More

How to update Geodatabase point file table from Excel data using FME


I have excel spreadsheet with data that I want to load into a GeoDatabase point table. The table already exists and I want to update the data not append. There is a unique ID to link the Excel data to the point file. When I use Joiner in FME or load data tool in ArcGIS it appends the data rather than updating.


When using FME, rather than using a joiner factory open the properties of the the writer and go to the parameters tab.
In there change the writer mode from INSERT to UPDATE. Then set your unique key field in the Update Key Columns field. Here's a link that may help Editing Writer Properties


Yes to MickyT's answer - and also the DatabaseUpdater transformer might be of use.

Also, there's an FME Tutorial on Spatial Databases at: http://www.safe.com/learning/training/on-demand/tutorials/ which I happen to know covers updates.


How to update Geodatabase point file table from Excel data using FME - Geographic Information Systems

Geog 353 Lab 5: Data Processing, Part 2
Update: 9/16/19
50 points
ASSIGNED: Monday September 30
DUE: Wednesday October 7

The data (historical population figures, by county, for your state) downloaded from the WWW must be processed in order to use it in ArcGIS. There are two basic data processing tasks involved in this project. In Lab 3, you used Excel to clean up and combine your data into an Excel file (.XLS). You then used Open Office to convert the Excel file into a .DBF file.

In Lab 5 you will import the .DBF files into ArcGIS. ArcGIS does support .XLS files. But the .DBF file seems to work with less hassles, so we will work with the .DBF file. Once in ArcGIS, you will join your data to a county map of your state or states. You will then use ArcGIS's spreadsheet/database capabilities to calculate the data on population change that you will eventually map.

Lab 5 Goal: Your .DBF files from Excel successfully imported into ArcGIS, linked to a county outline map of your state or states, and population change data calculated.


PROBLEMS and SOLUTIONS: Below find a few common problems you may experience when joining tables and processing data in this Lab. Clicking on the problem here will take you to the solution below:

1) Start by tidying up your 353 folders: using Windows Explorer delete old ArcGIS files (such as those generated in Lab 4) from your ArcGIS folder. If you want to save these files, please create a separate folder for them.

Make sure that you have your .DBF file, correctly processed and formatted, from Lab 3. Theses should be in your Data folder. You should back up both these files on a flash drive or some other device (it is always good to keep a back-up copy of your data files, especially those you have put a lot of work into).


2) Enter the realm of ArcGIS by starting ArcMap. Under the File menu hit New. Under File also Save. Navigate to your ArcGIS folder, and save the file there. I would call mine Wisconsin.mxd (after my home state, which sed to be, but is no longer a better state than Ohio).


3) Now we need to get a county map of our state or states. This, of course, will be a Layer in a Data Frame in ArcMap's Table of Contents. Under File select Add Data. or just hit the icon (yellow box with +). You may have to Connect to folder (as learned in the last lab). The file you need, counties.shp is in the ESRI2000 folder on the C: drive, in the USA folder.


4) You should be able to see the counties.shp layer in the data frame. Two problems should be FRIGHTENINGLY evident. First, we do not need the entire US, only our individual state or states. Second, the map projection is weird. Let's extract our state from the United States first, using the query and select features of GIS.

What you want, in essence, is a subset of the counties.shp layer. One way to do this is to select your state, and save it to a new layer file.

    Click once on Counties to highlight the layer.

Once you have selected your state's counties, you need to save those counties in a new map layer.

    Right click once on the counties layer, and from the menu select Data and Export Data. As you are creating a new map layer, navigate to your ArcGIS folder


Do this for xtra phun: Using the counties layer and the selection process reviewed above, please create a subset of at least three states together on the same layer. Some of you have to do this for your project, and all of you should know how to do it anyway. Save it as a layer (appropriately named), save a .jpg and place it in the Lab Log.


5) You should see your state (or states) on the screen. Change the projection of your map, and its center, to something more appropriate than the default (as discussed in the lectures on Map Projections / Geographic Framework). Note which projection you choose and why in your Lab Log.

    Right-click on the data frame (probably called Layers if you did not rename it) and select Properties.


Thrilling!: you now have your county level map, in a proper map projection.

Next step: Linking your Census data (.DBF file) to your base map.

6) Right click on your state layer and select Open Attribute Table. This is the attribute data that ESRI provides with the county level maps. This Table is akin to a simple version of Excel. You won't need most of this data (so we will hide it) and we need to join your population data for the 1900 to 2010 censuses (censi?) to this data.


Pause and ponder where we are: we have a base map (and linked data in a Table ) in ArcGIS. We also have a .DBF file of the additional data we need to link to both the base map and data for our state. This is a very common occurrence in GIS and computer mapping. So what to do?

One of the most important ways of combining data files is through a common column (field) of data. This is the idea behind a relational database. It is vital, however, that the columns (fields) are EXACTLY the same: that the data in the column (field) is the same and that the format - numeric, text, etc., is the same).

Are there any columns of data that your .DBF files and the table for counties.shp share? Obliviously, you have to plan ahead in any GIS or mapping project and make sure that you have a common column (field) if you are going to join tables of data. We did that: FIPS and the county names are (we hope) the same. Recall the FIPS codes are unique numeric identifiers for each geographic unit (counties, states, census tracts) in the country.

The next step is to join your .DBF file to the data associated with your state map. This might be quick or it might be a nightmare! Worst case scenario, a failed join may start a monitor fire.

Please pause to ask for divine intervention from the God or Gods or Goddesses or Godettes of your choice, to ensure that our data joins with minimal mahem.

PROBLEM: I am mapping more than one state and my data for each state are still in separate files. This means you skipped a step in the last exercise. Badness! If you are mapping more than one state you should combine your census data in a single file in Excel then open and save it as a single .dbf in Open Office before you bring it into ArcGIS:

  • Open Excel
  • Create a New Excel file (very important!)
  • Open your (alphabetically) first state .XLS file, select, and copy all the data including the headers (FIPS, Y1900, etc.).
  • Paste into the new .XLS file
  • Open your (alphabetically) second state .XLS file, select, and copy all the data, but NOT the headers.
  • Paste into the new .XLS file and save .

Join the new combined file in ArcGIS as noted in step 7 above.

7) Please make sure that your .DBF file is not open in Open Office and close the attribute table in ArcGIS. Your state or states should be visible on the screen in ArcMap.

    Right click on your state layer and select Joins and Relates and Join.


8) Export your state layer (map and attribute table): This combines your joined data attribute tables and will make it easier to work with your table.

    right mouse click on the layer containing your state map


If everything seems kosher at this point - cool.


9) Open and tidy up your Attribute table. Notice that you have many columns (fields) of data that you don't need. To make it easier to work with, hide all the fields you don't need: this won't change the table, only what you see.

    Right click on your state layer and select Properties.

At this point you have joined your historical census data to the existing ArcGIS Table which is linked to the county map of your state (or states). You have learned how to link different data files together (very common in GIS work) and how to view only part of a base map and data file (using queries and selecting ).

Recall that our project entails mapping out percent population change. What we have done so far is appended total values of historical Census figures to the base map and associated table for our state. We can calculate population change from the data we have. Percent population change from, say, 1900 to 1910 can be calculated as such:

    1910 population - 1900 population

We could do this with pencil and paper, or crayon and construction paper, for each pair of years, or we could turn to one of the marvels of modern technology and let the computer calculate this additional data for us. We can do this in ArcGIS using ArcGIS's database/spreadsheet capabilities.

10) To complete the processing of our data, we want to generate some additional data (population change) from the data we have (total population).

    Open up your attribute table

Think about what you are doing: The Field Calculator is telling you that your chg_00_10 field - whatever number shows up there - is a number that results from the equation you build. You will not see the equation in the Table, but you will see the number that results from the equation.

Copy the equation below into the field calculator:

    ( ( ( [ Y1910 ] - [ Y1900 ] ) / [ Y1900 ] ) * 100 )

You should see your percentages. They could be negative (sometimes counties lose population) or could be very large (hundreds, even thousands percent increase).

Calculate the rest of the years (1910-1920, etc.) by following the same procedure of creating a field and calculating the values. Be extremely careful that you do this right the syntax and order of the numbers matters much!

PROBLEM: When I calculate % population change, ArcGIS chokes and claims I am dividing by 0. You cannot divide by 0! If there are any zeros in your table, that means you forgot to change these to 9999999999 in Lab 3. You can fix this problem in ArcGIS by editing the table. Be careful, as any changes you make while editing an ArcGIS table are permanent. Do this:

  • Under the Editor menu (left side of screen, below File menu) select Start Editing. In the dialog box, make sure you tell ArcGIS which layer you want to edit.
  • Select the offending zero and change it to 9999999999 (ten nines).
  • Under the Editor menu Save Edits and Stop Editing.
  • Try calculating population change again.
  • Note: if this does not work you may have to un-join the tables ask instructor!


When all your population change data is calculated:

In this exercise, you have learned how to join data you found on the WWW and processed in Excel (.XLS) then Open Office (.DBF) to an existing ArcGIS Table and base map provided by ESRI. You have also learned how to change the map projection of the base map, and how to view only part of the base map and data file (using query and select functions). Finally, you have calculated additional data (percent population change) using ArcGIS's spreadsheet/database capabilities. These are all common GIS and computer mapping activities.

