BLM Natl MLRS Acquisitions

<div style='font-size:12pt; text-align:Left;'><div><div><pre style='background-color:rgb(255, 255, 255); color:rgb(76, 76, 76); font-family:&quot;Segoe UI&quot;, Arial, sans-serif; font-size:13.3333px; font-style:normal; font-variant-caps:normal; font-variant-ligatures:normal; font-weight:400; letter-spacing:normal; margin:0px; text-align:start; text-decoration-color:initial; text-decoration-style:initial; text-indent:0px; text-transform:none; width:1598.84px; word-spacing:0px;'><span style='font-size:14px;'>The BLM may purchase or acquire land and interests in land (including access easements, conservation easements, mineral rights and water rights) if funding is available, acquisition is supported in a land use plan, and there are no title defects, hazardous materials or other local issues. Acquiring land through purchase can enhance recreation opportunities, preserve open space, strengthen resource protection, and provide an alternative for transferring ownership to the BLM when a land exchange or other options are not available. Lands may also be acquired through donation. Such offers generally may be accepted as a gift to the United States if the lands are contiguous to, and “block-up” with, existing public lands. Donation may allow the donor an income tax deduction based on the appraised value of the donation.This dataset contains current and historic land acquisition cases with the dispositions of 'Authorized', Interim' or 'Status Record'  The data are derived from Legal Land Descriptions (LLD) contained in the US Bureau of Land Management's, BLM, Mineral and Land Record System(MLRS) and geocoded (mapped) using the Public Land Survey System (PLSS) derived from the most accurate survey data available through BLM Cadastral Survey workforce. Geospatial representations might be missing for some cases that can not be geocoded using the MLRS algorithm. 

Each case is given a data quality score based on how well it mapped.  These can be lumped into seven groups to provide a simplified way to understand the scores.  
Group 1: Direct PLSS Match. Scores "0", "1", "2", "3" should all have a match to the PLSS data. There are slight differences, but the primary expectation is that these match the PLSS.   
Group 2: Calculated PLSS Match. Scores "4", "4.1", "5", "6", "7" and "8" were generated through a process of creating the geometry that is not a direct capture from the PLSS. They represent a best guess based on the underlining PLSS   
Group 3: Mapped to Section. Score of "8.1", "8.2", "8.3", "9" and "10" are mapped to the Section for various reasons (refer to log information in data quality field).  
Group 4: Combination of mapped and unmapped areas. Score of 15 represents a case that has some portions that would map and others that do not. 
Group 5: No NLSDB Geometry, Only Attributes. Scores "11", "12", "20", "21" and "22" do not have a match to the PLSS and no geometry is in the NLSDB, and only attributes exist in the data.   
Group 6  :Mapped to County. Scores of "25" map to the County.  
Group 7: Improved Geometry. Scores of "100" are cases that have had their geometry edited by BLM staff using ArcGIS Pro or MLRS bulk upload tool.</span></pre><p>&nbsp;</p></div></div></div>

Tags: acquisitions, alabama, alaska, arizona, arkansas, authorization, boundaries, california, colorado, connecticut, delaware, district-of-columbia, eastern-states, energy, florida, geology, georgia, geoscientificinformation, geospatial, idaho, illinois, indiana, iowa, kansas, kentucky, land-grid, lease, louisiana, maine, management, maryland, massachusetts, michigan, mining, minnesota, mississippi, missouri, montana, nebraska, nevada, new-hampshire, new-jersey, new-mexico, new-york, north-carolina, north-dakota, ohio, oklahoma, oregon, pennsylvania, planningcadastre, plss, public-land-survey-system, range, rhode-island, section, south-carolina, south-dakota, tennessee, texas, township, utah, vermont, virginia, washington, west-virginia, wisconsin, wyoming
Organization: Department of the Interior