Elizabeth McDowell

1. ELIZABETH MCDOWELL
MESA COUNTY GIS
PYTHON & THE CERR CHALLENGE PROCESS

2. SEVERANCE TAX FUND
Fifty percent of the State’s receipts from the severance tax on minerals and mineral fuels are credited to the Local Government Severance Tax Fund. The Department of Local Affairs (DOLA) is directed to allocate 70% of these funds to local governments through discretionary grants and loans under the Energy and Mineral Impact Assistance Program. The remaining 30% is distributed directly to the municipalities and counties economically and socially impacted by mineral production based on certain measurable factors determined by the General Assembly.
Colorado Employee Residence Reports (CERR) are submitted by reporting parties and are used as one of the factors to determine the severance tax allocations to and within counties.
(State of Colorado. Department of Local Affairs. Federal Mineral Lease and State Severance Tax Direct Distribution: Program Guidelines, June 2011)

3. FOR 2013 REPORTING YEAR
•237 CERRs (with Mesa County addresses)
•233 Companies
•16,088 Total Employees
•3,261 Employee Addresses within Mesa County
•356 Not Challengeable
•106 Invalid Addresses (Incl. PO Boxes, bad addresses)
•879 Challenged Addresses
Jurisdiction
Company Reported Employees
Post-Challenge Employees
Net Gain
City of Fruita
328
298
-30
City of Grand Junction
1769
1150
-619
Mesa County
994
1689
695
Town of Collbran
28
18
-10
Town of De Beque
56
45
-11
Town of Palisade
86
72
-14
Garfield County
4
0
-4
Town of Parachute
7
0
-7
TOTAL
3272
3272
0
+21%

4. MESA COUNTY ALLOCATION
Direct Distribution Payment - 2014
$1,723,565.20

5. COLORADO EMPLOYEE RESIDENCE REPORTS
•Employers submit lists of employees involved in mineral extraction that meet the following:
•Worked ≥ 500 hours in Colorado in any 6 months during the reporting year
•Worked for the purpose of extraction within Colorado
•Submission guidelines:
•Report only employees during reporting year period
•Report only Colorado employees
•Report the PHYSICAL LOCATION of employee’s residence in Colorado (not mailing address)
•No PO Boxes

6. WHAT WE GET
•Lists of employees that do not meet mineral extraction requirements
•PO Boxes
•Incorrect / invalid addresses
•Addresses listed in the wrong jurisdiction
Communication from Local Governments back to Employers can
help improve the overall quality of employee reports

7. MESA COUNTY GIS
•Processes CERRs for municipalities within Mesa County
•City of Grand Junction
•City of Fruita
•Town of Palisade
•Town of De Beque
•Town of Collbran
•Include surrounding Counties/Municipalities in process
•Garfield County
•Town of Parachute

8. APPLICATIONS USED
•CERRs provided as Microsoft Excel workbooks
•VBA Script to merge multiple workbooks
•Manually add, populate and rename fields
•Work in batches as CERRs become available
•Maintain data in SQL database
•Scripts to prep data for geocoding in ArcGIS
•Union batch tables/views
•Reporting
•ESRI ArcMap 10.1 and PythonWin 2.7.2
•Geocoding
•Intersects, Spatial Joins
•Supporting Documentation

9. WHY PYTHON?
•ESRI Marketing
•Online Python class
•Python based sessions at the ESRI Developer Conference, March 2014
•Data Driven Pages enhanced with Python
GOAL:
Use python scripting with DDP to create supporting documentation for address challenges

10. CHALLENGE PROCESS OVERVIEW
1.Download / Prep CERRs for Processing
2.Geocoding / Address Verification
3.Supporting Documentation
4.Submit Challenges on DOLA’s Website
In previous years supporting documentation was created from Assessor’s Property Detail
-Individually created per challenge address
-For 2012 Reporting Year: >1000
http://emap.mesacounty.us/assessor_lookup/