Prepare to show your instructor your correctly linked data and your new fields (columns) of population change data on the lab due date. Make sure you hide all the fields we are not using. Also define the following ArcGIS terms in your Lab Blog:

  • .DBF / Dbase file
  • Select by Attributes
  • Query
  • Fields (in a Table)
  • Records (in a Table)
  • Attributes
  • Relational Database
  • Join function
  • Monitor Fire
  • Calculate / Field Calculator

Next: you will learn to make important decisions about how to classify and symbolize your population change data as a choropleth (value by county), dot, and graduated symbol map in Lab 6. But first, the beloved mid-term evaluation!

When you are done, by end of class on due date: email me the link to your blog entry for Lab 5. This should include


Overview

ESRI shapefiles store both geometry and attributes for features. No topological information is stored.

A shapefile is a logical construct that consists of a series of physical files with different extensions. These extensions are added to the base name of the shapefile. All files must reside in the same folder.

Shapefile does not support small integer (16bit) or integer (32bit) data types. Instead, it supports a number(x,y) data type. The equivalent data types are:

Shape format – the feature geometry itself. This is a variable-record-length file in which each record describes a shape (feature) with a list of its vertices. A single .shp file can contain only one type of geometry. Supported geometries are point, multipoint, polyline, polygon, and multipatch.

Each entity in a .shp file has a corresponding entry in the .shx index file and a corresponding row of attributes in the associated .dbf file. The order of the entries in each of these files is synchronized. For example, the third geometric entity in the .shp file is pointed to by the third entry in the .shx index file and has the attributes held in the third row of the .dbf file.

Index file that stores the index of the feature geometry. In the .shx file, each record contains the offset of the corresponding main file record from the beginning of the main file.

The dBASE file (.dbf) contains feature attributes, with one record per feature – that is, a one-to-one relationship between a record in the main file and its attributes in the dBASE file – based on record number. For example, if the type of geometry in the main file is multipoint, the .dbf file will have one row for each set of points held in the main file. If the type of geometry in the main file is point, there will be one row in the .dbf file for each point.

Attribute records in the dBASE file must be in the same order as records in the main file.

Any single DBFs (attribute) file can have a maximum file size of 2 GB, a limit imposed by the dBase III specification. Files larger than 2 GB may be readable, but not officially supported. Files larger than 2 GB are not writable, and will produce an error message.

Spatial index for the geometric data.

These two files will not be written unless Write Spatial Index is selected in the Shapefile writer parameters dialog.

Attribute index for the geometric data. These files are named as filename.attributename.atx .

Attribute indexes are created for any user attributes that are flagged for indexing. For more information, see Esri Shapefile Writer: User Attributes.

Zipped file that contains all the files that comprise a Shapefile dataset.

For example, coast.shz will contain coast.shp , coast.dbf , coast.shx , and optionally other shapefiles if applicable.

Shapefile datasets larger than 2 GB are considered invalid (and were probably not created with Esri software), due to the following:

  1. Internal pointers between the index file (.shx) and main file (.shp) are stored as signed 32-bit integers. This is a limitation of operating system architecture.
  2. Attribute files (.dbf) files also have a 2 GB size limit.
  3. The main file (.shp) header contains information on the size of the file, specified as a signed integer. Writing a shapefile dataset greater than 2 GB would invalidate the file header.

Because indexes are measured in "words", FME can read and write 4 GB files. However, these files may not function properly with other applications. Further, on some 32-bit operating systems, there is no way to reference a location in a file more than 2 GB from the beginning.

Shapefiles can hold both two- and three-dimensional geometry, as well as an optional measure value on each vertex. However, all features within a single shapefile will have the same dimensionality. Note that while older Esri products may only support two-dimensional shapefiles, FME can read and write both two- and three-dimensional shapefiles. FME can also handle measure data associated with features.

Note: Aggregate linear features and aggregate polygonal features may be created using the Aggregator transformer. They may be broken into their component pieces for output to formats that do not support aggregation using the Deaggregator transformer.

Note: If a polygon containing holes is written to a Shapefile, any adjacent holes will be merged into a single hole before the polygon is output.

If the FME feature contains an "unnamed" measure and the destination feature type is set to 2D + Measures or 3D + Measures, then FME will write the measure.

In the FME Data Inspector, these measures are labeled <default_measure> . If the feature has a named measure (for example, distanceMeasure ), the Shapefile writer will ignore it, and then measures on the destination geometry will be undefined.

The Shapefile reader will automatically load the <default_measure> if a geometry type with measures is read.

Note: Measures are currently not supported when reading or writing the shape_multipatch geometry type.


Document Management

Four documents are provided within the project framework.

  1. The Data Sources Workflow describes the steps to normalize the provided data into Facilities and Building.
  2. The Inventory Workflow describes the steps to create the Facilities Inventory (FI) and building Inventory (BI) modeling inventory.
  3. The Hazus Updates Workflow provides the methodology for integrating the FI and BI into a Hazus Flood or Eathquake Study Region.
  4. The Analysis Workflow outlines the steps and processes for each hazard analysis.

The workflows are provided as “best practice”, and may be universally applied. Staff will be trained on how to implement these tools on their various PDM projects.

The workflow documents are maintained for use on Pre-Disaster Mitigation (Hazus) projects at Polis. The names of the files are:

The following abbreviations are used throughout the document:

[TBD] To be determined – decision point

[PIO] Process improvement opportunities – ideas for development

[Caution] Proceed with care. Things to watch out for.

[Name] Contributions required by …

[Rev] Major revision marker

[Note] Miscellaneous hints to the reader

[Option] Optional or alternative workflow

Versions are incremented with each project milestone.

File Clarification, tool explanation


Optimize Online Geocoding

This exercise uses emergency response data that is summarized by street address to consolidate multiple reports with the same address. Using this technique, many more points can be mapped by geocoding many fewer records by summarizing records of responses to the same address. It uses ArcGIS for Desktop with ArcGIS Online.

Getting Started

Download the sample dataset [ZIP] . Unzip the archive on a local computer and note the folder directory structure. The KFDKFD_AGOL folder be used to store all data generated during the Desktop exercise.

  1. Start ArcMap in ArcGIS for Desktop and create a new blank map document.
  2. Click the Add Data button and navigate to KFDKFDKFD_AGOL. Load only these layer files: Kent Fire Department, KFD Fire RDs, and KFD Fire Stations. Do not load KFD Incidents file (which will be populated later).
  3. Inspect the layers and verify that they are linked to the data. If you need to repair a link to source data, right-click on that layer and choose Data > Repair Data Source and navigate to KFDKFDKFD_AGOLGDBFilesWASP83NFProtection.gdb. You should see eight fire stations, and a fire district boundary.

Check Coordinate Systems

The layers added to the map are in Washington State Plane North American 1983 (NAD83) US Feet, so the map’s data frame will also be in that projected coordinate system. ArcGIS Online uses the World Geodetic System 1984, a geographic coordinate system. Since the data will use both NAD 1983 and WGS 1984 datums, one of those datums will require transformation.

  1. Open the data frame properties, select the Coordinate System tab. It should show that the coordinate system is NAD_1983_StatePlane_Washington_North_FIPS_ 4601_Feet.
  2. Click the Transformation button. In the Geographic Coordinate System Transformation dialog, Convert from: should be set to GCS_North_American_1983.
  3. Click the Add button. Choose World > WGS 1984. Click OK.
  4. Under Using (choices are sorted by suitability for the layer’s extent): click the dropdown and choose WGS_1984_(ITRF00)_To_NAD_1983 which should be at the top of the list.
  5. Click OK twice to save these changes. Save the map.

Look at the KFD Fire RDs layer which shows the Kent Fire Department Response Districts. Kent Fire Department uses these 0.5 mile squares to assign primary, support, and multiple alarm run orders to manage individual station response loads and tabulate station performance.

This exercise will geocode 1,028 responses in RD 1516 and RD 1517, located north and west of Station 71. Save the map document in the KFD_AGOL folder with the name KFD_Incidents_AGOL. Make sure you save your work to the file, folder, feature class, and table names specified to avoid problems.

Set Up the Map Document and Environment

Choose File > Map Document Properties and add a title, summary, description, author, and tags for the map document. Open Catalog and set the default geodatabase to Incidents.gdb and check the box next to relative pathnames. Click OK to apply your updates and save your project. Finally sign into your ArcGIS Online account so you can use its geocoding utility. This will require less than 10 credits.

Preparing Incident Data for Geocoding

Click the Add Data dropdown and select Add Basemap and add the Open StreetMap basemap because you will be performing street-level address geocoding. If necessary, zoom to extent of the Kent Fire Department layer that shows the department’s service area. Look at relationships between KFD Fire Stations, the KFD RDs, and underlying streets. Save the map.

Load Incident Data from Excel

Load the Microsoft Excel spreadsheet containing selected incidents by clicking Add Data and navigate to KFD_AGOLXLSFiles, open KFD_Incidents.xls, and select KFD_Incidents$. After adding KFD_Insidents$ to the map document, open the worksheet and sort the Address2 field in ascending order. Notice that this field contains many multiple records with the same addresses. Only one record will be needed to geocode each location. To protect the data format before summarizing the Address2 field, let’s export this worksheet as a geodatabase table.

Export Data from Excel to a Geodatabase Table

  1. Make sure the KFD_Incidents$ table is visible.
  2. Right-click the KFD_Incidents$ table in the TOC and choose Data > Export.
  3. Save the exported file as KFD_Incidents_XY in Incidents.gdb.
  4. Click Yes when asked if you want to load this new table in the map.
  5. Open the table and verify that it contains all 1,028 exported records.
    Notice that each duplicate Address2 record contains the same values for City, County, State, and Zip_5. If they did not, additional steps might be required to edit or perform additional rematching steps. Remove KFD_Incidents$ from the TOC and save the map.

