made available across jurisdictional boundaries, providing consistent and uniform cadastral data to meet business need that includes connections to the source information from the data stewards or data custodians.  The data stewards determine which data are published and should be contacted for any questions on data content or for additional information.  The cadastral publication data is data provided by cadastral data producers in a standard form on a regular basis.  Cadastral publication data has two primary components, land parcel data and cadastral reference data.  It is important to recognize that the publication data are not the same as the operation and maintenance or production data.  The production data is structured to optimize maintenance processes, is integrated with internal agency operations, and contains much more detail than the publication data.  The publication data is a subset of the more complete production data and is reformatted to meet a national standard so data can be integrated across jurisdictional boundaries and be presented in a consistent and standard form nationally.

The PLSS CadNSDI is the standardized form of the CadNSDI containing PLSS data.  This publication format provides a suggested minimum.  It is fully compliant with the intent of the standard for a data producer to include additional feature classes.  For example, there could be an added feature classes for recently monumented corners or mining claim lodes.

Similarly, it is fully compliant with the intent of the standard to add attributes to any feature class.  For example some jurisdictions identify corners that are photo-identifiable in the PLSSCorners Feature Class. It is also compliant to extend the length of any fields in the feature classes.  The field sizes are suggested minimum lengths.

Data sets will contain only available information.  A data steward cannot publish what is not collected.  For example, some states will have township and first division level data; others will also include second division polygons.  Some data sets have PLSSPoints and others do not. Over time it is expected that the content of all data sets will be developed and grow to be more similar in completeness.

The two most common question or comments about this standardized data have been

1.     There are too many features class, attributes, etc.

2.     There are not enough features, attributes, etc.

The Cadastral Subcommittee, through its consensus data standard development process, has agreed that this level of detail supports most business processes and is the best compromise between too many and too few.

What does the PLSS CadNSDI Contain?

The PLSS Cad NSDI database has this structure.

The Parcel Feature Data Set is included but it has not been populated for any of the CadNSDI data sets.  The Cadastral Reference Feature Data Set is the primary focus of the standardized data.

Summary

These are the cadastral reference features that provide the basis and framework for parcel mapping and for other mapping.

Description

These are the cadastral reference features that provide the basis and framework for parcel mapping and for other mapping. This feature data set contains PLSS and Other Survey System data. The other survey systems include subdivision plats and those types of survey reference systems. This feature data set also include feature classes to support the special conditions in Ohio.

The Cadastral Reference Feature Data Set has the following feature classes

Each of these feature classes will be described more fully in other documents. 

The Cadastral Reference Local contains localized data that has not yet been integrated into the standardized data sets.  Typically these are localized data such as a portion of a county or a national forest area, where more accurate information or more complete information has been identified but the data steward has not incorporated this into the standardized data.  It is provide as a reference or as a notice that more current information may be available, although it is not in the standardized form and has not been fully evaluated.  After the localized data is incorporated into the standard it is removed from the Cadastral Reference Local Feature Data Set.  Feature classes will not have standardized names, but will include the localized data contributor in the name.  For example the following is an example from the Michigan PLSS CadNSDI, with BLM, Kent County and Oakland County PLSS Points submitted in the Cadastral Reference Local Feature Data Set.

Metadata for Aggregated Data Sets

Aggregated or "rolled up" data is a compilation of locally sourced data assembled into a single, often standardized data set.  This often occurs at the state level such as state standardized address or parcel data.

Many consumers of aggregated data use it as produced and need no additional information about it.  Some users need additional information from local providers or need information about how source data was generated or fitness for use and need locally sourced metadata.

Metadata for a compiled or aggregated data set might describe the compilation process, any data transformation or standardization by the aggregator, and the identity of source contributors.  This could be accomplished with current metadata tools and standards, but it could be lengthy and tedious to maintain.

Another approach could be to develop metadata about each contributor and their source data set and then link those individual metadata records from the compiled metadata.  It may be difficult to associate which record in the compiled file matches which metadata file, and it may be challenging to maintain, but it could be accomplished with the current tools and standard formats.

A third approach, and one that is advocated in the PLSS Cadastral Publication standard, is to develop a spatial feature, termed metadata at a glance, that provides the geometry of the extent for each contributor, the date the source information was added to the compiled data set, the identified custodian or steward for the data, and a link to the contributors metadata about their source data.  The compiled metadata record describes how the data are aggregated and focuses on describing the compiled product, but the metadata at a glance directs users to original sources. 

Using this metadata at a glance approach, the geographic extent for each contributor can be easily viewed and the last update and perhaps an accuracy summary can be seen at a glance.  The compiled data set metadata will focus on the methods and standards of aggregation and compilation.  The currency and contributors will be readily visible and accessible through the metadata at a glance.  Links to source custodians and source data can be found from the metadata at a glance. 