11. SUPPORTING DOCUMENTATION
•State Requirements:
•A current map or property tax record showing the address is within the municipal limits or located in unincorporated county territory may be used. In addition, a letter from both the municipality and the county agreeing to the address location may be used. (DOLA Direct Distribution Frequently Asked Questions (FAQ) July 2014)
•ArcMap Data Driven Pages
•Solution to combine a current map with property tax table for multiple properties
•Leverages existing County GIS data
•Greatly reduces tedium of individually creating PDFs from website

12. DATA DRIVEN PAGE SETUP
•Input Layer
•Spatial join of geo-coded address layer with parcel data
•Copy of input layer used as the Page Definition Query to only show the active address on each page
•DDP Extent
•Since parcel size varies greatly used “Best Fit” with a 1500% Margin to get suitable surrounding areas around small parcels.
•Inset Maps
•One of each municipality and one County-wide
•Extent indicator on main data frame to show where property is located in reference to municipal boundary

13. PYTHON SCRIPT COMPONENTS
•General Settings
•Map Document Variables
•Looping through Pages
•Map Layout
•Dynamic Layout Elements
•Export to PDF
Python script available on github:
https://github.com/emcdowell/Python-CERR.git

14. GENERAL SETTINGS
•Set Directory Paths / Output PDF
•Script Parameters
•Used to select different input without having to change code
inputLayer = arcpy.GetParameterAsText(0)
•Allow script to overwrite outputs
arcpy.env.overwriteOutputs = True

15. MAP DOCUMENT VARIABLES
Set up map document variables
mxd = arcpy.mapping.MapDocument(mxdpath)
ddp = mxd.dataDrivenPages
dataframes = arcpy.mapping.ListDataFrames(mxd, '')
actDataframe = arcpy.mapping.ListDataFrames(mxd, '')[0]
Define input table and temporary table variables
inputTable = r'S:ITGISLizPythonCERRDataPython_CERR.gdbMC_TAC_AgencyCodes'
tempTable = r'S:ITGISLizPythonCERRDataPython_CERR.gdbtempTable'
Define field variables

16. LOOPING
Script has multiple loops
•Primary Loop:
•Loop through each page using page number
for pgNum in range(1, ddp.pageCount + 1):
ddp.currentPageID = pgNum
•Secondary & Tertiary Loops:
•Script loops through scenarios within each page
•Search & Insert Cursor
•Layout element positioning
•if…elif statements with
nested for…in statements

17. # Set variables to set data frame insets
reportedDistrict = ddp.pageRow.getValue(qMuni_County)
newDistrict = ddp.pageRow.getValue(qDistrict)
if reportedDistrict == 'City of Grand Junction':
lblGrandJunction.elementPositionX = 8.27
lblGrandJunction.elementPositionY = 2.63
lblFruita.elementPositionX = 10.0
lblCollbran.elementPositionX = 10.0
lblDebeque.elementPositionX = 10.0
lblPalisade.elementPositionX = 10.0
lblCounty.elementPositionX = 12.0
txtCounty.elementPositionX = 3.18
txtCounty.elementPositionY = 1.07
txtGrandJunction.elementPositionX = 10.0
txtFruita.elementPositionX = 10.0
txtCollbran.elementPositionX = 10.0
txtDebeque.elementPositionX = 10.0
txtPalisade.elementPositionX = 10.0
for frames in dataframes:
if frames.name == 'df_GJT':
frames.elementPositionX = 5.7
frames.elementPositionY = 0.5
elif frames.name == 'df_Address':
frames.elementPositionX = 0.2745
frames.elementPositionY = 2.9452
else:
frames.elementPositionX = 8.8
frames.elementPositionY = 0.7
elif reportedDistrict == ‘Mesa County’:

18. MAP LAYOUT ELEMENTS
•Each layout item MUST be uniquely named
•Size and Position Tab
•Element Name
•Create and do preliminary setup in ArcMap then use Python to further manipulate the element