Summarize the Address2 Field

  1. To summarize all incident addresses, right-click Address2 and select Summarize.
  2. Expand City, County, State, and Zip_5 and check First. Remember, each of these fields contain identical text for each unique address string.
  3. Save the summary output as KFD_Incidents_Address2_Sum_1_XY in Incidents.gdb.
  4. When the summarized table is complete, add it to the map document.

This naming conventions references the source table (KFD_Incidents_Address2_Sum1_XY), the summarized field (Address2), the sequential summary (Sum1), and it identifies the dataset as a table only (XY). It is long, yet it is very descriptive.

Open KFD_Incidents_Address2_Sum_1_XY, and sort Count_Address2 in descending order. Notice that the summary table contains only 129 unique addresses, while the source table, KFD_Incidents_XY, contains 1,028 responses. Geocoding summary addresses, calculating the coordinates for those addresses, joining coordinates back into the response table, and posting that table as an XY event theme will save considerable credits. Saving the geocoded coordinates in a reference table will allow the coordinates for those addresses to be used for geocoding responses in other years and requiring only new addresses to be geocoded.

Geocode Addresses

In the TOC, right-click KFD_Incidents_Address2_Sum1_XY and choose Geocode Addresses. Select the World Geocode Service (ArcGIS Online). Sign into ArcGIS Online. In the Geocode Address wizard, make the following choices, scrolling down the dialog as necessary:

  • Set Address to Address2.
  • Skip Neighborhood
  • Set City to First_City,
  • Set Subregion to First_County,
  • Set Region to First_State
  • Set Postal to First_Zip_5
  • Skip PostalExt

Save the geocoded points in Incidents.gdb and name the file KFD_Incidents_Address2_Sum1_X. Click OK when done These settings provide a very precise geocoding schema and the match rate should be 100 percent. If you want to try rematching, set only the Address parameter and leave the others alone. Click OK to continue and watch the geocoding process. The summary geocoded points will be in the WGS 1984 geographic coordinate system. You could consider reprojecting these points to save them in a local coordinate system.

Review Geocoded Addresses

If you need to rematch to fix ties or unmatched records, click the Rematch button and explore the Interactive Rematch wizard. In the address area, verify and, if necessary, correct information for each problem record. Click inside the Candidates area, select the best candidate, and click Match. When finished, close Interactive Rematch and save the map.

Preview geocoding results on the map. Zoom to the extent of geocoded locations, if desired. All 129 points should map just north west of Station 71 in or near FD 1516 and FD 1517. Open and inspect the Geocoding Results table. Locate longitude and latitude fields X and Y. These fields contain WGS 1984 geographic coordinates that will be joined to KFD_Incidents_XY.

Create Longitude, Latitude Fields in Incidents Table

Verify that the KFD_Incidents_XY and KFD_Incidents_Address2_Sum1_X tables are visible. Open the KFD_Incidents_XY table and add two new fields, LonDec84 and LatDec84, to KFD_Incidents_XY to store coordinates. Set both field types to Double. Verify the Names and formats of both fields.

Join Geocoded Attributes to Incident Table

  1. In the TOC, right-click KFD_Incidents_XY and choose Joins and Relates > Join. In the Join Data wizard, Assigning Address2 to Item 1 (the join field)
  2. KFD_Incidents_Address2_Sum1_X to Item 2 (the join to table)
  3. Assign Address (not Address2) to item 3 the join on field and make sure Keep all records is selected.
  4. Click OK to create the join and inspect the resulting KFD_Incidents_XY table.
  5. Save the map.

Calculating Coordinates Using Joined Data

With the KFD_Incidents_XY table open, locate and right-click LonDec84. Choose Field Calculator. In the Field Calculator, scroll down through fields and locate KFD_Incidents_Address2_Sum1_X.X. Load it into the calculation window and click OK. Next, calculate LatDec83, using KFD_Incidents_Address2_Sum1_X.Y. Verify that LonDec84 and LatDec84 contain valid coordinates, remove the join, and save your project. This step shows the importance of applying naming conventions carefully.

Display XY Incident Data

Inspect the unjoined KFD_Incidents_XY table, right-click its name in the TOC, and open Display XY Data. In the Display XY Data wizard, make sure the X Field is set to LonDec84 and the Y Field is set to LatDec84. Z Field set to . Click the Edit button to edit the Coordinate System. Choose Geographic Coordinate System > World > WGS 1984. Before clicking OK, check these entries carefully. This step is crucial.

Export XY Incident Data to a Feature Class

  1. With no features selected, open the KFD_Incidents_XY Events table and inspect the 1,028 records, noting field IncTypeNo.
  2. Right-click the KFD_Incidents_XY Events table and choose Data > Export Data.
  3. Save the exported points as KFD_Incidents_X in Incidents.gdb.
  4. Choose Use the coordinate system as the data frame.
  5. Click OK.
  6. Add the exported incident points into the map. Zoom to this layer and inspect the locations mapped. All points should fall in or near FD 1516 or FD 1517.

Mapping Incidents by NFIRS Type

The last step will be to assign a meaningful thematic legend. The National Fire Incident Reporting System (NFIRS) uses a three-digit system to identify emergency incidents by incident type. The IncTypeNo field in KFD_Incidents_X references an empty layer file that assigns incidents to nine type groups.

Click Add Data, navigate to KFD_ARCGIS ONLINE_GDBFilesWASP83NF and locate KFD Incidents.lyr, and add it to the map. If you have followed all naming conventions specified previously, the KFD_Incidents_X should map correctly. If you encounter a broken link, right-click the layer file, choose Properties > Data > Repair Data Sources and connect the layer file to KFD_Incidents_X.

Inspect Map and Attribute Data

If you are curious, look at the symbology properties for KFD_Incidents to see how NFIRS type numbers are used, right-click on the layer file, click on the Symbology tab, and click the Advanced button > Symbol levels. This will show how the larger size Rescue, EMS incidents symbols map below symbols for less frequent types of incidents. After inspecting the mapped incidents and save the map. Click Cancel. Save the map one final time.

Conclusion

By summarizing 1,028 incidents, this exercise showed that geocoding only unique 129 records was required to map all incidents and this process consumed only four ArcGIS Online credits. This exercise uses a subset of Kent Fire Department incident data. The full Kent Fire incident dataset included nearly 16,000 incidents that represented more than 6,200 unique addresses. The overall geocoding efficiency ratio using the method described here is approximately 2.5, which is still considerable improved in efficiency and produces a reference set that can be used to summarize and geocode only new incident addresses.


How to update Geodatabase point file table from Excel data using FME - Geographic Information Systems

Microsoft Excel format

Detailed chemical and isotopic analysis of host and associated rocks within an area that is currently the largest producer of gold in the US. Part of a study of the hydrothermal activity leading to the formation of these gold deposits.

Geochemical analyses of rocks, mineral separates, bulk samples from lake and stream sediments, heavy mineral concentrates, and soils collected by USGS from 1962 to 2009.

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one “best value” determination for each analyzed species, greatly improving speed and efficiency of use. Like the

Geochemical analyses of rocks, mineral separates, bulk samples from lake and stream sediments, heavy mineral concentrates, and soils collected by USGS from 1962 to 2009.

Analyses of 72 water samples include pH, conductivity, water temperature, major cation and anion concentrations, trace-element concentrations, and dissolved organic-carbon concentrations.

This study delineated permissive areas for undiscovered porphyry copper deposits, compiled a database of known deposits and significant prospects, estimated undiscovered deposits within those areas and made probabilistic estimates of their metal content.

Analyses to establish the background and baseline geochemistry for bedrock and soil in the region, and to determine terrestrial mercury sources and sinks in the park

Geochemical analyses conducted as part of a larger study of the Midcontinent Rift in the Lake Superior region.

<div author" lang="en"><div Southern High Plains (SHP) is a new and emerging U.S. uranium province. Here, uranyl vanadates form deposits in Pliocene to Pleistocene sandstone, dolomite, and limestone

Chemical analysis of 47,478 samples of various geological materials to facilitate geologic mapping, petrologic studies, mineral resource assessment, and similar regional environmental assessements.

Chemical analyses for 31 samples collected from various phosphate mine sites in southeastern Idaho (25), northern Utah (2), and western Wyoming (4)

This report examines potential sources of lanthanide elements [rare earth elements (REE)] with the objective of providing up-to-date geologic information regarding mineral commodities likely to have increased demand in the near term.

Regional data for pH, alkalinity, and specific conductance for stream waters and a multi-element geochemical dataset for stream sediments collected in the New England.

This map sheet with accompanying Geographic Information System (GIS) project is an update of the existing U.S. Geological Survey (USGS) Conterminous U.S. Coal Fields map compiled using data primarily from the USGS National Coal Resource Assessment (NCRA).

Geologic and mineral resource information for 1635 deposits of phosphate worldwide

Geochemical, petrographic, and geochronologic data for samples, principally those of unmineralized Tertiary volcanic rocks, from the Tonopah, Divide, and Goldfield mining districts of west-central Nevada.

It has long been recognized that the largest mineral deposits contain most of the known mineral endowment (Singer and DeYoung, 1980). Sometimes called giant or world-class deposits, these largest deposits account for a very large share of historic and cur

Location and descriptive data for aggregate producers in New Mexico that are believed to have been active during the period 1997-1999.

Epithermal gold-silver deposits are vein, stockwork, disseminated, and replacement deposits that are mined primarily for their gold and silver contents some deposits also contain substantial resources of lead, zinc, copper, and (or) mercury. These deposi

This report describes compilation and evaluation results of data from the USGS global mineral assessment including new descriptive mineral-deposit and grade and tonnage models and spatial databases for deposits and occurrences, ore bodies and open pits.