Spatially enabled geospatial metadata.

Aereo, ABC and Parcel Data

-->

In late April the Supreme Court heard arguments on if Aereo, a company with thousands of small (tiny actually) digital antennas, could collect programs broadcasted over the air, package them and distribute the freely collected information over the internet, for a price.  The data is compiled onto cloud storage and can be consumed for a price on any screen. 

Several news articles called this a disruptive business model  (http://freakonomics.com/2014/04/28/whats-at-stake-in-the-aereo-case-maybe-the-future-of-the-cloud/). The issue at hand was could Aereo take the freely broadcasted signals and sell them through cloud services.  The problem being that ABC and other broadcasters sell those same signals to cable and dish networks for a fairly significant amount.

In my opinion there are many elements of this case that are similar to parcel data, of course.  Many states and counties and cities produce parcel data for their internal use and then “broadcast” or provide the data freely.  As we have seen time and time again, commercial vendors assemble that free data, repackage it, and sell it.

Most recently for example, a firm sent out a notice that users could purchase high-resolution aerial photography for southeastern Ohio (www.emap-int.com).  This is the part of Ohio with the Utica Oil Shale fracking activities.  But this very same product is freely available through the State of Ohio OGROP program http://ogrip.oit.ohio.gov/ProjectsInitiatives/OSIPDataDownloads.aspx.

So why is capturing freely broadcast programming and reselling it disruptive and taking freely available GIS data and reselling it not disruptive?  Maybe neither are disruptive, maybe it’s just the American free market. 

Geospatial Metadata and ITunes

Listening to the remembrances of Pete Seeger this week and being reminded again of how he used music to build communities and reach across ages gave me an idea.

What if we used music to date our geospatial data and standards?  We could link to ITunes songs and it would universally convey not only the time period of the data set but also would relate the data or standards to other things going on at the time.

For example, what if I told you the Cadastral Data Content Standards were published when Pearl Jam’s Better Man and Daughter, Madonna’s Secret, and Melissa Etheridge’s Come to My Window were getting a lot airplay.  Does that convey a sense of what was going on, what you were working on at the same time, and how much time has passed?

Or what if you thought about when we first explored statewide parcel data sets in the modern GIS era Jon Bon Jovi’s Miracle, Phil Collins’ Do You Remember and Another Day in Paradise, and Vanilla Ice with Ice Ice Baby were on everyone’s walkman or car radio probably more like it. 

Maybe Baha Men and Who Let the Dogs Out would be a good tune for the 2000 Census, one of those benchmark data sets.  What if Who Let the Dogs Out played on your computer when you opened a 2000 Census data set?  Would that be sufficient warning about the data vintage?

We should give some thought to adding an ITune link that would open with the data set to our metadata.  It might make it easier to date it, easier to relate its vintage to other data sets, and maybe even make the metadata memorable.  You could scroll through a play list instead of clicking the metadata links.  It would definitely make geospatial metadata and dating our standards more interesting.

Next time you publish a data set think about what your are listening to, what's your data set's tune?

Geospatial in the Technology Revolution

The January 2014 issue of Harpers has an intriguing article by Jeff Madrick, “The Anti-Economist - The Digital Revolution That Wasn’t”. 

Mr Madrick restates one of the Adam Smith’s principles; rising productivity is the source of the wealth of nations, in terms of technology. "Technologies that disrupt the labor force in one area eventually tend to benefit everyone because of increases in productivity.  Increases in the amount of goods and services delivered per hour of work generally lead to greater prosperity. Some jobs are eliminated but more and better paying jobs often replace them."

"Productivity growth actually started to fall well before the 2008 recession.  While some of the early hardware and software developments did create as much as 3% productivity gains, this did not last." 

At the heart of his thesis is that the new information companies, and in fact the new communication/information technology revolution does not create jobs.  We are four decades into the digital age and the economic benefits have not been manifested.

Granted this one view of the information age and its economic impact, but there are some interesting challenges for us in the geospatial information industry.  Geospatial technologies are part of the information age revolution. There are certainly new jobs in geospatial information that did not exist 5, 10, or 20 years ago.  If one of the trends in the geospatial industry is toward web based solutions and apps that can present answers to common questions, where are the high-end geospatial analysis professionals? As geospatial professionals are we really pushing forward and looking forward?  Are we being disruptive and contributing to productivity gains?  Some interesting challenges to consider as we move into the New Year.

The full article can be found here (it may require a login to get to the full article) http://harpers.org/archive/2014/01/