20. SCALE BAR
# Set scale bar based on page extent
if actDataframe.scale < 4000:
scale1.elementPositionX = 0.33
scale1.elementPositionY = 2.98
scale2.elementPositionX = 11.6
scale3.elementPositionX = 11.6
scale4.elementPositionX = 11.6
elif 4001 <= actDataframe.scale <= 10000:
scale1.elementPositionX = 11.6
scale2.elementPositionX = 0.33
scale2.elementPositionY = 2.98
scale3.elementPositionX = 11.6
scale4.elementPositionX = 11.6
elif 10001 <= actDataframe.scale <= 20000:
scale1.elementPositionX = 11.6
scale2.elementPositionX = 11.6
scale3.elementPositionX = 0.33
scale3.elementPositionY = 2.98
scale4.elementPositionX = 11.6
elif actDataframe.scale > 20001:
scale1.elementPositionX = 11.6
scale2.elementPositionX = 11.6
scale3.elementPositionX = 11.6
scale4.elementPositionX = 0.33
scale4.elementPositionY = 2.98
•Create four scale bars in ArcMap
•Set Division Value for each
•Determine placement on layout
•Use Python to change X,Y location based on the scale of the active data frame

21. SEARCH & INSERT CURSORS
•Search Cursor
•Use For … In… to match record in input table to page variable
•Insert Cursor
•Populate temporary table with matching records
cursor = arcpy.da.SearchCursor(inputTable, ('TAC_Code', 'Year_', 'Agency_Name'), ''' "Year_" = 2013 ''')
insertcursor = arcpy.da.InsertCursor(tempTable, ('TAC_Code', 'Year_', 'Agency_Name'))
for row in cursor:
if row[0] == pgTAC:
insertcursor.insertRow(row)
del row
del insertcursor
del cursor
cursor = arcpy.da.SearchCursor(inputTable, ('TAC_Code', 'Year_', 'Agency_Name'), ''' "Year_" = 2013 ''')
insertcursor = arcpy.da.InsertCursor(tempTable, ('TAC_Code', 'Year_', 'Agency_Name'))

22. DYNAMIC TABLE
•Built using Python’s Search and Insert cursors
•Lists taxing authorities per challenge address
•Verifies that a parcel is located inside or outside of municipal boundary
•Based on the number of rows in the table, script will adjust text size, row height, grid line positioning
#Set and clone cell text elements
cellTxt.fontSize = rowHeight / 0.0155
y = upperY - headerHeight
rows = arcpy.SearchCursor("in_memorysort1")
for row in rows:
x = upperX + 0.05
col1CellTxt = cellTxt.clone("_clone")
col1CellTxt.text = row.getValue("Agency_Name")
col1CellTxt.elementPositionX = x
col1CellTxt.elementPositionY = y
col2CellTxt = cellTxt.clone("_clone")
col2CellTxt.text = row.getValue("TAC_Code")
col2CellTxt.elementPositionX = x + 2.3
col2CellTxt.elementPositionY = y
y = y - rowHeight
if col1CellTxt.elementWidth > 2:
col1CellTxt.elementWidth = 2

23. EXPORT TO PDF
# Set Output PDF (Beginning of script)
pdfPath = outDir + r"FinalMapBook.pdf"
if os.path.exists(pdfPath): # Check to see if file already exists, delete if it does
os.remove(pdfPath)
finalPdf = arcpy.mapping.PDFDocumentCreate(pdfPath)
…
# Name and export individual pages (Inside Loop through each page)
ddp.exportToPDF(pgLabel + ".pdf", "CURRENT")
finalPdf.appendPages(pgLabel + ".pdf")
…
# Commit Changes and delete variable references (Outside of loop)
finalPdf.saveAndClose()
del finalPdf

24. THREE HOURS I WAS ABLE TO SPEND WORKING ON SOMETHING ELSE!
For batch size between 150 – 200 addresses

25. MOVING FORWARD
•Significant time-saver (not counting the time it took to put the script together)
•Will continue to work on script
•Incorporate intersect and spatial join
•Do more with parameters

26. LINKS
•DDPwithDynamicTablesAndGraphs_10.1_v1
http://www.arcgis.com/home/item.html?id=3a525b986b774a3f9cbbd8daf2435852
•Merge Excel Workbooks
http://msdn.microsoft.com/en-us/library/office/gg549168(v=office.14).aspx
•My full script:
https://github.com/emcdowell/Python-CERR.git