This report makes available a large well dataset so that subsurface stratigraphic data are available in digital form for use within a GIS and in construction of three-dimensional models to address regional stratigraphic and structural questions.

This paper reports on a methodology that is used with estimates of undiscovered copper resources to evaluate the likely contribution that the predicted resources will make to mineral supplies in the foreseeable future.

Geochemical analysis of 47 elements in more than 700 soil samples that appeared to be unaffected by mineralization or anthropogenic contamination.

Data from field sampling of tailings, waste rock, sediment, and water at the mines, along with water, sediment, and biota in a pond and tributaries that drain from the mine area, to help assess the chemical hazards due to historic mining.

Data from seismic reflection and refraction, electromagnetic sounding, and resistivity profiling in a study area in Indiana. Exact location is not provided.

Distribution of fluorine in specific age-lithologic categories, ranging from precambrian metamorphic rocks to modern soils.

Results of analysis in tabular form

Regional samples of rock, sediment, leachates, and water were collected in and around the TCL site and analyzed for major and minor elements and radionuclides

Chemical composition of sediments and soils are of interest because of the potential for human and wildlife health impacts from high metal contents due to over 100 years of mining activity.

Systematic geochemical and mineralogical survey of soil horizons analyzed using a consistent set of methods, sample spacing 1 per 1,600 sq km.

Systematic geochemical and mineralogical survey of soil horizons analyzed using a consistent set of methods, sample spacing 1 per 1,600 sq km.

The Paleocene Fort Union Formation hosts a compositionally diverse array of Eocene plugs, dikes, and sills arrayed around the Eocene Big Timber stock in the Crazy Mountains of south-central Montana. The geochemistry and petrography of the sills have no

Major and trace element concentration data for 960 sites, providing a baseline for the natural variation in soil geochemistry in Colorado.

In this area of historical lead-zinc mining activity, studies such as this help to assess the possibility of human exposure to cadmium and lead through eating fish.

Analytical results for geochemical samples collected in 2007 from the Pebble deposit and surrounding environs

Chemical analysis of surface samples taken over a well known but concealed deposit to understand metal migration into the surface materials and to assess the power of surface sampling for detecting concealed deposits of this type.

Geochemical data for stream sediment, surface-water, rock, precipitate, and vegetation samples from Red Mountain

Study of sources and resulting transport of radionuclides and other dissolved constituents in the surrounding ground water.

Groundwater geochemistry data from samples that were collected in June 2011 to better understand the hydrogeologic system in the area surrounding the proposed mine site.

Water samples near this abandoned mine were analyzed for specific conductance, pH, temperature, and dissolved oxygen with handheld field meters, and metals were analyzed using inductively coupled plasma-mass spectrometry.

Compilation of all available chemical data (more than 2,000 samples) for igneous rocks that constitute the southern segment of the ancestral Cascades magmatic arc.

Geochemical analyses of Cretaceous igneous intrusions in order to establish the geologic framework for world-class mineral deposits of the Butte district

Data from a study intended to explore how well the chemical analysis of water and sediment in an area reflects that of the rock from which the sediments are eroded and through which the water flows.

This report presents chemical characteristics of transient unsaturated-zone water collected by lysimeter from the Manning Canyon repository site in Utah. Data collected by U.S. Geological Survey and U.S. Department of the Interior, Bureau of Land Manageme

This report presents and makes data available to ongoing petrogenetic investigations of these rocks. It develops an accurate and current portrayal of their spatial distribution in GIS format while analyzed samples are presented via Excel workbooks.

Chemical and mineralogical results of a soil study conducted in the lower Rio Grande valley, Texas. 210 samples were collected from soils formed on Holocene alluvial flood-plain and distributary channel deposits of the river valley.

A study conducted collected 57 surface rock samples from nine types of intrusive rock to reexamine the chemistry and metallic content of the major rock units of the Iron Hill complex

Surficial geochemical methods used to identify subsurface mineral deposits covered by alluvium. Can a geophysical anomaly, interpreted as a buried granite pluton, be discerned from the geochemical characteristics of overlying Quaternary sediments?

40Ar/39Ar dating, electron microprobe analyses and geologic mapping help clarify chronology and correlations of the numerous volcanic and intrusive units exposed in the area and interpret the petrogenesis of a few of the volcanic units.

Compilation of isotopic and fission track age determinations, some previously published. Data for the tephrochronology of Pleistocene volcanic ash, carbon-14, Pb-alpha, common-lead, and U-Pb determinations on uranium ore minerals are not included.

Presents data for mineral deposits and unaltered and hydrothermally altered volcanic rocks. Data presented were acquired in three USGS labs by three different geochronologists. Analytical methods and data derived from each lab are presented separately.

Contains geologic, gravity anomaly, and aeromagnetic anomaly maps and the associated geologic and geophysical databases (ArcMap), as well as complete descriptions of the geologic map units and the structural relations in the mapped area.

Tabular presentation of mineral activities for mining and exploration in Washington during 1985 to 1997.

Field notes, locations, rock descriptions, and geochemical analyses from a 1988 to 1994 study to facilitate new research efforts in IOCG (iron-oxide deposits in context with other iron oxide-copper ± uranium) deposit types.

Major element oxide and trace element geochemical analyses, including those for gold, silver, and base metals, for representative rock units and for grab samples from quartz veins and mineralized zones within the quadrangle

Geologic map and associated digital databases of central Alaska. Compilation and reinterpretation of previously published and unpublished mapping at 1:250,000 scale, with limited 1:125,000 and 1:63,360 scale mapping.

Spatial geologic map with rock type, vents, arcs, landslides, etc. provided in an ESRI geodatabase and KMZ format for Google Earth viewing. Mapsheet with accompanying descriptive pamphlet is provided as a PDF. Petrographic rock summary is also included.

Includes data on mineral deposit locations and geochemical analyses

This metal ore deposit formed over a period of time in which its tectonic setting was changing, consequently it shows a range of characteristics typical of several different types of deposits. Rock geochemistry data are provided in tabular form.

Uninterpreted geophysical measurements and geochemical and mineralogical analytical data from samples collected during the summer field seasons from 2007 to 2010. Data are available in a single Geographic Information System (GIS) database.

Existing and new gravity station data and magnetometer measurements used to infer geologic structure of this area

Gravity and aeromagnetic data analysis described in a report, with a few physical property analyses

Data from more than 660 gravity stations, 100 line-km of truck-towed magnetometer traverses, and 260 physical-property sites in this area.

Gravity station data and physical property measurements used to infer subsurface geologic structure

Data for 90 rock samples collected from 2001 to 2007 for geophysical, geochemical, mineralogical, and environmental rock properties of acid neutralizing capacity and net acid production.

Study focusing on the effects of historical mining on Forest Service lands in this area.

Database of large-tonnage, generally low-grade, hydrothermal deposits related to igneous intrusions emplaced at high crustal levels. Includes tri-partite classification based on deposit type, metal commodities, and source/host pluton chemistry.

Locations of focus areas to be used for planning and collection of geophysical, geological, and topographic (lidar) data pertaining to the study of rare earth element resources in the US.

For each deposit group, a map indicates relative density and certainty of favorable data. Tables list datasets used in analysis, parameters queried, their contribution to mineral estimates and allow further user analysis. GIS files enable spatial query.

Regional locations and estimates of the probable amounts of undiscovered resources of copper, platinum-group elements, and potash in selected types of mineral deposits to depths of 1 to 3 kilometers below the surface of the earth worldwide.

Report with gravity station data in a spreadsheet

Report with generalized interpretation and Excel tables of gravity and physical property data including an explanations of their formats.

Gravity station data, isostatic anomaly map, and depth to basement map derived from geophysical inverse modeling.

321 observed gravity values with free-air, simple Bouguer, and complete Bouguer anomaly values high-precision relative-gravity surveys repeated over time, useful for aquifer-storage-change monitoring and absolute gravity values for use as base stations.

Data on the southern portion of the Marine Corps Air Ground Combat Center (MCAGCC) Twentynine Palms and the Yucca Valley-Joshua Tree-Twentynine Palms area and presents the map and basin analyses.

Aerial, truck-towed, and hand-held magnetometer measurements, gravity station data, and sample density and magnetic susceptibility measures

Electromagnetic, resistivity, and magnetic surveys used to determine the extent of contamination in an abandoned mine area.

Geochemical analysis of mine dumps, mill tailings, mine drainage, and surface waters indicate contamination from these historic mines is generally less than that asserted or suggested by some geochemical models

<p (SnO<sub>2</sub>), a main ore mineral in tin deposits, is suitable for U–Pb isotopic dating because of its relatively high U/Pb ratios and typically low common Pb. We report a LA-ICPMS analytical procedure for U–Pb dating of

Reconnaissance summary and survey of 1118 indium analyses of a diverse suite of minerals, including monomineralic and polymineralic ores and mineral concentrates, from 12 mineral-deposit types, principally zinc-lead deposits, from 16 countries.

Extensive commentary but minimal location information for more than 4000 mineral exploration targets worldwide, by year.

Contour map of isostatic gravity anomaly with bedrock geology, and principal facts for the gravity stations as a separate data file.

Data represent work completed when the study ended due to lack of funding. Geologic and site information on each specific deposit or occurrence are provided. Analytical and geologic data are summarized and site descriptions for samples analyzed are listed

Chemical analysis of 201 samples of rocks thought to be altered or mineralized in an area containing numerous mines.

