Understanding Map Integration Using GIS Software › ... › 2016 › 07 ›...
Transcript of Understanding Map Integration Using GIS Software › ... › 2016 › 07 ›...
Annual Meeting 2016
Understanding Map Integration
Using GIS Software
7/28/16 Michelle Pasco
USRIP Symposium
Introduction to GIS � Geographic Information
System (GIS)
� Used to study all kinds of data with a geospatial component
� Digitizes maps using vector components (points, lines, polygons)
� Representation of the real world and its attributes
Credit: desktop.arcgis.com
Map Integration � Also known as “conflation”
� Combination of two or more datasets to provide new
perspectives and insight on existent geo-enabled data sets
� May result in a number of problems
� This project attempted to research and conflate two data sets within GIS � Virginia Department of Transportation’s (VDOT) Linear
Referencing System (LRS) � INRIX XD (XD)
Study Area
Interstates I-64, I-564, I-95, I-395, and I-495 highlighting the study areas.
Issues faced � Spatial displacement and attribute disparity
� Length, position, direction, size, shape � Feature representation
� Unequal updating periods, equal data models acquired
by different operators, unequal data models, and content differences
Methods of Conflation � Spatial Join – combines two datasets by comparing their
digitized geometries and creating a count recording either features in close proximity or complete matches
Spatial join conflation on part of I-64.
Methods of Conflation � Transfer Attributes – matches a feature from one dataset
to another feature by selecting one attribute that is similar in both datasets within a certain distance
Transfer attributes conflation on part of I-64.
Comparison Cases
Spatial Join � Matching Geographic
Coordinate Systems (GCS) vs Original = possibly different GCS (ORG)
� LRS EDGE (EDGE) vs LRS Non-EDGE (NON)
Transfer Attributes � Search Distances
� 0.1 miles � 0.3 miles � 0.5 miles � 1 mile
Accuracy Assessment � Spatial Join:
� Transfer Attributes:
Results: Spatial Join
Visual representation of spatial join cases on part of I-64.
EDGE_ORG
EDGE_GCS
NON_ORG
NON_GCS
LRS EDGE = EDGE LRS Non-EDGE = NON
XD Original = ORG XD Geographic Coordinate System = GCS
Results: Spatial Join Road Name &
Spatial Join # of features (count>0) features
Conflation Accuracy, ca (%)
I-64 EDGE_ORG 728 632 86.81
I-564 EDGE_ORG 12 10 83.33
I-95 EDGE_ORG 573 452 78.88
I-395 EDGE_ORG 136 89 65.44
I-495 EDGE_ORG 167 102 61.08
Road Name & Spatial Join # of features (count>0)
features Conflation
Accuracy, ca (%)
I-64 EDGE_GCS 728 359 49.31
I-564 EDGE_GCS 12 10 83.33
I-95 EDGE_GCS 573 287 50.09
I-395 EDGE_GCS 136 27 19.85
I-495 EDGE_GCS 167 30 17.96
Results: Transfer Attributes
Visual representation of transfer attribute cases on part of I-64.
0.1 mile Search Distance
0.3 mile Search Distance
0.5 mile Search Distance
1 mile Search Distance
Road Name & Search Distance # of features No <Null>
features Conflation
Accuracy, ca (%)
I-64_0.1 mi 754 686 90.98
I-564_0.1 mi 11 8 72.73
I-95_0.1 mi 477 435 97.32
I-395_0.1 mi 64 60 93.75
I-495_0.1 mi 52 50 96.15
Road Name & Search Distance # of features No <Null>
features Conflation
Accuracy, ca (%)
I-64_0.3 mi 754 689 91.38
I-564_0.3 mi 11 8 72.73
I-95_0.3 mi 477 435 97.32
I-395_0.3 mi 64 61 95.31
I-495_0.3 mi 52 50 96.15
Results: Transfer Attributes
Results: Buffer Tool
0.1 0.3 0.5 Flat
Seg 1 1 1 1 1
Seg 2 3 3 3 2
Seg 3 2 2 2 2
Seg 4 2 2 2 1
0.1 0.3 0.5 Flat
4100330 2 2 2 2
4100331 3 3 3 2
4100515 3 3 3 2
Visual representation of the types of buffers on part of I-64.
LRS Matching XD Matching
Conclusions � Transfer attributes is overall more accurate
� Covers the two most important aspects in the conflation process: spatial data and attributes
� Spatial joining is better to use if the datasets are comprised of many, potentially small, features
� Either way, larger-scale projects will be more vulnerable to issues
Questions?
Acknowledgements
� Simona Babiceanu, who advised me and kept me on the right track
� Dr. Emily Parkany, for the constant support � Daniela Gonzales, for encouraging me to
apply to this program
References � Davis, Curt H., Haithcoat, Timothy L., Keller, James M., Song, Wenbo. Relaxation-
Based Point Feature Matching for Vector Map Conflation, 2011. Transactions in GIS, 15(1), pg. 43-60. http://onlinelibrary.wiley.com/doi/ 10.1111/j.1467-9671.2010.01243.x/full. Accessed June 20, 2016.
� Environmental Systems Research Institute (ESRI). ArcGIS for Desktop, 2016. arcgis.com. Accessed July 18, 2016.
� G. v. Gösseln, M. Sester. Integration of Geoscientific Data Sets and the German Digital Map Using A Matching Approach. Commission IV, WG IV/7. http:// www.cartesia.org/geodoc/isprs2004/comm4/papers/534.pdf. Accessed June 15, 2016.
� INRIX. I-95 Vehicle Protection Project II Interface Guide, 2014. http:// i95coalition.org/projects/vehicle-probe-project/. Accessed June 17, 2016.
� Virginia Department of Transportation. Roadway Network System. Release Notes, Linear Referencing System, Version 15.2, 2015. https://www.arcgis.com/ home/item.html?id=60916ea827544412ad209ea5192ad7fd. Accessed June 2, 2016.