Differential Leveling Conversion and Analysis Toolset Lisa Berry University of Redlands, MS GIS Program

Project Overview Masters of Science in GIS Partnered with: Image:

Problem Inability to spatially and temporally pinpoint height changes over time along transportation infrastructure in southern California Images: and

How to Measure this Change Elevation changes along the transportation infrastructure using differential leveling survey methods Image:

Differential Leveling Survey Methods Differences in height rather than actual elevation - Helps determine geodetic elevation Image from:

What is Differential Leveling? Measured between Benchmarks Difference in height between these benchmarks Images from: gallery.usgs.govImages from: gallery.usgs.gov, onlinemanuals.txdot.gov onlinemanuals.txdot.gov Differential Leveling Observation Run

Benchmark and Run Visualization Runs store differential height value Benchmarks (Points) Runs (Lines)

Spatial and Temporal Problem Finding regions of significant change

Blue Book Format National Geodetic Survey (NGS) Vertical Observation text files - Collects and validates geodetic leveling data

Blue Book Format 1 Survey per file - Line records store raw observations

Goals of Project Build a geodatabase to manage datasets for efficient analysis Tool to automate conversion into ArcGIS Tool to perform analysis between surveys Image: esri.com

What Needs to be in the Geodatabase? Types of survey information within file: Basic information about survey Latitude/Longitude of Benchmarks Differential height between benchmarks Date/Time of measurements Temperatures and instrument specifications

Methods ArcGIS Geodatabase schema Python and ArcPy methods - Conversion and analysis

Conversion Process Step 1: Creation of Geodatabase Schema

Conversion Process Step 2: Python Script Tool - Handling different line records

Python Script Line Record “key”

Conversion Process Step 3: Field type conversion within Python - Integer and date fields to match geodatabase schema +=

Writing to the Geodatabase Insert Cursor method Sparse/Convert Line Data Python List Insert Row to Table

Conversion Tool Interface Batch process surveys Pre-made Geodatabase schema (location)

Conversion Output

Pilot Dataset Results 9 Surveys 2700 Benchmarks and 4200 Runs Filling 2 Feature Classes and 2 additional tables 15 seconds

Analysis Tool Searches Feature Class for: - Same start and end benchmark - Significant difference in the differential leveling value Search Cursor Method - Comparing the table to itself

Analysis Tool Interface

Analysis Tool Result Join with Runs Feature Class - Spatial visualization of the runs with significant change

Overall Solution BeforeAfter

The Big Picture Image: aaroads.cahighways.org and

Conclusions Creation of geodatabase Automating the conversion process Creating a tool for analysis Allowing further analysis of datasets Runs Benchmarks Equipment Differences

Thank you Lisa Berry