Magnetic and gravity investigations were undertaken in Mono Lake to study regional crustal structures and to aid in understanding the geologic framework, in particular regarding potential geothermal resources and volcanic hazards throughout Mono Basin.

Magnetic susceptibility measurements with statistical analysis

In-place rock magnetic susceptibility measurements for 746 sites are reported in a database. These data, collected from 1991 through 2012, are intended to help improve geophysical modeling of the Earth’s crust in the Western United States.

The purpose of this report is to release one north-south and two east-west regional magnetotelluric sounding profiles collected where drillhole data are sparse. No interpretation of the data is included.

138 rock geochemistry samples collected during the 2006 field season were analyzed using the ICP-AES/MS42, ICP-AES10, fire assay, and cold vapor atomic absorption methods

Regional-scale geochemical survey designed to serve as a baseline for further analyses, and to understand effects of historical mining in the area.

Spreadsheets containing geochemical data for soils

Analyses of about 900 samples by ICP-MS, ICP-AES, and hydride generation-atomic absorption spectrometry. The samples collected in 2005 were also analyzed by a cold vapor-atomic absorption method for mercury.

Total mercury and methylmercury measured in sediments, tailings, and water in an area where historical gold mining has occurred proposed habitat restoration work may release some of the mercury that is currently buried there.

This report contains explanatory material and summary tables for lode mineral deposits and placer districts and metallogenic belts of Northeast Asia.

Methods used to create several styles for lithology or geologic time, including style files for Lithclass lithologic categories, and ISC and DNAG timescales.

This map displays over 1,500 records of mineral facilities throughout the continent of Asia and the countries of the Pacific Ocean. Each record represents one commodity and one facility type at a single geographic location. Facility types include mines, o

This map displays over 1,700 records of mineral facilities within the countries of Europe and western Eurasia. Each record represents one commodity and one facility type at a single geographic location. Facility types include mines, oil and gas fields, an

This map displays almost 900 records of mineral facilities within the countries that formerly constituted the Union of Soviet Socialist Republics (USSR). Each record represents one commodity and one facility type at a single geographic location. Facility

This report releases data for waters in which naturally occurring acid-rock drainage occurs but acid-mine drainage from historical mining continues to contribute dissolved metals further degrading preexisting water quality.

Geochemical data generated during mineral and environmental assessments for the Bureau of Land Management in northern Nevada, northeastern California, southeastern Oregon, and southwestern Idaho.

Radiometric ages of geological materials by K-Ar, Rb-Sr, Sm-Nd, fission track, and U-Th-Pb dating methods. A revision of DDS-14 correcting locations and providing the data in more convenient formats.

Location, type, mineralogy, name, tonnage and grade, and geological setting for 120 deposits of this type. The US has no active nickel mines or nickel reserves.

Morphological data on fibrous mineral particles from an old vermiculite mine that has been a subject of environmental health investigations

Analytical results for Pb-concentrations and isotopic ratios from about 150 samples of soil A horizon and about 145 samples of soil C horizon collected along a 4000-km east-west transect across the USA.

The purpose of this report is to present available petrographic and geochemical data for unmineralized rock samples, principally volcanic rocks and to make those data available to ongoing petrogenetic investigations of these rocks.

Miocene calc-alkaline volcanic rocks, part of the southern segment of the ancestral Cascades magmatic arc, are spatially, temporally, and likely genetically associated with precious metal epithermal deposits in the Tonopah, Divide, and Goldfield Districts

Presents data and describes the methods used to determine the physical attributes, as well as the chemical and mineralogical composition of surficial deposits groundwater levels and water composition in this area.

Density and magnetic susceptibility measurements to assist in interpretation of gravity and magnetic anomaly data

This study summarizes the identified mineral inventory of PGE in South Africa and Zimbabwe and estimates the potential amount of undiscovered PGE resources that may be present in these countries.

Location and characteristics of 1,124 individual mineral deposits of this type, with grade and tonnage models for chromium as well as several related elements.

Delineated permissive areas for undiscovered porphyry copper deposits, compiled a database of known deposits and significant prospects, estimated undiscovered deposits within those areas and made probabilistic estimates of their metal content.

This study delineated permissive areas for undiscovered porphyry copper deposits, compiled a database of known deposits and significant prospects, estimated undiscovered deposits within those areas and made probabilistic estimates of their metal content

This study delineated permissive areas for undiscovered porphyry copper deposits, compiled a database of known deposits and significant prospects, estimated undiscovered deposits within those areas and made probabilistic estimates of their metal content.

This study delineated permissive areas for undiscovered porphyry copper deposits, compiled a database of known deposits and significant prospects, estimated undiscovered deposits within those areas and made probabilistic estimates of their metal content.

A compilation of worldwide mineral deposits of this type. This older version was revised and updated in OFR-2008-1155.

A compilation of worldwide mineral deposits of this type. This older version was revised and updated in OFR-2008-1155.

Revised and updated information about mineral deposit models for porphyry copper deposits to facilitate assessment of undiscovered resources

Map with excel tables showing principal facts (gravity observation data).

Gravity station data from three separate surveys

Includes 2,604 (170 not previously published) gravity station measurements with calculations of free-air, simple Bouguer, complete Bouguer, and isostatic anomaly.

The purpose of the data collection was to investigate possible changes in gravity across mapped Quaternary faults and to improve regional gravity coverage, adding to the existing national gravity database.

Data collected as part of a geological and geophysical framework study

Gravity observations from 99 locations in this area, with free-air, isostatic, and complete Bouguer anomalies.

Regional tracts of land where the geology is permissive for the occurrence of undiscovered porphyry copper deposits, with probabilistic estimates of undiscovered resources, along with tables of discovered deposits and prospects.

Results from sediment cores collected during 1997-1999 oxbow lakes and beaver ponds to assess impact of historic mining and exposed mineral deposits on the quality of the water and the aquatic and riparian habitats.

Analysis of soil samples in the Texas borderland area

This dataset contains location, geologic and mineral economic data for world rare earth mines, deposits, and occurrences. The data in this compilation were derived from published and non-published sources.

Detailed descriptions of more than 200 mineral districts, mines, and mineral occurrences (deposits, prospects, and showings) within the United States that are reported to contain substantial enrichment of rare earth elements.

Detailed descriptions of more than 200 mineral districts, mines, and mineral occurrences (deposits, prospects, and showings) within the United States that are reported to contain substantial enrichment of rare earth elements.

The site, on the Wind River Indian Reservation, has Eastern Shoshone and Northern Arapahoe tribes concerned about groundwater and soil contamination. This effort studies geochemical soil variation and compares concentrations with levels established by EPA

Regional maps of phyllic and argillic hydrothermal alteration are presented on 10 separate plates available in either low or high resolution. A GIS package includes points features for potential porphyry copper sites also provided in tabular form.

Currently (2011) asbestos is not mined commercially in the US. This shows where it has been found or mined in the past.

Currently (2011) asbestos is not mined commercially in the US. This data set shows occurrences and historic mines.

Map and data tables show location, mineralogy, geology, and relevant literature for each mine, prospect, or occurrence

Currently (2011) asbestos is not mined commercially in the US. This data set shows occurrences and historic mines, and includes information on talc deposits.

Location, mineralogy, host rocks, and references for 51 natural occurrences of asbestos in Washington and Oregon, using descriptions found in the geologic literature.

Currently (2011) asbestos is not mined commercially in the US. This data set describes historical mines and occurrences.

Data in this report will be used to better understand the processes that control epithermal mineral-deposit formation by attempting to relate the geochemistry of the primary structures that hosted hydrothermal fluid flow to their heat and fluid sources.

Characteristics of 785 deposits, including grade, tonnage, rocks, mineralogy, and references

Location and characteristics of 123 sediment-hosted gold deposits worldwide. These deposits have disseminated micron-sized invisible gold in sedimentary rocks.

This study area encompasses the greatest sediment-hosted copper-cobalt province in the world, containing 152 million metric tons of copper in greater than 80 deposits.

This report provides information on sediment-hosted zinc-lead mineral deposits based on the geologic settings that are observed on regional geologic maps.

Data on a variety of mineral deposit types that have variable environmental effects when exposed at the ground surface by mining or natural processes.

Weight percent of 15 mineral groups from 331 samples, geochemical analyses of 58 major and trace-element chemical species from 502 samples taken at 175 sites, along with data from geochemical reference standards and analyses of splits.

Geochemical and mineralogical analysis of uranium-bearing sandstone in order to help understand mineral deposits of this type.

Analysis of stream sediments to assess the geochemistry, in particular the mercury and selenium contents, of mining-impacted sediments in the study area

New analyses of 1,075 previously collected stream sediment samples, measuring more elements with better precision. Includes arsenic, gold, palladium, and platinum and 55 major, rare earth, and trace element concentrations.

New data for 366 previously collected stream-sediment samples, because recently developed analytical methods can detect additional elements of interest and have lower detection limits than the methods used when these samples were originally analyzed.

Geochemical and mineralogical analyses of plutonic rock samples

Seasonal geochemical data intended to understand formation of ferricretes and to help understand the ground-water flow system.

Geophysical modeling used to study mineral deposit geometry. Includes 227 new gravity stations.

Interpretation of aeromagnetic data, with geologic structure, to infer thickness and estimate capacity of an aquifer.

<p ore-genesis model for world-class deposits of the Butte mining district, Montana, USA, is deep pre-Main Stage porphyry Cu-Mo and overlying Main Stage Ag-Zn-Cu zoned-lode deposits, both of which formed from hydrothermal fluids driven by

<p Cornwall and Devon vein- and greisen-type copper and tin deposits of southwest England are spatially and genetically related to shallow-seated granitic intrusions. These late Variscan intrusions, collectively known as the Cor

Contains results of age dating studies interpreted in other reports. Samples in each study shown as separate sheets within Excel files. Analyses of each zircon are listed separately on a sheet according to filtering scheme and type of instrument used.

The report provides insight into the dependence of the United States on foreign supply to meet the country’s mineral needs. This overview highlights the importance of understanding what is happening at each point along the supply chain.

Geochemical analysis of stream waters and sediments in mountainous areas immediately west of Denver, Colorado, to evaluate the concentration and distribution of uranium-bearing acid drainage originating from historical mines

U-Pb geochronology and isotopic studies of select plutons across the Salmon River suture in western Idaho.

Information on VMS deposits from around the world with new grade and tonnage models for three subtypes of VMS deposits and data allowing locations of all deposits to be plotted using GIS.

This report releases data, cross section, and model information related to the identification of travel paths for groundwater supply and principal stratigraphic and structural features that serve as constraints or conduits for groundwater movement.

Investigates Cr and Ni geochemistry providing insight into the mobility and bioaccessibility of these toxic trace elements throughout northern California and perhaps the origin of elevated dissolved Cr(VI) concentrations in the western Sacramento Valley

This report presents geochemical analyses for 220 mostly altered and mineralized rock samples from the Red Dog Zn-Pb-Ag district in the western Brooks Range of northern Alaska.

This report presents geochemical analyses for 210 unaltered and unmineralized rock samples of Paleozoic age in the western Brooks Range of northern Alaska.

Chemical analysis of geological materials at this EPA Superfund site located on lands of the Hopi and Navajo nations, which may contain mine waste.

The data consist of major- and trace-element whole-rock geochemical analyses and major- and trace-element analyses of sulfide minerals determined by electron microprobe and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) techniques


Rare Earth Element Occurrences in the United States

Concentrated, mineable deposits of the REEs are rare, such that most of the sites within this data release are for unmined locations where the published information may not contain thorough descriptions (Van Gosen and others, 2014). Therefore, decisions had to be made by the authors regarding the addition or exclusion of specific REE occurrences in the dataset, based principally on the available descriptions of the REE concentrations and the apparent size of the mineralized body. The level of detail of this type of information varied widely amongst the occurrences, ranging from general descriptions to detailed sampling and analysis of some deposits.

The entries and descriptions in the database were derived from published papers, reports, data, and internet documents representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Although an attempt was made to capture as many examples as possible, this dataset is a progress report that is part of an ongoing effort. The authors welcome additional published information in order to continually update and refine this dataset.

In addition to the conventional resources described in this report, every year approximately 56,000 metric tons of REEs are mined, beneficiated, and put into solution, but not recovered, by operations associated with the global phosphate fertilizer industry (Emsbo and others, 2015, 2016). As indicated by Emsbo and others (2015, 2016), recovery of byproduct REEs from the phosphate industry has the potential to substantially increase the supply of REEs to the market.

The significant increases in applications and demands for REEs has led to an increased interest in identifying new sources that include extraction not only from mineral deposits, but also the potential for REE extraction from coal-based resources, and recycling of products containing REEs. The Department of Energy is currently (2019) evaluating technologies to recover REEs and other critical minerals from coal and coal-based resources (https://www.netl.doe.gov/coal/rare-earth-elements). Recycling efforts have focused on recovering REEs from light bulbs and electronics. The dataset provided in this data release is restricted to non-fuel, REE-bearing mineral deposits and does not include energy resources (such as coal).

Van Gosen, B.S., Verplanck, P.L., Long, K.R., Gambogi, Joseph, and Seal, R.R., II, 2014, The rare-earth elements—Vital to modern technologies and lifestyles: U.S. Geological Survey Fact Sheet 2014–3078, 4 p., https://dx.doi.org/10.3133/fs20143078.

Emsbo, Poul, McLaughlin, P.I., Breit, G.N., du Bray, E.A., and Koenig, A.E., 2015, Rare earth elements in sedimentary phosphate deposits—Solution to the global REE crisis?: Gondwana Research, v. 27, p. 776–785, accessed March 13, 2019, at https://doi.org/10.1016/j.gr.2014.10.008.

Emsbo, Poul, McLaughlin, P.I., du Bray, E.A., Anderson, E.D., Vandenbroucke, T.R.A., and Zielinski, 2016, Rare earth elements in sedimentary phosphorite deposits—A global assessment, chap. 5 of Verplanck, P.L, and Hitzman, M.W., eds., Rare earth and critical elements in ore deposits: Reviews in Economic Geology, v. 18, p. 101–114, accessed March 13, 2019, at https://www.segweb.org/store/detail.aspx?id=EDOCREV18.

The rare earth elements (REEs) represent a prime example of a “critical mineral resource”. In the 21st century, the REEs have gained visibility due to: (1) the recognition of the essential, specialized properties that REEs contribute to modern technology, as well as (2) China's dominance in production and supply of the REEs, and (3) international dependence on China for the majority of the world's REE supply. Since the late 1990s, China has provided 85–95 percent of the world’s REEs, while the United States and other nations are highly dependent on REEs for their use in high technology devices, clean energy components, and defense technologies.

This dataset was compiled to provide base layers of information that identify and describe the known REE deposits, prospects, and showings in the United States. This compilation is intended to contribute to our geologic understanding of REE deposits in the United States, and to assist in evaluating their resource potential.

Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., 2017, Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, 797 p., http://doi.org/10.3133/pp1802.

DATABASE LAYERS AND TABLES

The Loc_Pt feature class contains point locations of mines, mineral occurrences (which includes deposits, prospects, and showings), and mineral regions, and the attribute information describing the location, source report, scale of the map used to obtain the location, and if the location has a polygonal footprint in the Loc_Poly feature class. In the database, all features have a point location, except for surface workings.

The Loc_Poly feature class contains footprints or polygons of areas, deposits, mineral districts, mining districts, placer districts, and prospects. If a source report shows a location as a polygon, the polygon is digitized, and the approximate centroid of the polygon is added to the Loc_Pt layer. Attribute information about the location is provided in the Loc_Pt layer. Mines are represented as points in the database, even when footprints are presented in source reports. Where possible, the approximate extent of the mining operation area, determined from imagery, is presented in the surface workings layer (see Loc_Poly_Sw).

The Loc_Poly_Sw feature class contains the approximate area of mining-related activity, or “surface workings” as shown on Esri imagery. These polygonal outlines have no corresponding point location in the database, nor do they have links to other tables. The attribute information for surface workings contains the date of the imagery and basic location information including state and county names. Surface workings must be at least 1,000 feet (300 meters) in one dimension to be digitized, and multiple workings that are 500 feet (150 meters) or less apart are combined into one outline. No attempt is made to distinguish between the types of surface workings (for example, roads, pits, leach pads, waste piles, etc.), even when presented in source reports.

The Site table is used to identify related features, such as a deposit and the mine(s) operating on it, or a mine and its related deposits. Each site has a unique identification value in the Site_ID field. The Site_ID is used in all tables except the References table. The Site table also indicates where information about a site occurs within the database. For example, if the Resources field in the Site table contains the value “Yes”, resource information is available in the Resources table.

The GeolMinOcc table contains information about the geology of mineral deposits, prospects, and showings. Every attempt is made to compile information as reported in the source report. For example, if one source report states the valuable material as "thorite and monazite" and another reports "cenosite", the attribute field Value_Mat will contain all values. The value in the Ref_ID field is the primary source report for the record, for example, Jackson and Christiansen (1993). All information in the record comes from the primary source report unless an attribute field value contains a footnote denoted as a number in parentheses. If a record value is followed by a footnote, the Ref_ID is given in the Remarks field. Full citations for source reports are provided in the References table.

The Resources table contains reported resource and reserve information for mineral deposits. Initial (or earliest resource data found by authors) and current resource data were compiled, even if information from intervening years was reported. Resource values were recorded as shown in source reports, including year reported, resource amount, units, and classification system(s). The definition of terms (for example, inferred, proven, probable, etc.) used in various resource classification systems may change through time. Resources extracted from older sources might not be compliant with current rules and guidelines in minerals industry standards such as National Instrument 43-101 (NI 43-101) or the Joint Ore Reserves Committee Code (JORC Code). Inclusion of material in the database is for descriptive purposes only and does not imply endorsement by the U.S. Government. If resources or reserves are reported for a group of features rather than an individual deposit, the Ftr_ID will show “-1111” and the resource or reserve is assigned to the “site” or Site_ID that groups those deposits in the Site table. A value ending with “111” as a decimal trailer indicates the value was calculated by USGS authors. For example, if a grade is calculated by USGS authors to be 0.05 percent, the value recorded in the database will be 0.05111. Where a range in values is provided for attribute fields such as Mat_Amnt, Grade Contained, etc., the average of the range is reported within the field and the range of values is noted within the Remarks field. For consistency, resource values are converted to the International System of Units (SI units) by the USGS authors. When gold and silver values are reported in ounces in the source report, troy ounces were assumed when converting to SI units.

The Production table contains published production data for mines. Production is listed by commodity and reported as shown in the source reports. If production is reported annually, production is totaled by the USGS authors for the time period defined by the Year_From and Year_To values. If production is reported for a group of features, the Ftr_ID will show “-1111” and the production is assigned to the “site” or Site_ID that groups those mines in the Site table. A value ending with “111” as a decimal trailer indicates the value was calculated by USGS authors. For example, if a grade is calculated by USGS authors to be 0.05 percent, the value recorded in the database will be 0.05111. Where a range in values are provided for attribute fields such as Mat_Amnt, Grade Contained, etc., the average of the range is reported within the field and the range of values are noted within the Remarks field. For consistency, production values are converted to the International System of Units (SI units) by the USGS authors. When gold and silver values are reported in ounces in the source report, troy ounces were assumed when converting to SI units.

The History table contains information derived from publicly available sources regarding the status of a mine, prospect, deposit, or mineral region through time. Values in the Status field indicate a condition or phase for the time period stated in the Year_From and Year_To fields. This information may not reflect the current status of a feature. For example, if the attribute record shows the status of a mine is “Active” and the Year_From and Year_To dates are 1963 and 2001 respectively, the mine was active from 1963 to 2001 it is unknown if the mine is still active. The Last_Updt field shows the date that the record was last updated by the authors.

The Dep_Model table contains mineral deposit model and geoenvironmental model classifications for a deposit. When deposit model classifications could not be determined from published sources, the deposit model was assigned based on available geologic information and denoted as “USGS Authors (2018)” in the DpMD_RefID field.

The Descr_Sum table contains relevant descriptions found in source reports. These descriptions are attributed according to the type of description, such as Geology, Resource, Production, History, etc. Descriptions pertain to individual features or to larger sites. The authors do not paraphrase or combine descriptions, therefore, when a database feature is described in multiple reports, the feature will have multiple entries.

The References table contains the citation of the map or report(s) from which the point, polygon, or attribute information was obtained. The table also assigns a short reference Ref_ID which is used throughout the database.

Mines are a man-made feature associated with the process of extracting, processing, or concentrating ore materials. In this database, mines have a point location, and where possible, the polygon boundary showing the extent of surface workings identified from imagery. No attempt is made to distinguish specific mine features like pits, dumps, tailings, etc. within the surface workings outline.

Mineral occurrences, defined as a concentration of a mineral considered potentially valuable, are attributed as deposits, prospects, and showings in the database. Mineral deposits have defined size and grade indicated by current and (or) past production, and (or) a resource estimate. Prospects have sufficient data to describe at least two dimensions and the presence of useful or valuable minerals or materials. Showings have the occurrence of potentially valuable minerals as indicated by geological examination or analyses of samples.

Mineral regions are attributed as “areas”, mineral districts, mining districts, or placer districts. Areas have similar geology and deposit types. Mineral districts are areas, usually designated by name, defined by a group of deposits of similar type, origin, and/or commodity. Mining districts represent historic administrative areas organized by miners under the mining laws of the United States. Mining districts are typically an area containing a group of mines that exploited the same or related commodity. Placer districts are areas of placer mining operations. Placer district polygons were defined by the USGS authors. Mineral region polygons may overlap.

The locations of mines, mineral occurrences, and mineral regions are commonly represented as points in source maps and reports, and occasionally as footprints (polygon outlines). In this database, all features have a point location, and some have an additional polygonal footprint. Surface workings in the Loc_Poly_Sw feature class are the exception -- they do not have a corresponding point location or attribute information in the point layer. Otherwise, for points that have polygonal boundaries, the point attribute field Loc_Poly contains the value “Yes” and type of boundary is described in the field Poly_Def (for example, “Trace of placer districts” or “Outline of Indicated and Inferred Resource”).

Each point and polygon feature is uniquely identified by a Ftr_ID. The Site_ID is used to indicate groups of related features, or “sites”. Tables are linked (related) using the Ftr_ID or the Site_ID fields. Some tables have more than one record describing a feature. For example, a point denoting a mine location may have many records in the Production table summarizing the dates and amounts of material produced. The database is designed to allow the user to navigate from the point or polygon layers to the linked table information or from the tables to the point and polygon layers.

All database information is derived from publicly available sources. The Last_Updt field shows the date that the record information was last updated by the authors. Full citations are listed in the References table, and each citation is assigned a short citation, REF_ID that is used for identification in the database. With the exception of the Loc_Poly feature class, the primary reference(s) is noted in the Ref_ID field. Additional references are enumerated after attribute field values, and the corresponding short reference is in the Remarks field. For example, the Commodity field shows “rare earth elements iron (1)”. This indicates the commodity “rare earth elements” was derived from the primary reference denoted in the Ref_ID field as “McKeown and Klemic (1956)” and “iron” was derived from a secondary reference denoted in the Remarks field as “(1) Jackson and Christiansen (1993)”.

There is no relevance to the order of data presented in lists. For example, if the Commodity field shows “rare earth elements thorium uranium”, that is the order in which those commodities were compiled by the authors and does not represent the order of importance. Additionally, in the GeolMinOcc table, lists in different fields do not relate. For example, if the Commodity field shows “rare earth elements thorium uranium”, the Value_Mat field may list related ore minerals in a different order. Similarly, the data lists reflect the order in which the information was compiled. Listed fields are present in the Site, Loc_Pt, and GeolMinOcc tables.

Field or attribute records that contain "Null" values in the file geodatabase, were checked for available data, and no data were found. In some cases, an entire field may contain no information. These "Null" fields are maintained in the database structure for consistency with related USGS products and for possible future use if information becomes available.

Two points may occupy the same location. This occurs when there is a deposit with a mine, and the location of either the mine or the deposit is unknown. For example, a report provides a map showing the location of a deposit. The report also provides production data for underground “Mine X” that is mining the deposit, but does not provide the location of “Mine X”. In this case, a second point representing “Mine X” is placed at the point location of the deposit.

Polygon features may overlap. Viewing polygons as outlines without color fills will show where polygon overlap occurs.

In the attribute section of this metadata, attribute fields from all tables and feature classes are listed in alphabetic order corresponding feature classes and tables are listed in parentheses after the field name in the ‘Attribute_Label’. For example, “Mat_Amnt (Production, Resources)” indicates the attribute field Mat_Amnt (material amount) occurs in the Production table and in the Resources table.


SPATIAL DATA STANDARDS

This section describes the DataBC standards that apply when storing spatial data in the BC Geographic Warehouse.

Geometry Type and Content

All spatial tables in the BC Geographic Warehouse will store geometry using the Oracle Spatial Data Option (SDO) MDSYS.SDO_GEOMETRY object type.

The following SDO_GTYPE values for SDO_GEOMETRY objects are supported:

SDO GTYPE Value Corresponding ArcGIS Geometry Type Description
DL01 POINT Single point per feature
DL02 or DL06 LINE Two or more points connected into a line possibly multiple lines per feature
DL03 or DL07 POLYGON Three or more points connected into a line possibly multiple lines per feature
DL05 MULTIPOINT More than one disconnected point per feature

All features in an Oracle table or view must have the same ArcGIS geometry type.

The maximum number of vertices in any SDO_GEOMETRY object is:

Type of geometry Maximum number of vertices
Two dimensions (latitude/longitude or northing/easting (Y/X)) 524,288
Three dimensions (Y/X plus height or measure) 349,525
Four dimensions (Y/X plus height and measure) 262,144

Spatial Reference IDs (SRID)

The following two EPSG Spatial Reference ID (SRID) values are supported for spatial datasets stored in the BC Geographic Warehouse:

SRID value Description Use Link
3005 NAD 83 / BC Albers Datasets with coordinates falling into the following ranges (approximately the extents of BC) when projected to SRID 3005:
X (easting): 0 to 2,000,000
Y (northing): 0 to 2,000,000
https://epsg.io/3005
4326 WGS 84 - World Geodetic System 1984 (longitude/latitude) Datasets that extend far beyond the borders of BC https://epsg.io/4326

Note that ArcGIS maintains its own set of SRID values, one for each distinct combination of the EPSG SRID, the number of dimensions, and the resolution and tolerance values. These values may change over time and must not be referenced directly.

Registration

All tables and views containing a spatial column must be registered with the geodatabase.

Tables and Views Containing Spatial Column

All tables and views containing a spatial column must have a corresponding entry in the owner schema’s USER_SDO_GEOM_METADATA table.

Note that the process of registering a table or view with the geodatabase automatically creates a row for that table or view. Similarly, dropping a table or view in ArcCatalog removes the row.

Resolution and Tolerance

When registering a table or view with the geodatabase, the default XY Resolution value (.0001) must be used.

When registering a table or view with the geodatabase, the default XY Tolerance value (.001) must be used.

Shape (Geometry) Column Name

The standard column name for spatial geometry is SHAPE . Under some circumstances GEOMETRY can be used instead. Contact the DataBC Architecture Services team for more information.

ObjectID Column Name

The standard column for a spatial table or view’s [ObjectID column](https://desktop.arcgis.com/en/arcmap/latest/manage-data/using-sql-with-gdbs/object-id.htm) is OBJECTID.

Supporting Columns and Triggers

In addition to SHAPE and OBJECTID , all spatial tables and views will have additional columns as noted in the table below:

COLUMN_NAME Data Type/Precision Use
FEATURE_LENGTH_M NUMBER(19,4) Contains the length in metres of a linear feature or the perimeter of a polygonal feature. Not used for POINT or MULTIPOINT datasets.
FEATURE_AREA_SQM NUMBER(19,4) Contains the area in square meters of a polygonal feature. Not used for LINE, POINT, or MULTIPOINT datasets.
SE_ANNO_CAD_DATA BLOB Reserved for storing complex curve parameters. Note that storage of complex curve information is not supported in the BCGW however, this column must still be present.

FEATURE_LENGTH_M and FEATURE_AREA_SQM are populated by database triggers of the form

for line feature tables, and

for polygonal feature tables.

See Naming and Describing Standards for definitions of container_name and table_short_name.

Column Order

The following columns, if present, appear in the following order, as the last columns in the table or view.


2 Answers 2

You will need create variables to hold references to the newly created CommandButtons. By adding the WithEvents modifier you will be able to receive the CommandButton events.

Naming the controls after cell values is problematic. A better solution is to use the MSForms Control Tag property to hold your references. In my example below I add a qualified reference to the target cell.

Changed the subroutines name from addLabel to something more meaningful Show_UserForm6.

Combobox values as they are added.


Rhenium Occurrences in the United States

Rhenium is one of the rarest elements in the Earth's crust. Most rhenium occurs in the mineral molybdenite, where the rhenium substitutes for molybdenum. Rhenium is produced as a byproduct from roasting molybdenum concentrates recovered from mining porphyry copper deposits. Because the United States contains many porphyry copper mines and deposits, decisions had to be made by the authors regarding the addition or exclusion of copper and molybdenum deposits in the dataset, based principally on the published descriptions of the occurrence of rhenium in those deposits. The level of detail describing the rhenium occurrence varies widely, ranging from rhenium resources to general descriptions about the occurrence of rhenium.

The entries and descriptions in the database were derived from published papers, reports, data, and internet documents, published from 1917 to 2018, representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Although an attempt was made to capture as many examples as possible, this dataset is a progress report that is part of an ongoing effort. The authors welcome additional published information in order to continually update and refine this dataset.

DATABASE LAYERS AND TABLES

The Loc_Pt feature class contains point locations of mines, mineral occurrences (which includes deposits and prospects), and mineral regions, and the attribute information describing the location, source report, scale of the map used to obtain the location, and if the location has a polygonal footprint in the Loc_Poly feature class. In the database, all features have a point location, except for surface workings. In this database, all mineral regions are mining districts.

The Loc_Poly feature class contains footprints or polygons of areas, deposits, mineral districts, mining districts, and prospects. If a source report shows a location as a polygon, the polygon is digitized and the approximate centroid of the polygon is added to the Loc_Pt layer. Attribute information about the location is provided in the Loc_Pt layer. Mines are represented as points in the database, even when footprints are presented in source reports. Where possible, the approximate extent of the mining operation area, determined from imagery, is presented in the surface workings layer (see Loc_Poly_Sw).

The Loc_Poly_Sw feature class contains the approximate area of mining-related activity, or “surface workings” as shown on Esri imagery. These polygonal outlines have no corresponding point location in the database, nor do they have links to other tables. The attribute information for surface workings contains the date of the imagery and basic location information including state and county names. Surface workings must be at least 1,000 feet (300 meters) in one dimension to be digitized, and multiple workings that are 500 feet (150 meters) or less apart are combined into one outline. No attempt is made to distinguish between the types of surface workings (for example, roads, pits, leach pads, waste piles, etc.), even when presented in source reports.

The Site table is used to identify related features, such as a deposit and the mine(s) operating on it, or a mine and its related deposits. Each site has a unique identification value in the Site_ID field. The Site_ID is used in all tables except the References table. The Site table also indicates where information about a site occurs within the database. For example, if the Resources field in the Site table contains the value “Yes”, resource information is available in the Resources table.

The GeolMinOcc table contains information about the geology of mineral deposits and prospects. Every attempt was made to compile information as reported in the source report. For example, if one source report states the valuable material as “chalcopyrite”, and another reports "chalcocite and bornite", the attribute field Value_Mat will contain all values. The value in the Ref_ID field is the primary source report for the record, for example, Fournier (1967). All information in the record comes from the primary source report unless an attribute field value contains a footnote denoted as a number in parentheses. If a record value is followed by a footnote, the Ref_ID is given in the Remarks field. Full citations for source reports are provided in the References table.

The Resources table contains reported resource and reserve information for mineral deposits. Initial (or earliest resource data found by authors) and current resource data were compiled, even if information from intervening years was reported. Resource values were recorded as shown in source reports, including year reported, resource amount, units, and classification system(s). The definition of terms (for example, inferred, proven, probable, etc.) used in various resource classification systems may change through time. If resources or reserves are reported for a group of features rather than an individual deposit, for example, the Copper Creek deposits, the Ftr_ID will show “-1111” and the resource or reserve is assigned to the “site” or Site_ID that groups those deposits in the Site table. For consistency, resource values are converted to International System of Units (SI) units by the USGS authors. Where gold and silver values are reported in ounces in the source report, troy ounces were assumed when converting to SI units.

The Production table contains published production data for mines. Production is listed by commodity and reported as shown in the source reports. If production is reported annually, production is totaled by the authors for the time period defined by the Year_From and Year_To values. If production is reported for a group of features, the Ftr_ID will show “-1111” and the production is assigned to the “site” or Site_ID that groups those mines in the Site table. For consistency, production values are converted to International System of Units (SI) units by the USGS authors. Where gold and silver values are reported in ounces in the source report, troy ounces were assumed when converting to SI units.

The History table contains information derived from publically available sources regarding the status of a mine, prospect, deposit, or mineral region through time. Values in the Status field indicate a condition or phase for the time period stated in the Year_From and Year_To fields. This information may not reflect the current status of a feature. For example, if the attribute record shows the status of a mine is “Active” and the Year_From and Year_To dates are 1920 and 1992 respectively, the mine was active from 1920 to 1992 it is unknown if the mine is still active. The Last_Updt field shows the date that the record was last updated by the authors.

The Dep_Model table contains mineral deposit model and geoenvironmental model classifications for a deposit. If a deposit model classification could not be determined from published sources, the deposit model was assigned based on available geologic information and denoted as “USGS Authors (2018)” in the DpMD_RefID field.

The Descr_Sum table contains relevant descriptions found in source reports. These descriptions are attributed according to the type of description, such as Geology, Resource, Production, History, etc. Descriptions pertain to individual features or to larger sites. The authors do not paraphrase or combine descriptions, and therefore, when a database feature is described in multiple reports, the feature will have multiple entries.

The References table contains the citation of the map or report(s) from which the point, polygon, or attribute information was obtained. The table also assigns a short reference Ref_ID that is used throughout the database.

Mines are a man-made feature associated with the process of extracting, processing, or concentrating ore materials. In this database, mines have a point location, and where possible, the polygon boundary showing the extent of surface workings identified from imagery. No attempt is made to distinguish specific mine features like pits, dumps, tailings, etc. within the surface workings outline.

Mineral occurrences, defined as a concentration of a mineral considered potentially valuable, are attributed as deposits and prospects in the database. Mineral deposits have defined size and grade indicated by current and (or) past production, and (or) a resource estimate. Prospects have sufficient data to describe at least two dimensions and the presence of useful or valuable minerals or materials. Mineral showings, or the occurrence of potentially valuable minerals as indicated by geological examination or analyses of samples, are not included in the database.

Mining districts represent historic administrative areas organized by miners under the mining laws of the United States. Mining districts are typically an area containing a group of mines that exploited the same or related commodity, such as gold or silver in the example of the Carlin area deposits. Mining district polygons may overlap.

The locations of mines, mineral occurrences, and mineral regions are commonly represented as points in source maps and reports, and occasionally as footprints (polygon outlines). In this database, all features have a point location, and some have an additional polygonal footprint. Surface workings in the Loc_Poly_Sw feature class are the exception—they do not have a corresponding point location or attribute information in the point layer. Otherwise, for points that have polygonal boundaries, the point attribute field Loc_Poly contains the value “Yes” and type of boundary is described in the field Poly_Def (for example, “Outline of ore”, “Block model of 0.01 to 0.5 percent copper”, or “Boundary of mining district”).

Each point and polygon feature is uniquely identified by a Ftr_ID. The Site_ID is used to indicate groups of related features, or “sites”. Tables are linked (related) using the Ftr_ID or the Site_ID fields. Some tables have more than one record describing a feature. For example, a point denoting a mine location may have many records in the Production table summarizing the dates and amounts of material produced. The database is designed to allow the user to navigate from the point or polygon layers to the linked table information or from the tables to the point and polygon layers.

All database information is derived from publically available sources. The Last_Updt field shows the date that the record information was last updated by the authors. Full citations are listed in the References table, and each citation is assigned a short citation used as an identification (ID) in the database. In each feature class and table, the primary reference is noted in the Ref_ID field. Auxiliary references are enumerated after attribute field values, and the corresponding short reference is in the Remarks field. For example, the Commodity field shows “copper rhenium (1)”. This indicates the commodity “copper” was derived from the primary reference denoted in the Ref_ID field as “Browne and Miller (2002)” and “rhenium” was derived from a secondary reference denoted in the Remarks field as “(1) John and Taylor (2016)”.

Attribute records that are blank, or contain a "Null" value in the file geodatabase, were checked for available data, and no data were found. Some fields have all blank values if the authors were unable to locate relevant published information. These blank fields are maintained in the database structure for consistency with related USGS products and for possible future use if information becomes available.

Two points may occupy the same location. This occurs when there is a deposit with a mine, and the location of either the mine or the deposit is unknown. For example, a report provides a map showing the location of a deposit. The report also provides production data for underground “Mine X” that is mining the deposit, but does not provide the location of “Mine X”. In this case, a second point representing “Mine X” is placed at the point location of the deposit.

Polygon features may overlap. Viewing polygons as outlines without color fills will show where polygon overlap occurs.

In the attribute section of this metadata, attribute fields from all tables and feature classes are listed in alphabetic order corresponding feature classes and tables are listed in parentheses after the field name in the ‘Attribute_Label’. For example, “Mat_Amnt (Production, Resources)” indicates the attribute field Mat_Amnt (material amount) occurs in the Production table and in the Resources table.